Aurore Malet-Karas

Marion Noulhiane

PhD Valérie Doyère
email: valerie.doyere@u-psud.fr

Dynamic of spatio-temporal binding in rats

Keywords: spatio-temporal binding, interval timing, nose poke, peak interval procedure

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Introduction

Can we access time without having another dimension associated to it, such as an action, or space, or a sequential event? Aristotle proposed that the spatial and temporal contingencies are intrinsically linked together, and cannot be dissociable. Since the 19 th century, with the emergence of behavioral experiments, several scientists studied the interaction of these two dimensions; for example, Piaget found that children base their judgements of time and duration on their experience of distance (Piaget, 1927(Piaget, /1969)). Fairly recent data confirm that representation of duration is expressed through distance notions, but not the reverse, showing that space and time are asymmetrically dependent on each other [START_REF] Casasanto | Time in the mind: Using space to think about time[END_REF]. To what extent these are bound together, in terms of learning and adaptation to spatio-temporal changes is not yet clear.

Skinner pointed out the difficulty to experimentally dissociate between where and when an event occurred, as the memory of the event itself is built on this very association [START_REF] Skinner | A Case History in Scientific Method[END_REF][START_REF] Meck | Hippocampus, time, and memory[END_REF][START_REF] Crystal | Theoretical and conceptual issues in time-place discrimination[END_REF][START_REF] Meck | Hippocampus, time, and memory--a retrospective analysis[END_REF][START_REF] Meck | Hippocampus, time, and memory--a retrospective analysis[END_REF] for a more recent review see [START_REF] Church | A resolution of the debate about associative and temporal learning[END_REF]. Many studies have focused on the animal's abilities to learn, consolidate and retrieve spatial or temporal information, although to a lesser extent for the latter, disregarding the potential involvement of the other (space or time) parameter. Spatial memory in animals has been extensively studied through the use of arena exploration, in radial maze [START_REF] Olton | Spatial memory and radial arm maze performance of rats[END_REF] and the Morris water maze [START_REF] Morris | Allocentric spatial learning by hippocampectomised rats: A further test of the "spatial mapping" and "working memory" theories of hippocampal function[END_REF] in rodents, and many other more complex and ethological experimental setups, such as arena with food cached for monkeys (Lavanex and Lavanex, 2006), rodents (Jacobs and Limans, 1991;[START_REF] Jacobs | Memory for cache locations in Merriam's kangaroo rats[END_REF]Davenport et al, 2000;[START_REF] Gould | What scatter-hoarding animals have taught us about smallscale navigation[END_REF], and even pigs [START_REF] Mendl | Pigs in space: spatial memory and its susceptibility to interference[END_REF]. In comparison, learning of the temporal dimension has been less investigated. Mainly, time processing has been assessed either through the sequential order of events [START_REF] Fortin | Critical role of the hippocampus in memory for sequences of events[END_REF][START_REF] Kesner | The role of the hippocampus in memory for the temporal order of a sequence of odors[END_REF], or through the time interval (the duration of a segment of time) [START_REF] Church | Duration discrimination by rats[END_REF]Holders and Roberts, 1985; for a review see [START_REF] Buhusi | What makes us tick? Functional and neural mechanisms of interval timing[END_REF]. In a Pavlovian task combining reinforcement time with locations and reinforcement probabilities, a recent study indicates that mice could learn to associate time intervals and locations in order to optimize their behavior [START_REF] Tosun | Mice plan decision strategies based on previously learned time intervals, locations, and probabilities[END_REF]. However, it is not known to what extent this behavior is flexible when new spatio-temporal rules are learned, and to what extent time and location are bound in memory.

Considering their ecological occurrence, time and space are two dimensions that cannot be dissociated. Indeed, the ability to learn the spatio-temporal variability of important events can confer considerable advantages to animals in the context of both resources seeking as well as predators' avoidance [START_REF] Crystal | Theoretical and conceptual issues in time-place discrimination[END_REF]. The associative learning of space and time is also referred to as place-time learning, and it has been demonstrated possible with classical conditioning in animals [START_REF] Wilkie | Conditional time-place learning[END_REF][START_REF] Thorpe | Unequal interval time-place learning[END_REF]. Gallistel (1990) proposed that whenever a biologically significant event occurs, a memory code is created that contains the nature of the event, as well as the time and place in which it occurred, constituting a tripartite memory. However, still, very little is known regarding the learning of a spatio-temporal (-event or -reward) memory.

Animals can learn time fast [START_REF] Dallérac | Temporal specificity of fear conditioning: effects of different conditioned stimulus-unconditioned stimulus intervals on the fear-potentiated startle effect[END_REF]Bevins and Ayres, 1995;[START_REF] Balsam | Timing at the start of associative learning[END_REF]Diaz-Mataix et al, 2013), and adapt rapidly (i.e. within a few sessions) to changes in temporal rules in a peak interval protocol [START_REF] Lejeune | Adjusting to changes in the time of reinforcement: peak-interval transitions in rats[END_REF][START_REF] Guilhardi | Dynamics of temporal discrimination[END_REF][START_REF] Sanabria | Adaptation of timing behavior to a regular change in criterion[END_REF]Dallérac et al, 2017). Similarly, animals can also adapt quickly to changes in a spatial rule from session to session [START_REF] Morris | Allocentric spatial learning by hippocampectomised rats: A further test of the "spatial mapping" and "working memory" theories of hippocampal function[END_REF][START_REF] Blanco | Area and peak shift effects in a navigation task with rats[END_REF]. [START_REF] Tosun | Mice plan decision strategies based on previously learned time intervals, locations, and probabilities[END_REF] demonstrated that animals can make Bayesian inferences based on spatio-temporal knowledge they previously learned to optimize their time-switching behavior to get the food reward. How the animal learns these two dimensions, and to what extent they are independent of each other, remains unclear.

The aim of the present study was to investigate the dynamics of learning location and time when associated together. For this purpose, learning of spatio-temporal rules was manipulated in a memory paradigm including 3 phases (See Figure 1 Instrumental training following the new spatio-temporal rule to assess the impact of the preceding session and the dynamic of learning a spatio-temporal association: during nine days, the new spatio-temporal rule was enforced for all animals under the PI procedure. We expected that the rat exposed to the shift in phase 2 will benefit from this training, and show a better learning of the new spatio-temporal rule in phase 3 than the control group. Our analysis aimed at determining whether the benefit is observed for either location or time, or both, and at deciphering to what extent the learning of new location and new time are linked.

Material and Method

Animals 12 adult male Sprague-Dawley rats (Janvier, France; average weight 320g) were housed in pairs in Plexiglas cages, with food and water ad libitum. After one week of adaptation, food access was restricted to keep the animals at 85% of their normal weight. The experiment was performed in accordance with the recommendations of the European Economic Community (86/609/EEC) and the French National Committee (87/848) for care and use of laboratory animals. spatio-temporal rule in context B (Hole#1 lit is reinforced with 3 pellets at 30s after tone onset), while the control group is presented the previous rule (hole#4, reinforced at 60s). Both groups are then presented the intact context A (hole#2 lit, reinforced at 15s) in a Pavlovian manner as well. (C) Phase 3: Dynamic of spatio-temporal learning (days 1 to 9 after phase 2). Both groups are presented the new rule (hole#1, 30s) in context B, while the paradigm associated to context A (hole#2, 15s) remains the same as in initial training. For these sessions, all the nose-poke holes are lit.

Apparatus

The experiment took place inside cages (Rat Test Cage, Coulbourn Instruments, USA; dimensions in cm: 30Lx25lx30h) with a 5-holes nose-poke wall on the left (each hole being 2.5 cm of diameter and spaced by 2 cm, « 5-holes nose poke », Coulbourn Instruments), a magazine (4x3x3cm) connected to a feeder on the opposite side to deliver the reward (45 mg pellets, Rodent Grain-Based Diet, Bioserv). The magazine was lit when the reward was delivered. Two house-lights were the only light source in the cage during the trials, one green and one red placed at two distinct extremities of the cage, and served as contextual cues in addition to auditory cues-see below. A speaker was placed on the top of the chamber for delivery of sounds as timing cues. Each of the 5 holes and the magazine were equipped with photoelectric cells to register the activity (nose-poke, NP) of the animals. The cages were placed into a ventilated (background noise, 65dB) soundproof box. The experiment was monitored by the software Graphic State (Coulbourn instruments, USA).

Protocol Phase 1: Training

Pre-training: On the first day of the experiment, the rat was placed into the operant box for a 30 min session on Continuous Reinforcement (CRF). At the beginning of the session, two pellets were placed into each of the 5 lit holes of the nose-poke wall to encourage the animal to visit them. Each time the animal entered one of the holes, the magazine lit and a pellet was delivered. When the animal entered its nose in the magazine, the light switched off and a new trial began.

Training: The aim of this phase was to train the animals to learn a complex spatio-temporocontextual rule. From day 2 for 41 sessions (8 weeks), the animals were submitted to a Peak-Interval (PI) procedure in which they were presented with two different spatio-temporal rules, each associated with a specific context, presented each day in a pseudo randomized order (A followed by B, or B followed by A).

Those two contexts were made distinguishable by a house light, red for context A vs. green for context B, and two different timing cues (1kHz-80dB and 11kHz-80dB tones, for A and B respectively). Each context was associated with a spatial rule indicated by a light in one of the nose-poke holes (hole#2 in A and #4 in B). The reward was delivered according to a different Fixed-Interval (FI) in each context (15s after tone onset in A, and 60s after tone onset in B) : the first nose-poke after the time into the appropriate hole triggered the delivery of reinforcement and switched on the magazine light and turned off the tone. Probe trials, in which no reinforcement delivery was programmed, consisted of 45s for the tone associated with the FI 15s, and 120s for the FI 60s. Thus, the animal learnt two spatio-temporal rules, each associated to a specific context (context A: hole#2-15s; context B: hole#4-60s, see figure 1A).

In order to keep the animal motivated at the beginning of training, we began with 8 Probe trials for 49 reinforcement trials in each context (the first trial in each context was always reinforced), for 10 sessions.

Then, we used a ratio of 24 Probe for 37 reinforcement trials until the 8 weeks of training were completed.

The inter-trial-interval (ITI) randomly varied between 5 and 30 seconds (15 s on average) in the context A and between 10 and 30 seconds (20 s on average) in B.

Phase 2: Shift of the spatio-temporal association

In this part of the experiment (day 41), two groups (n=6) were formed, and equilibrated according to their performances. All rats were submitted to a single session during which 3 pellets were delivered in a Pavlovian manner at the end of the timing cue, independently of the animal's behavior. We began with 8 trials in context B (figure 1B, right side) , during which the control group received the rewards according to the previously learnt association (hole#4 lit -60s), whereas the experimental group was submitted to a shift of this associative rule, with the rewards delivered in another hole at another time (hole#1 lit -30s).

Immediately following this phase, 8 trials rewarded with 3 pellets in context A were also presented in a Pavlovian manner, while keeping its corresponding spatio-temporal rule (hole#2 lit-15s, figure 1B, left side).

Phase 3: Dynamic of spatio-temporal learning

The Phase 3 was designed to assess, in the 9 following days, the impact of the single Pavlovian session during which the spatio-temporal rule was shifted, through the measurement of the behavioral adaptation to this new rule in the instrumental task. Therefore, in the context B, only the new rule hole#1 -30s was reinforced (all holes were lit) while in the context A the rules remained unchanged (hole#2, 15s). A rapid learning of the spatio-temporal We hypothesized thatthe experimental group would benefit from the previous exposure to the new spatio-temporal rule, and will show better or faster adaptation in comparison to the control group. The duration of the Probe trials remained as in phase 1. The first day after phase 2, the rats were submitted to context B first, and then to context A, but the order was pseudo-randomized for the following sessions.

Statistical analyses

Only the unreinforced probe trials were used to analyze the behavior of the animals. The spatial behavior was assessed by measuring for each rat the percentage of number of nose-pokes (NP) in each hole, then averaged per group. The temporal behavior associated to each hole was analyzed by plotting the function for rate of response (NP) against 1s time bins in context A and 2s time bins in context B. For comparison of temporal behavior between holes for the sessions after the shift, the temporal activity was normalized by the overall activity of the rats for the hole of interest.

The statistical analyses were performed by SPSS, JASP and Prism. Frequentist ANOVAs Student ttests were used to assess the spatial and temporal learning. We used an alpha level of .05 for all statistical tests. Bayesian ANOVAs were further conducted to better characterize findings in the temporal behavior.

Results

Training phase

The temporal behavior associated to each hole in each context over the course of the 8 weeks of training of phase 1 is shown in figure 2A andB. A differential behavior associated to each spatio-temporal rule developed progressively with training up to a well segregated temporal behavior, with numbers of nose-poke peaking around 15s in Hole #2 in context A and around 60s in Hole #4 in context B. The spatial behavior was also well segregated, as shown in figure 2C andD.

In effect, during the last week of training (week 8), the number of visits for the holes of interest of each context were significantly higher than for the other holes, as revealed by the Paired t-tests analyses: the Hole #2 was more visited in context A (NP= 88%, SE=2.7) than in B (NP=3.8%, SE=1.1) (t(10)=29.4, p<.001), while the Hole#4 was more visited in context B (NP=86%, SE=2.6) than in context A (NP=1.4%, SE=0.3) (t(10)=32.6, p<.001).

Dynamic of spatio-temporal association

In Phase 2, half of the animals (experimental group) were subjected to a change of the spatiotemporal rule in context B only, from 60s-Hole#4 to 30s-Hole#1, in a Pavlovian session. The other half (control group) were also submitted to a Pavlovian session, but keeping the initial spatiotemporal association. From day 1 to day 9 of Phase 3, the new spatio-temporal (30s-Hole#1) rule was enforced in context B for both groups. The impact of Phase 2 (Shift of the association) was expected to be observable during the next session (expecting a benefit for the experimental group), when both groups were returned to the peak interval procedure with the new spatio-temporal rule enforced in context B and all the holes lit. The analysis of behavior on day 9 aimed to determine whether the benefit might have weakened because both groups were further trained under the new FI schedule.

Day +1 after the Shift of the association-Context B

From day 1 to day 9 of Phase 3, the new spatio-temporal (30s-Hole#1) rule was enforced in context B for both groups. The impact of Phase 2 (Shift of the association) was assessed during the first session of the instrumental task (day+1), expecting a benefit for the experimental group. As shown in figure 3A, the spatial behavior of the animals at Day+1 was distributed over several holes, and clearly differed from their behavior at the end of training during Phase 1 (see figure 2D). We focused our analyses on three of the holes, the new and old reinforced holes (#1, #4, respectively), as well as the hole reinforced in the other context (#2). There was no difference between groups for any of these holes (t(10)<1.12, ns). Compared to their behavior in Phase 1, the animals decreased significantly their visits in the old hole #4 (t(10)=-17.84, p< .001), and increased their visits in the new hole #1 (t(10)=9.56, p< .001). However, even if the hole #2 was not reinforced in this context, we also observed a significant increase of visits of this hole (t(10)= 8.74, p< .001). Surprisingly, the hole #3, which had never been reinforced, was more visited by the animals now than during the phase 1 (t(10) = 4.11, p<.05). In contrast, the number of visits in the hole #5 remained low, and not significantly different from phase 1 (t(10)=1.65, ns). The temporal behavior of the animals is shown in the figure 3 (BCD) for the holes #1, #2 and #4, respectively. For hole #2, hole #3 and hole #4, there was a discriminable temporal pattern (significant effect of time, F(59,590)=3.61, F(59,590) = 2.41 and F(59,590)=2.82, p<.001 for hole#2, #3 and #4, respectively), with no group x time interaction (F(59,590)<1.02 for all, ns).

Interestingly, the animals tended to visit these holes with a maximum activity centered at the old 60-s temporal rule. The temporal pattern in hole #3 was similar to hole #2 and #4 (data not shown), confirming the spatial generalization . The lack of differential behavior between groups was confirmed with Bayesian analyses: a JZS Bayes factor ANOVA with default prior scales revealed that the main effect model was preferred to the group x time interaction model by a Bayes factor of 30.82 for hole #2, 6.36 for the hole #3, and 22.74 for the hole #4.

Importantly however, the two groups showed a differential temporal behavior in the hole #1 (significant group x time interaction, F(59,590)=1.44, p=.021), with the activity of experimental animals peaking earlier, according to the new 30-s temporal rule (figure 3B). Additionally, a more refined analysis of this Day+1 session revealed that only the animals exposed to the new rule in phase 2 presented an adjusted spatio-temporal binding to this new rule. As shown in figure 4A, the temporal behavior of the control animals did not differ significantly in the new (#1) and old (#4) reinforced holes (no hole x time interaction: F(59,590)=1.14, p= .22), suggesting that the animals did not adapt to the change of rule. Bayesian Repeated measure ANOVA confirmed that the main effect model was preferred to the hole x time interaction model by a Bayes factor of 7.81. In contrast, the behavior of experimental animals did differ temporally between those two holes (interaction hole x time: F(59, 590)=2.16, p< .001) (figure 4B). Bayesian statistics further confirmed this result, as the hole x time interaction model was preferred to the main effects by a Bayes factor of 9811.33. The animals visited the new hole (#1) at the new time (30s) and additionally kept the old spatio-temporal association, visiting the old hole more around the old time (hole #4, 60s). This pattern of results indicates that the behavior of the experimental animals was linked to the spatio-temporal associations. was subtracted to the mean activity during a temporal window centered at 30s (new time) for comparison between the two groups for the new (C) and old (D) holes. Significant differences are displayed as: from zero ="0"and between groups = "*" when p<.05.

In order to assess more specifically the learning of the spatio-temporal association, we compared the mean level of activity in those two holes when restraining to temporal windows centered at the new and old timing rules. For each animal and each hole, the mean activity for the 60-s window (from 52-68s) was subtracted to the mean activity for the 30-s window (from 26-34s) and compared between the two groups for the new and old holes (figures 4C and D). Following this calculation, a behavior linked to a spatio-temporal learning would thus be positive (more activity in the new hole/new time and no differential or less activity in the other combinations new hole/old time, old hole/new time or old hole/old time). For the hole #1 (new hole) this difference was positive only for the experimental group, indicating that the animals visited more the hole #1 at 30s (in concordance with the new rule) than at 60s (t-test to zero: t(5)=3.03, p=.029). However, no such difference was observed for the control group (t(5)=-0.75, p=0.49) confirming that the animals did not have a discernible temporal behavior in this hole. This analysis also revealed a difference between the two groups (t(10)=2.249, p=.048) in this hole (figure 4C). The same analysis for the old hole (hole #4, figure 4D) showed no differential spatio-temporal behavior (difference from zero, (t(5)=-1.33, p=.24 and t(5)=-1.33, p=.24 for control and experimental groups), and no statistical differences between the two groups (t(10)=0.70, p=.50).

Day +1 after the Shift of the spatio-temporal association -Context A

The context A was not associated to a change of the spatio-temporal rule. Therefore, no change was expected in the behavior of the animals, and no difference between the two groups either. Indeed, as shown in the figure 5, all the animals kept an adjusted spatio-temporal behavior.

With regard to their spatial behavior, the animals visited mainly the hole #2, reinforced in this context, but showed also a slight increased activity in the hole newly reinforced in the other context B, hole #1 (figure 5A). We focused our analyses on these two holes (#2 and #1). There was no significant difference between the two groups in neither of these two holes (t(10)<1.31, ns). Despite the fact that the spatial rule had not been changed in this context, the animals decreased significantly their visits in the hole #2 (t(10)=-5.45, p< .001), and increased their visits in the hole #1 (t(10)=9.25, p< .001), compared to their behavior before phase 2. The numbers of visits in the holes #3, #4 and #5 were not significantly different from before the phase 2 (respectively: t(10) = 2.03, t(10) = 2.17, t(10) = .08; ps>.05).

However, with regard to the temporal behavior in holes #1 and #2 (depicted in figure 5BC), all the animals showed an increased activity centered at the peak interval time for this context (15s) in both holes (significant effect of time, F(44,440)=1.75, p<.01 for hole #1, and F(44,440)=8.31, p<.001 for hole #2; and no hole x time interaction in either case Fs<1.14, ns,), with no difference between groups (no group x time interaction, Fs<1, ns confirmed by a Bayesian analysis, with the main effect model preferred to the interaction model by a Bayesian factor of 148.77 for hole #1 and 75.83 for hole #2).

Day +9 after the Shift of the spatio-temporal association-All contexts (B&A)

In order to assess the evolution of the impact of the shift of the spatio-temporal rule, the animals were trained under the new context B spatio-temporal rule for 9 days. The results obtained at Day+9 for the two contexts are shown in figure 6. Second, interestingly, the benefit of the shift session was visible on the spatial behavior in context B (figure 6A), as the experimental group showed a higher level of activity in hole #1 compared to hole #2 (t(5)=-4.48, p=.007), whereas it was not the case for the control group (t(5)=-1.33, p=.24). This differential behavior was confirmed with between group differences with higher activity for the experimental group in the reinforced hole #1 (t(10)=2.20, p=.026) and lower activity for the unreinforced hole #2 in this context (t(10)=2.19, p=.027), as compared to the control group. No differential spatial behavior was observed in context A (figure 6D), with a similar level of activity between groups for both hole #1 and #2 (t(10)<1.50, ns). These results indicate that the control animals did not differentiate the hole #1 from the hole #2 in context B, showing that they did not yet fully learn the new spatial component of the rule, contrarily to the experimental group who showed an adjusted spatial behavior in this task.

The temporal behavior did not differ between groups in neither hole #1 or #2 for both contexts (no significant group x time interaction, Fs(59,590)<1, ns with a Bayes factor of 33.37 for #1 and of 23.54 for #2 in favor of a main effect for context B ; Fs(44,440)≤1.0, ns for context A, also confirmed by a Bayes factor of 4.58 for hole #1 and 242.86 for hole #2). Both groups showed an activity peaking around 30s in context B, and around 15s in context A, with a sharper temporal function in the reinforced hole (hole#1 in context B, and hole #2 in context A) than in the nonreinforced holes. These results show a correct learning of the temporal rule for both groups that is well controlled by the context.

Discussion

The aim of the present study was to investigate the learning of space and time when both dimensions are associated and to assess whether the two dimensions are learnt independently of each other. For this purpose, we developed an experimental paradigm based on a nose-poke setup and peak-interval procedure to assess the learning of a new spatio-temporal association, once the rules have been acquired. The main results showed (1) a spatio-temporal binding, which is conserved when the rule is changed, and a fast and robust adaptation to the new associative rule; (2) a faster learning of the timing rule which is not dissociable neither from a spatial nor a contextual dimension.

After learning the two spatio-temporal associations in each context during Phase 1, the animals were subjected once to a shift of this rule (Phase 2). They benefited from this single session (or shift of the spatio-temporal rule) in adapting faster to this new rule in Phase 3. Indeed, we observed that the experimental animals expressed a differential spatio-temporal binding according to both the old rule (hole #4, 60s) and the new rule (hole #1, 30s), indicating that the spatial and temporal components of the rule had been learned bound together, in a coherent association within the context. At the same time, the spatio-temporal association learned in context A (hole #2 reinforced at 15s) was spared, which confirms that the animals were able to discriminate between the two contexts, despite a slight spatial generalization to the hole #1.

Interestingly, the impact of the shift session was still visible after 8 additional days of training, as only the experimental group presented the appropriate spatio-temporal behavior (hole #1, 30s, in context B). The spatial generalization observed for the control group suggests that the spatial component might be learned later in comparison to the timing rule, as we will discuss below.

Spatio-temporal binding and adaptation to the change

In this experiment, we submitted the animals to a shift of the spatio-temporal rule after the learning phase. As the initial rule had been learned, this paradigm permitted to isolate the learning of location and time, independently of the acquisition of the instrumental task. The main question was then: would one dimension shift before the other, or would the two dimensions be learned at the same rate?

The results showed a facilitated adaptation to a change in an associative rule combining place and interval time learning. The pattern of results indicates that a single session was sufficient for the animal to learn a new spatio-temporal association. First and foremost, this result supports the work of our colleagues (Molet and Miller, 2013;[START_REF] Buhusi | Associative and temporal learning: new directions[END_REF][START_REF] Tosun | Mice plan decision strategies based on previously learned time intervals, locations, and probabilities[END_REF] by confirming that animals are able to encode the location and temporal attributes of a given situation. Secondly, this study shows that interval time can be learnt fast which echoes the findings of several studies showing that animals can learn time in one session. For example, Guilhardhi and [START_REF] Guilhardi | Dynamics of temporal discrimination[END_REF] showed that rats were able to adapt quickly to repeated shifts in a PI task. Similarly, Dallérac et al (2017) observed that rats can exhibit fast adaptation of temporal fear conditioned responses when changing the time between the CS and US in a conditioned suppression task. Other reports have shown that time is learned in a single trial (Diaz-Mataix et al, 2013;[START_REF] Dallérac | Temporal specificity of fear conditioning: effects of different conditioned stimulus-unconditioned stimulus intervals on the fear-potentiated startle effect[END_REF].Recent research also reported that mice are able to adjust their temporal behavior quickly and abruptly (only one trial) to a change of reinforcement rule, by using probabilistic estimate of the rule [START_REF] Kheifets | Mice take calculated risks[END_REF]. In a comparable manner, it is well known that animals can rapidly adapt to changes in spatial representation. For example, animals are able to rapidly adapt to changes in spatial memory tasks, accompanied with a remapping of the hippocampal place cells (O'Keefe and Dostrovsky, 1976;[START_REF] Morris | Allocentric spatial learning by hippocampectomised rats: A further test of the "spatial mapping" and "working memory" theories of hippocampal function[END_REF][START_REF] Moser | Place Cells, Grid Cells, and the Brain's Spatial Representation System[END_REF] for review see [START_REF] Eichenbaum | The hippocampus, memory, and place cells: is it spatial memory or a memory space[END_REF]Hartley et al , 2013). In addition, a recent study in monkeys by Kowk and collaborators (2015) confirmed that animals can exhibit flexibility to rapidly adapt to spatio-temporal changes in a delayed matching-to-position task. Our results thus comfort the previous results of the literature, extending it further. In sum, our present findings confirm that rodents do have the capacity to perceive and adjust quickly their behavior to a complex change of a rule, supporting the view that animal may represent time and space in their memory, therefore offering a framework for decision making, as suggested in a recent review (Gur et al, 2108).

More importantly, our results show that when learned in association, there is a spatiotemporal binding formed that governs behavior in a selective manner according to each of the old and new rules. Surprisingly, though, the behavior of the control animals was not spatially adjusted 8 days after the shift, while their temporal behavior was not significantly different from the experimental animals in the new reinforced location (hole #1). The imprecision in nose-poke choice might be due to the fact that, in phase 3, all nose-pokes were lit. In a similar situation, [START_REF] Tosun | Mice plan decision strategies based on previously learned time intervals, locations, and probabilities[END_REF] showed that when two previously reinforced locations are lit at the same time, the animals explore the two locations with a switching behavior between the two locations governed by the temporal rules associated with them. Furthermore, the behavior of experimental animals in the present experiment clearly shows that time and space were bound together (see figure 4). The fact that the temporal pattern of the visits to the hole #2 was not related to its specific association in the other context, nor to the old or new temporal rule in this context (see figure 6), suggests that the animal's behavior reflected some imprecision in spatial memory (as also suggested by the activity in hole #3). Nevertheless, the results may indicate that the spatial dimension needs more time to be learned than the temporal one. One may argue, however, that the situation was not symmetrical for both dimensions, as the long-to-short shift in the temporal dimension could be seen as without ambiguity for the animal (because reinforcement is available earlier than expected) contrary to the spatial situation where all possible locations were lit. Further experiments will be needed to question the hierarchical organization in memory of spatial and temporal representations. In the framework of working memory, an asymmetry of the representation of spatial and temporal information has been found regarding the type of task and/or the sensory modality engaged [START_REF] Buhusi | Impaired interval timing and spatial-temporal integration in mice deficient in CHL1, a gene associated with schizophrenia[END_REF][START_REF] Noyce | Space Depends On Time: Informational Asymmetries in Visual and Auditory Short-Term Memory[END_REF][START_REF] Michalka | Short-Term Memory for Space and Time Flexibly Recruit Complementary Sensory-Biased Frontal Lobe Attention Networks[END_REF]. [START_REF] Gallistel | Space, Time and Number in the Brain: Searching for the Foundations of Mathematical Thought -Mental Magnitudes[END_REF] proposed that abstract dimensions such as time, space and numbers could be represented as magnitudes. But little is known about the link of this representation within a contextual event and the formation of a meaningful memory articulating those parameters together.

In sum, regarding the processing of spatial and temporal dimensions, it appears from these results that when the association is presented to the animals in an explicit manner (with a Pavlovian shift of the rule), time and space are learned together, exhibiting spatio-temporal binding. However, without precise indications, such as visual cues, time may be learnt before space.

Binding of time and space within context

In the present work, in addition to the spatio-temporal association, the animals had to learn the difference between two different contexts (A and B) which differed by the lighting of the experimental box and the frequency of the discriminative timing stimuli (tone). While this situation provides a control for the specificity of the obtained effects, it also gives some indication of whether there had been some tripartite (time-location-context) memory formed. The rats' ability to learn a complex association of space-event-context has been well documented in many behavioral paradigms assessing episodic-like memory in rodents, and is referred to as the what/where/which context association (for similar studies, see [START_REF] Eacott | Integrated memory for object, place, and context in rats: a possible model of episodic-like memory?[END_REF][START_REF] Eacott | Recollection in an episodic-like memory task in the rat[END_REF]Easton and Eacott 2008;[START_REF] Eacott | Episodic memory in animals: remembering which occasion[END_REF]. A recent work from [START_REF] Crystal | Binding of episodic memories in the rat[END_REF] also reported that rats can dissociate episodic events based on contextual cues in order to form a bound representation of multiple dimensions of the memory. But these studies focused on episodic-like memory, and used a very different time scale than the second-to-minute range. To our knowledge, no experiment has studied a contextual dimension in the framework of interval timing.

Our results show a clear time-location-context associated behavior, as shown by the fact that the animals were able to discriminate between the two contexts during the initial training (Phase 1).

In addition, the representation of the context appeared to be conserved in Phase 3, as shown by an appropriate spatio-temporal behavior in context A (hole #2-15s) and the correct adaptation to the new rule of the context B (hole #1-30s) by the experimental animals. Nevertheless, two observations can be made. First, while the spatio-temporal rule was not changed in context A, a slight increase in visits was observed in the hole #1 in this context (hole newly reinforced in context B). Second, the hole #2 (reinforced in context A) was still visited in context B even at the end of phase 3 (especially in control animals), although it had never been reinforced in that context. Thus, there was some generalization of the spatial learning between the two contexts despite their very different characteristics, especially the highly discriminable timing cues (1 kHz vs. 11 kHz). One may suspect that the fact that all the nose-poke holes were lit in Phase 3 may have played a role, although proximity between the holes could not be the sole factor, as no such increase in responding was found in the opposite side, equally distant to hole #2 (i.e., hole # 3). Importantly, this uncertainty was weakened by the Pavlovian shift session in the experimental animals. Furthermore, the temporal behavior was properly adjusted to the context from day one in the experimental animals, suggesting that a time-context association is learned faster than a space-context association. Further experiments will be necessary to clearly compare the speed of learning of each of the components of the space-time-context triplet, and the rules governing their binding.

Neuronal representation of the association

Where could the binding between space and time be encoded in the brain? Are space and time represented in the same anatomical structures? Up to date, two brain areas have been reported to support spatial and interval timing processes.

Firstly, the basal ganglia, in particular the dorsal striatum, is known to be critical in interval timing and temporal processes (for a review see [START_REF] Buhusi | What makes us tick? Functional and neural mechanisms of interval timing[END_REF]. A rapid adaptation to a change of rule has been previously linked to a change in the firing rate of the task-modulated striatal neurons [START_REF] Portugal | Behavioral Sensitivity of Temporally Modulated Striatal Neurons[END_REF]. The author of this study suggested that rather than event times, the striatum might encode the timing of adaptive behavioral transitions between options based on the environmental statistics. Similarly, [START_REF] Mello | A scalable population code for time in the striatum[END_REF] demonstrated in an electrophysiological experiment that striatal neuronal populations can quickly adapt to the immediate task demands by rescaling their temporal tuning to abrupt changes in delays to reinforcement, similar to our present task. Furthermore, the striatum has been reported to be involved in spatial memory; for example, it has been shown that lesions in the dorsal part of the striatum leads to deficits in spatial learning [START_REF] Block | Quinolinic acid lesion of the striatum induces impairment in spatial learning and motor performance in rats[END_REF]McDonald and White, 1994;[START_REF] Lee | Lesions of the dorsomedial striatum delay spatial learning and render cue-based navigation inflexible in a water maze task in mice[END_REF]. A recent study in rats also confirmed that a unilateral lesion of this structure can lead to a disruption of spatio-temporal integration [START_REF] Blankenship | Unilateral lesions of the dorsocentral striatum (DCS) disrupt spatial and temporal characteristics of food protection behavior[END_REF], which may lead to think that the dorsal striatum might be involved in our present task.

Secondly, the hippocampal formation has been extensively studied regarding spatial navigation [START_REF] O'keefe | The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat[END_REF][START_REF] Save | Object exploration and reactions to spatial and nonspatial changes in hooded rats following damage to parietal cortex or hippocampal formation[END_REF][START_REF] Maguire | Recalling routes around London: Activation of the right hippocampus in taxi drivers[END_REF]Goodrich-Hunsaker et al, 2008). In addition, it is known that the hippocampal place cells are able to code for interval time as well (Pastalkova et al, 2008;[START_REF] Macdonald | Hippocampal "time cells" bridge the gap in memory for discontiguous events[END_REF]Kraus et al, 2013) and that fornix lesions (initially found to impair spatial learning) impair the detection of temporal changes [START_REF] Kwok | Adaptability to changes in temporal structure is fornixdependent[END_REF]. The involvement of hippocampus in interval timing has been highlighted in a recent study [START_REF] Jacobs | Critical role of the hippocampus in memory for elapsed time[END_REF], although in a supra-minute time range. Another recent finding also suggests that hippocampus and memory-related mechanisms are involved in spatio-temporal integration bringing evidence that the hippocampi are critical components of internal clock mechanism [START_REF] Buhusi | Impaired interval timing and spatial-temporal integration in mice deficient in CHL1, a gene associated with schizophrenia[END_REF]. All these results are clues pointing into the direction that the hippocampal formation might also be involved in the spatio-temporal binding that we report in our present study, and more research needs to be conducted to further clarify this question.

In conclusion, using a behavioral paradigm that enables the study of the interaction of space, interval time and context, and their overall binding, we have found differential dynamics of space and interval time learning, and a tripartite space-time-context binding. The matters of probabilistic estimate of rules, animal cognitive representation and behavioral adaptation to a change of rule still demand more investigation. We believe that our experimental design can be a basis for further investigations related to the hierarchical learning of such a complex association. We hope that it will foster to a better understanding of the neural mechanisms supporting the integrated representation of space, interval time and context, and therefore contribute to the development of more refined therapeutic approaches for memory impairment and related diseases.

  for the experimental design): (phase 1) Instrumental training phase of specific spatio-temporal rules associated to different contexts: a Peak Interval (PI) task combining the learning of contextual (different house lights and tone frequencies), spatial (nose poke) and temporal (peak interval) information; (phase 2) Spatiotemporal shift in a single Pavlovian session to control the amount of exposure to stimuli and reinforcement during the learning of a new spatio-temporal rule: some rats were shifted to a new spatio-temporal association (new location and new time) specific to one context using a Pavlovian paradigm, while the others were kept to the trained spatio-temporal rule; (phase 3)

  refaite

Figure 1 :

 1 Figure 1: Experimental design

Figure 2 :

 2 Figure 2: spatio-temporal training

Figure 3 :

 3 Figure 3: Impact of the Shift session at Day+1 in context B

Figure 4 :

 4 Figure 4: Spatio-temporal binding

Figure 5 :

 5 Figure 5: Impact of the shift session at Day+1 in context A

Figure 6 :

 6 Figure 6: Impact of the shift session at Day+9 in the two contexts

This work was supported by grants from Agence Nationale de la Recherche (ANR).


Rodrigo A Ibata
email: rodrigo.ibata@astro.unistra.fr

Alan Mcconnachie

Jean-Charles Cuillandre

Nicholas Fantin

Misha Haywood

Nicolas F Martin

Piere Bergeron

Volker Beckmann

Edouard Bernard

Piercarlo Bonifacio

Elisabetta Caffau

Raymond Carlberg

Patrick Côté

Rémi Cabanac

Scott Chapman

Pierre-Alain Duc

Florence Durret

Benoît Famaey

Sébastien Frabbro

Stephen Gwyn

Francois Hammer

Vanessa Hill

Michael J Hudson

Ariane Lanc ¸on

Geraint Lewis

Khyati Malhan

Paola Di Matteo

Henry Mccracken

Simona Mei

Yannick Mellier

Julio Navarro

Sandrine Pires

Chris Pritchet

Celine Reylé

Harvey Richer

Annie C Robin

Rubén Sánchez Jannsen

Marcin Sawicki

Douglas Scott

Vivien Scottez

Kristine Spekkens

Else Starkenburg

Guillaume Thomas

Kim Venn

THE CANADA-FRANCE IMAGING SURVEY: FIRST RESULTS FROM THE u-BAND COMPONENT

Keywords: Galaxy, halo -Galaxy, stellar content -surveys -galaxies, formation -Galaxy, structure -(stars, ) white dwarfs

The Canada-France Imaging Survey (CFIS) will map the northern high Galactic latitude sky in the uband ("CFIS-u", 10,000 deg 2 ) and in the r-band ("CFIS-r", 5,000 deg 2 ), enabling a host of stand-alone science investigations, and providing some of the ground-based data necessary for photometric redshift determination for the Euclid mission. In this first contribution we present the u-band component of the survey, describe the observational strategy, and discuss some first highlight results, based on approximately one third of the final area. We show that the Galactic anticenter structure is distributed continuously along the line of sight, out to beyond 20 kpc, and possesses a metallicity distribution that is essentially identical to that of the outer disk sampled by APOGEE. This suggests that it is probably a buckled disk of old metal-rich stars, rather than a stream or a flare. We also discuss the future potential for CFIS-u in discovering star-forming dwarf galaxies around the Local Group, the characterization of the white dwarf and blue straggler population of the Milky Way, as well as its sensitivity to low-surface brightness structures in external galaxies.

INTRODUCTION

Progress in astrophysics has depended, to a great extent, on the large imaging surveys that have provided the community with sources for study. From the Schmidt telescope campaigns of the 1950s to 1990s, to the digital era with the Sloan Digital Sky Survey (SDSS, York et al. 2000) and Pan-STARRS1 (PS1, Chambers et al. 2016), almost every area of astrophysics has been influenced by the rich trove of morphological and photometric information large optical imaging surveys have provided.

Over the coming years, several new important sky surveys will undoubtedly continue this trend. These include the Large Synoptic Survey Telescope (LSST, LSST Collaboration 2012) which will explore the time domain by repeatedly imaging approximately half the sky in six bands over the course of a decade; the Gaia space mission (Brown et al. 2016) which will unveil the astrometric sky; and the Euclid satellite (Laureijs et al. 2011;[START_REF] Racca | Proceedings of the SPIE[END_REF]), which will gather data of superb image quality over 15,000 deg 2 to probe the dark universe via gravitational lensing. Mining and understanding this massive new parameter space will undoubtedly provide the basis of much of our scientific discussion for decades to come.

In the optical bands, the most challenging component of a sky survey to acquire is the u-band (centered around 3500 • A). The difficulty comes from the intrinsic faintness of most sources, whose spectral energy distributions peak red-wards of u, it also stems from the poor transparency of the atmosphere in this band (together with the strong variation with airmass), and from the relatively poor efficiency of most optical CCD cameras in this wavelength region. This typically means that u-band observations are more expensive to obtain in telescope time than longer wavelength optical bands. It is the reason that the SDSS u-band is relatively shallow compared to the SDSS g, r, i-bands, and it probably accounts for the lack of a u-band in the PS1 survey.

Nevertheless, the u-band contains very important astrophysical information: in stars, the many metal lines in this spectral region render the u-band very useful for measuring metallicity; young or hot stellar populations have their greatest contrast in the UV, making this an important band to study star-formation in the nearby Universe; while for distant galaxies the u-band is very powerful for helping us to distinguish between photometric redshift solutions.

These are some of the reasons why undertaking an allsky u-band survey is of prime astrophysical importance. These reasons undoubtedly also motivated the SkyMapper (Keller et al. 2007) and SCUSS (Zou et al. 2016) projects. SkyMapper is currently surveying the whole southern sky to u ∼ 20 at S/N = 30, while SCUSS has mapped the Southern Galactic Cap, reaching u ∼ 23 at S/N = 5. Over the course of the next decade, LSST will also survey the southern sky in the u-band, but plans for a deep northern sky u-band survey have been missing, until now. One of the main motivations of our community in undertaking a northern deep u-band survey was to contribute to the photometric redshift measurements that are needed for the Euclid mission. Groundbased photometric redshifts are essential for Euclid, since the gravitational lensing and baryon acoustic oscillation analyses depend on distance, for which redshift can be used as a proxy. 25 However, the immediate scientific driver for the present u-band survey is Galactic Archaeology, in particular by using metallicity as a population discriminant, as well as as a means to improve photometric distance measurements for main sequence stars. The sensitivity of the u-band to metallicity was beautifully demonstrated by Ivezić et al. (2008, hereafter I08), who undertook an analysis of SDSS photometry that allowed them to map out the metallicity structure of the Milky Way within about 9 kpc of the Sun. The limiting distance was set by the photometric depth of the SDSS u-band data, which substantially limited the volume that I08 could study in this way. As we discuss in the companion paper to this contribution (Ibata et al. 2017;hereafter, Paper II), for the purpose of measuring photometric metallicities, the SDSS is effectively 2.7 mag too shallow in the uband to make optimal use of its g-band depth. Providing this missing information is one of the aims of our survey, and we argue in paper II that it is essential to study the Galactic halo in combination with Gaia. The reason for this is that most halo stars in Gaia will have faint magnitudes, beyond the threshold for accurate Gaia parallax or metallicity measurements. By providing a reliable photometric metallicity and thereby a good photometric distance, it will be possible to convert Gaia's proper motions (which remain excellent for all of their surveyed stars) into transverse velocities, which are physically much more useful.

The outline of this paper is as follows. In Section 2 we explain the survey strategy, observations and data reduction. In Section 3 we explore the stellar populations and structure towards the Galactic Anticenter, and we briefly discuss Galactic white dwarfs in Section 4. We show how CFIS-u can be used to uncover nearby starforming dwarf galaxies in Section 5, and discuss its sensitivity to low surface brightness structures in Section 6. We draw conclusions for our study in Section 7.

CFIS-U

It was for the afore-mentioned reasons that we decided in late 2013 to propose a large u-band survey in response to a call for Large Programs at the Canada-France-Hawaii Telescope (CFHT) for the four semesters 2015A-2016B. This first survey was named the "CFHT Legacy for the u-band All-Sky Universe" (Luau) survey, and was awarded an initial 63.6 nights over this period to survey ∼3,500 deg 2 . This program was designed to take advantage of the unique u-band sensitivity of CFHT with respect to all other current, large, ground-based facilities. In a subsequent call for the 2017-2019 period, we extended the scope of this wide-field survey to include extensive r-band, as well as u-band, imaging. The new program is called the Canada-France Imaging Survey, and 25 The core science of Euclid requires photometric redshifts derived from griz ground-based photometry, plus the wide optical ("VIS") and near infrared (JHK) photometry measured on-board the satellite. Ideally, Euclid would have access to u-band photometry over its entire 15,000 deg 2 survey area to u = 24.2 (S/N = 10, integrated over 2 diameter apertures). Although CFIS-u only reaches u = 23.7 with this metric, it will nevertheless be very helpful for rejecting nearer and more compact sources. It is expected that LSST will provide the required ground-based photometry in the South. consists of 271 nights of new data, in addition to the original allocation for Luau. The completed CFIS-u component (which includes the original Luau survey) will cover 10,000 deg 2 , and the new r-band component will cover 5,000 deg 2 . CFIS is intended to enable a broad swath of stand-alone science investigations, to contribute some of the necessary data for the derivation of photometric redshifts for the Euclid space mission, and to provide a high quality legacy dataset for the northern hemisphere sky. More details on CFIS, in particular with relation to the r-band component, will be presented in a forthcoming contribution. Here, we focus on the data acquisition and processing and some science highlights using the existing u-band data.

One of the primary aims of CFIS-u is to improve upon I08 by providing much deeper u-band data that can be used to reach the full depth of the SDSS and PS1 surveys. The plan is for CFIS-u to cover the entire northern sky not optimally accessible to LSST in the u-band, and away from the Galactic plane (|b| > 25 • ), to a depth of u = 24.4 with a photometric uncertainty of δu ∼ 0.2 mag. The survey footprint is shown in Figure 1. In combination with grizy data from SDSS and PS1, this enables the application of the techniques pioneered by I08 to a volume a factor of about 100 greater than what can be reached with existing SDSS u-band data.

The imaging data for CFIS-u presented herein were obtained at the Canada-France Hawaii Telescope in semesters 2015A-2016B and are scheduled to continue until the end of 2019B (Jan 2020). The survey uses the MegaCam wide-field camera [START_REF] Boulade | Instrument Design and Performance for Optical/Infrared Ground-based Telescopes[END_REF] with a new u-band filter procured in late 2014 which is physically wider than the u-band filter previously installed, allowing for the illumination of the full 40 CCDs of the MegaCam mosaic (previous filters only allowed for 36 CCDs to be illuminated). The new u-band filter is also bluer than the old one, being closer to the SDSS u-band, but with a significantly different transmission curve (see Figure 2). We adopted a very simple tiling pattern, using the central rectangular (9 × 4 CCD) regions butted against each other in the East-West direction with only a small overlap that was optimised over the course of the survey. The four lateral CCDs (of which there are two on each of the Eastern and Western ends of the camera -see Figure 3) then overlap 100% in the adjacent fields, both East and West.

As of 2017 February 25th, a total of 10,827 useful images have been processed for this large survey, covering an area of approximately 2,909 deg 2 of sky, shown in black in Figure 1. The median seeing over this set of images is 0 .78. The strategy we adopted was to take exposures of 80 s per field, but to stagger the tiling pattern so that successive East-West rows are displaced from the previous row by one third of a field both in the North and East directions. Thus the final exposure depth is 240 s over most of the survey. However, there are small horizontal gaps (80 ) in the MegaCam camera between CCD rows, and in those areas the coadded exposure is only 160 s. In about 10% of the survey the overlap with the four lateral CCDs gives 320 s of total exposure. The images were initially pre-processed (de-biassed and flat-fielded) at the CFHT using the Elixir pipeline software (Magnier & Cuillandre 2004). We then used the Cambridge Astronomical Survey Unit (CASU) photometric pipeline (Irwin & Lewis 2001) to detect sources and derive aperture magnitudes for all sources 1.5σ above the local background in each individual frame (i.e. not stacked). An aperture radius of four pixels was used (0 .75). In this process, the astrometry was calibrated with respect to the Gaia Data Release 1 (DR1) catalog (Brown et al. 2016), although in a small fraction of frames ( 1%) we were forced to use PS1 positions due to a paucity of Gaia stars in these high-latitude fields. The typical rms scatter in the astrometric solutions is excellent, being 0 .034.

The source classification was undertaken using a customized version of the CASU image classification tool, which examines the morphology of individual sources. The main improvement we implemented to this algorithm was to train the point-source criteria in each field using Gaia stars (which allows better rejection of extended sources and noise).

To compare the CFHT photometry to the SDSS DR13 photometry (Albareti et al. 2016), we made use of the following color transformation (used for calibration at the Canadian Astronomy Data Centre):

u CFHT = u SDSS + 0.038(u SDSS -g SDSS ) -0.165 , (1)
which is found to be valid for u SDSS -g SDSS > 1.3 (note that this transformation is only valid for stars that are redder than the main sequence stars studied in Paper II). After comparing our photometry to the SDSS u-band (transformed with this color equation), we found that there was a strong pattern of residuals over the camera. This problem has been noted before (e.g., [START_REF] Regnault | [END_REF]Ibata et al. 2014), and is due to the CFHT pipeline creating slightly incorrect flats. The master flats are created by the pipeline from all the twilight observations during a run (MegaCam is mounted on the CFHT typically once per month for a two-week period, or "run"), but this introduces a flat-fielding error with respect to nighttime science images. (In principle, it should be possible to use the dark-sky CFIS-u data themselves to generate a better u-band flat, but we did not attempt to implement this). The SDSS-MegaCam pattern for the u-band appears to have remained stable over the course of the survey, but since we have sufficiently good statistics, we decided to model it on a run-by-run basis. For each CCD the residual pattern was modeled as a twodimensional Legendre polynomial with up to cubic terms in x and y (i.e., 10 parameters in all), and the resulting models were used to flatten all the data. An example of this correction for one of the runs is shown in Figure 3. The typical rms scatter for stars in the range 16 < u < 19 (and 1.3 < u -g < 2.5) is 0.02 mags. Note that even with perfectly calibrated CFHT and SDSS photometry, there would be scatter in the photometric differences due to the fact that the two u-band filters are significantly different (see Figure 2).

The photometric offsets between overlapping fields, and with the SDSS, were then used to derive a global solution to the zero point of the survey. Due to the size of the matrix inversion problem (> 98, 000 equa- tions with 10,827 unknown zero points), we simplified the task by solving for the zero points using data in bands of 30 • in RA, but offset in 10 • intervals. In this way we solve for the central 10 • , while maintaining the information of the photometric offsets 10 • on either side of that region. Any equation solutions (i.e., field-to-field photometric differences) that were discrepant at more than 2.5σ were discarded, and the procedure was iterated (keeping the plausible field-to-field photometric offsets) until convergence. We checked that the zero points in the overlapping RA bands were consistent to better than 0.01 mag.

Finally, the individual sources between frames were matched using a 0 .3 search radius, and their photometric measurements were combined using the photometric uncertainties to construct a weighted flux average, and hence a magnitude. This results in a catalog containing > 3.0 × 10 7 sources. These detections were then matched against the SDSS, or PS1, using a 0 .5 search radius, thus matching all but the very highest, rare, proper motion sources.

In comparing the corrected CFHT photometry with the SDSS DR13 data (which was re-calibrated by the SDSS team as detailed in Finkbeiner et al. 2016), we noticed that strong residuals remain. However, these residuals do not the MegaCam footprint, but quite obviously track the SDSS observing pattern of "stripes", as we show in Figure 4. Once the CFIS-u survey is completed, we will be able to use it to fully recalibrate the SDSS u-band. In the meantime, some caution is needed when using the SDSS u, which clearly contains spatiallycorrelated zero point errors.

The photometric uncertainties in the CFHT data are derived from the square root of the variance of the weighted flux measurements. The distribution of these uncertainties is displayed in Figure 5, along with the uncertainties of the SDSS u-band photometry of the same targets. The CFHT photometry can be seen to be significantly deeper than the SDSS, typically reaching ∼ 3 magnitudes fainter at the same uncertainty level.

Two additional photometric catalogs were generated, using the SDSS DR13 positions of point sources and all PS1 sources as input centroids for forced photometry on the CFIS u-band images. For the SDSS, it was necessary first to shift the astrometric solution of all CFIS frames onto the SDSS solution, which is slightly different to that of Gaia (this is not necessary for PS1, as it is already on the Gaia astrometric reference frame). As before, we measured u-band aperture magnitudes within a four pixel radius centered on all those source positions. The resulting forced photometry catalogs have (at present) > 2.0 × 10 7 and > 1.4 × 10 8 measurements, at the positions of the SDSS point-sources and PS1 detections, respectively.

THE GALACTIC ANTICENTER

As a first example of what will be possible with CFIS-u, we will examine the populations towards the Galactic Anticenter, making use of the CFIS photometric metallicity determinations. In Paper II, we describe in detail the procedure we follow to derive [Fe/H]. Briefly, this consists of using an empirically-determined three-dimensional Legendre polynomial to interpolate an [Fe/H] value from a star's (u -g) 0 , (g -r) 0 and (g -i) 0 colors (we use "Method 2" from Paper II). The resulting metallicity is then supplied to the I08 photometric parallax calibration (Equations 1-3 in Paper II), together with a de-reddened r-band magnitude to obtain the star's distance. The g, r, i magnitudes that we use here to probe the Galactic Anticenter are combined SDSS and PS1 magnitudes on the SDSS photometric system, with the color selection and quality criteria of the "Wide cut," as described in Paper II.

In Figure 6 we display a metallicity map of our Galaxy in the R-z plane, as viewed by CFIS-u. A striking aspect of this map is the strong metal-rich population in the outer Galaxy at distances 15 kpc R 25 kpc. This structure, named the Galactic Anticenter Stellar Structure (GASS), was discovered over a decade ago in SDSS counts towards the Anticenter direction (Newberg et al. 2001), and has been found to encircle a large swath of the Milky Way, approximately parallel to the disk over at least 100 • (Ibata et al. 2003;Rocha-Pinto et al. 2004;Martin et al. 2006Martin et al. , 2007;;Conn et al. 2008;Sharma et al. 2010;Martin et al. 2014). Slater et al. (2014) and Morganson et al. (2016) show recent panoramic views of this structure. Early spectroscopic follow-up indicated that the structure is composed of metal rich stars with [Fe/H] = -0.4 ± 0.3, on roughly circular orbits, with relatively cold kinematics σ v = 20 ± 4 km s -1 (Crane et al. 2003;Rocha-Pinto et al. 2003). The stars that make up the structure are not young: the so-called Triangulum-Andromeda overdensity (located between 15 and 20 kpc along the line of sight towards M31), that appears to be related to the GASS, has been measured to have an age of 6-10 Gyr (Sheffield et al. 2014).

Since the structure is located on the edge of the disk, it appears natural to suppose that it may be a warp or feature of that Galactic component, similar to the various overdensities that have been detected in the outskirts of the disk of the Andromeda galaxy (Ferguson et al. 2002;Ibata et al. 2005). However, the streamlike aspect of the GASS would be considered proof of an accretion origin if it inhabited almost any other orbital plane. Indeed, Penarrubia et al. (2005) considered this possibility, presenting an N -body model for the GASS in which it forms from stars that are tidally disrupted from an accreted dwarf galaxy, as this undergoes dynamical friction and is assimilated into the outer disk of the Milky Way. The initial orbital mis-alignment of the satellite with respect to the disk then naturally leads to a stream-like structure that snakes in and out of the disk, similar to the behavior of the observed overdensity. Further support for the accreted dwarf scenario of formation of the GASS was found in the chemical abundances derived from high-resolution spectra, which show an abundance pattern similar to what is seen in dwarf spheroidal galaxies and unlike that of the disk (Chou et al. 2010). This evidence is not conclusive, however, since star formation in the low density environments in the outer disks of galaxies will have different gas retention and chemical evolution compared to the inner disk regions (Barnes et al. 2012).

Recent studies have explored again the Galactic origin of the GASS, suggesting that it is due to material that has been kicked out of the Galactic disk (Xu et al. 2015;Price-Whelan et al. 2015), possibly due to the reaction of the close passage of an interloper (Kazantzidis et al. 2008), and/or the action of subsequent dynamical waves traveling through the disk. 26 This latter possibility is particularly interesting in the light of the discovery of vertical waves in stellar density and mean vertical stellar motions in the Solar vicinity (Widrow et al. 2012). Subsequently, Widrow et al. (2014) analyzed a simulated Galactic disk interacting with multiple satellites, in which the inner Galactic disk was shown to be prone to a strong bar instability and affected by vertical breathing modes, 27 whilst the outer disk was shown to be affected by bending modes. 28 Such a vertical bending mode could precisely be responsible for the GASS. Interestingly, Monari et al. (2015Monari et al. ( , 2016a,b) ,b) managed to explain qualitatively the properties of the inner disk breathing modes of the Widrow et al. (2014) simulation from the effects of its bar and spirals (see also Faure et al. 2014). A vertical breathing mode is actually observed locally in the kinematics of red clump stars out to |z| ∼ 2 kpc (Williams et al. 2013), but with a rather high vertical velocity amplitude compared to the predictions from spiral density waves in Monari et al. (2016a,b), a discrepancy potentially due to the transient nature of spirals, which could themselves be induced by interactions with satellites. Thus, the inner disk breathing mode, as seen by Williams et al. (2013), could be related to satelliteinduced non-axisymmetries of the disk, whilst the GASS could be the signature of the bending mode induced by those same satellite interactions. In this picture, the Solar vicinity would then be in the transition zone between the inner breathing Galactic disk and the outer bending Galactic disk. The consequences of this emerging complex picture on our future dynamical modeling of the Milky Way in the Gaia/CFIS era are profound. It was for instance recently shown that local dynamical determinations of the dark matter density could be off by ∼ 25% if one wrongly assumes that the Solar neighborhood is in equilibrium (Banik et al. 2017).

The high-metallicity sub-component seen in the CFISu map of Figure 6a is present at high vertical distance z over the entire Galactic anticenter direction that CFIS-u currently probes. Figures 6b-d show the distribution of three metallicity slices towards the Galactic anticenter, selected from the spatial box 185 • < l < 195 • , 18 • < 26 The reader may find it useful to refer to figure 8 of Li et al. (2017), which sketches a possible configuration for the oscillatory behavior of the outer disk.

27 A vertical breathing mode is a density perturbation with even parity with respect to the Galactic plane (i.e. no North-South asymmetry in density) and a vertical velocity field perturbation with odd parity. It is thus a vertical "rarefaction-compression" wave.

28 A vertical bending mode has odd parity in density with respect to the plane, and even parity in the vertical velocity field. It is thus a vertical "corrugation" wave. 7). The line of sight distribution is approximately smooth, although there may be some discontinuities at the locations marked by the arrows. These will be investigated further when the survey covers a greater area of the Galactic Anticenter region. nearer component (blue line) has a significantly lower peak metallicity (around -0.7 dex) than the outer disk in APOGEE. Indeed, its MDF is similar to the intermediate metallicity component identified in the metallicitydistance decomposition in Paper II (black line), which we attribute to the Solar neighborhood thick disk. As we probe out further in distance, however (green and red lines), the distributions become substantially more metal rich. Given that these more distant samples also contain a contribution from the intermediate component as well as halo stars, the metal-rich remainder population probably peaks at slightly higher values that what we see here. Taking into account the small but significant negative radial metallicity gradient observed in the outer disk, the mean metallicity of the disk population probably decreases between R = 16 and 26 kpc compared to what it is at R < 15 kpc. The outer disk, as it is known from APOGEE within R < 15 kpc, therefore appears compatible with these CFIS-u data, giving support to the hypothesis that the GASS is probably the result of the outer disk being pushed out of the Galactic plane.

Although the MDF of Figure 7 peaks at a metallicity typical of the thick disk, we exclude the possibility that the metal-rich subcomponent is the thick disk itself -defined from its chemical properties as an alpha-rich population, implying a number of specific characteristics (see Haywood et al. 2013). It has been shown that the short scale-length of this population essentially confines the thick disk to the inner 10 kpc of the Milky Way (Bensby et al. 2011;Cheng et al. 2012;[START_REF] Bovy | [END_REF]. In fact, the outer disk (as least as far out as R = 15 kpc) is exclusively populated by a low-alpha population, as shown by APOGEE (Hayden et al. 2015).

The distance distribution of the metal-rich stars towards the Galactic Anticenter is shown in Figure 8, where we plot stars with -0.5 < [Fe/H] < 0 in the same window of sky as before. The distribution is approximately smooth, although there are hints of discontinuities at the locations marked with arrows. We will attempt to verify whether these features exist in adjacent regions of the sky once the CFIS-u survey covers the anticenter more completely.

The relative smoothness of the stellar distribution along this line of sight to the Galactic anticenter, as well as the chemical similarity with the outer disk (Figure 7) suggests that the GASS resembles a warped disk of old stars. It will therefore be very interesting to obtain further u-band data closer to the Galactic plane so as to probe the asymmetry of the outer disk as a function of distance.

WHITE DWARFS AND HORIZONTAL BRANCH STARS

White dwarfs are the remnants of some of the oldest stars in the Galaxy and have provided vital information regarding the formation, evolution, and structure of the Milky Way. Recent large surveys, such as the SDSS, have increased the number of spectroscopically confirmed white dwarfs by more than an order of magnitude, allowing for large statistical analyses of the Galactic population. Much of the focus has been on the coolest and oldest white dwarfs, since they can provide age constraints for the Galactic components (see, e.g., Leggett et al. 1998;Kalirai 2012;Gianninas et al. 2015). On the other hand, studies of the hot, and hence young, white dwarf population focus on constraining the initialfinal mass relationship -an important input for stellar population synthesis codes (see, e.g., Bianchi et al. 2011).

The combination of wide sky coverage, u-band depth, and the ability to cross-match with other deep photometric surveys (e.g., SDSS, PS1, or the Next Generation Virgo Survey - Ferrarese et al. 2012) makes CFIS-u ideally suited to select a large number of white dwarfs. Figure 9 shows a (u -g), (g -r) color-color diagram of all CFIS-u sources with δu, δg, δr < 0.05 mag and u > 17 mag. Spectroscopically confirmed white dwarfs identified by Kleinman et al. (2013), sorted by type, are shown as colored points. The theoretical sequence for white dwarfs with a mass of M = 0.6 M and atmospheres of pure hydrogen or pure helium (computed from Holberg & Bergeron 2006 model atmospheres 29 ) are shown in green and purple respectively. Figure 9 shows the clear separation between the white dwarf cooling sequence, QSOs, and main-sequence stars.

The positions and u-band magnitudes will also be important for the cool end of the white dwarf luminosity function. White dwarf colors turn blueward at low temperatures as a result of collision induced opacity from molecular hydrogen, and hence deep u-band photometry will aid in the detection of these cool objects. However, cool white dwarfs are difficult to detect as they are faint and have similar colors to main sequence stars. Previous attempts to disentangle the coolest white dwarfs from other populations have relied on their proper motions (see, e.g., Harris et al. 2006;Kilic et al. 2006;Rowell & Hambly 2011). Combining the astrometry from SDSS with that of CFIS will yield proper motions for sources with deep u-band photometry. The combination of u-band magnitude and proper motions will allow for a comprehensive selection of cool white dwarfs for followup spectroscopy.

29 http://www.astro.umontreal.ca/~bergeron/CoolingModels Sub-dwarf O and B stars are the most common hot stars in the halo apart from white dwarfs, and CFIS-u should readily allow their photometric identification (Scibelli et al. 2014). The depth of CFIS-u is also sufficient to identify effectively all A-type stars, including horizontal branch stars, out to beyond the virial radius of the Galaxy (D∼300kpc), which will allow us to study the global shape and substructure of the distant outer halo. This will provide targets for follow-up spectroscopy that will be used to constrain the mass distribution of the Milky Way at very large radius. While blue stragglers will contaminate the sample of horizontal branch stars, the former have higher surface gravity and can be distinguished from the latter with ugr photometry (Deason et al. 2011).

STAR-FORMATION ON THE EDGE OF THE LOCAL

GROUP

Another very interesting issue that can be investigated with a survey such as CFIS-u is to quantify the prevalence of star-forming dwarf galaxies in the environment surrounding the Local Group. These objects may trace the recent accretion of dark and baryonic matter into our environment and can potentially tell us about the suppression of star-formation in isolated low-mass haloes [START_REF] Read | [END_REF].

The prototypical object of this class is Leo-P, discovered by Giovanelli et al. (2013) in a follow-up of compact high-velocity H I clouds detected by the ALFALFA survey (Giovanelli et al. 2005). Located at a distance of (1.62 ± 0.15) Mpc (McQuinn et al. 2015), this small galaxy appears to be an unquenched analogue to the dwarf spheroidals that are observed in abundance around the Milky Way and Andromeda.

Whether such objects are numerous or not remains an open question, as they have been very difficult to detect. In the SDSS, Leo-P appears as a very low-surface brightness structure with 2-3 faint point sources superimposed (Figure 10b and10c). In contrast, the greater depth and much better image quality of CFIS-u allows us to resolve the structure into around 20 point sources (Figure 10a).

This demonstrates the feasibility of detecting similar galaxies simply by searching for localised overdensities of point sources in our u-band maps.

THE u-BAND LOW SURFACE BRIGHTNESS UNIVERSE

We have investigated the use of the CFIS-u exposures for uncovering low-surface brightness structures in ultraviolet light. This is potentially a very interesting application of the CFIS-u data, since it may allow us to uncover and quantify low-levels of star-formation in the extreme outskirts of galaxies, opening up the possibility of studying star-formation as a function of environment in a large volume of the local Universe.

To this end we adapted the Elixir-LSB software, which was initially developed to process the Next Generation Virgo Survey (NGVS, Ferrarese et al. 2012), to the particular case of the CFIS-u signal properties and observing strategy. A sky model is constructed using a sliding window of a minimum of nine sequential frames in time: with a new frame captured every four minutes, the intrinsic variation of the sky in time can be frozen. The NGVS showed that a window of up to one hour long exhibits stable behaviour in the u-band for a moonless sky, reaching after Elixir-LSB processing 29 mag arcsec -2 in direct detection performance (32 mag arcsec -2 when integrating a galaxy profile).

With CFIS-u, we have the luxury of having many short exposures and find that reducing the window to ∼ 30 minutes, while still having enough frames to reject astronomical sources from the background, gives the best results. To restore the true sky signal in an image, we associate with each image a list of input frames captured within that time window, out of which a map correction frame is extracted. For long periods of CFIS-u observations, the list consists in the four preceding and four following frames (and skewed lists for the very end and beginning of the CFIS-u set on that sequence of the night). For each list of nine images, we derive the precise sky level for each frame and perform a median combination after scaling each image to a common median background value, and after having also smoothed each frame with a 2 wide Gaussian kernel. This smoothing ensures the sigma-clipping algorithm properly rejects faint sig-nals above the background, such as the extended faint features we aim to detect or optics reflection halos and other artifacts. This median background map is then precisely scaled back to the background of the central image of the set it is associated with, to enable a precise background subtraction, restoring the true sky signal.

Using a (min -max)/max metric on the corrected image background (searching for the faintest true signal above the noisy background, i.e., detecting by eye the faintest extended feature), the CFIS-u data show a limit at the 1% level (to be compared to 0.2% for the NGVS u-band), or equivalently, this indicates that we detect features that are in contrast 5 magnitudes fainter than the sky background (∼ 28 mag arcsec -2 ). The performance is not as good as the NGVS: this is due to the noise properties of the signal, which is not in the pure photon-noise dominated regime: the typical CFIS-u 80 s exposure has a background level of ∼ 30 electrons, giving a photon noise of just ∼ 6 electrons, which is comparable to the CCD readout noise. This limits the Elixir-LSB performance, since the true background level remains biased. The pipeline will be updated to include a more accurate noise model when running the sigma-clipping rejection.

We selected NGC 3486, a nearby spiral galaxy, to test the procedure described above. The result is shown in Figure 11, where the CFIS-u image is compared to the identical region from the GALEX Medium Imaging Survey (MIS, Martin et al. 2005). Compared to the 3000 s GALEX MIS image, the 240 s CFIS-u LSB-stack appears competitive in depth, and has much better spatial resolution. We will explore the properties of the low surfacebrightness components of nearby galaxies in forthcoming contributions in this series.

DISCUSSION AND CONCLUSIONS

We have presented the u-band component of the new Canada-France Imaging Survey, a community effort to obtain u-and r-band photometry of the northern sky that will be used for a host of stand-alone science studies as well as being part of the requirements of the Euclid mission to measure photometric redshifts of galaxies at cosmological distances. Our plan is to release CFIS calibrated images and photometric catalogues to the international community in early 2021, one year after the end of the observing campaign. Here we have focussed on some highlights of the Local Universe science that will be possible with the u-band, thanks to the depth and excellent image quality of the survey, using the ∼2,900 deg 2 already observed, approximately one third of the final u-band survey.

CFIS-u currently probes only a relatively small region towards the Galactic Anticenter, but the data in-hand already allow us to measure the chemical properties of the outskirts of the Galactic disk in this direction, and probe the variation of the structures along the line of sight. The metallicity distribution of the metal-rich component in the outer disk is remarkably narrow, given the volume sampled, with a dispersion only slightly larger than the uncertainties in the metallicities, pointing to a possibly very homogeneous population. Finally, the resemblance of the metallicity distribution in the outer disk at high extraplanar distance (z > 5 kpc) with the disk sampled by APOGEE suggests that the metal-rich stars found towards the Galactic Anticenter are most likely simply the outer disk population that has been kinematically heated or warped by minor accretion events.

Upcoming contributions in this series will investigate white dwarf populations in our Galaxy, as well as attempting to identify blue straggler and blue horizontal branch stars to probe the outermost reaches of the halo of the Milky Way, and presenting a new metallicity calibration for giant branch stars. We expect the CFIS-u metallicities and distances (presented in detail in Paper II) to be particularly useful when they are joined with faint sources from Gaia, since they will allow us to convert proper motions to transverse velocities, hence placing the populations in distance, and allowing population discrimination; all of this will greatly increase the power of the Gaia dataset for halo science. The u-band sensitivity will also be used to attempt to quantify the prevalence of nearby star-forming dwarf galaxies, and we will investigate the low-surface brightness Universe in the u band.

Figure 1 .

 1 Figure 1. Current footprint of the CFIS-u survey (solid black areas) on an equatorial projection of the northern sky. The light blue background shows the expected final footprint of the survey (|b| > 25 • ), once it is completed in 2020. The colormap that increases towards the Galactic plane shows the interstellar reddening, clipped so that the lowest value (in brown) marks E(B -V ) = 0.15. The red lines show Galactic coordinates, with the Galactic Plane and Galactic minor axis highlighted with a solid line. The isolated black "islands" that are disconnected from the main survey region are calibration targets, containing star clusters and the Draco dSph.

Figure 2 .

 2 Figure 2. Transmission curves for the SDSS and both old and new CFHT u-band filters. The new CFHT u-band (solid purple line) remains highly transmissive into the red (> 50% transmission until ∼ 3970

Figure 3 .

 3 Figure3. Map of the photometric differences between the CFHT and SDSS DR13 measurements, plotted in standard (gnomonic) projection with respect to the center of the CFHT field of view. The data shown are all bright stars (16 < u 0 < 18) taken on the first run of the survey (run "15Am04"). (a) shows the raw differences, whereas (b) shows these differences after correction using the two-dimensional Legendre polynomial model described in the text. While the raw photometry displays large systematic differences exceeding ±0.05 mag, the corrected photometry is flat to better than 0.02 mag rms. The data on other runs show a very similar distribution of photometric differences.

Figure 4 .

 4 Figure4. (a) Zoomed-in sky map of the photometric differences between the CFHT and SDSS DR13 measurements. The objects that are plotted are high signal to noise stars with 16 < u 0 < 19 within the color range 1.3 < u SDSS,0 -g SDSS,0 < 1.7. The striking stripy pattern that is visible all over this region (and elsewhere in the survey) follows precisely the SDSS "stripes". (b) This behaviour is even more obvious when the CFIS-u stars are plotted in SDSS η, λ coordinates, which is a coordinate system in which the SDSS stripes are arranged horizontally. This clearly shows that the SDSS has substantial position-dependent errors in the u-band. In contrast, in panel (a) one does not see any pattern of differences that could be obviously associated with the CFHT (which would cause a roughly 1 • × 1 • pattern aligned with the cardinal directions).

Figure 5 .

 5 Figure 5. Distribution of uncertainties as a function of magnitude for the SDSS (blue) and CFIS-u (red). At the limiting uncertainty for good metallicity measurements of δu = 0.03, CFIS-u is approximately 3 mag deeper than the SDSS (black dashed line).

Figure 6 .Figure 8 .

 68 Figure 6. (a) Metallicity distribution in the Galactic windows surveyed to date by CFIS-u. A large number of high-metallicity stars are seen at large R and z, appearing to fan-out above the disk. Panels b-d concentrate on the region in the longitude range 185 • < < 195 • , where the survey currently probes the Galactic Anticenter. It is clear that the metal-poor stars have a very different distribution to the intermediate (c) and metal-rich populations (d), as they possess a smoother spatial distribution. A "boxy" structure extending to z ∼ 8 kpc is present both in (c) and (d), and additionally we detect a strong metal-rich component closer to the Galactic plane, extending out to R ∼ 25 kpc. b < 40 • . The flattened feature is present predominantly in metal-rich stars ([Fe/H] > -0.75), where it forms a continuous extended structure out to R ∼ 25The metallicity distribution in this Anticenter field at z > 5 kpc is shown in Figure7. The limit of z = 5 kpc was chosen as this corresponds to the distance in figures 12 and 13 of Paper II where the metal-poor halo population (the "pop C" component of the metallicitydistance decomposition) becomes dominant. However, it can be seen from this diagram that that is clearly not the case towards the Galactic Anticenter, since the metalrich population is dominant far above the plane.It is interesting to compare these properties with what we have learned in recent years about the outer disk of the Milky Way, in particular from APOGEE. The results from APOGEE(Hayden et al. 2015, their figure4) show

Figure 9 .

 9 Figure 9. Color-color diagram showing the location of spectroscopically confirmed white dwarfs from Kleinman et al. (2013) sorted by type. Model QSO tracks from Bianchi et al. (2009) are shown in purple and model white dwarfs with pure hydrogen or pure helium atmospheres from Holberg & Bergeron (2006) are shown in green and blue respectively, transitioning from solid to dashed in the region of the main sequence (for clarity).

Figure 10 .Figure 11 .

 1011 Figure 10. The Leo-P star-forming dwarf galaxy in CFIS-u (a) and in the SDSS u-band (b) and g-band (c). The structure stands out as a clear overdensity of ∼ 20 point sources in CFIS-u.

We thank the staff of the Canada-France-Hawaii Telescope for taking the CFIS data and their extraordinary support throughout the project. We are especially indebted to Todd Burdulis for the care and dedication given to planning and observing this survey. RAI and NFM gratefully acknowledge support from a "Programme National Cosmologie et Galaxies" grant. This work is based on data obtained as part of the Canada-France Imaging Survey, a CFHT large program of the National Research Council of Canada and the French Centre National de la Recherche Scientifique. Based on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA Saclay, at the Canada-France-Hawaii Telescope (CFHT) which is operated by the National Research Council (NRC) of Canada, the Institut National des Science de l'Univers (INSU) of the Centre National de la Recherche Scientifique (CNRS) of France, and the University of Hawaii. This research used the facilities of the Canadian Astronomy Data Centre operated by the National Research Council of Canada with the support of the Canadian Space Agency. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. The SDSS web site is www.sdss.org. SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, the French Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU) / University of Tokyo, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatário Nacional / MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University.

The Pan-STARRS1 Surveys (PS1) have been made possible through contributions of the Institute for Astronomy, the University of Hawaii, the Pan-STARRS Project Office, the Max-Planck Society and its participating institutes, the Max Planck Institute for Astronomy, Heidelberg and the Max Planck Institute for Extraterrestrial Physics, Garching, The Johns Hopkins University, Durham University, the University of Edinburgh, Queen's University Belfast, the Harvard-Smithsonian Center for Astrophysics, the Las Cumbres Observatory Global Telescope Network Incorporated, the National Central University of Taiwan, the Space Telescope Science Institute, the National Aeronautics and Space Administration under Grant No. NNX08AR22G issued through the Planetary Science Division of the NASA Science Mission Directorate, the National Science Foundation under Grant No. AST-1238877, the University of Maryland, and Eotvos Lorand University (ELTE).