Waldo Gonzalo

Cancino Ticona

Nadia Boukhelifa
email: nadia.boukhelifa@inria.fr

Anastasia Bezerianos
email: anastasia.bezerianos@lri.fr

Evelyne Lutton
email: evelyne.lutton@grignon.inria.fr

Waldo Cancino
email: waldo.cancino@inria.fr

Evolutionary Visual Exploration: Experimental Analysis of Algorithm Behaviour

Keywords: I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods, and Search-Heuristic methods, H.5.m [Information Interfaces and Presentation]: Miscellaneous Interactive evolutionary computation, visual analytics, genetic programming

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INTRODUCTION

Recent work combining visualisation and optimisation [START_REF] Hauser | The iterative process of interactive visual analysis[END_REF] shows a growing interest of the Visual Analytics community toward optimisation and interactive evolutionary algorithms, and of the evolutionary computation community toward visualisation [START_REF] Guettala | A user assistant for the selection and parameterization of the visualizations in visual data mining[END_REF][START_REF] Hayashida | Visualized IEC: interactive evolutionary computation with multidimensional data visualization[END_REF][START_REF] Llorà | Analyzing active interactive genetic algorithms using visual analytics[END_REF].

The purpose of visual exploration is to find meaningful patterns in the data that can lead to insight [START_REF] Thomas | Illuminating the Path: The Research and Development Agenda for Visual Analytics[END_REF]. In highdimensional datasets, this task becomes challenging as viewers may be faced with a large space of alternative views on their data. The quest for finding an interesting subset of elements, such as data dimensions or projections, is shared by other research fields. In machine learning and data mining this issue is often treated as a classification problem where dimensions refer to features [START_REF] Seo | A rank-by-feature framework for unsupervised multidimensional data exploration using low dimensional projections[END_REF]. Formalised as an optimization problem (e.g. Evolutionary Computation), interesting features are selected based on a fitness function [START_REF] Cancino | Evographdice: Interactive evolution for visual analytics[END_REF].

For scientific and engineering applications, Interactive Evolutionary Computation (IEC) is interesting when the exact form of a more generalised fitness function is not known or is difficult to compute, say for producing a visual pattern that would interest a particular user. Here, the human visual system, together with the emotional and psychological responses of the user in question can outperform a pattern detection or learning algorithm. Similarly, Evolutionary Visual Exploration1 (EVE) relies on the premise that IEC can help guide users in exploring complex datasets.

EvoGraphDice [START_REF] Boukhelifa | Evolutionary Visual Exploration: Evaluation With Expert Users[END_REF][START_REF] Cancino | Evographdice: Interactive evolution for visual analytics[END_REF] is an EVE prototype for the exploration of multidimensional datasets: the system proposes interesting views based on both objective measures, such as visual patterns in the two-dimensional projections of the data, and subjective measures corresponding to user satisfaction with the presented view. EvoGraphDice was built as an extension of an existing scatterplot matrix inspection tool. It uses low dimension projection to handle data multidimensionality, and linear and non-linear combinations of dimensions for an axis of the projection plane to propose alternative views. User exploration is guided by an Interactive Evolutionary Algorithm (IEA) which can both generate new views and adapt to user interest (see figure 1).

The paper is organised as follows. Section 2 presents the main mechanisms of EvoGraphDice. The experimental setup and analysis are described in section 3. We discuss our main findings in section 4, before giving a conclusion and future research in section 5.

EVOLUTIONARY VISUAL EXPLORATION

In this section we first describe the visual interface of EvoGraphDice and the main components of the genetic engine we implemented behind the visualization tool. Combining visual analytics with stochastic optimisation using our framework (EVE) raises interesting issues related to algorithm convergence, described next, that motivated our experimental study.

EvoGraphDice Visual Interface

EvoGraphDice has been built over an existing visualization tool (GraphDice [START_REF] Bezerianos | GraphDice: A System for Exploring Multivariate Social Networks[END_REF][START_REF] Elmqvist | Rolling the Dice: Multidimensional Visual Exploration using Scatterplot Matrix Navigation[END_REF]) to manage the various projections of the data. Views are organised in a scatterplot matrix (SPLOM) of 2D projections, figure 1-a. Users can do brushing and linking using a lasso tool. EvoGraphDice displays the dimensions proposed by the IEA as additional rows (and columns) in the SPLOM. The system initially displays dimensions returned by a PCA, after which the user can evolve new dimensions by pressing the "evolve" button, figure 1-d. The proposed views are displayed in yellow background; the darker the color the more interesting the view. The system provides an initial score (1 to 5) for each new view but the user can adapt this score using the slider in figure 1-d. User evaluated cells are flagged (using a small black square) to distinguish them from system evaluated cells. EvoGraphDice can be initialised at any time using the "restart" button which resets parameters of the IEA. Users can save views (figure 1-f) and bring them back into the SPLOM if they have been replaced during the exploration.

The current population of evolved dimensions is also displayed as a table (figure 1-h) where each row corresponds to a combined dimension described by a mathematical expression and various components of the fitness function such as the scagnostics measures defined in section 2.2. The user can edit an individual (i.e. a combined dimension) using the "dimension editor" in figure 1-j, or limit the dimension search space (figure 1-i), which results in a system reset similar to pressing the "restart" button. Note that many EA parameters can be tuned, such as the fitness threshold and crossover/mutation/replacement rates (see [START_REF] Cancino | Evographdice: Interactive evolution for visual analytics[END_REF]).

A first version of EvoGraphDice [START_REF] Cancino | Evographdice: Interactive evolution for visual analytics[END_REF] was based on an IEA that only manipulated linear combinations of dimensions. Recent extensions, described in [START_REF] Boukhelifa | Evolutionary Visual Exploration: Evaluation With Expert Users[END_REF], are (i) a Genetic Programming (GP) algorithm allowing the manipulation of nonlinear combinations of dimensions as variable size mathematical formulae, (ii) user assessment of proposed views is explicitly captured via a slider, (iii) a surrogate function based on some specific geometric measurements (scagnos- tics) is used to predict and simplify the interactions of the user with the IEA, (iv) color highlighting of cells is used to draw user attention to the most interesting views.

In [START_REF] Boukhelifa | Evolutionary Visual Exploration: Evaluation With Expert Users[END_REF], we validated our general approach of combining visual analytics with stochastic optimisation to aid data exploration, by conducting an observational study with expert users from various domains. Our results showed that EvoGraphDice can help users quantify qualitative hypotheses and try out different scenarios to dynamically transform their data. Importantly, it allowed our experts to think laterally, better formulate their research questions and build new hypotheses for further investigation. Our work demonstrated that tightly combined visualization and optimisation techniques yields exciting results in data analysis, and opens new venues for research. It also highlighted challenges such as monitoring algorithm convergence which we try to address in this paper in more detail.

User fatigue

Despite efforts to design good user interfaces for IEA, human interaction raises "user bottleneck" problems [START_REF] Banzhaf | Handbook of Evolutionary Computation, chapter Interactive Evolution[END_REF][START_REF] Poli | Genetic programming with user-driven selection: Experiments on the evolution of algorithms for image enhancement[END_REF][START_REF] Takagi | Interactive Evolutionary Computation: System Optimization Based on Human Subjective Evaluation[END_REF]. Among the various possible strategies to deal with this issue, we choose to use a small population size of suggested dimensions, and to deploy an approximated user model: a surrogate function based on a series of geometric measures that have proven statistical properties and are computable for moderately large data sets [START_REF] Wilkinson | Scagnostics Distributions[END_REF]. Scagnostics2 are based on geometric graphs which are calculated from areas, perimeters and lengths of these graphs. They include nine measures to characterise scatterplots (figure 2) and are useful for quickly discovering regularities and anomalies in scatterplot matrices. The underlying algorithm detects different types of point distributions including multivariate normal, log normal, multinomial, sparse, dense, convex and clusters. It does so by binning, detecting outliers and computing measures based on the following three statistical properties: shape for convex, skinny and stringy distributions; trend for monotonic distributions; and density for skewed, clumpy, outlying, sparse and striated.

Search Space and Genetic Engine

The space searched by the evolutionary process is the set of all dimensions that can be built by combining the initial dimensions with operators and constants, encoded as trees according to the Genetic Programming (GP) framework [START_REF] Koza | Genetic Programming[END_REF]. Evolved expressions can be any combination using +, -, * , /, (.) (.) , exp and log operators.

A small set of combined dimensions is evolved: if n is the number of initial dimensions, a population of another n combined dimensions is evolved. Each time the user clicks on the "evolve" button, a new generation is computed, and the user can explicitly evaluate a cell in the SPLOM with a slider attached to each new view.

Users evaluate cells, while the evolutionary engine manipulates individuals, i.e. new dimensions. A user evaluation of a dimension is computed by averaging the user evaluations of all cells that involve the new dimension.

A set of a priori interesting dimensions has been chosen as a starting point: a PCA analysis is performed [START_REF] Smith | A tutorial on Principal Component Analysis[END_REF] on the original data and the corresponding n linear combinations form the initial population.

The fitness function, that is optimised by the genetic engine, is a sum of three terms: w k (max

j SC k (yi, xj)). (1)
The weights w k that govern the relative importance of each scagnostic measurement are initialised to a uniform weight (1/9). Then, when there are more than m > n user evaluations recorded (n, the number of variables), the w k are updated via a simple multilinear regression on the m past interactions (m ≥ n corresponds to the length of a "memory" of the system).

2.

A complexity term that favours dimensions made of a small number of variables and simple mathematical expressions :

fc(yi) = 1 - nvars(yi) n × 1 depth(yi) , (2)
nvars(yi) is the number of original variables involved in the mathematical expression of yi, and depth(yi) is the depth of the GP tree representing yi.

3.

A user evaluation term, fu(yi), that is an average of the user evaluation for each cell corresponding to yi (range of 1 to 5 from "bad" to "excellent").

Diversity management: for small populations sizes, there is a major risk of premature convergence if no diversity preservation mechanism exists. To deal with this issue in EvoGraphDice each time a new dimension y i is generated, its Euclidean distance to the current population is computed. If y i is too close to one of the individuals of the current population, it is replaced by a random individual.

Issues of Algorithm Convergence

As we are dealing with a small population of dimensions evolved during only a few generations (see experimental section next), the algorithm cannot be considered as having converged in the classical sense. Theoretical analysis considers two main mechanisms that govern the behaviour of EAs: focus (convergence or exploitation) and diversity (random search or exploration). In their most classical uses, i.e. computationally expensive optimisation, the exploitation mechanism is privileged and the exploration component is used only to ensure the robustness of the results. In the interactive framework where creativity or new feature discovery are sought, the same mechanisms operate but with a different balance: exploration capability seems to have a bigger impact.

Additionally, talking about convergence for EVE systems is even more difficult as usually the users themselves do not clearly know what they are searching for. In previous experimental analysis [START_REF] Boukhelifa | Evolutionary Visual Exploration: Evaluation With Expert Users[END_REF], it has been noticed that the "guided" exploration ability of EvoGraphDice is exploited in different ways by experts: some explored, thus it can be said they focused on the random search ability of the algorithm, while others exploited with longer runs (>10 generations) thus focused on guided search/convergence. In both cases, the IEA provides a unified framework for users that sometimes are interested in focussed search, e.g. if they know what they want, or in explorative suggestions if they know less what they may find.

The experiments described in the next sections have been designed to evaluate the focused search/exploitation mechanisms of EvoGraphDice where the user has a precise and well defined task to perform.

ALGORITHM BEHAVIOUR: EXPERIMENTAL ANALYSIS

We conducted an experiment to collect data about user interactions and the fitness function. In particular, we wanted to monitor the IEA's convergence focusing on (i) the learning behaviour of the algorithm; and (ii) its ability to adapt to user focus.

Experiment Setup and Procedure

We run our experiment with 12 participants (5 female), ages 23 -43 (mean 28.5). Participants were researchers from two different institutions who had limited experience with SPLOM-based visualizations (only 3 previously used a SPLOM-based visualization tool). We ran EvoGraphDice on an HP Z800 workstation PC with a 1900 dual monitor (1280 x 1024 screen resolution), and on a MacBookAir connected on a 30 monitor (same resolution). Each session with a user lasted 1 -2 hours.

A 5D dataset was synthesised with two enclosed curvilinear dependencies between two variables (x0 and x1) and random data for the rest of the dimensions. Participants were asked, after a brief introduction to the tool, to evolve a scatterplot where it is possible to separate the two curves in figure 3 (left) with a straight line, and were not given a time limit to complete the task (this task is equivalent to separating the two convex hulls in figure 3). Two levels of difficulty for the game were generated, where difficulty relates to the amount of enclosure between the curves-the bigger the overlap area between the curves the more difficult it is to find the solution. Participants started with level 0 and depending on their performance, they also did level 1. For struggling participants we allowed them to restrict the search space to dimensions x0 and x1 in the hope of helping them find a solution more quickly. Participants stopped the game when they found a solution or when they felt the tool is no longer proposing interesting views (for struggling users, a minimum exploration time of 20 minutes was always respected). With the exception of one user, all participants successfully evolved a view separating the two convex hulls for level 0 (average time to find a solution was 17 minutes), but only 6 out of the 11 participants who tried level 1 managed to find a solution (average time 11.5 minutes). We note that some users needed several tries (i.e. game restarts) in order to solve the game.

Data Collection

Log data was gathered for further analysis and includes two types of information related to: (a) user interactions with the tool such as cell selections and evaluations via the slider; and (b) genetic engine status at each generation (see Table 1), such as details about the individuals in each generation (including their fitness components and scagnostics scores), the cells these individuals participate in (i.e. the yellow cells in figure 1-a); and (c) the overall learned scagnostics weights. A total of 27 log files were collected and further analysed.

Data Analysis

System Evaluation vs. User Evaluation

User scores of evaluated cells (i.e. when the user moves the slider) have been compared to their "predicted" values, based on the current scagnostic weights learned at each generation and the scagnostics values of the corresponding cell (see equation 1): k=1..9 w k (SC k (yi, xj)). These values are averaged per generation and are plotted in figure 4 (for successful players) and figure 5 (for unsuccessful players) for level 0 of the game. The predicted values seem to roughly follow the user evaluations in the majority of cases, independently from the output of the game (success or failure). An exception to this is figure 4-d (generations 17 -23) where a user decided to rate the cells according to a different criterion (aesthetics) after having obtained the result.

Learning User Strategy Through Scagnostics

A second type of analysis was performed on the scagnostics weights distribution along generations. For comparison purposes (and since the number of generations per exploration session differ between users), generation values have been averaged in three bins, corresponding to the start, middle and end of the game session. We tried to get the same bin size for all groups when possible, and at the very least ensure start and end bins always have the exact same size when integer division by 3 was not possible. Figures 6 to 9 display the 9 histograms of various game sessions, for levels 0 and 1, with limited and unlimited search spaces.

For level 0-no restricted space (figure 6), successful explorations are concentrated around either sparse, and/or a combination of skinny and stringy distributions (if we focus on the end generation bins). For the unsuccessful session, there was no strong pattern, as expected. Also, for sessions where sparse was a dominant exploration pattern, we can see a clear increase from start to end generations, e.g. figure 6-c, e andf. When restricting the search space for level 0, the solution to the game was found much quickly than when the search space was not limited (5 generations as opposed to on average 23 generations as seen in figure 7-a). The successful exploration strategy was a combined approach between sparse and stringy. For the unsuccessful exploration session (figure 7-b), it might show that just 'skinny' as the dominant scagnostic might not be enough to reach a solution, or that the user did not explore enough generations (given the chosen search strategy, a solution might be just around the corner).

Again, and for level 1 this time, in figure 8 the unsuccessful candidate had a random exploration pattern, while focusing on sparse distributions still leads to a successful exploration strategy. When limiting the search space (figure 9), the average number of dimensions ran to get a successful solution was 24 (slightly higher than for level 0), but in general the same observations regarding successful pattern searches mentioned above also hold for this game level.

DISCUSSION

EvoGraphDice seems to exhibit a learning behaviour controlled by the diversity component which aims at maintaining an exploration capability (section 3.3.1). This effect is noticeable even in the case of unsuccessful game sessions (e.g. figure 5-b) and corresponds to an exploitation component (focus) of the genetic engine, regardless of user aim. Analysis of section 3.3.2 shows different exploration patterns for successful and unsuccessful sessions. The analysis of the content of the surrogate function, via the observation of the variation of the learned weights of the scagnostics measurement, highlights a difference in users' focus of attention (i.e. sought after pattern). For successful game sessions, there are clearly two strategies, one tending to "unfold" the curved shapes by favouring linear scagnostic measurements (e.g. middle solution in figure 3), the other trying to spread the figures laterally by favouring sparse scagnostics (e.g. right solution in figure 3).

The surrogate function that "approximates" the user evaluation, is clearly not able to embed the explicit aim of the game (that is separating the convex hulls of two geometrical subsets) as it only performs calculations on the whole set of points of the scatterplot. However, what is clear from the analysis is that the surrogate function seems to be discriminative enough to allow various search strategies.

CONCLUSION AND FUTURE WORK

This work is focussed on the evaluation of the convergence behaviour of the EvoGraphDice system through a user study having a well specified task, while previous studies [START_REF] Boukhelifa | Evolutionary Visual Exploration: Evaluation With Expert Users[END_REF] assessed the tool's exploration and lateral thinking capabilities. The experiments and analysis presented above exhibit an obvious learning behaviour that seems to facilitate the algorithm's convergence toward a satisfying solution, while still allowing users to adopt various search strategies. The data we collected for this study have not been fully explored and future work will involve more in-depth analysis, for example examining trends for different GP runs per user exploration session (instead of concatenating all generation runs). In addition, we would like to re-examine our notion of startmiddle-end of an exploration session to find more meaningful ways to characterise various stages of exploration (e.g. change of user focus).

Our current formal and informal experimental analysis will guide our future developments. We will for instance improve the surrogate function calculation in order to more complex visual patterns (beyond those currently detected by Scagnostics, e.g. kurtosis and entropy). A bridging of EvoGraphDice with existing statistical packages will also be considered, in order to combine powerful statistical analysis with flexible and intuitive visual exploration.

Figure 2 :

 2 Figure 2: Nine scagnostics measures from [16].

Figure 1 :

 1 Figure 1: EvoGraphDice prototype showing an exploration session of a synthetic dataset (the spiral game). Widgets: (a) an overview scatterplot matrix showing the original data set of 5 dimensions (x0..x4) and the new dimensions (1..5) as suggested by the evolutionary algorithm, (b) main plot view, (c) tool bar for main plot view, (d) a tool bar with (top to bottom) "favourite" toggle button, "evolve" button, a slider to evaluate cells and a restart (PCA) button, (e) the selection history tool, (f ) the favourite cells window, (g) the selection query window, (h) IEA main control window, (i) window to limit the search space, and (j) dimension editor.

1 .

 1 The surrogate function fsc, that plays the role of a predictor. It is based on scagnostics measurements computed for every scatterplot cell yi, xj of each dimension yi. The corresponding fitness term is a linear combination of the highest values of the scagnostics (SC k (yi, xj)) of each scatterplot cell (yi, xj): fsc(yi) = k=1..9

Figure 3 :

 3 Figure 3: Two different solutions (screenshots of plots) for the training game problem-level0 (left) that involve a simple dimension combination (middle) and a complex formula (right).

Figure 4 :

 4 Figure 4: Average user evaluation of cells versus scagnostics-based prediction over generations for four successful participants [a-d] for level 0 of the game. The orange curves correspond to user evaluations while the pink ones represent the predicted values.

Figure 5 :

 5 Figure 5: Average user evaluation of cells versus scagnostics-based prediction over generations for two unsuccessful participants [a-b] for level 0 of the game. The orange curves correspond to user evaluations while the pink ones represent the predicted values.

Figure 6 :

 6 Figure 6: Scagnostics weights over time for successful participants [a-h] and unsuccessful participants [i] for level 0 the game without limiting the search space.

Figure 7 :

 7 Figure 7: Scagnostics weights over time for successful participants [a] and unsuccessful participants [b] for level 0 of the game when restricting the search space to dimensions x0 and x1.

Figure 8 :Figure 9 :

 89 Figure 8: Scagnostics weights over time for successful participants [a] and unsuccessful participants [b] for level 1 of the game without restricting the search space.

Table 1 :

 1 Log data capturing information on the GP.

	For each evaluated cell generation number,
	9 scagnostics,
	predicted evaluation
	( =	w k x Cell Scagnostics),
	user evaluation.

For each generation 9 scagnostics weights, w k . For each individual generation number, genome (math. formula), surrogate function term, fsc, complexity term, fc, average user evaluation, fu, resulting fitness.

http://www.aviz.fr/Research/EVE

Available as a free downloadable package in R from http: //www.rforge.net/scagnostics/

Acknowledgements:

We thank all the users who participated in our experiment.


P.-E Bernaudin

GANIL R Ferdinand

P Bosland

Y Gómez Martínez

ASSEMBLING, TESTING AND INSTALLING THE SPIRAL2 SUPERCONDUCTING LINAC

Assembly and tests of the SPIRAL2 superconducting linac's components are now proceeding smoothly. Cryomodules are being processed in CEA Saclay and IPN Orsay, inter-cryomodules "warm" sections in GANIL. While installation of the accelerators components is going on in the new SPIRAL2 building in Caen, installation of the cryomodules will begin during the last quarter of 2013. The latest results of the cryomodules tests as well as the installation strategy are depicted in this paper.

INTRODUCTION

The driver of the GANIL's SPIRAL2 Project [1] consists of two high performance ECR sources, a RFQ, and the superconducting (SC) linac. The driver is also asked to provide all the energies from 2 MeV/u to the maximum designed value.

The SC linac is composed of low E and high E cryomodules, each one including one E=0.07 cavity or two E=0.12 cavities respectively. This paper describes the status of the construction of the superconducting linac and the strategy for its installation in the new SPIRAL2 building.

ASSEMBLING AND TESTING CRYOMODULES

Low E cryomodules are developed, assembled and tested by CEA/Irfu at Saclay, and high E cryomodules by IPN Orsay. Both types of cavities are equipped with the same 40 kW power coupler, developed, assembled and conditioned in a third laboratory, LPSC Grenoble.

Cryomodules A

The E=0.07 cryomodule (type A) design has been described in this reference [START_REF] Bernaudin | Design of the low beta, quarterwave resonator and its cryomodule for the SPIRAL2 project[END_REF].

All E=0.07 cavities have now been qualified in vertical cryostat and have reached the required specifications: less than 10 W of dynamic losses at nominal, 6.5 MV/m accelerating gradient (see Fig. 1). The 13 th , spare cavity is being treated following beam vacuum leaks.

Assembly and test of these cryomodules are now proceeding routinely. Four cryomodules have been successfully RF tested at 4 K. Two of those cryomodules have been completely qualified; two other ones have been tested and only require some alignment corrections. Two more fully assembled cryomodules are awaiting cold test, while two other ones are in different stages of assembly (beam vacuum being sealed).

Cryomodules B

The E=0.12 cryomodule (type B) design has been described in these references [3][4].

As for cryomodules A, contamination problems have been solved. Two cryomodules have been assembled. One of them is qualified, the other one showed good RF performances; cavities have reached the nominal gradient and total cryogenic losses are on the safe side (lower than 30 W) but one cavity is misaligned which could lead to partial disassembly (no intervention on beam vacuum is foreseen). Main concern was the Cold Tuning System (CTS), which showed what we called "negative backlash": upon changing action direction, frequency variation was not changing accordingly until several dozens of Hertz. Extensive tests at room temperature correlated this effect with very small lateral movement of the plunger inside the cavity upon any direction change. The guiding columns have been optimized to ensure these movements are directed along the cavity axis. This suppressed the phenomenon, as confirmed by latest 4K tests of the cryomodules. On the other hand, this rotation of the plunger system and some observed defect on the plunger surface led us to test in vertical cryostat all cavities equipped with the plunger in the proper orientation.

Power Couplers

The power coupler design has been described in this reference [START_REF] Martínez | Last SPIRAL2 10 kW CW RF coupler design[END_REF]. Both cryomodules share the same power coupler with two different external conductor length, in order to provide the coupling differences.

Improvement of the preparation and conditioning procedures, ranging from electropolishing the antenna to seemingly minor aspects like the orientation of the coupler during clean room assembly, provided good results: conditioning time both on the test bench in Grenoble and in the cryomodules in Saclay and Orsay was significantly reduced. Coupler conditioning up to 40 kW (open circuit conditions) on the dedicated bench in Grenoble is now shorter than two days.

In the meantime, it was discovered that the EPDM seals of the valves used on the power coupler pumping port and warm sections were unacceptable, outgasing a significant mass flow of heavy elements. In order to reduce the risks of contamination of the cavities, and to avoid loss of time with the supplier, the valves not yet assembled on the cryomodules were baked in Grenoble, reducing significantly the partial pressure of masses above 45 (see Fig. 2).

Inter-cryomodules Warm Sections

The warm section design required a difficult consensus to allow the insertion of a number of elements in the short available space: two quadrupoles for transverse focusing, steering magnets, one BPM for beam position, transverse matching and phase measurements, one longitudinal beam extension monitor, and the pumping and vacuum diagnostics system. Assembly work on the inter-cryomodules warm sections started during summer 2012 (see Fig. 3). Assembly of all components connected to beam vacuum is performed in an ISO5 clean room in Saclay by the GANIL team.

Assemblies were stopped after four sections when it was discovered that the EPDM seals of the gate and angle valves were outgasing heavy elements. The flow rate was so high that it limited the vacuum in the chambers above 2e-8 mbar (specifications: 1e-9 mbar without beam). Assembly is now restarting from scratch after the manufacturer changed all seals and cleaned the valves according to clean room assembly requirements. EPDM seals were changed to Viton ones after cross-checking the integrated radiation doses requirements for the linac.

INSTALLING THE SUPERCONDUCTING LINAC Transportation

Transportation of the cryomodules (250 km by road) from Saclay and Orsay to GANIL has been identified as being a critical operation. Therefore one transportation test has been recently organised. During March 2013, one up-to-specifications, low E cryomodule was transported from Saclay to GANIL, unloaded in GANIL, then loaded again on the truck and transported back to CEA Saclay for tests (see Fig. 4). Shock sensors have recorded all events during the whole transportation. As expected, strongest shocks happen during loading/unloading phases. They stay below 5 g, well under the power coupler limits (10 g in the horizontal plane). Damping system having proved satisfactory will be used for all cryomodules transportations. Alignment of cavity inside the cryomodule has been checked before and after transportation. No movement was recorded.

After transportation the cavity was once again cold tested in order to verify that performances were not altered. Performances were slightly better after transportation, with reduced field emission (see table 3).

In view of all these results, method and tooling for transportation are qualified and will be used similarly for all 19 cryomodules; cryomodules type A will be transported two at a time, type B ones only one by one.

Installation

Installation of the valve boxes platform in the linac tunnel already started. In order to speed up operations, installation of the cryomodules and warm sections will start before all cabling and tubing operations are completed. Nevertheless, a high standard of cleanliness will be kept in the tunnel at any time in order to avoid any contamination problem.

After the linac supports, which will soon be positioned and aligned inside the tunnel, cryogenic components (helium heaters, valves boxes) will be installed, followed by the warm sections and finally cryomodules will be positioned as they become available.

The most critical step of the linac installation is the connection between inter-cryomodules warm sections and cryomodules. Cavities are being kept under static vacuum (vacuum level being checked regularly and kept below a 0.1 mbar threshold), and the sealing face of the beam valves are kept dust-free by covers put during the clean room assembly phase. On the other side, warm sections stay at atmospheric pressure (but dust-free sealed). Connection will be performed under a mobile, ISO5 laminar flow, specially designed to fit inside the very constraint space between two cryomodules and under the valves box support platform. Meticulous cleaning and particles counts will ensure that the environment is clean enough to perform the connection operation, which is itself very delicate: a Helicoflex™ seal shall be inserted and sealed in a very tight space. This connection has been tested in CEA Saclay with an empty cryomodule.

The whole procedure to be implemented for this connection will be tested on a single cryomodule (high E) and warm section in IPN Orsay.

Start up

Reception tests should start when utilities of the building become operational, which is expected first half of 2014. First cool down of the linac is expected before the end of 2014, with the first particle on the beam dump for Christmas.

Figure 1 :

 1 Figure 1: Q 0 vs. E acc curves of E=0.07 cavities.

Figure 2 :

 2 Figure 2: Outgasing of EPDM valves used on the power couplers before and after baking. Blue zone show pollutants above specifications.

Figure 3 :

 3 Figure 3: Inter-cryomodules warm sections during clean room assembly (left) and on alignement bench (right).

Figure 4 :

 4 Figure 4: Transportation test of the cryomodule CMA2 from Saclay to GANIL. Right picture shows the cryomodule inside his crate, fixed on its damping support.

Table 1 :

 1 Measured Performances of E=0.07 Cryomodules already Tested None of the cryomodules prepared this way failed the RF qualification tests.

		Specs	CMA4	CMA6	CMA7	CMA2
	Max. acc. gradient [MV/m]	> 6.5	8.8	8.3	9.0	9.1
	Total losses at 4K
	and 6.5 MV/m	< 20.5	20.8	11.4	11.8	13.2
	[W]
	4K static losses [W]	< 8.5	6.5	4.0	4.1	3.2
	Pressure sensitivity [Hz/mbar]	< 5.00	1.58	1.32	1.45	1.31
	Beam vacuum [mbar]	< 5.0e-7	1.5e-7	2.0e-8	1.1e-8	4.3e-8
	Beam vacuum leaks [mbar.l/s]	< 5e-10	9.5e-10	< 1e-10	< 1e-10	< 1e-10
	Cavity alignment
	(cylindricity)	1.30	0.52	0.40	0.48	1.46
	[mm]
	RF cold tests of the cryomodules show good
	performances (see table 1). Previous field emission
	problems caused admittedly by dust pollution have been
	eradicated, thanks to optimised preparation procedures.
	Notice that, contrary to what is usually done, cavities are
	not HPR rinsed anymore between the vertical cryostat test
	and cryomodule assembly in the clean room; between
	these two steps, the cavity is refilled with filtered air to
	avoid contamination.

Table 2 :

 2 Measured Performances of E=0.12 Cryomodules already Tested

		Specs	CMB1	CMB2
	Max. acc. gradient [MV/m]	> 6.5	>8.0	>8.0	>8.0	>8.0
	Pressure sensitivity [Hz/mbar]	< 8.0	5.7	5.1	5.3	5.0
	Cavity alignment (cylindricity) [mm]	1.20	0.16	0.34	0.88	2.54
	Total losses at 4K and 6.5 MV/m [W]	< 36.0	29.5	32.0
	Beam vacuum [mbar]	< 5.0e-7	<6.0e-8	<6.0e-8
	Beam vacuum leaks [mbar.l/s]	< 5e-10	< 1e-10	< 1e-10

Table 3 :

 3 Performances of E=0.07 Cryomodule CMA2 before and after Transportation

		Specs	before	after
	Max. acc. gradient [MV/m]	> 6.5	7.8	9.1
	Total losses at 4K [W]	< 20.5	15.4	13.2
	X-rays at nominal gradient [PSv/h]		730	9

Proceedings of IPAC2013, Shanghai, China ISBN 978-3-95450-122-9