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LHC Collimation Working Group – 20 February 2012 Collimator Setup Software in 2012 G. Valentino R. W. Assmann, S. Redaelli and N. Sammut.

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Presentation on theme: "LHC Collimation Working Group – 20 February 2012 Collimator Setup Software in 2012 G. Valentino R. W. Assmann, S. Redaelli and N. Sammut."— Presentation transcript:

1 LHC Collimation Working Group – 20 February 2012 Collimator Setup Software in 2012 G. Valentino R. W. Assmann, S. Redaelli and N. Sammut

2 Acknowledgements Colleagues who contributed to data-taking in the collimator setups (BE/ABP): R. Bruce, F. Burkart, M. Cauchi, D. Deboy, L. Lari, A. Rossi, B. Salvachua, D. Wollmann BE/BI: B. Dehning, S. Jackson, C. Zamantzas EN/STI: A. Masi Gianluca Valentino2

3 Outline Gianluca Valentino3 The Setup Application in 2011 Setup Application for 2012 Demonstration Video Ongoing Work Summary

4 The Setup Application in 2011 Main features: –BLM feedback (jaws stop moving when losses exceed pre-defined threshold) –Improved GUI –Automated data logging (not fully exploited) –Parallel collimator setup made possible Achievements: –Automation reduced the amount of operator intervention required –Setup time reduced by up to a factor 6 for TCT alignment from 2010 –High BLM losses and resulting beam dumps from human error avoided Issues: –Optics had to be input manually: it was easier to use the excel sheets for logging –Full automation not yet achieved (loss threshold, step size, time interval input manually) Gianluca Valentino4

5 Application Structure for 2012 Gianluca Valentino5 Setup Task Sequencer GUI User Parallel Setup Algorithm Loss Threshold Selection BLM Spike Recognition Fast BLM Data Acquisition BLM Feedback Available in 2011 Tested in 2011 MD Data Logging Available in 2011 NEW

6 Automatic BLM Spike Recognition Automatic recognition of BLM spikes achieved using Support Vector Machines (SVMs) Loss spikes are classified into 2 classes: optimal and non-optimal spikes SVMs maximize the margin between the data points of different classes and the decision boundary 97% prediction accuracy from 480 samples (improvement from 90% achieved from data of July MD) Gianluca Valentino6 Discriminating Features: (a)ratio of maximum BLM value to average of 10 preceding BLM values (b) coefficient of power fit to loss temporal decay (c) correlation coefficient of power fit Optimal Loss Spike 20 seconds loss pattern fed into the SVM predictor

7 Automatic Threshold Selection A model based on Exponentially Weighted Moving Average (EWMA) was developed. The largest weight is given to the most recent value. The EWMA was calculated for 475 sets of 10 BLM values. Gianluca Valentino7 Threshold set by the operator

8 Setup Task Sequencer Gianluca Valentino8 Start Move in B1 & B2 TCP Move B1 & B2 collimators in parallel Align collimators separately in sequence Stop All collimators aligned ? Loop executed for each plane YES NO

9 Input Parameter Heuristics Heuristics derived from experience with manual and semi-automatic setup Gianluca Valentino9 Input ParameterHeuristic Jaw Step Size at 450 GeVAll collimators: 10 µm Jaw Step Size at 4 TeVTCT: 10 µm (larger beam size) All other collimators: 5 µm Jaw Movement Time Interval1 s for 1 Hz BLM data 0.125 s for 12.5 Hz BLM data BLM ThresholdCalculated from the latest 10 BLM values before every jaw movement After an optimal loss spikeMove in other jaw to align both jaws After a non-optimal loss spikeIncrease step size by 5 µm and move in jaw again

10 Parallel Setup Algorithm Gianluca Valentino10 Start sequential alignment After first jaw stops, wait for 2 s in case of other stopping jaws Stop all movements and move each of the stopped jaws separately by a further 50 µm Start parallel jaw movement Are there other stopped jaws? Are all collimators close to the beam? YES NO YES

11 Demo Video Gianluca Valentino11

12 Fast BLM Data Acquisition Fast BLM data acquisition possible thanks to C. Zamantzas and S. Jackson. Data is sent via UDP packets from all BLM crates at a rate of 12.5 Hz (RS07, 81.92 ms). A UDP client is implemented in setup application to receive packets and convert data to Gy/s. Gianluca Valentino12

13 Fast BLM Data Video Gianluca Valentino13

14 For = 1 Hz, = 8 Hz and = 84 : = 2 hours 30 minutes factor 7 better than 2011 Setup Time: Theoretical Limit Assumptions: (a) Each plane for B1 and B2 set up in parallel. (b) Step size: 10 µm for all collimators. (c) Each jaw needs to be moved for 7 mm (average) until it touches the beam. (d) In sequential setup, each jaw needs to be moved in further by 200 µm (average), B1 and B2 separately. Gianluca Valentino14 Parallel Setup Sequential Setup 60 s delay per collimator for pattern recognition and threshold 10 minutes per group for parallel setup recovery after cross talk

15 Ongoing Work 1 hour without beam required for final testing and to determine the maximum number of collimators that can be moved in parallel at 8 Hz. Tentative: Week 9 after MP tests. When crosstalk occurs during setup, the collimators moving in parallel will be saved to avoid these combinations in future. BPM-interpolated orbit at the collimators will be compared to the beam-based centres. If the BPM interpolation is consistently correct to within a certain value, the jaws can be moved immediately to one location assuming a certain beam size, instead of parallel setup. Models for threshold selection and spike recognition will be developed for the 12.5 Hz data. Work on collimator setup LSA tables which will store a timestamp and the jaw alignment positions is in progress (P. Pera Mira). Logging application of fast BLM (and collimator?) data: ~45 GB/day – issue of storage Gianluca Valentino15

16 Summary A fully automatic setup procedure has been developed for 2012. Models for automatic loss threshold selection and spike recognition were built based on 2011 setup data. Setup task sequencer uses heuristics learned through setup experience to make decisions. Increased automation and faster data rates expected to reduce the setup time hopefully by factor > 3 from 2011. Gianluca Valentino16


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