2. Data Analytics & Machine Learning in BI
M. Gonzalez Berges, J.J. Gras, B. Salvachua
With input from
D. Alves, G. Azzopardi, E. Bravin, L. Coyle, M. Di Castro, L. Grech, A. Guerrero,
R. Jones, T. Levens, T. Pieloni, G. Valentino, M. Wendt, C. Zamantzas

3. Agenda Introduction OAF (Offline Analysis Framework) Current Use Cases
Improve diagnostics with ML/DA
BI wishes
Conclusions
28th May 2019 – ML Workshop

4. Introduction Data Analysis is key for BI systems
Going from instrument measurement to beam parameters
Machine Learning techniques
Not much done so far compared with other standard signal treatments
Big interests in evaluating potential
Evaluate instrument status (predictive maintenance)
Asses instrument performance (aging)
Improve instrument response (calibration).
28th May 2019 – ML Workshop
BE-BI

5. Offline Analysis Framework (OAF)
BI centralized tool that provides:
Automatic daily reports based on analysis of logging data
About 50 reports generated per day
Processing based on data set configuration files (extension with python code is possible, currently 5% of cases)
CALS + Python
Relies on “snapshots”, this functionality should be kept in the new API with NXCALS
28th May 2019 – ML Workshop
BE-BI

6. Offline Analysis Framework (OAF)
Example: BLM card temperature measurements, with statistical analysis to identify trends, outliers, etc.
Daily report (24 h of data), average and sigma distributions
28th May 2019 – ML Workshop
BE-BI

7. OAF Analysis (some examples)
Accelerator
Instrument - Analysis
LINAC 4
Transmission efficiency / Line BCTs cross-calibration check
BLM crate humidity monitoring
LINAC 3
PSB
Overview of PSB beam Instrumentation
Wire scanner usage survey and analyze PS
Overview of PS beam Instrumentation
BLM: Comparison of the old and newly installed electronics results
SPS
Monitoring of the BCT used for safety for the EA
BPM: MOPOS vs ALPS – evaluation of the new orbit system
Wire scanner usage survey and analyses
LHC
BPM – Electronics Racks and acq card Temperature Survey
BLM – Acq Cards temp survey
DCCT BCT cross calibration check
AD,LEIR… …
28th May 2019 – ML Workshop
BE-BI

8. Other Analysis Tools BLMs health system checks
Additional daily cron reports: connectivity-dac, optical link errors, LSA BLM threshold changes, voltages status.
CALS + LSA DB + Python and post processing
Reports are produced with summarized information
28th May 2019 – ML Workshop
BE-BI

9. Other Analysis Tools
Example: dedicated analysis tools for specific tasks such as BSRT calibration, BLM lifetime calibration and fill-by-fill monitoring, dBLM fill-by-fill analysis, etc.
28th May 2019 – ML Workshop
BE-BI

10. Current ML use cases BLM radiation tests with TIM.
Renovation of the LHC beam-based feedback systems.
LHC BLM spike classification applied to the collimation alignment.
LHC beam lifetime optimization at injection.
28th May 2019 – ML Workshop
BE-BI

11. Current ML use cases BLM radiation tests with TIM
Collaboration with EN-SMM (M. Di Castro)
BLM radiation tests with TIM
Radiation BLM tests done autonomously with the TIM train
Faster-RCNN network for online 2D Beam Loss Monitors (BLM) localization
Multiple RGB-D cameras used for 3D reconstruction of the environment
3D pose will be used by the robotic arm path planner to calculate a safe approach to the BLM in the reconstructed environment
Image recognition
28th May 2019 – ML Workshop
BE-BI

12. Current ML use cases Renovation of the LHC beam-based feedback systems
Collaboration with OP-LHC and U. Malta (G. Valentino)
Renovation of the LHC beam-based feedback systems
The LHC OFC is currently being upgraded to FESA3.
As part of an academic exercise, we are investigating the use of Reinforcement Learning for orbit feedback control as opposed to the SVD beam response matrix.
The objective is to respond more quickly to BPM or COD failures, and achieve equal, if not better performance in the orbit feedback.
A simulation environment is being set up using OpenAI Gym.
Anomaly detection of BPMs used for the orbit feedback is also being investigated using machine learning techniques such as Local Outlier Factor
28th May 2019 – ML Workshop
BE-BI

13. Current ML use cases
Collaboration OP-LHC and LHC Collimation
LHC BLM spike classification applied to the collimation alignment.
Study the prediction of the LHC beam lifetime at injection and the optimization of the tune working point using ML algorithms.
Gabriella Azzopardi will present on the 4th June
Collaboration OP-LHC and EPFL
Loic Coyle presented today
28th May 2019 – ML Workshop
BE-BI

14. Improve of beam diagnostics using ML/DA
Keeping the same goals:
Evaluate instrument status (predictive maintenance)
Asses instrument performance (aging)
Improve instrument response (calibration).
We have discussed within BI how beam diagnostics could be improved if applying more sophisticated techniques.
We came out with a list of subjects where improving ML/DA could have a direct impact
28th May 2019 – ML Workshop
BE-BI

15. Improve of beam diagnostics using ML/DA
Identify outliers in different instruments or measurements. Currently done in most cases by comparison of simple thresholds (like BPM temperatures):
Extend the work started in ABP for identifying misbehaviors of BPMs.
The next generation of acquisition cards are equipped with Ethernet connection and higher computation power (FPGA) could envisage NN algorithm to detect anomalies (example of BLM patterns).
Study Wire-scanner distributions of power, position and profile. Disentangle real beam effects vs instrumental problems.
Find the correct tune in a noisy spectrum:
Feedback the tune finder with information on noise peaks.
Head-tail triggering too often with TBytes of data.
Identify the type of instability like a 2nd level trigger and reduce the data stored.
28th May 2019 – ML Workshop
BE-BI

16. Improve of beam diagnostics using ML/DA
Cross-calibration or online recalibration of instruments.
BSRT-WS, explore analysis of images.
BLM lifetime-BCT / losses in IRs vs losses in other locations, improve pattern recognition.
Beam size measurements using quadrupolar moment of BPMs.
Development of direct e-cloud measurements/observation using BPMs.
Complex, requiring correlation with other data like cryogenics, bunch-by-bunch patterns on beam size and charges.
Virtual instruments combining signal from different devices: schottky, lifetime, luminosity prediction.
Asses performance of instruments or algorithms, example OFC by analysis BPM signal and COD current, trained with fills data.
28th May 2019 – ML Workshop
BE-BI

17. Use Case Analysis Use Case Data Source Impact
Identify Outliers for BLMs, BPMs, Wire-scanners
CALS (NXCALS)
Improve instrument availability/performance
Tune measurement
Improve tune signal
Head-tail triggering
Files (TBytes)
Reduce data volumen / better analysis
Cross-calibration / online calibration
Images needed
Better measurements
BPMs quadrupolar moment
Online
Additional beam size measurement
ecloud measurement with BPMs
TBD
Direct ecloud measurement
Virtual instruments
Several
Additional mesurements
Algorithms assesment
Performance monitoring …
28th May 2019 – ML Workshop
BE-BI

18. BI wishes Ideally to be provided centrally Centralized software
Because python is intuitive many have started here:
Support of main data analysis libraries (numpy, scipy, pandas, matplotlib, etc.)
Support a (or several) machine learning packages (scikit-Learn, pytorch, tensorflow, keras)
28th May 2019 – ML Workshop
BE-BI

19. BI wishes Support on data collection and preparation:
Data preparation: align time data, clean data, etc.
Logging Flexibility: ML relies in many cases on the analysis of “big data” samples. Flexibility on increasing the logging rate for certain periods, like MD or commissioning is desirable. Example: G.Azzopardi: training of BLM spike using dedicated 100Hz BLM stream data, stored in csv files. This was crucial in order to be able to measure the shape of the signal. Similar cases might apply to UFO studies
28th May 2019 – ML Workshop
BE-BI

20. BI wishes BI will heavily rely on NXCALS
Guidelines on performance for data insertion/extraction avoiding custom setups
Currently files used in some cases, some ad-hoc infrastructure (servers + net links)
Backwards compatibility API to keep our tools running
Evaluation of online analysis
28th May 2019 – ML Workshop
BE-BI

21. Conclusion Data Analysis is part of BI core activities
ML has only been started
Rely as much as possible in NXCALS provided features
28th May 2019 – ML Workshop
BE-BI

22. Contributions are welcome!
Workshop on
“Data Science and Machine Learning”
Sunday 6th of October
Morning: tutorials
Afternoon: presentations / demonstrations
Full Details
Contributions are welcome!
28th May 2019 – ML Workshop
BE-BI