ICALEPCS 2019: 1st Data Science and Machine Learning Workshop

Session

Contributions 2

6 Oct 2019, 16:00

Marriott at The Brooklyn Bridge

9. Machine Learning for Automatic LHC Collimator Alignment

Gabriella Azzopardi (University of Malta (MT))

06/10/2019, 16:00

Presentation

The collimation system is installed in the Large Hadron Collider to protect its super-conducting magnets and sensitive equipment from potentially dangerous beam halo particles. The collimator settings are determined following an alignment procedure, whereby collimator jaws are moved towards the beam until a suitable spike pattern, consisting of a sharp rise followed by a slow decay, is...

7. Reinforcement Learning for FEL performance optimization

Niky Bruchon (University of Trieste)

06/10/2019, 16:15

Presentation

The theory behind various optimization techniques is often not trivial, however their implementation on complex systems such as a Free Electron Lasers could be even more challenging.
In this talk we will present the experience gained using a Machine Learning technique called Reinforcement Learning for the performance optimization of the seeded FEL FERMI.
In particular, the difficulties...

8. Machine Learning Based Reconfiguration of the BNL ATR Line

Dr Jonathan Edelen (RadiaSoft LLC)

06/10/2019, 16:30

Presentation

The Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory will undergo a beam energy scan over the next several years. To execute this scan, the transfer line between the Alternating Gradient Synchrotron (AGS) and RHIC or the so-called the ATR line, must be re-tuned for each energy. Control of the ATR line has four primary constraints: match the beam trajectory into RHIC,...

13. Machine Learning for High Energy Photon Source (HEPS)

Paul Chu (Institute of High Energy Physics, Chinese Academy of Science)

06/10/2019, 16:45

Presentation

A 4th generation, 6-GeV high energy synchrotron radiation light source, High Energy Photon Source (HEPS), is being built in suburban Beijing, China. The complexity level and high accuracy requirements for the accelerator and beamlines will need a state-of-art controls approach such as machine learning technology. It is essential to design a modern software architecture with proper...