General Information
The workshop will take place in person in Strasbourg at IPHC from Wed 20 Nov 1 PM to Friday 22 Nov 1pm. A social reception will take place Thursday night.
Directions will be provided
Introduction
Machine Learning is now potentially impacting many aspects of particle physics, nuclear physics and astroparticle physics.
This workshop covers current developments with Machine Learning at IN2P3 and CEA-IRFU.
The call for contribution is not limited to finished work, work in progress or even Expression of Interest are very welcome. (Since this is a national workshop, experimental contributions by students need not be approved by the collaboration, but please refer to the experiment national contact for the exact rules).
The call for contributions will be open shortly
Tracks
- ML for object identification and reconstruction
- ML for analysis : event classification, statistical analysis and inference, including anomaly detection
- ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model
- Fast ML : Application of Machine Learning to DAQ/Trigger/Real Time Analysis
- ML infrastructure : Hardware and software for Machine Learning
- ML training, courses, tutorial, open datasets and challenges
- ML for phenomenology and theory (only if does not fit in Tracks above)
- ML for particle accelerators (only if does not fit in Tracks above)
- Others
Mailing list
Please make sure you are subscribed to MACHINE-LEARNING-L@in2p3.fr on IN2P3 listserv to keep up to date with ML.
Organisation : Alexandre Boucaud (APC/IN2P3), Valérie Gautard (CEA/IRFU), David Rousseau (IJCLab/IN2P3)
