Abstract submission is open and registration will follow soon.
The workshop will be organised in a hybrid format (with a Zoom connection option). We expect speakers to attend in-person.
Machine learning has become a hot topic in particle physics over the past several years. In particular, there has been a lot of progress in the areas of particle and event identification, reconstruction, generative models, anomaly detection and more. In this conference, we will discuss current progress in these areas, focusing on new breakthrough ideas and existing challenges. The ML4Jets workshop will be open to the full community and will include LHC experiments as well as theorists and phenomenologists interested in this topic. We explicitly welcome contributions and participation from method scientists as well as adjacent scientific fields such as astronomy, astrophysics, astroparticle physics, hadron- and nuclear physics and other domains facing similar challenges.
This year's conference is organised jointly by LPNHE, LPTHE and IJCLab and hosted by LPNHE on the Sorbonne Campus. It follows conferences in 2017, 2018, 2020, 2021, 2022 and 2023.
Registration for both in-person and Zoom-participation will be free of charge and (at the minimum) include coffee-breaks for in-person participants. We are looking into an opt-in dinner and announce details and potential extra costs closer to the event.
Join the ML4Jets Slack Channel for discussions.
Anja Butter (LPNHE)
International Advisory Committee:
Florencia Canelli (University of Zurich)
Kyle Cranmer (NYU)
Vava Gligorov (LPNHE)
Gian Michele Innocenti (CERN)
Ben Nachman (LBNL)
Mihoko Nojiri (KEK)
Maurizio Pierini (CERN)
Tilman Plehn (Heidelberg)
David Shih (Rutgers)
Jesse Thaler (MIT)
Sofia Vallescorsa (CERN)
