Gregor Kasieczka
@kasieczka.bsky.social
Full Professor for Machine Learning in Particle Physics at Universität Hamburg | Searching for new physics with #CMSExperiment | He/Him
We show that a hybrid (conserving+breaking) architecture combines the advantages of symmetry-conserving networks (fast learning) and breaking ones (higher maximum performance).
Great work by Seth with @aishikghosh.bsky.social, Ed, @danielwhiteson.bsky.social
Full paper: arxiv.org/abs/2412.18773
Great work by Seth with @aishikghosh.bsky.social, Ed, @danielwhiteson.bsky.social
Full paper: arxiv.org/abs/2412.18773
January 6, 2025 at 8:44 AM
We show that a hybrid (conserving+breaking) architecture combines the advantages of symmetry-conserving networks (fast learning) and breaking ones (higher maximum performance).
Great work by Seth with @aishikghosh.bsky.social, Ed, @danielwhiteson.bsky.social
Full paper: arxiv.org/abs/2412.18773
Great work by Seth with @aishikghosh.bsky.social, Ed, @danielwhiteson.bsky.social
Full paper: arxiv.org/abs/2412.18773
We prepared 180M jets from 2016 CMS data taking to be easily used for machine learning & demonstrate their use for pre-training.
Data at: www.fdr.uni-hamburg.de/record/16505
Paper at: arxiv.org/abs/2412.10504
w/ @ozamram.bsky.social, @joschkabirk.bsky.social, @mikraemer.bsky.social & others
Data at: www.fdr.uni-hamburg.de/record/16505
Paper at: arxiv.org/abs/2412.10504
w/ @ozamram.bsky.social, @joschkabirk.bsky.social, @mikraemer.bsky.social & others
Aspen Open Jets: a real-world ML-ready dataset for jet physics
This dataset contains approximately 180 M boosted jets, derived from open data collected by the CMS experiment at the Large Hadron Collider (LHC) in 2016 — specifically the JetHT datastream — and pres...
www.fdr.uni-hamburg.de
December 17, 2024 at 7:21 AM
We prepared 180M jets from 2016 CMS data taking to be easily used for machine learning & demonstrate their use for pre-training.
Data at: www.fdr.uni-hamburg.de/record/16505
Paper at: arxiv.org/abs/2412.10504
w/ @ozamram.bsky.social, @joschkabirk.bsky.social, @mikraemer.bsky.social & others
Data at: www.fdr.uni-hamburg.de/record/16505
Paper at: arxiv.org/abs/2412.10504
w/ @ozamram.bsky.social, @joschkabirk.bsky.social, @mikraemer.bsky.social & others