Masked particle modeling on sets: towards self-supervised high energy physics foundation models
We propose masked particle modeling (MPM) as a self-supervised method for learning
generic, transferable, and reusable representations on unordered sets of inputs for use in …
generic, transferable, and reusable representations on unordered sets of inputs for use in …
Improving the performance of weak supervision searches using transfer and meta-learning
A bstract Weak supervision searches have in principle the advantages of both being able to
train on experimental data and being able to learn distinctive signal properties. However, the …
train on experimental data and being able to learn distinctive signal properties. However, the …
Sharpening the A→ Z (*) h signature of the Type-II 2HDM at the LHC through advanced Machine Learning
W Esmail, A Hammad, S Moretti - Journal of High Energy Physics, 2023 - Springer
A bstract The A→ Z (*) h decay signature has been highlighted as possibly being the first
testable probe of the Standard Model (SM) Higgs boson discovered in 2012 (h) interacting …
testable probe of the Standard Model (SM) Higgs boson discovered in 2012 (h) interacting …
OmniJet-: The first cross-task foundation model for particle physics
J Birk, A Hallin, G Kasieczka - arXiv preprint arXiv:2403.05618, 2024 - arxiv.org
Foundation models are multi-dataset and multi-task machine learning methods that once pre-
trained can be fine-tuned for a large variety of downstream applications. The successful …
trained can be fine-tuned for a large variety of downstream applications. The successful …
Probing Undiscovered Particles with Theory and Data-Driven Tools
K Fraser - 2024 - dash.harvard.edu
The Standard Model has been precisely tested by a plethora of experiments and has proved
extremely successful at describing the fundamental interactions of subatomic particles …
extremely successful at describing the fundamental interactions of subatomic particles …