12 papers
Towards Engineering Scaling Laws with Pretraining Data Composition
Jan-Lucas Uslu, Kevin Greif, Daniel Whiteson +1
Neural scaling laws describe how model performance improves as a power law in compute, model size, and dataset size. While well-established for large language models, these relatio…
On Focusing Statistical Power for Searches and Measurements in Particle Physics
James Carzon, Aishik Ghosh, Rafael Izbicki +3
Particle physics experiments rely on the (generalised) likelihood ratio test (LRT) for searches and measurements, which consist of composite hypothesis tests. However, this test is…
Time-dependent signals of new physics at the LHC
Max H. Fieg, Patrick J. Fox, Jinbo Zhang +3
The Large Hadron Collider (LHC) is sensitive to signals of beyond the Standard Model physics through a variety of channels including missing energy and resonance searches. In most…
Towards AI-assisted Neutrino Flavor Theory Design
Jason Benjamin Baretz, Max Fieg, Vijay Ganesh +4
Particle physics theories, such as those which explain neutrino flavor mixing, arise from a vast landscape of model-building possibilities. A model's construction typically relies…
Generative Unfolding of Jets and Their Substructure
Antoine Petitjean, Anja Butter, Kevin Greif +4
Unfolding, for example of distortions imparted by detectors, provides suitable and publishable representations of LHC data. Many methods for unbinned and high-dimensional unfolding…
Learning to Reconstruct Quirky Tracks
Qiyu Sha, Daniel Murnane, Max Fieg +4
Analysis of data from particle physics experiments traditionally sacrifices some sensitivity to new particles for the sake of practical computability, effectively ignoring some pot…