1.2k citations · 7.8k across the 194 of their papers we have counts for
197 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…
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…
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…
Characterizing Heavy Neutral Leptons: Measuring Parameters, Discriminating Majorana versus Dirac, and Using FASER2 as a Trigger for ATLAS
Jonathan L. Feng, Alec Hewitt, Daniel La Rocco +1
This work explores the potential of the proposed FASER2 experiment at the LHC to determine the properties of a discovered heavy neutral lepton (HNL), including its mass, couplings,…
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…
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…