99 citations · 191 across the 16 of their papers we have counts for
6 papers · 1 filter
Neural Scaling Laws for Jet Generation
Oz Amram, Darius A. Faroughy, Tjarko Gerdes +5
Recently observed empirical scaling laws describe the performance of foundation-type models as three independent key quantities -- dataset size, compute, and model parameters -- ar…
Data-Driven High-Dimensional Statistical Inference with Generative Models
Oz Amram, Manuel Szewc
Crucial to many measurements at the LHC is the use of correlated multi-dimensional information to distinguish rare processes from large backgrounds, which is complicated by the poo…
Weakly supervised anomaly detection with event-level variables
Liam Brennan, Tamas Almos Vami, Oz Amram +7
We introduce a new topology for weakly supervised anomaly detection searches, di-object plus~X. In this topology, one looks for a resonance decaying to two standard model particles…
Aspen Open Jets: Unlocking LHC Data for Foundation Models in Particle Physics
Oz Amram, Luca Anzalone, Joschka Birk +7
Foundation models are deep learning models pre-trained on large amounts of data which are capable of generalizing to multiple datasets and/or downstream tasks. This work demonstrat…
The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Gregor Kasieczka, Benjamin Nachman, David Shih +44
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. I…
Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data
Oz Amram, Cristina Mantilla Suarez
There has been substantial progress in applying machine learning techniques to classification problems in collider and jet physics. But as these techniques grow in sophistication,…