442 citations · 7.9k across the 282 of their papers we have counts for
12 papers · 1 filter
Pre-Training for Simulation-Based Science: A Study on Jet Foundation Model Training Objectives
Ibrahim Elsharkawy, Joschka Birk, Vinicius Mikuni +3
Foundation models (FMs) trained on large datasets and fine-tuned on downstream tasks have emerged as a powerful paradigm in AI for science. Industrial FMs are typically trained usi…
Kitchen Sink Anomaly Detection
Ranit Das, Marie Hein, Gregor Kasieczka +6
An enormous amount of R&D effort has resulted in many new resonant anomaly detection methods being proposed in recent years. However, the vast majority of previous R&D studies have…
Learning Broken Symmetries with Approximate Invariance
Seth Nabat, Aishik Ghosh, Edmund Witkowski +2
Recognizing symmetries in data allows for significant boosts in neural network training, which is especially important where training data are limited. In many cases, however, the…
Energy-scaling behavior of intrinsic transverse momentum parameters in Drell-Yan simulation
CMS Collaboration
An analysis is presented based on models of the intrinsic transverse momentum (intrinsic ) of partons in nucleons by studying the dilepton transverse momentum in Drel…
Accelerating Resonance Searches via Signature-Oriented Pre-training
Congqiao Li, Antonios Agapitos, Jovin Drews +9
The search for heavy resonances beyond the Standard Model (BSM) is a key objective at the LHC. While the recent use of advanced deep neural networks for boosted-jet tagging signifi…
OmniJet-: The first cross-task foundation model for particle physics
Joschka Birk, Anna Hallin, Gregor Kasieczka
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 d…
Classifier Surrogates: Sharing AI-based Searches with the World
Sebastian Bieringer, Gregor Kasieczka, Jan Kieseler +1
In recent years, neural network-based classification has been used to improve data analysis at collider experiments. While this strategy proves to be hugely successful, the underly…
Les Houches guide to reusable ML models in LHC analyses
Jack Y. Araz, Andy Buckley, Gregor Kasieczka +10
With the increasing usage of machine-learning in high-energy physics analyses, the publication of the trained models in a reusable form has become a crucial question for analysis p…
Back To The Roots: Tree-Based Algorithms for Weakly Supervised Anomaly Detection
Thorben Finke, Marie Hein, Gregor Kasieczka +6
Weakly supervised methods have emerged as a powerful tool for model-agnostic anomaly detection at the Large Hadron Collider (LHC). While these methods have shown remarkable perform…
Classifying Anomalies THrough Outer Density Estimation (CATHODE)
Anna Hallin, Joshua Isaacson, Gregor Kasieczka +6
We propose a new model-agnostic search strategy for physics beyond the standard model (BSM) at the LHC, based on a novel application of neural density estimation to anomaly detecti…
Boosted Top Tagging Method Overview
Gregor Kasieczka
We briefly review common tools and methods to identify boosted, hadronically decaying top quarks at the LHC experiments. This includes generic jet substructure variables, specific…
Towards an Understanding of the Correlations in Jet Substructure
D. Adams, A. Arce, L. Asquith +83
Over the past decade, a large number of jet substructure observables have been proposed in the literature, and explored at the LHC experiments. Such observables attempt to utilize…