204 citations · 572 across the 28 of their papers we have counts for
29 papers
Anomaly detection for multijet scenarios
Gregor Kasieczka, Sung Hak Lim, Louis Moureaux +3
Signals of physics beyond the Standard Model continue to resist discovery at the LHC. Recent years have seen the proliferation of new anomaly detection techniques, promising discov…
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…
AI and the Research-Education Environment of Physics
Savannah Thais, Koji Hashimoto, David S. Berman +6
In the current era of AI transforming the research-education environment of physics, variety of issues and concerns arise. The KITP program "Generative AI for High and Low Energy P…
A Scientific Human-Agent Reproduction Pipeline
Joschka Birk, Gregor Kasieczka, Siddharth Mishra-Sharma +3
Reproducing scientific analyses is essential for preserving knowledge, building extensible codebases, and deepening researcher understanding - yet the effort often outweighs its ac…
Enhancing next token prediction based pre-training for jet foundation models
Joschka Birk, Anna Hallin, Gregor Kasieczka +3
Next token prediction is an attractive pre-training task for jet foundation models, in that it is simulation free and enables excellent generative capabilities that can transfer ac…
SURFing to the Fundamental Limit of Jet Tagging
Ian Pang, Darius A. Faroughy, David Shih +2
Beyond the practical goal of improving search and measurement sensitivity through better jet tagging algorithms, there is a deeper question: what are their upper performance limits…
How to pick the best anomaly detector?
Marie Hein, Gregor Kasieczka, Michael Krämer +3
Anomaly detection has the potential to discover new physics in unexplored regions of the data. However, choosing the best anomaly detector for a given data set in a model-agnostic…
Quirk SUEP
David Curtin, Sascha Dreyer, Max Fusté Costa +6
We propose searching for physics beyond the Standard Model in the low-transverse-momentum tracks accompanying hard-scatter events at the LHC. TeV-scale resonances connected to a da…
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…
Accurate and robust methods for direct background estimation in resonant anomaly detection
Ranit Das, Thorben Finke, Marie Hein +4
Resonant anomaly detection methods have great potential for enhancing the sensitivity of traditional bump hunt searches. A key component of these methods is a high quality backgrou…
Universal New Physics Latent Space
Anna Hallin, Gregor Kasieczka, Sabine Kraml +4
We develop a machine learning method for mapping data originating from both Standard Model processes and various theories beyond the Standard Model into a unified representation (l…
Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
Thorsten Buss, Frank Gaede, Gregor Kasieczka +2
In the quest to build generative surrogate models as computationally efficient alternatives to rule-based simulations, the quality of the generated samples remains a crucial fronti…
Complete Optimal Non-Resonant Anomaly Detection
Gregor Kasieczka, John Andrew Raine, David Shih +1
We propose the first-ever complete, model-agnostic search strategy based on the optimal anomaly score, for new physics on the tails of distributions. Signal sensitivity is achieved…
Residual ANODE
Ranit Das, Gregor Kasieczka, David Shih
We present R-ANODE, a new method for data-driven, model-agnostic resonant anomaly detection that raises the bar for both performance and interpretability. The key to R-ANODE is to…
Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information
Joschka Birk, Erik Buhmann, Cedric Ewen +2
We introduce the first generative model trained on the JetClass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizi…
Full Phase Space Resonant Anomaly Detection
Erik Buhmann, Cedric Ewen, Gregor Kasieczka +3
Physics beyond the Standard Model that is resonant in one or more dimensions has been a longstanding focus of countless searches at colliders and beyond. Recently, many new strateg…
Combining Resonant and Tail-based Anomaly Detection
Gerrit Bickendorf, Manuel Drees, Gregor Kasieczka +2
In many well-motivated models of the electroweak scale, cascade decays of new particles can result in highly boosted hadronic resonances (e.g. ). This can make these models…
The Interplay of Machine Learning--based Resonant Anomaly Detection Methods
Tobias Golling, Gregor Kasieczka, Claudius Krause +6
Machine learning--based anomaly detection (AD) methods are promising tools for extending the coverage of searches for physics beyond the Standard Model (BSM). One class of AD metho…
Two-particle Bose-Einstein correlations and their Lévy parameters in PbPb collisions at = 5.02 TeV
CMS Collaboration
Two-particle Bose-Einstein momentum correlation functions are studied for charged-hadron pairs in lead-lead collisions at a center-of-mass energy per nucleon pair of $\sqrt{s_\math…
Feature Selection with Distance Correlation
Ranit Das, Gregor Kasieczka, David Shih
Choosing which properties of the data to use as input to multivariate decision algorithms -- a.k.a. feature selection -- is an important step in solving any problem with machine le…
Resonant anomaly detection without background sculpting
Anna Hallin, Gregor Kasieczka, Tobias Quadfasel +2
We introduce a new technique named Latent CATHODE (LaCATHODE) for performing "enhanced bump hunts", a type of resonant anomaly search that combines conventional one-dimensional bum…
Anomaly Detection under Coordinate Transformations
Gregor Kasieczka, Radha Mastandrea, Vinicius Mikuni +3
There is a growing need for machine learning-based anomaly detection strategies to broaden the search for Beyond-the-Standard-Model (BSM) physics at the Large Hadron Collider (LHC)…
New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
Andreas Adelmann, Walter Hopkins, Evangelos Kourlitis +8
The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources. To overcome this challenge,…
Machine Learning in the Search for New Fundamental Physics
Georgia Karagiorgi, Gregor Kasieczka, Scott Kravitz +2
Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new…