28 citations · 75 across the 9 of their papers we have counts for
9 papers
RODEM Jet Datasets
Knut Zoch, John Andrew Raine, Debajyoti Sengupta +1
We present the RODEM Jet Datasets, a comprehensive collection of simulated large-radius jets designed to support the development and evaluation of machine-learning algorithms in pa…
PIPPIN: Generating variable length full events from partons
Guillaume Quétant, John Andrew Raine, Matthew Leigh +2
This paper presents a novel approach for directly generating full events at detector-level from parton-level information, leveraging cutting-edge machine learning techniques. To ad…
SkyCURTAINs: Model agnostic search for Stellar Streams with Gaia data
Debajyoti Sengupta, Stephen Mulligan, David Shih +2
We present SkyCURTAINs, a data driven and model agnostic method to search for stellar streams in the Milky Way galaxy using data from the Gaia telescope. SkyCURTAINs is a weakly su…
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…
Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models
Tobias Golling, Lukas Heinrich, Michael Kagan +4
We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high e…
Improving new physics searches with diffusion models for event observables and jet constituents
Debajyoti Sengupta, Matthew Leigh, John Andrew Raine +2
We introduce a new technique called Drapes to enhance the sensitivity in searches for new physics at the LHC. By training diffusion models on side-band data, we show how background…
EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion
Erik Buhmann, Cedric Ewen, Darius A. Faroughy +7
Jets at the LHC, typically consisting of a large number of highly correlated particles, are a fascinating laboratory for deep generative modeling. In this paper, we present two nov…
Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation
Tobias Golling, Samuel Klein, Radha Mastandrea +2
Many components of data analysis in high energy physics and beyond require morphing one dataset into another. This is commonly solved via reweighting, but there are many advantages…
Calorimeter shower superresolution
Ian Pang, John Andrew Raine, David Shih
Calorimeter shower simulation is a major bottleneck in the Large Hadron Collider computational pipeline. There have been recent efforts to employ deep-generative surrogate models t…