Zobrazeno 1 - 10
of 4 340
pro vyhledávání: '"John, Andrew"'
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 address the challenge of multiplicity variations between parton and reco
Externí odkaz:
http://arxiv.org/abs/2406.13074
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 supervised machine learning algorithm that builds a background enriched
Externí odkaz:
http://arxiv.org/abs/2405.12131
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 via a classifier trained on auxiliary features in a weakly-supervised
Externí odkaz:
http://arxiv.org/abs/2404.07258
Autor:
Oleksiyuk, Ivan, Raine, John Andrew, Krämer, Michael, Voloshynovskiy, Svyatoslav, Golling, Tobias
We propose a new model-independent method for new physics searches called Cluster Scanning. It uses the k-means algorithm to perform clustering in the space of low-level event or jet observables, and separates potentially anomalous clusters to constr
Externí odkaz:
http://arxiv.org/abs/2402.17714
Autor:
Golling, Tobias, Heinrich, Lukas, Kagan, Michael, Klein, Samuel, Leigh, Matthew, Osadchy, Margarita, Raine, John Andrew
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 energy physics (HEP) scientific data. This work provides a novel scheme
Externí odkaz:
http://arxiv.org/abs/2401.13537
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 templates for the signal region can be generated either directly from
Externí odkaz:
http://arxiv.org/abs/2312.10130
Autor:
Buhmann, Erik, Ewen, Cedric, Faroughy, Darius A., Golling, Tobias, Kasieczka, Gregor, Leigh, Matthew, Quétant, Guillaume, Raine, John Andrew, Sengupta, Debajyoti, Shih, David
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 novel methods that generate LHC jets as point clouds efficiently and accu
Externí odkaz:
http://arxiv.org/abs/2310.00049
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 of preserving weights and shifting the data points instead. Normalizi
Externí odkaz:
http://arxiv.org/abs/2309.06472