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pro vyhledávání: '"Klein, Samuel P"'
Machine learning is becoming increasingly popular in the context of particle physics. Supervised learning, which uses labeled Monte Carlo (MC) simulations, remains one of the most widely used methods for discriminating signals beyond the Standard Mod
Externí odkaz:
http://arxiv.org/abs/2411.09296
Autor:
Leigh, Matthew, Klein, Samuel, Charton, François, Golling, Tobias, Heinrich, Lukas, Kagan, Michael, Ochoa, Inês, Osadchy, Margarita
In this work, we significantly enhance masked particle modeling (MPM), a self-supervised learning scheme for constructing highly expressive representations of unordered sets relevant to developing foundation models for high-energy physics. In MPM, a
Externí odkaz:
http://arxiv.org/abs/2409.12589
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
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
Language models demonstrate remarkable capacity to generalize representations learned in one modality to downstream tasks in other modalities. Can we trace this ability to individual neurons? We study the case where a frozen text transformer is augme
Externí odkaz:
http://arxiv.org/abs/2308.01544
Model independent techniques for constructing background data templates using generative models have shown great promise for use in searches for new physics processes at the LHC. We introduce a major improvement to the CURTAINs method by training the
Externí odkaz:
http://arxiv.org/abs/2305.04646
Resonant anomaly detection is a promising framework for model-independent searches for new particles. Weakly supervised resonant anomaly detection methods compare data with a potential signal against a template of the Standard Model (SM) background i
Externí odkaz:
http://arxiv.org/abs/2212.11285
Normalizing flows are constructed from a base distribution with a known density and a diffeomorphism with a tractable Jacobian. The base density of a normalizing flow can be parameterised by a different normalizing flow, thus allowing maps to be foun
Externí odkaz:
http://arxiv.org/abs/2211.02487
Autor:
Klein, Samuel, Golling, Tobias
The sensitivity of many physics analyses can be enhanced by constructing discriminants that preferentially select signal events. Such discriminants become much more useful if they are uncorrelated with a set of protected attributes. In this paper we
Externí odkaz:
http://arxiv.org/abs/2211.02486