Zobrazeno 1 - 10
of 1 939
pro vyhledávání: '"Krämer Michael"'
Autor:
Amram, Oz, Anzalone, Luca, Birk, Joschka, Faroughy, Darius A., Hallin, Anna, Kasieczka, Gregor, Krämer, Michael, Pang, Ian, Reyes-Gonzalez, Humberto, Shih, David
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 demonstrates how data collected by the CMS experiment at the Large Hadron Collid
Externí odkaz:
http://arxiv.org/abs/2412.10504
Autor:
Geuskens, Joep, Gite, Nishank, Krämer, Michael, Mikuni, Vinicius, Mück, Alexander, Nachman, Benjamin, Reyes-González, Humberto
Identifying the origin of high-energy hadronic jets ('jet tagging') has been a critical benchmark problem for machine learning in particle physics. Jets are ubiquitous at colliders and are complex objects that serve as prototypical examples of collec
Externí odkaz:
http://arxiv.org/abs/2411.02628
Autor:
Das, Ranit, Finke, Thorben, Hein, Marie, Kasieczka, Gregor, Krämer, Michael, Mück, Alexander, Shih, David
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 background template used to produce an anomaly score. Using the LHC Olympics R
Externí odkaz:
http://arxiv.org/abs/2411.00085
Publikováno v:
JCAP11(2024)017
Cosmic-ray antimatter, particularly low-energy antideuterons, serves as a sensitive probe of dark matter annihilating in our Galaxy. We study this smoking-gun signature and explore its complementarity with indirect dark matter searches using cosmic-r
Externí odkaz:
http://arxiv.org/abs/2406.18642
Autor:
Fischer, Kirsten, Lindner, Javed, Dahmen, David, Ringel, Zohar, Krämer, Michael, Helias, Moritz
A key property of neural networks driving their success is their ability to learn features from data. Understanding feature learning from a theoretical viewpoint is an emerging field with many open questions. In this work we capture finite-width effe
Externí odkaz:
http://arxiv.org/abs/2405.10761
Autor:
Beenakker, Wim, Borschensky, Christoph, Krämer, Michael, Kulesza, Anna, Laenen, Eric, Mamužić, Judita, Valero, Laura Moreno
Publikováno v:
SciPost Phys. Core 7, 072 (2024)
We report on updated precision predictions for total cross sections of coloured supersymmetric particle production at the LHC with a centre-of-mass energy of $\sqrt S$ = 13.6 TeV, computed with the modern PDF4LHC21 set. The cross sections are calcula
Externí odkaz:
http://arxiv.org/abs/2404.18837
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
Publikováno v:
JHEP 06 (2024) 179
We study a simplified Dark Matter model in the Dark Minimal Flavour Violation framework. Our model complements the Standard Model with a flavoured Dark Matter Majorana triplet and a coloured scalar mediator that share a Yukawa coupling with the right
Externí odkaz:
http://arxiv.org/abs/2312.09274
We present DarkCLR, a novel framework for detecting semi-visible jets at the LHC. DarkCLR uses a self-supervised contrastive-learning approach to create observables that are approximately invariant under relevant transformations. We use background-en
Externí odkaz:
http://arxiv.org/abs/2312.03067
Autor:
Finke, Thorben, Hein, Marie, Kasieczka, Gregor, Krämer, Michael, Mück, Alexander, Prangchaikul, Parada, Quadfasel, Tobias, Shih, David, Sommerhalder, Manuel
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 performance on specific signatures such as di-jet resonances, their applicati
Externí odkaz:
http://arxiv.org/abs/2309.13111