Zobrazeno 1 - 10
of 93
pro vyhledávání: '"Dillon, Barry M."'
Publikováno v:
SciPost Phys. Core 7, 056 (2024)
We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC2021. The AnomalyCLR technique is data-driven and uses augmentations of the background data to
Externí odkaz:
http://arxiv.org/abs/2301.04660
Discriminating quark-like from gluon-like jets is, in many ways, a key challenge for many LHC analyses. First, we use a known difference in Pythia and Herwig simulations to show how decorrelated taggers would break down when the most distinctive feat
Externí odkaz:
http://arxiv.org/abs/2212.10493
Autoencoders are an effective analysis tool for the LHC, as they represent one of its main goal of finding physics beyond the Standard Model. The key challenge is that out-of-distribution anomaly searches based on the compressibility of features do n
Externí odkaz:
http://arxiv.org/abs/2206.14225
Publikováno v:
Phys. Rev. D 106, 056005 (2022)
We investigate a method of model-agnostic anomaly detection through studying jets, collimated sprays of particles produced in high-energy collisions. We train a transformer neural network to encode simulated QCD "event space" dijets into a low-dimens
Externí odkaz:
http://arxiv.org/abs/2205.10380
Autor:
Nachman, Ben, Rappoccio, Salvatore, Tran, Nhan, Bonilla, Johan, Chachamis, Grigorios, Dillon, Barry M., Chekanov, Sergei V., Erbacher, Robin, Gouskos, Loukas, Hinzmann, Andreas, Höche, Stefan, Huffman, B. Todd, Kotwal, Ashutosh. V., Kar, Deepak, Kogler, Roman, Lange, Clemens, LeBlanc, Matt, Lemmon, Roy, McLean, Christine, Neubauer, Mark S., Plehn, Tilman, Roy, Debarati, Stark, Giordan, Roloff, Jennifer, Vos, Marcel, Yeh, Chih-Hsiang, Yu, Shin-Shan
Even though jet substructure was not an original design consideration for the Large Hadron Collider (LHC) experiments, it has emerged as an essential tool for the current physics program. We examine the role of jet substructure on the motivation for
Externí odkaz:
http://arxiv.org/abs/2203.07462
Autor:
Buss, Thorsten, Dillon, Barry M., Finke, Thorben, Krämer, Michael, Morandini, Alessandro, Mück, Alexander, Oleksiyuk, Ivan, Plehn, Tilman
Publikováno v:
SciPost Phys. 15, 168 (2023)
Searches for anomalies are a significant motivation for the LHC and help define key analysis steps, including triggers. We discuss specific examples how LHC anomalies can be defined through probability density estimates, evaluated in a physics space
Externí odkaz:
http://arxiv.org/abs/2202.00686
Autor:
Dillon, Barry M., Kasieczka, Gregor, Olischlager, Hans, Plehn, Tilman, Sorrenson, Peter, Vogel, Lorenz
Publikováno v:
SciPost Phys. 12, 188 (2022)
Collider searches face the challenge of defining a representation of high-dimensional data such that physical symmetries are manifest, the discriminating features are retained, and the choice of representation is new-physics agnostic. We introduce Je
Externí odkaz:
http://arxiv.org/abs/2108.04253
Autor:
Alvarez, Ezequiel, Dillon, Barry M., Faroughy, Darius A., Kamenik, Jernej F., Lamagna, Federico, Szewc, Manuel
Monte Carlo (MC) generators are crucial for analyzing data in particle collider experiments. However, often even a small mismatch between the MC simulations and the measurements can undermine the interpretation of the results. This is particularly im
Externí odkaz:
http://arxiv.org/abs/2107.00668
Publikováno v:
SciPost Phys. 11, 061 (2021)
Autoencoders as tools behind anomaly searches at the LHC have the structural problem that they only work in one direction, extracting jets with higher complexity but not the other way around. To address this, we derive classifiers from the latent spa
Externí odkaz:
http://arxiv.org/abs/2104.08291
Unsupervised anomaly detection could be crucial in future analyses searching for rare phenomena in large datasets, as for example collected at the LHC. To this end, we introduce a physics inspired variational autoencoder (VAE) architecture which perf
Externí odkaz:
http://arxiv.org/abs/2103.06595