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Esser, F.; Madigan, M.; Sanz, V.; Ubiali, M. |
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Title |
On the coupling of axion-like particles to the top quark |
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2023 |
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Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
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09 |
Issue |
9 |
Pages |
063 - 39pp |
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Abstract  |
In this paper we explore the coupling of a light axion-like particle (ALP) to top quarks. We use high-energy LHC probes, and examine both the direct probe to this coupling in associated production of a top-pair with an ALP, and the indirect probe through loop-induced gluon fusion to an ALP leading to top pairs. Using the latest LHC Run II data, we provide the best limit on this coupling. We also compare these limits with those obtained from loop-induced couplings in diboson final states, finding that the +MET channel is the best current handle on this coupling. |
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no |
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yes |
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yes |
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IFIC @ pastor @ |
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6083 |
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Author |
Kasieczka, G. et al; Sanz, V. |
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Title |
The LHC Olympics 2020: a community challenge for anomaly detection in high energy physics |
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Journal Article |
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Year |
2021 |
Publication |
Reports on Progress in Physics |
Abbreviated Journal |
Rep. Prog. Phys. |
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84 |
Issue |
12 |
Pages |
124201 - 64pp |
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Keywords |
anomaly detection; machine learning; unsupervised learning; weakly supervised learning; semisupervised learning; beyond the standard model; model-agnostic methods |
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A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders. |
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[Kasieczka, Gregor] Univ Hamburg, Inst Expt Phys, Hamburg, Germany, Email: gregor.kasieczka@uni-hamburg.de; |
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IOP Publishing Ltd |
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English |
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0034-4885 |
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WOS:000727698500001 |
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no |
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yes |
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yes |
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IFIC @ pastor @ |
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5039 |
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