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Author Huang, F.; Sanz, V.; Shu, J.; Xue, X.
Title LIGO as a probe of dark sectors Type Journal Article
Year (down) 2021 Publication Physical Review D Abbreviated Journal Phys. Rev. D
Volume 104 Issue 10 Pages 095001 - 9pp
Keywords
Abstract We show how current LIGO data is able to probe interesting theories beyond the Standard Model, particularly dark sectors where a dark Higgs boson triggers symmetry breaking via a first-order phase transition. We use publicly available LIGO O2 data to illustrate how these sectors, even if disconnected from the Standard Model, can be probed by gravitational wave detectors. We link the LIGO measurements with the model content and mass scale of the dark sector, finding that current O2 data are testing a broad set of scenarios that can be mapped into many different types of dark-sector models where the breaking of SU(N) theories with Nf fermions is triggered by a dark Higgs boson at scales ? similar or equal to 108-109 GeV with reasonable parameters for the scalar potential.
Address [Huang, Fei; Shu, Jing; Xue, Xiao] Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China, Email: huangf4@uci.edu;
Corporate Author Thesis
Publisher Amer Physical Soc Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2470-0010 ISBN Medium
Area Expedition Conference
Notes WOS:000716446500001 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5021
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Author Bonilla, J.; Brivio, I.; Gavela, M.B.; Sanz, V.
Title One-loop corrections to ALP couplings Type Journal Article
Year (down) 2021 Publication Journal of High Energy Physics Abbreviated Journal J. High Energy Phys.
Volume 11 Issue 11 Pages 168 - 57pp
Keywords Beyond Standard Model; Effective Field Theories; Renormalization Group
Abstract The plethora of increasingly precise experiments which hunt for axion-like particles (ALPs), as well as their widely different energy reach, call for the theoretical understanding of ALP couplings at loop-level. We derive the one-loop contributions to ALP-SM effective couplings, including finite corrections. The complete leading-order – dimension five – effective linear Lagrangian is considered. The ALP is left off-shell, which is of particular impact on LHC and accelerator searches of ALP couplings to gamma gamma, ZZ, WW, Z gamma gluons and fermions. All results are obtained in the covariant Rg gauge. A few phenomenological consequences are also explored as illustration, with flavour diagonal channels in the case of fermions: in particular, we explore constraints on the coupling of the ALP to top quarks, that can be extracted from LHC data, from astrophysical sources and from Dark Matter direct detection experiments such as PandaX, LUX and XENONIT. Furthermore, we clarify the relation between alternative ALP bases, the role of gauge anomalous couplings and their interface with chirality-conserving and chirality-flip fermion interactions, and we briefly discuss renormalization group aspects.
Address [Bonilla, J.; Gavela, M. B.] Univ Autonoma Madrid, Dept Fis Teor, E-28049 Madrid, Spain, Email: jesus.bonilla@ua.m.es;
Corporate Author Thesis
Publisher Springer Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1029-8479 ISBN Medium
Area Expedition Conference
Notes WOS:000721914800006 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5029
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Author Kasieczka, G. et al; Sanz, V.
Title The LHC Olympics 2020: a community challenge for anomaly detection in high energy physics Type Journal Article
Year (down) 2021 Publication Reports on Progress in Physics Abbreviated Journal Rep. Prog. Phys.
Volume 84 Issue 12 Pages 124201 - 64pp
Keywords anomaly detection; machine learning; unsupervised learning; weakly supervised learning; semisupervised learning; beyond the standard model; model-agnostic methods
Abstract 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.
Address [Kasieczka, Gregor] Univ Hamburg, Inst Expt Phys, Hamburg, Germany, Email: gregor.kasieczka@uni-hamburg.de;
Corporate Author Thesis
Publisher IOP Publishing Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0034-4885 ISBN Medium
Area Expedition Conference
Notes WOS:000727698500001 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5039
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Author Khosa, C.K.; Mars, L.; Richards, J.; Sanz, V.
Title Convolutional neural networks for direct detection of dark matter Type Journal Article
Year (down) 2020 Publication Journal of Physics G Abbreviated Journal J. Phys. G
Volume 47 Issue 9 Pages 095201 - 20pp
Keywords dark matter; dark matter detection; neural networks; xenon1T; WIMPs
Abstract The XENON1T experiment uses a time projection chamber (TPC) with liquid xenon to search for weakly interacting massive particles (WIMPs), a proposed dark matter particle, via direct detection. As this experiment relies on capturing rare events, the focus is on achieving a high recall of WIMP events. Hence the ability to distinguish between WIMP and the background is extremely important. To accomplish this, we suggest using convolutional neural networks (CNNs); a machine learning procedure mainly used in image recognition tasks. To explore this technique we use XENON collaboration open-source software to simulate the TPC graphical output of dark matter signals and main backgrounds. A CNN turns out to be a suitable tool for this purpose, as it can identify features in the images that differentiate the two types of events without the need to manipulate or remove data in order to focus on a particular region of the detector. We find that the CNN can distinguish between the dominant background events (ER) and 500 GeV WIMP events with a recall of 93.4%, precision of 81.2% and an accuracy of 87.2%.
Address [Khosa, Charanjit K.; Mars, Lucy; Richards, Joel; Sanz, Veronica] Univ Sussex, Dept Phys & Astron, Brighton BN1 9QH, E Sussex, England, Email: charanjit.kaur@sussex.ac.uk;
Corporate Author Thesis
Publisher Iop Publishing Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0954-3899 ISBN Medium
Area Expedition Conference
Notes WOS:000555607800001 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 4485
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Author LHC BSM Reinterpretation Forum (Abdallah, W. et al); Mitsou, V.A.; Sanz, V.
Title Reinterpretation of LHC results for new physics: status and recommendations after run 2 Type Journal Article
Year (down) 2020 Publication Scipost Physics Abbreviated Journal SciPost Phys.
Volume 9 Issue 2 Pages 022 - 45pp
Keywords
Abstract We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum. We detail current experimental offerings in direct searches for new particles, measurements, technical implementations and Open Data, and provide a set of recommendations for further improving the presentation of LHC results in order to better enable reinterpretation in the future. We also provide a brief description of existing software reinterpretation frameworks and recent global analyses of new physics that make use of the current data.
Address [Abdallah, Waleed; Dutta, Juhi] Harish Chandra Res Inst HBNI, Allahabad 211019, Uttar Pradesh, India, Email: Andy.Buckley@glasgow.ac.uk;
Corporate Author Thesis
Publisher Scipost Foundation Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2542-4653 ISBN Medium
Area Expedition Conference
Notes WOS:000573102600007 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 4547
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