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Author |
Desai, N.; Domingo, F.; Kim, J.S.; Ruiz de Austri, R.; Rolbiecki, K.; Sonawane, M.; Wang, Z.S. |
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Title |
Constraining electroweak and strongly charged long-lived particles with CheckMATE |
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Journal Article |
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Year |
2021 |
Publication |
European Physical Journal C |
Abbreviated Journal |
Eur. Phys. J. C |
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Volume |
81 |
Issue |
11 |
Pages |
968 - 19pp |
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Abstract |
Long-lived particles have become a new frontier in the exploration of physics beyond the Standard Model. In this paper, we present the implementation of four types of long-lived particle searches, viz. displaced leptons, disappearing track, displaced vertex with either muons or with missing transverse energy, and heavy charged tracks. These four categories cover the signatures of a large range of physics models. We illustrate their potential for exclusion and discuss their mutual overlaps in mass-lifetime space for two simple phenomenological models involving either a U(1)-charged or a coloured scalar. |
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Address |
[Desai, Nishita] Tata Inst Fundamental Res, Dept Theoret Phys, Mumbai 400005, Maharashtra, India, Email: nishita.desai@tifr.res.in; |
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Springer |
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English |
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ISSN |
1434-6044 |
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Notes |
WOS:000714374500002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5015 |
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Author |
De Romeri, V.; Kim, J.S.; Martin Lozano, V.; Rolbiecki, K.; Ruiz de Austri, R. |
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Title |
Confronting dark matter with the diphoton excess from a parent resonance decay |
Type |
Journal Article |
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Year |
2016 |
Publication |
European Physical Journal C |
Abbreviated Journal |
Eur. Phys. J. C |
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Volume |
76 |
Issue |
5 |
Pages |
262 - 13pp |
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Abstract |
A diphoton excess with an invariant mass of about 750 GeV has been recently reported by both ATLAS and CMS experiments at LHC. While the simplest interpretation requires the resonant production of a 750 GeV (pseudo) scalar, here we consider an alternative setup, with an additional heavy parent particle which decays into a pair of 750 GeV resonances. This configuration improves the agreement between the 8 and 13 TeV data. Moreover, we include a dark matter candidate in the form of a Majorana fermion which interacts through the 750 GeV portal. The invisible decays of the light resonance help to suppress additional decay channels into Standard Model particles in association with the diphoton signal. We realise our hierarchical framework in the context of an effective theory, and we analyse the diphoton signal as well as the consistency with other LHC searches. We finally address the interplay of the LHC results with the dark matter phenomenology, namely the compatibility with the relic density abundance and the indirect detection bounds. |
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Address |
[De Romeri, Valentina; Kim, Jong Soo; Martin-Lozano, Victor; Rolbiecki, Krzysztof] Univ Autonoma Madrid, Inst Fis Teor UAM CSIC, E-28049 Madrid, Spain, Email: valentina.deromeri@uam.es |
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Springer |
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English |
Summary Language |
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Series Editor |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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ISSN |
1434-6044 |
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Expedition |
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Conference |
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Notes |
WOS:000399931700001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
3088 |
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Permanent link to this record |
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Author |
Caron, S.; Kim, J.S.; Rolbiecki, K.; Ruiz de Austri, R.; Stienen, B. |
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Title |
The BSM-AI project: SUSY-AI-generalizing LHC limits on supersymmetry with machine learning |
Type |
Journal Article |
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Year |
2017 |
Publication |
European Physical Journal C |
Abbreviated Journal |
Eur. Phys. J. C |
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Volume |
77 |
Issue |
4 |
Pages |
257 - 25pp |
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Abstract |
A key research question at the Large Hadron Collider is the test of models of new physics. Testing if a particular parameter set of such a model is excluded by LHC data is a challenge: it requires time consuming generation of scattering events, simulation of the detector response, event reconstruction, cross section calculations and analysis code to test against several hundred signal regions defined by the ATLAS and CMS experiments. In the BSM-AI project we approach this challenge with a new idea. A machine learning tool is devised to predict within a fraction of a millisecond if a model is excluded or not directly from the model parameters. A first example is SUSY-AI, trained on the phenomenological supersymmetric standard model (pMSSM). About 300,000 pMSSM model sets – each tested against 200 signal regions by ATLAS – have been used to train and validate SUSY-AI. The code is currently able to reproduce theATLAS exclusion regions in 19 dimensions with an accuracy of at least 93%. It has been validated further within the constrained MSSM and the minimal natural supersymmetric model, again showing high accuracy. SUSY-AI and its future BSM derivatives will help to solve the problem of recasting LHC results for any model of new physics. SUSY-AI can be downloaded from http://susyai.hepforge.org/. An on-line interface to the program for quick testing purposes can be found at http://www.susy-ai.org/. |
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Address |
[Caron, Sascha; Stienen, Bob] Radboud Univ Nijmegen, IMAPP, Nijmegen, Netherlands, Email: krolb@fuw.edu.pl |
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Corporate Author |
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Thesis |
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Publisher |
Springer |
Place of Publication |
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Editor |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
1434-6044 |
ISBN |
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Medium |
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Expedition |
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Conference |
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Notes |
WOS:000400079300001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
3097 |
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Permanent link to this record |