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Author Ellis, J.; Gomez, M.E.; Lola, S.; Ruiz de Austri, R.; Shafi, Q.
Title Confronting grand unification with lepton flavour violation, dark matter and LHC data Type Journal Article
Year 2020 Publication Journal of High Energy Physics Abbreviated Journal J. High Energy Phys.
Volume 09 Issue 9 Pages 197 - 29pp
Keywords Supersymmetry Phenomenology
Abstract We explore possible signatures for charged lepton flavour violation (LFV), sparticle discovery at the LHC and dark matter (DM) searches in grand unified theories (GUTs) based on SU(5), flipped SU(5) (FSU(5)) and SU(4)(c) x SU(2)(L) x SU(2)(R) (4-2-2). We assume that soft supersymmetry-breaking terms preserve the group symmetry at some high input scale, and focus on the non-universal effects on different matter representations generated by gauge interactions at lower scales, as well as the charged LFV induced in Type-1 see-saw models of neutrino masses. We identify the different mechanisms that control the relic DM density in the various GUT models, and contrast their LFV and LHC signatures. The SU(5) and 4-2-2 models offer good detection prospects both at the LHC and in LFV searches, though with different LSP compositions, and the SU(5) and FSU(5) models offer LFV within the current reach. The 4-2-2 model allows chargino and gluino coannihilations with neutralinos, and the former offer good detection prospects for both the LHC and LFV, while gluino coannihilations lead to lower LFV rates. Our results indicate that LFV is a powerful tool that complements LHC and DM searches, providing significant insights into the sparticle spectra and neutrino mass parameters in different models.
Address [Ellis, J.] Kings Coll London, Dept Phys, Theoret Particle Phys & Cosmol Grp, London WC2R 2LS, England, Email: John.Ellis@cern.ch;
Corporate Author Thesis
Publisher Springer Place of Publication Editor
Language (up) 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:000576973000003 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 4566
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Author MoEDAL Collaboration (Acharya, B. et al); Bernabeu, J.; Mamuzic, J.; Mitsou, V.A.; Papavassiliou, J.; Ruiz de Austri, R.; Santra, A.; Vento, V.; Vives, O.
Title First Search for Dyons with the Full MoEDAL Trapping Detector in 13 TeV pp Collisions Type Journal Article
Year 2021 Publication Physical Review Letters Abbreviated Journal Phys. Rev. Lett.
Volume 126 Issue 7 Pages 071801 - 7pp
Keywords
Abstract The MoEDAL trapping detector consists of approximately 800 kg of aluminum volumes. It was exposed during run 2 of the LHC program to 6.46 fb(-1) of 13 TeV proton-proton collisions at the LHCb interaction point. Evidence for dyons (particles with electric and magnetic charge) captured in the trapping detector was sought by passing the aluminum volumes comprising the detector through a superconducting quantum interference device (SQUID) magnetometer. The presence of a trapped dyon would be signaled by a persistent current induced in the SQUID magnetometer. On the basis of a Drell-Yan production model, we exclude dyons with a magnetic charge ranging up to five Dirac charges (5g(D)) and an electric charge up to 200 times the fundamental electric charge for mass limits in the range 870-3120 GeV and also monopoles with magnetic charge up to and including 5g(D) with mass limits in the range 870-2040 GeV.
Address [Acharya, B.; Alexandre, J.; Ellis, J. R.; Fairbairn, M.; Mavromatos, N. E.; Sakellariadou, M.; Sarkar, S.] Kings Coll London, Phys Dept, Theoret Particle Phys & Cosmol Grp, London, England, Email: jpinfold@ualberta.ca
Corporate Author Thesis
Publisher Amer Physical Soc Place of Publication Editor
Language (up) English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0031-9007 ISBN Medium
Area Expedition Conference
Notes WOS:000620021300009 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 4723
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Author Lopez-Fogliani, D.E.; Perez, A.D.; Ruiz de Austri, R.
Title Dark matter candidates in the NMSSM with RH neutrino superfields Type Journal Article
Year 2021 Publication Journal of Cosmology and Astroparticle Physics Abbreviated Journal J. Cosmol. Astropart. Phys.
Volume 04 Issue 4 Pages 067 - 35pp
Keywords dark matter theory; dark matter detectors
Abstract R-parity conserving supersymmetric models with right-handed (RH) neutrinos are very appealing since they could naturally explain neutrino physics and also provide a good dark matter (DM) candidate such as the lightest supersymmetric particle (LSP). In this work we consider the next-to-minimal supersymmetric standard model (NMSSM) plus RH neutrino superfields, with effective Majorana masses dynamically generated at the electroweak scale (EW). We perform a scan of the relevant parameter space and study both possible DM candidates: RH sneutrino and neutralino. Especially for the case of RH sneutrino DM we analyse the intimate relation between both candidates to obtain the correct amount of relic density. Besides the well-known resonances, annihilations through scalar quartic couplings and coannihilation mechanisms with all kind of neutralinos, are crucial. Finally, we present the impact of current and future direct and indirect detection experiments on both DM candidates.
Address [Lopez-Fogliani, Daniel E.] Univ Buenos Aires, Fac Ciencia Exactas & Nat, Inst Fis Buenos Aires UBA, RA-1428 Buenos Aires, DF, Argentina, Email: daniel.lopez@df.uba.ar;
Corporate Author Thesis
Publisher Iop Publishing Ltd Place of Publication Editor
Language (up) English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1475-7516 ISBN Medium
Area Expedition Conference
Notes WOS:000644501000049 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 4824
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Author Otten, S.; Caron, S.; de Swart, W.; van Beekveld, M.; Hendriks, L.; van Leeuwen, C.; Podareanu, D.; Ruiz de Austri, R.; Verheyen, R.
Title Event generation and statistical sampling for physics with deep generative models and a density information buffer Type Journal Article
Year 2021 Publication Nature Communications Abbreviated Journal Nat. Commun.
Volume 12 Issue 1 Pages 2985 - 16pp
Keywords
Abstract Simulating nature and in particular processes in particle physics require expensive computations and sometimes would take much longer than scientists can afford. Here, we explore ways to a solution for this problem by investigating recent advances in generative modeling and present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physical events, but to also ensure that these events occur with the correct frequencies. We investigate the feasibility of learning the event generation and the frequency of occurrence with several generative machine learning models to produce events like Monte Carlo generators. We study three processes: a simple two-body decay, the processes e(+)e(-)-> Z -> l(+)l(-) and pp -> tt<mml:mo><overbar></mml:mover> including the decay of the top quarks and a simulation of the detector response. By buffering density information of encoded Monte Carlo events given the encoder of a Variational Autoencoder we are able to construct a prior for the sampling of new events from the decoder that yields distributions that are in very good agreement with real Monte Carlo events and are generated several orders of magnitude faster. Applications of this work include generic density estimation and sampling, targeted event generation via a principal component analysis of encoded ground truth data, anomaly detection and more efficient importance sampling, e.g., for the phase space integration of matrix elements in quantum field theories. Here, the authors report buffered-density variational autoencoders for the generation of physical events. This method is computationally less expensive over other traditional methods and beyond accelerating the data generation process, it can help to steer the generation and to detect anomalies.
Address [Otten, Sydney; Caron, Sascha; de Swart, Wieske; van Beekveld, Melissa; Hendriks, Luc; Verheyen, Rob] Radboud Univ Nijmegen, Inst Math Astro & Particle Phys IMAPP, Nijmegen, Netherlands, Email: Sydney.Otten@ru.nl
Corporate Author Thesis
Publisher Nature Research Place of Publication Editor
Language (up) English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2041-1723 ISBN Medium
Area Expedition Conference
Notes WOS:000658761600003 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 4862
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Author van Beekveld, M.; Caron, S.; Hendriks, L.; Jackson, P.; Leinweber, A.; Otten, S.; Patrick, R.; Ruiz de Austri, R.; Santoni, M.; White, M.
Title Combining outlier analysis algorithms to identify new physics at the LHC Type Journal Article
Year 2021 Publication Journal of High Energy Physics Abbreviated Journal J. High Energy Phys.
Volume 09 Issue 9 Pages 024 - 33pp
Keywords Phenomenological Models; Supersymmetry Phenomenology
Abstract The lack of evidence for new physics at the Large Hadron Collider so far has prompted the development of model-independent search techniques. In this study, we compare the anomaly scores of a variety of anomaly detection techniques: an isolation forest, a Gaussian mixture model, a static autoencoder, and a beta-variational autoencoder (VAE), where we define the reconstruction loss of the latter as a weighted combination of regression and classification terms. We apply these algorithms to the 4-vectors of simulated LHC data, but also investigate the performance when the non-VAE algorithms are applied to the latent space variables created by the VAE. In addition, we assess the performance when the anomaly scores of these algorithms are combined in various ways. Using supersymmetric benchmark points, we find that the logical AND combination of the anomaly scores yielded from algorithms trained in the latent space of the VAE is the most effective discriminator of all methods tested.
Address [van Beekveld, Melissa] Clarendon Lab, Rudolf Peierls Ctr Theoret Phys, 20 Pks Rd, Oxford OX1 3PU, England, Email: mcbeekveld@gmail.com;
Corporate Author Thesis
Publisher Springer Place of Publication Editor
Language (up) 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:000695421600003 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 4973
Permanent link to this record