| Records |
| Author |
Gomez, M.E.; Lola, S.; Ruiz de Austri, R.; Shafi, Q. |
| Title |
Confronting SUSY GUT With Dark Matter, Sparticle Spectroscopy and Muon (g – 2) |
Type |
Journal Article |
| Year |
2018 |
Publication |
Frontiers in Physics |
Abbreviated Journal |
Front. Physics |
| Volume |
6 |
Issue |
|
Pages |
127 - 9pp |
| Keywords |
grand unification; supersymmetry; dark matter; LHC; sparticle spectroscopy |
| Abstract |
We explore the implications of LHC and cold dark matter searches for supersymmetric particle mass spectra in two different grand unified models with left-right symmetry, SO(10) and SU(4)(c) x SU(2)(L) x SU(2)(R) (4-2-2). We identify characteristic differences between the two scenarios, which imply distinct correlations between experimental measurements and the particular structure of the GUT group. The gauge structure of 4-2-2 enhances significantly the allowed parameter space as compared to SO(10), giving rise to a variety of coannihilation scenarios compatible with the LHC data, LSP dark matter and the ongoing muon g-2 experiment. |
| Address |
[Gomez, Mario E.] Univ Huelva, Fac Ciencias Expt, Dept Ciencias Integradas, Huelva, Spain, Email: mario.gomez@dfa.uhu.es |
| Corporate Author |
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Thesis |
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| Publisher |
Frontiers Media Sa |
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  |
2296-424x |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000450940000001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
3808 |
| Permanent link to this record |
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| Author |
Begone, G.; Deisenroth, M.P.; Kim, J.S.; Liem, S.; Ruiz de Austri, R.; Welling, M. |
| Title |
Accelerating the BSM interpretation of LHC data with machine learning |
Type |
Journal Article |
| Year |
2019 |
Publication |
Physics of the Dark Universe |
Abbreviated Journal |
Phys. Dark Universe |
| Volume |
24 |
Issue |
|
Pages |
100293 - 5pp |
| Keywords |
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| Abstract |
The interpretation of Large Hadron Collider (LHC) data in the framework of Beyond the Standard Model (BSM) theories is hampered by the need to run computationally expensive event generators and detector simulators. Performing statistically convergent scans of high-dimensional BSM theories is consequently challenging, and in practice unfeasible for very high-dimensional BSM theories. We present here a new machine learning method that accelerates the interpretation of LHC data, by learning the relationship between BSM theory parameters and data. As a proof-of-concept, we demonstrate that this technique accurately predicts natural SUSY signal events in two signal regions at the High Luminosity LHC, up to four orders of magnitude faster than standard techniques. The new approach makes it possible to rapidly and accurately reconstruct the theory parameters of complex BSM theories, should an excess in the data be discovered at the LHC. |
| Address |
[Begone, Gianfranco; Liem, Sebastian] Univ Amsterdam, GRAPPA, Sci Pk 904, NL-1098 XH Amsterdam, Netherlands, Email: jongsoo.kim@tu-dortmund.de |
| Corporate Author |
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Thesis |
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| Publisher |
Elsevier Science Bv |
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 |
2212-6864 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000465292500018 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
3994 |
| Permanent link to this record |
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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 |
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| 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 |
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Thesis |
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| Publisher |
Nature Research |
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 |
2041-1723 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000658761600003 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
4862 |
| Permanent link to this record |
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| Author |
Roszkowski, L.; Ruiz de Austri, R.; Trotta, R. |
| Title |
Efficient reconstruction of constrained MSSM parameters from LHC data: A case study |
Type |
Journal Article |
| Year |
2010 |
Publication |
Physical Review D |
Abbreviated Journal |
Phys. Rev. D |
| Volume |
82 |
Issue |
5 |
Pages |
055003 - 12pp |
| Keywords |
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| Abstract |
We present an efficient method of reconstructing the parameters of the constrained MSSM from assumed future LHC data, applied both on their own right and in combination with the cosmological determination of the relic dark matter abundance. Focusing on the ATLAS SU3 benchmark point, we demonstrate that our simple Gaussian approximation can recover the values of its parameters remarkably well. We examine two popular noninformative priors and obtain very similar results, although when we use an informative, naturalness-motivated prior, we find some sizeable differences. We show that a further strong improvement in reconstructing the SU3 parameters can by achieved by applying additional information about the relic abundance at the level of WMAP accuracy, although the expected data from Planck will have only a very limited additional impact. Further external data may be required to break some remaining degeneracies. We argue that the method presented here is applicable to a wide class of low-energy effective supersymmetric models, as it does not require one to deal with purely experimental issues, e.g., detector performance, and has the additional advantages of computational efficiency. Furthermore, our approach allows one to distinguish the effect of the model's internal structure and of the external data on the final parameters constraints. |
| Address |
[Roszkowski, Leszek] Univ Sheffield, Dept Phys & Astron, Sheffield S3 7RH, S Yorkshire, England, Email: L.Roszkowski@sheffield.ac.uk |
| Corporate Author |
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Thesis |
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| Publisher |
Amer Physical Soc |
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 |
1550-7998 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ISI:000281517100002 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ elepoucu @ |
Serial |
385 |
| Permanent link to this record |
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| Author |
Bertone, G.; Cerdeño, D.G.; Fornasa, M.; Ruiz de Austri, R.; Trotta, R. |
| Title |
Identification of dark matter particles with LHC and direct detection data |
Type |
Journal Article |
| Year |
2010 |
Publication |
Physical Review D |
Abbreviated Journal |
Phys. Rev. D |
| Volume |
82 |
Issue |
5 |
Pages |
055008 - 7pp |
| Keywords |
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| Abstract |
Dark matter (DM) is currently searched for with a variety of detection strategies. Accelerator searches are particularly promising, but even if weakly interacting massive particles are found at the Large Hadron Collider (LHC), it will be difficult to prove that they constitute the bulk of the DM in the Universe Omega(DM). We show that a significantly better reconstruction of the DM properties can be obtained with a combined analysis of LHC and direct detection data, by making a simple Ansatz on the weakly interacting massive particles local density rho(0)((chi) over bar1), i.e., by assuming that the local density scales with the cosmological relic abundance, (rho(0)((chi) over bar1)/rho(DM)) = (Omega(0)((chi) over bar1)/Omega(DM)). We demonstrate this method in an explicit example in the context of a 24-parameter supersymmetric model, with a neutralino lightest supersymmetric particle in the stau coannihilation region. Our results show that future ton-scale direct detection experiments will allow to break degeneracies in the supersymmetric parameter space and achieve a significantly better reconstruction of the neutralino composition and its relic density than with LHC data alone. |
| Address |
[Bertone, G.] Univ Zurich, Inst Theoret Phys, CH-8057 Zurich, Switzerland |
| Corporate Author |
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Thesis |
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| Publisher |
Amer Physical Soc |
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 |
1550-7998 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ISI:000281741400005 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ elepoucu @ |
Serial |
380 |
| Permanent link to this record |
