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Author |
van Beekveld, M.; Beenakker, W.; Caron, S.; Kip, J.; Ruiz de Austri, R.; Zhang, Z.Y. |
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Title |
Non-standard neutrino spectra from annihilating neutralino dark matter |
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Journal Article |
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Year |
2023 |
Publication |
Scipost Physics Core |
Abbreviated Journal |
SciPost Phys. Core |
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Volume |
6 |
Issue |
1 |
Pages |
006 - 23pp |
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Abstract |
Neutrino telescope experiments are rapidly becoming more competitive in indirect de-tection searches for dark matter. Neutrino signals arising from dark matter annihilations are typically assumed to originate from the hadronisation and decay of Standard Model particles. Here we showcase a supersymmetric model, the BLSSMIS, that can simulta-neously obey current experimental limits while still providing a potentially observable non-standard neutrino spectrum from dark matter annihilation. |
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Address |
[van Beekveld, Melissa] Univ Oxford, Rudolf Peierls Ctr Theoret Phys, Clarendon Lab, Parks Rd, Oxford OX1 3PU, England, Email: melissa.vanbeekveld@physics.ox.ac.uk; |
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English |
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WOS:000928492200001 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5480 |
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Author |
MoEDAL Collaboration (Acharya, B. et al); Musumeci,E.; Mitsou, V.A.; Papavassiliou, J.; Ruiz de Austri, R.; Santra, A.; Vento, V.; Vives, O. |
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Title |
Search for highly-ionizing particles in pp collisions at the LHC's Run-1 using the prototype MoEDAL detector |
Type |
Journal Article |
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Year |
2022 |
Publication |
European Physical Journal C |
Abbreviated Journal |
Eur. Phys. J. C |
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Volume |
82 |
Issue |
8 |
Pages |
694 - 16pp |
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Abstract |
A search for highly electrically charged objects (HECOs) and magnetic monopoles is presented using 2.2 fb(-1) of p – p collision data taken at a centre of mass energy (E-CM) of 8 TeV by the MoEDAL detector during LHC's Run-1. The data were collected using MoEDAL's prototype Nuclear Track Detectord array and the Trapping Detector array. The results are interpreted in terms of Drell-Yan pair production of stable HECO and monopole pairs with three spin hypotheses (0, 1/2 and 1). The search provides constraints on the direct production of magnetic monopoles carrying one to four Dirac magnetic charges and with mass limits ranging from 590 GeV/c(2) to 1 TeV/c(2). Additionally, mass limits are placed on HECOs with charge in the range 10e to 180e, where e is the charge of an electron, for masses between 30 GeV/c(2) and 1 TeV/c(2). |
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[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: Laura.Patrizii@bo.infn.il; |
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Springer |
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English |
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1434-6044 |
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WOS:000838674800001 |
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no |
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yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5326 |
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Author |
Stoppa, F.; Vreeswijk, P.; Bloemen, S.; Bhattacharyya, S.; Caron, S.; Johannesson, G.; Ruiz de Austri, R.; van den Oetelaar, C.; Zaharijas, G.; Groot, P.J.; Cator, E.; Nelemans, G. |
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Title |
AutoSourceID-Light Fast optical source localization via U-Net and Laplacian of Gaussian |
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Journal Article |
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Year |
2022 |
Publication |
Astronomy & Astrophysics |
Abbreviated Journal |
Astron. Astrophys. |
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Volume |
662 |
Issue |
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Pages |
A109 - 8pp |
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Keywords |
astronomical databases; miscellaneous; methods; data analysis; stars; imaging; techniques; image processing |
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Abstract |
Aims. With the ever-increasing survey speed of optical wide-field telescopes and the importance of discovering transients when they are still young, rapid and reliable source localization is paramount. We present AutoSourceID-Light (ASID-L), an innovative framework that uses computer vision techniques that can naturally deal with large amounts of data and rapidly localize sources in optical images. Methods. We show that the ASID-L algorithm based on U-shaped networks and enhanced with a Laplacian of Gaussian filter provides outstanding performance in the localization of sources. A U-Net network discerns the sources in the images from many different artifacts and passes the result to a Laplacian of Gaussian filter that then estimates the exact location. Results. Using ASID-L on the optical images of the MeerLICHT telescope demonstrates the great speed and localization power of the method. We compare the results with SExtractor and show that our method outperforms this more widely used method. ASID-L rapidly detects more sources not only in low- and mid-density fields, but particularly in areas with more than 150 sources per square arcminute. The training set and code used in this paper are publicly available. |
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[Stoppa, F.; Vreeswijk, P.; Bloemen, S.; Groot, P. J.; Nelemans, G.] Radboud Univ Nijmegen, Dept Astrophys, IMAPP, POB 9010, NL-6500 GL Nijmegen, Netherlands, Email: f.stoppa@astro.ru.nl |
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Edp Sciences S A |
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English |
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0004-6361 |
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Notes |
WOS:000818665600009 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5291 |
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Author |
Aarrestad, T. et al; Mamuzic, J.; Ruiz de Austri, R. |
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Title |
Benchmark data and model independent event classification for the large hadron collider |
Type |
Journal Article |
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Year |
2022 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
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Volume |
12 |
Issue |
1 |
Pages |
043 - 57pp |
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Abstract |
We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of > 1 billion simulated LHC events corresponding to 10 fb(-1) of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge. |
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Address |
[Aarrestad, Thea; Heinrich, Lukas A.; Jawahar, Pratik; Pierini, Maurizio; Touranakou, Mary; Wozniak, Kinga A.] European Org Nucl Res CERN, CH-1211 Geneva 23, Switzerland |
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Scipost Foundation |
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English |
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2542-4653 |
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Notes |
WOS:000807448000038 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5256 |
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Author |
Balazs, C. et al; Mamuzic, J.; Ruiz de Austri, R. |
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Title |
A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications |
Type |
Journal Article |
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Year |
2021 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
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Volume |
05 |
Issue |
5 |
Pages |
108 - 46pp |
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Keywords |
Phenomenology of Field Theories in Higher Dimensions; Supersymmetry Phenomenology |
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Abstract |
Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensive to evaluate. We describe a number of global optimisation algorithms that are not yet widely used in particle astrophysics, benchmark them against random sampling and existing techniques, and perform a detailed comparison of their performance on a range of test functions. These include four analytic test functions of varying dimensionality, and a realistic example derived from a recent global fit of weak-scale supersymmetry. Although the best algorithm to use depends on the function being investigated, we are able to present general conclusions about the relative merits of random sampling, Differential Evolution, Particle Swarm Optimisation, the Covariance Matrix Adaptation Evolution Strategy, Bayesian Optimisation, Grey Wolf Optimisation, and the PyGMO Artificial Bee Colony, Gaussian Particle Filter and Adaptive Memory Programming for Global Optimisation algorithms. |
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[Balazs, Csaba] Monash Univ, Sch Phys & Astron, Melbourne, Vic 3800, Australia, Email: bstienen@science.ru.nl; |
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Publisher |
Springer |
Place of Publication |
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English |
Summary Language |
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Edition |
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ISSN |
1029-8479 |
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Notes |
WOS:000762408900002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5149 |
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