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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 2021 Publication Reports on Progress in Physics Abbreviated Journal Rep. Prog. Phys.
Volume 84 Issue 12 Pages 124201 - 64pp
Keywords (up) 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
Permanent link to this record
 
 
Author Caron, S.; Eckner, C.; Hendriks, L.; Johannesson, G.; Ruiz de Austri, R.; Zaharijas, G.
Title Mind the gap: the discrepancy between simulation and reality drives interpretations of the Galactic Center Excess Type Journal Article
Year 2023 Publication Journal of Cosmology and Astroparticle Physics Abbreviated Journal J. Cosmol. Astropart. Phys.
Volume 06 Issue 6 Pages 013 - 56pp
Keywords (up) dark matter simulations; gamma ray experiments; Machine learning; millisecond pulsars
Abstract The Galactic Center Excess (GCE) in GeV gamma rays has been debated for over a decade, with the possibility that it might be due to dark matter annihilation or undetected point sources such as millisecond pulsars (MSPs). This study investigates how the gamma-ray emission model (-yEM) used in Galactic center analyses affects the interpretation of the GCE's nature. To address this issue, we construct an ultra-fast and powerful inference pipeline based on convolutional Deep Ensemble Networks. We explore the two main competing hypotheses for the GCE using a set of-yEMs with increasing parametric freedom. We calculate the fractional contribution (fsrc) of a dim population of MSPs to the total luminosity of the GCE and analyze its dependence on the complexity of the ryEM. For the simplest ryEM, we obtain fsrc = 0.10 f 0.07, while the most complex model yields fsrc = 0.79 f 0.24. In conclusion, we find that the statement about the nature of the GCE (dark matter or not) strongly depends on the assumed ryEM. The quoted results for fsrc do not account for the additional uncertainty arising from the fact that the observed gamma-ray sky is out-of-distribution concerning the investigated ryEM iterations. We quantify the reality gap between our ryEMs using deep-learning-based One-Class Deep Support Vector Data Description networks, revealing that all employed ryEMs have gaps to reality. Our study casts doubt on the validity of previous conclusions regarding the GCE and dark matter, and underscores the urgent need to account for the reality gap and consider previously overlooked “out of domain” uncertainties in future interpretations.
Address [Caron, Sascha; Hendriks, Luc] Radboud Univ Nijmegen, Theoret High Energy Phys, Heyendaalseweg 135, NL-6525 AJ Nijmegen, Netherlands, Email: scaron@nikhef.nl;
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 1475-7516 ISBN Medium
Area Expedition Conference
Notes WOS:001025516000009 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5576
Permanent link to this record
 
 
Author Amerio, A.; Cuoco, A.; Fornengo, N.
Title Extracting the gamma-ray source-count distribution below the Fermi-LAT detection limit with deep learning Type Journal Article
Year 2023 Publication Journal of Cosmology and Astroparticle Physics Abbreviated Journal J. Cosmol. Astropart. Phys.
Volume 09 Issue 9 Pages 029 - 39pp
Keywords (up) gamma ray theory; Machine learning
Abstract We reconstruct the extra-galactic gamma-ray source-count distribution, or dN/dS, of resolved and unresolved sources by adopting machine learning techniques. Specifically, we train a convolutional neural network on synthetic 2-dimensional sky-maps, which are built by varying parameters of underlying source-counts models and incorporate the FermiLAT instrumental response functions. The trained neural network is then applied to the Fermi-LAT data, from which we estimate the source count distribution down to flux levels a factor of 50 below the Fermi-LAT threshold. We perform our analysis using 14 years of data collected in the (1, 10) GeV energy range. The results we obtain show a source count distribution which, in the resolved regime, is in excellent agreement with the one derived from cataloged sources, and then extends as dN/dS " S-2 in the unresolved regime, down to fluxes of 5 center dot 10-12 cm-2 s-1. The neural network architecture and the devised methodology have the flexibility to enable future analyses to study the energy dependence of the source-count distribution.
Address [Amerio, A.] Univ Valencia, Inst Fis Corpuscular IFIC, Calle Catedrat Jose Beltran 2, Paterna 46980, Spain, Email: aurelio.amerio@ific.uv.es;
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 1475-7516 ISBN Medium
Area Expedition Conference
Notes WOS:001097055700001 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5785
Permanent link to this record
 
 
Author Balibrea-Correa, J.; Lerendegui-Marco, J.; Babiano-Suarez, V.; Caballero, L.; Calvo, D.; Ladarescu, I.; Olleros-Rodriguez, P.; Domingo-Pardo, C.
Title Machine Learning aided 3D-position reconstruction in large LaCl3 crystals Type Journal Article
Year 2021 Publication Nuclear Instruments & Methods in Physics Research A Abbreviated Journal Nucl. Instrum. Methods Phys. Res. A
Volume 1001 Issue Pages 165249 - 17pp
Keywords (up) Gamma-ray; Position sensitive detectors; Monolithic crystals; Compton imaging; Machine Learning; Convolutional Neural Networks; Total Energy Detector; Neutron capture cross-section
Abstract We investigate five different models to reconstruct the 3D gamma-ray hit coordinates in five large LaCl3(Ce) monolithic crystals optically coupled to pixelated silicon photomultipliers. These scintillators have a base surface of 50 x 50 mm(2) and five different thicknesses, from 10 mm to 30 mm. Four of these models are analytical prescriptions and one is based on a Convolutional Neural Network. Average resolutions close to 1-2 mm fwhm are obtained in the transverse crystal plane for crystal thicknesses between 10 mm and 20 mm using analytical models. For thicker crystals average resolutions of about 3-5 mm fwhm are obtained. Depth of interaction resolutions between 1 mm and 4 mm are achieved depending on the distance of the interaction point to the photosensor surface. We propose a Machine Learning algorithm to correct for linearity distortions and pin-cushion effects. The latter allows one to keep a large field of view of about 70%-80% of the crystal surface, regardless of crystal thickness. This work is aimed at optimizing the performance of the so-called Total Energy Detector with Compton imaging capability (i-TED) for time-of-flight neutron capture cross-section measurements.
Address [Balibrea-Correa, J.; Lerendegui-Marco, J.; Babiano-Suarez, V.; Caballero, L.; Calvo, D.; Ladarescu, I.; Olleros-Rodriguez, P.; Domingo-Pardo, C.] Univ Valencia, CSIC, Inst Fis Corpuscular, Valencia, Spain, Email: javier.balibrea@ific.uv.es
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0168-9002 ISBN Medium
Area Expedition Conference
Notes WOS:000641308300007 Approved no
Is ISI yes International Collaboration no
Call Number IFIC @ pastor @ Serial 4803
Permanent link to this record
 
 
Author HAWC Collaboration (Alfaro, R. et al); Salesa Greus, F.
Title Gamma/hadron separation with the HAWC observatory Type Journal Article
Year 2022 Publication Nuclear Instruments & Methods in Physics Research A Abbreviated Journal Nucl. Instrum. Methods Phys. Res. A
Volume 1039 Issue Pages 166984 - 13pp
Keywords (up) High energy; Crab Nebula; G/H separation; Machine Learning
Abstract The High Altitude Water Cherenkov (HAWC) gamma-ray observatory observes atmospheric showers produced by incident gamma rays and cosmic rays with energy from 300 GeV to more than 100 TeV. A crucial phase in analyzing gamma-ray sources using ground-based gamma-ray detectors like HAWC is to identify the showers produced by gamma rays or hadrons. The HAWC observatory records roughly 25,000 events per second, with hadrons representing the vast majority (> 99.9%) of these events. The standard gamma/hadron separation technique in HAWC uses a simple rectangular cut involving only two parameters. This work describes the implementation of more sophisticated gamma/hadron separation techniques, via machine learning methods (boosted decision trees and neural networks), and summarizes the resulting improvements in gamma/hadron separation obtained in HAWC.
Address [Alfaro, R.; Angeles Camacho, J. R.; Avila Rojas, D.; Belmont-Moreno, E.; Espinoza, C.; Garcia, D.; Hernandez, S.; Leon Vargas, H.; Sandoval, A.; Serna-Franco, J.] Univ Nacl Autonoma Mexico, Inst Fis, Mexico City, DF, Mexico, Email: tcapistran@astro.unam.mx;
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0168-9002 ISBN Medium
Area Expedition Conference
Notes WOS:000861747900006 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5371
Permanent link to this record