| Records |
| Author |
Panes, B.; Eckner, C.; Hendriks, L.; Caron, S.; Dijkstra, K.; Johannesson, G.; Ruiz de Austri, R.; Zaharijas, G. |
| Title |
Identification of point sources in gamma rays using U-shaped convolutional neural networks and a data challenge |
Type |
Journal Article |
Year  |
2021 |
Publication |
Astronomy & Astrophysics |
Abbreviated Journal |
Astron. Astrophys. |
| Volume |
656 |
Issue |
|
Pages |
A62 - 18pp |
| Keywords |
catalogs; gamma rays: general; astroparticle physics; methods: numerical; methods: data analysis; techniques: image processing |
| Abstract |
Context. At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Aims. Here we present the first application of deep learning based algorithms to automatically detect and classify point sources from gamma-ray data. For concreteness we refer to this approach as AutoSourceID. Methods. To detect point sources, we utilized U-shaped convolutional networks for image segmentation and k-means for source clustering and localization. We also explored the Centroid-Net algorithm, which is designed to find and count objects. Using two algorithms allows for a cross check of the results, while a combination of their results can be used to improve performance. The training data are based on 9.5 years of exposure from The Fermi Large Area Telescope (Fermi-LAT) and we used source properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth Fermi-LAT source catalog in addition to several models of background interstellar emission. The results of the localization algorithm are fed into a classification neural network that is trained to separate the three general source classes (AGNs, PSRs, and FAKE sources). Results. We compared our localization algorithms qualitatively with traditional methods and find them to have similar detection thresholds. We also demonstrate the robustness of our source localization algorithms to modifications in the interstellar emission models, which presents a clear advantage over traditional methods. The classification network is able to discriminate between the three classes with typical accuracy of similar to 70%, as long as balanced data sets are used in classification training. We published online our training data sets and analysis scripts and invite the community to join the data challenge aimed to improve the localization and classification of gamma-ray point sources. |
| Address |
[Panes, Boris] Pontificia Univ Catolica Chile, Ave Vicuna Mackenna 4860, Macul, Region Metropol, Chile, Email: bapanes@gmail.com |
| Corporate Author |
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Thesis |
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| Publisher |
Edp Sciences S A |
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 |
0004-6361 |
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:000725877600001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5053 |
| Permanent link to this record |
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| Author |
ANTARES Collaboration (Albert, A. et al); Barrios-Marti, J.; Hernandez-Rey, J.J.; Illuminati, G.; Lotze, M.; Tönnis, C.; Zornoza, J.D.; Zuñiga, J. |
| Title |
Model-independent search for neutrino sources with the ANTARES neutrino telescope |
Type |
Journal Article |
| Year |
2020 |
Publication |
Astroparticle Physics |
Abbreviated Journal |
Astropart Phys. |
| Volume |
114 |
Issue |
|
Pages |
35-47 |
| Keywords |
Neutrino astronomy; Astroparticle physics; Pattern recognition; Anisotropy |
| Abstract |
A novel method to analyse the spatial distribution of neutrino candidates recorded with the ANTARES neutrino telescope is introduced, searching for an excess of neutrinos in a region of arbitrary size and shape from any direction in the sky. Techniques originating from the domains of machine learning, pattern recognition and image processing are used to purify the sample of neutrino candidates and for the analysis of the obtained skymap. In contrast to a dedicated search for a specific neutrino emission model, this approach is sensitive to a wide range of possible morphologies of potential sources of high-energy neutrino emission. The application of these methods to ANTARES data yields a large-scale excess with a post-trial significance of 2.5 sigma. Applied to public data from IceCube in its IC40 configuration, an excess consistent with the results from ANTARES is observed with a post-trial significance of 2.1 sigma. |
| Address |
[Albert, A.; Drouhin, D.; Racca, C.; Saldana, M.] Univ Haute Alsace, Inst Univ Technol Colmar, GRPHE, 34 Rue Grillenbreit,BP Colmar 50568, F-68008 Mulhouse, France, Email: stefan.geisselsoeder@fau.de; |
| Corporate Author |
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Thesis |
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| Publisher |
Elsevier |
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 Issue |
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Edition |
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| ISSN |
0927-6505 |
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:000489353300005 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
4167 |
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| Author |
ATLAS Collaboration (Aad, G. et al); Aparisi Pozo, J.A.; Bailey, A.J.; Barranco Navarro, L.; Cabrera Urban, S.; Castillo, F.L.; Castillo Gimenez, V.; Cerda Alberich, L.; Costa, M.J.; Escobar, C.; Estrada Pastor, O.; Ferrer, A.; Fiorini, L.; Fullana Torregrosa, E.; Fuster, J.; Garcia, C.; Garcia Navarro, J.E.; Gonzalez de la Hoz, S.; Gonzalvo Rodriguez, G.R.; Guerrero Rojas, J.G.; Higon-Rodriguez, E.; Jimenez Pena, J.; Lacasta, C.; Lozano Bahilo, J.J.; Madaffari, D.; Mamuzic, J.; Marti-Garcia, S.; Melini, D.; Miñano, M.; Mitsou, V.A.; Moreno Llacer, M.; Rodriguez Bosca, S.; Rodriguez Rodriguez, D.; Ruiz-Martinez, A.; Salt, J.; Santra, A.; Soldevila, U.; Sanchez, J.; Valero, A.; Valls Ferrer, J.A.; Vos, M. |
| Title |
ATLAS data quality operations and performance for 2015-2018 data-taking |
Type |
Journal Article |
| Year |
2020 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
| Volume |
15 |
Issue |
4 |
Pages |
P04003 - 43pp |
| Keywords |
Large detector systems for particle and astroparticle physics; Large detector-systems performance |
| Abstract |
The ATLAS detector at the Large Hadron Collider reads out particle collision data from over 100 million electronic channels at a rate of approximately 100 kHz, with a recording rate for physics events of approximately 1 kHz. Before being certified for physics analysis at computer centres worldwide, the data must be scrutinised to ensure they are clean from any hardware or software related issues that may compromise their integrity. Prompt identification of these issues permits fast action to investigate, correct and potentially prevent future such problems that could render the data unusable. This is achieved through the monitoring of detector-level quantities and reconstructed collision event characteristics at key stages of the data processing chain. This paper presents the monitoring and assessment procedures in place at ATLAS during 2015-2018 data-taking. Through the continuous improvement of operational procedures, ATLAS achieved a high data quality efficiency, with 95.6% of the recorded proton-proton collision data collected at root s = 13 TeV certified for physics analysis. |
| Address |
[Banerjee, S.; Dang, N. P.; Duvnjak, D.; Jackson, P.; Oliver, J. L.; Petridis, A.; Qureshi, A.; Sharma, A. S.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, SA, Australia |
| Corporate Author |
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Thesis |
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| Publisher |
Iop Publishing Ltd |
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 |
1748-0221 |
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:000534740000003 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
4402 |
| Permanent link to this record |
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| Author |
KM3NeT Collaboration (Aiello, S. et al); Alves Garre, S.; Calvo, D.; Carretero, V.; Colomer, M.; Corredoira, I; Gozzini, S.R.; Hernandez-Rey, J.J.; Illuminati, G.; Khan Chowdhury, N.R.; Manczak, J.; Pieterse, C.; Real, D.; Salesa Greus, F.; Thakore, T.; Zornoza, J.D.; Zuñiga, J. |
| Title |
gSeaGen: The KM3NeT GENIE-based code for neutrino telescopes |
Type |
Journal Article |
| Year |
2020 |
Publication |
Computer Physics Communications |
Abbreviated Journal |
Comput. Phys. Commun. |
| Volume |
256 |
Issue |
|
Pages |
107477 - 15pp |
| Keywords |
Astroparticle physics; High energy neutrinos; Monte Carlo event generator; Neutrino telescopes; Neutrino oscillations; KM3NeT; GENIE |
| Abstract |
The gSeaGen code is a GENIE-based application developed to efficiently generate high statistics samples of events, induced by neutrino interactions, detectable in a neutrino telescope. The gSeaGen code is able to generate events induced by all neutrino flavours, considering topological differences between tracktype and shower-like events. Neutrino interactions are simulated taking into account the density and the composition of the media surrounding the detector. The main features of gSeaGen are presented together with some examples of its application within the KM3NeT project. Program summary Program Title: gSeaGen CPC Library link to program files: http://dx.doi.org/10.17632/ymgxvy2br4.1 Licensing provisions: GPLv3 Programming language: C++ External routines/libraries: GENIE [1] and its external dependencies. Linkable to MUSIC [2] and PROPOSAL [3]. Nature of problem: Development of a code to generate detectable events in neutrino telescopes, using modern and maintained neutrino interaction simulation libraries which include the state-of-the-art physics models. The default application is the simulation of neutrino interactions within KM3NeT [4]. Solution method: Neutrino interactions are simulated using GENIE, a modern framework for Monte Carlo event generators. The GENIE framework, used by nearly all modern neutrino experiments, is considered as a reference code within the neutrino community. Additional comments including restrictions and unusual features: The code was tested with GENIE version 2.12.10 and it is linkable with release series 3. Presently valid up to 5 TeV. This limitation is not intrinsic to the code but due to the present GENIE valid energy range. References: [1] C. Andreopoulos at al., Nucl. Instrum. Meth. A614 (2010) 87. [2] P. Antonioli et al., Astropart. Phys. 7 (1997) 357. [3] J. H. Koehne et al., Comput. Phys. Commun. 184 (2013) 2070. [4] S. Adrian-Martinez et al., J. Phys. G: Nucl. Part. Phys. 43 (2016) 084001. |
| Address |
[Aiello, S.; Leonora, E.; Longhitano, F.; Randazzo, N.] Ist Nazl Fis Nucl, Sez Catania, Via Santa Sofia 64, I-95123 Catania, Italy, Email: distefano_c@lns.infn.it |
| Corporate Author |
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Thesis |
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| Publisher |
Elsevier |
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 |
0010-4655 |
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:000564482200008 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
4520 |
| Permanent link to this record |
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| Author |
KM3NeT Collaboration (Aiello, S. et al); Alves Garre, S.; Calvo, D.; Carretero, V.; Colomer, M.; Corredoira, I; Gozzini, S.R.; Hernandez-Rey, J.J.; Illuminati, G.; Khan Chowdhury, N.R.; Manczak, J.; Pieterse, C.; Real, D.; Salesa Greus, F.; Thakore, T.; Zornoza, J.D.; Zuñiga, J. |
| Title |
Event reconstruction for KM3NeT/ORCA using convolutional neural networks |
Type |
Journal Article |
| Year |
2020 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
| Volume |
15 |
Issue |
10 |
Pages |
P10005 - 39pp |
| Keywords |
Cherenkov detectors; Large detector systems for particle and astroparticle physics; Neutrino detectors; Performance of High Energy Physics Detectors |
| Abstract |
The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches. |
| Address |
[Aiello, S.; Leonora, E.; Longhitano, F.; Randazzo, N.] Ist Nazl Fis Nucl, Sez Catania, Via Santa Sofia 64, I-95123 Catania, Italy, Email: thomas.eberl@fau.de; |
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Thesis |
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| Publisher |
Iop Publishing Ltd |
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 Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
1748-0221 |
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:000577278000005 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
4570 |
| Permanent link to this record |
