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Author |
Pierre Auger Collaboration (Abreu, P. et al); Pastor, S. |
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Title |
Search for Point-like Sources of Ultra-high Energy Neutrinos at the Pierre Auger Observatory and Improved Limit on the Diffuse Flux of Tau Neutrinos |
Type |
Journal Article |
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Year |
2012 |
Publication |
Astrophysical Journal Letters |
Abbreviated Journal |
Astrophys. J. Lett. |
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Volume  |
755 |
Issue |
1 |
Pages |
L4 - 7pp |
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Keywords |
astroparticle physics; cosmic rays; neutrinos; telescopes |
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Abstract |
The surface detector array of the Pierre Auger Observatory can detect neutrinos with energy E-nu between 10(17) eV and 10(20) eV from point-like sources across the sky south of +55 degrees and north of -65 degrees declinations. A search has been performed for highly inclined extensive air showers produced by the interaction of neutrinos of all flavors in the atmosphere (downward-going neutrinos), and by the decay of tau leptons originating from tau neutrino interactions in Earth's crust (Earth-skimming neutrinos). No candidate neutrinos have been found in data up to 2010 May 31. This corresponds to an equivalent exposure of similar to 3.5 years of a full surface detector array for the Earth-skimming channel and similar to 2 years for the downward-going channel. An improved upper limit on the diffuse flux of tau neutrinos has been derived. Upper limits on the neutrino flux from point-like sources have been derived as a function of the source declination. Assuming a differential neutrino flux k(PS) . E-nu(-2). from a point-like source, 90% confidence level upper limits for k(PS) at the level of approximate to 5x10(-7) and 2.5x10(-6) GeV cm(-2) s(-1) have been obtained over a broad range of declinations from the searches for Earth-skimming and downward-going neutrinos, respectively. |
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Address |
[Abreu, P.; Andringa, S.; Assis, P.; Brogueira, P.; Cazon, L.; Conceicao, R.; Diogo, F.; Espadanal, J.; Goncalves, P.; Pimenta, M.; Santo, C. E.; Santos, E.; Tome, B.] Univ Tecn Lisboa, LIP, Lisbon, Portugal |
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Publisher |
Iop Publishing Ltd |
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Language |
English |
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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 |
2041-8205 |
ISBN |
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Expedition |
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Conference |
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Notes |
WOS:000306930700004 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
1121 |
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Author |
ANTARES Collaboration (Adrian-Martinez, S. et al); Aguilar, J.A.; Bigongiari, C.; Dornic, D.; Emanuele, U.; Gomez-Gonzalez, J.P.; Hernandez-Rey, J.J.; Mangano, S.; Ruiz-Rivas, J.; Salesa, F.; Sanchez-Losa, A.; Toscano, S.; Yepes, H.; Zornoza, J.D.; Zuñiga, J. |
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Title |
First Search For Point Sources Of High-Energy Cosmic Neutrinos With The Antares Neutrino Telescope |
Type |
Journal Article |
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Year |
2011 |
Publication |
Astrophysical Journal Letters |
Abbreviated Journal |
Astrophys. J. Lett. |
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Volume |
743 |
Issue |
1 |
Pages |
L14 - 6pp |
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Keywords |
astroparticle physics; cosmic rays; neutrinos |
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Abstract |
Results are presented of a search for cosmic sources of high-energy neutrinos with the ANTARES neutrino telescope. The data were collected during 2007 and 2008 using detector configurations containing between 5 and 12 detection lines. The integrated live time of the analyzed data is 304 days. Muon tracks are reconstructed using a likelihood-based algorithm. Studies of the detector timing indicate a median angular resolution of 0.5 +/- 0.1 deg. The neutrino flux sensitivity is 7.5 x 10(-8)(E(v)/GeV)(-2) GeV(-1) s(-1) cm(-2) for the part of the sky that is always visible (delta < -48 deg), which is better than limits obtained by previous experiments. No cosmic neutrino sources have been observed. |
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Address |
[Adrian-Martinez, S.; Ardid, M.; Bou-Cabo, M.; Camarena, F.; Ferri, M.; Larosa, G.; Martinez-Mora, J. A.] Univ Politecn Valencia, Inst Invest Gestio Integrada Zones Costaneres IGI, Gandia 46730, Spain |
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Publisher |
Iop Publishing Ltd |
Place of Publication |
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Language |
English |
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Series Volume |
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ISSN |
2041-8205 |
ISBN |
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Expedition |
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Conference |
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Notes |
WOS:000297372600014 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ elepoucu @ |
Serial |
826 |
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Author |
Trotta, R.; Johannesson, G.; Moskalenko, I.V.; Porter, T.A.; Ruiz de Austri, R.; Strong, A.W. |
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Title |
Constraints on Cosmic-Ray Propagation Models from a Global Bayesian Analysis |
Type |
Journal Article |
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Year |
2011 |
Publication |
Astrophysical Journal |
Abbreviated Journal |
Astrophys. J. |
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Volume |
729 |
Issue |
2 |
Pages |
106 - 16pp |
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Keywords |
astroparticle physics; cosmic rays; diffusion; Galaxy: general; ISM: general; methods: statistical |
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Abstract |
Research in many areas of modern physics such as, e. g., indirect searches for dark matter and particle acceleration in supernova remnant shocks rely heavily on studies of cosmic rays (CRs) and associated diffuse emissions (radio, microwave, X-rays, gamma-rays). While very detailed numerical models of CR propagation exist, a quantitative statistical analysis of such models has been so far hampered by the large computational effort that those models require. Although statistical analyses have been carried out before using semi-analytical models (where the computation is much faster), the evaluation of the results obtained from such models is difficult, as they necessarily suffer from many simplifying assumptions. The main objective of this paper is to present a working method for a full Bayesian parameter estimation for a numerical CR propagation model. For this study, we use the GALPROP code, the most advanced of its kind, which uses astrophysical information, and nuclear and particle data as inputs to self-consistently predict CRs, gamma-rays, synchrotron, and other observables. We demonstrate that a full Bayesian analysis is possible using nested sampling and Markov Chain Monte Carlo methods (implemented in the SuperBayeS code) despite the heavy computational demands of a numerical propagation code. The best-fit values of parameters found in this analysis are in agreement with previous, significantly simpler, studies also based on GALPROP. |
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Address |
[Trotta, R.] Univ London Imperial Coll Sci Technol & Med, Astrophys Grp, Blackett Lab, London SW7 2AZ, England |
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Publisher |
Iop Publishing Ltd |
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Language |
English |
Summary Language |
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Original Title |
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Series Volume |
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Edition |
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ISSN |
0004-637x |
ISBN |
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Conference |
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Notes |
ISI:000288608700029 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
541 |
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Author |
Panes, B.; Eckner, C.; Hendriks, L.; Caron, S.; Dijkstra, K.; Johannesson, G.; Ruiz de Austri, R.; Zaharijas, G. |
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Title |
Identification of point sources in gamma rays using U-shaped convolutional neural networks and a data challenge |
Type |
Journal Article |
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Year |
2021 |
Publication |
Astronomy & Astrophysics |
Abbreviated Journal |
Astron. Astrophys. |
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Volume |
656 |
Issue |
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Pages |
A62 - 18pp |
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Keywords |
catalogs; gamma rays: general; astroparticle physics; methods: numerical; methods: data analysis; techniques: image processing |
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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. |
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Address |
[Panes, Boris] Pontificia Univ Catolica Chile, Ave Vicuna Mackenna 4860, Macul, Region Metropol, Chile, Email: bapanes@gmail.com |
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Publisher |
Edp Sciences S A |
Place of Publication |
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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 Volume |
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ISSN |
0004-6361 |
ISBN |
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Conference |
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Notes |
WOS:000725877600001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5053 |
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Author |
Gammaldi, V.; Zaldivar, B.; Sanchez-Conde, M.A.; Coronado-Blazquez, J. |
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Title |
A search for dark matter among Fermi-LAT unidentified sources with systematic features in machine learning |
Type |
Journal Article |
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Year |
2023 |
Publication |
Monthly Notices of the Royal Astronomical Society |
Abbreviated Journal |
Mon. Not. Roy. Astron. Soc. |
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Volume |
520 |
Issue |
1 |
Pages |
1348-1361 |
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Keywords |
astroparticle physics – methods; data analysis – methods; observational – methods; statistical – dark matter – gamma-rays; general |
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Abstract |
Around one-third of the point-like sources in the Fermi-LAT catalogues remain as unidentified sources (unIDs) today. Indeed, these unIDs lack a clear, univocal association with a known astrophysical source. If dark matter (DM) is composed of weakly interacting massive particles (WIMPs), there is the exciting possibility that some of these unIDs may actually be DM sources, emitting gamma-rays from WIMPs annihilation. We propose a new approach to solve the standard, machine learning (ML) binary classification problem of disentangling prospective DM sources (simulated data) from astrophysical sources (observed data) among the unIDs of the 4FGL Fermi-LAT catalogue. We artificially build two systematic features for the DM data which are originally inherent to observed data: the detection significance and the uncertainty on the spectral curvature. We do it by sampling from the observed population of unIDs, assuming that the DM distributions would, if any, follow the latter. We consider different ML models: Logistic Regression, Neural Network (NN), Naive Bayes, and Gaussian Process, out of which the best, in terms of classification accuracy, is the NN, achieving around 93 . 3 per cent +/- 0 . 7 per cent performance. Other ML evaluation parameters, such as the True Ne gativ e and True Positive rates, are discussed in our work. Applying the NN to the unIDs sample, we find that the de generac y between some astrophysical and DM sources can be partially solved within this methodology. None the less, we conclude that there are no DM source candidates among the pool of 4FGL Fermi-LAT unIDs. |
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Address |
[Gammaldi, V; Sanchez-Conde, M. A.; Coronado-Blazquez, J.] Univ Autonoma Madrid, Departamentode Fis Teor, E-28049 Madrid, Spain, Email: viviana.gammaldi@uam.es; |
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Publisher |
Oxford Univ Press |
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Language |
English |
Summary Language |
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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 |
0035-8711 |
ISBN |
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Area |
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Expedition |
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Conference |
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Notes |
WOS:000937053400014 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
5489 |
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