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Author |
Salesa Greus, F.; Sanchez Losa, A. |
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Title |
Multimessenger Astronomy with Neutrinos |
Type |
Journal Article |
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Year |
2021 |
Publication |
Universe |
Abbreviated Journal |
Universe |
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Volume |
7 |
Issue |
11 |
Pages |
397 - 11pp |
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Keywords |
multimessenger astronomy; astroparticle physics; neutrinos |
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Abstract |
Multimessenger astronomy is arguably the branch of the astroparticle physics field that has seen the most significant developments in recent years. In this manuscript, we will review the state-of-the-art, the recent observations, and the prospects and challenges for the near future. We will give special emphasis to the observation carried out with neutrino telescopes. |
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Address |
[Salesa Greus, Francisco; Sanchez Losa, Agustin] Univ Valencia, CSIC, IFIC Inst Fis Corpuscular, C Catedratico Jose Beltran 2, E-46980 Paterna, Spain, Email: sagreus@ific.uv.es; |
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Mdpi |
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English |
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Notes |
WOS:000724957500001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5036 |
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Permanent link to this record |
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Author |
Massimi, C.; Cristallo, S.; Domingo-Pardo, C.; Lederer-Woods, C. |
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Title |
n_TOF: Measurements of Key Reactions of Interest to AGB Stars |
Type |
Journal Article |
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Year |
2022 |
Publication |
Universe |
Abbreviated Journal |
Universe |
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Volume |
8 |
Issue |
2 |
Pages |
100 - 19pp |
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Keywords |
s process; MACS; time of flight |
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Abstract |
In the last 20 years, the neutron time-of-flight facility nTOF at CERN has been providing relevant data for the astrophysical slow neutron capture process (s process). At nTOF, neutron-induced radiative capture (n,gamma) as well as (n,p) and (n,alpha) reaction cross sections are measured as a function of energy, using the time-of-flight method. Improved detection systems, innovative ideas and collaborations with other neutron facilities have lead to a considerable contribution of the n_TOF collaboration to studying the s process in asymptotic giant branch stars. Results have been reported for stable and radioactive samples, i.e.,Mg- 24,Mg-25,Mg-26, Al-26, S-33,Fe- 54,Fe-57, Ni-58,Ni-59,Ni-62,Ni-63, Ge-70,Ge-72,Ge-73, Zr-90,Zr-91,Zr-92,Zr-93,Zr-94,Zr-96, La-139, Ce-140, Pm-147, Sm-151,Gd- 154,Gd-155,Gd-157, Tm-171, Os-186,Os-187,Os-188, Au-197, Tl-203,Tl-204,Pb- 204,Pb-206,Pb-207 and Bi-209 isotopes, while others are being studied or planned to be studied in the near future. In this contribution, we present an overview of the most successful achievements, and an outlook of future challenging measurements, including ongoing detection system developments. |
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Address |
[Massimi, Cristian] Univ Bologna, Dept Phys & Astron, I-40127 Bologna, Italy, Email: cristian.massimi@unibo.it; |
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Mdpi |
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English |
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WOS:000762514400001 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5144 |
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Permanent link to this record |
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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 |
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English |
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0004-6361 |
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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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Permanent link to this record |
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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 |
Type |
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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Address |
[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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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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Series Editor |
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Series Volume |
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ISSN |
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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Permanent link to this record |
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Author |
Stoppa, F.; Ruiz de Austri, R.; Vreeswijk, P.; Bhattacharyya, S.; Caron, S.; Bloemen, S.; Zaharijas, G.; Principe, G.; Vodeb, V.; Groot, P.J.; Cator, E.; Nelemans, G. |
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Title |
AutoSourceID-FeatureExtractor Optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation |
Type |
Journal Article |
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Year |
2023 |
Publication |
Astronomy & Astrophysics |
Abbreviated Journal |
Astron. Astrophys. |
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Volume |
680 |
Issue |
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Pages |
A108 - 14pp |
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Keywords |
astronomical databases: miscellaneous; methods: data analysis; stars: imaging; techniques: image processing |
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Abstract |
Aims. In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources' features and their uncertainties from single-band image cutouts, given the approximated locations of the sources provided by the previously developed code AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a proof of concept, showing the potential of machine learning in estimating astronomical features when trained on meticulously crafted synthetic images and subsequently applied to real astronomical data.Methods. The algorithm presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band cutouts of 32x32 pixels around the localised sources to estimate flux, sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a two-step mean variance estimation (TS-MVE) approach to first estimate the features and then their uncertainties without the need for additional information, for example the point spread function (PSF). For this proof of concept, we generated a synthetic dataset comprising only point sources directly derived from real images, ensuring a controlled yet authentic testing environment.Results. We show that ASID-FE, trained on synthetic images derived from the MeerLICHT telescope, can predict more accurate features with respect to similar codes such as SourceExtractor and that the two-step method can estimate well-calibrated uncertainties that are better behaved compared to similar methods that use deep ensembles of simple MVE networks. Finally, we evaluate the model on real images from the MeerLICHT telescope and the Zwicky Transient Facility (ZTF) to test its transfer learning abilities. |
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Address |
[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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Publisher |
Edp Sciences S A |
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English |
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ISSN |
0004-6361 |
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Conference |
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Notes |
WOS:001131898100003 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5887 |
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Permanent link to this record |