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Author |
ATLAS Collaboration (Aad, G. et al); Amos, K.R.; Aparisi Pozo, J.A.; Bailey, A.J.; Bouchhar, N.; Cabrera Urban, S.; Cantero, J.; Cardillo, F.; Castillo Gimenez, V.; Chitishvili, M.; Costa, M.J.; Didenko,, M.; Escobar, C.; Fiorini, L.; Fullana Torregrosa, E.; Fuster, J.; Garcia, C.; Garcia Navarro, J.E.; Gomez Delegido, A.J.; Gonzalez de la Hoz, S.; Gonzalvo Rodriguez, G.R.; Guerrero Rojas, J.G.R.; Lacasta, C.; Lozano Bahilo, J.J.; Marti-Garcia, S.; Martinez Agullo, P.; Miralles Lopez, M.; Mitsou, V.A.; Monsonis Romero, L.; Moreno Llacer, M.; Munoz Perez, D.; Navarro-Gonzalez, J.; Poveda, J.; Prades Ibañez, A.; Rubio Jimenez, A.; Ruiz-Martinez, A.; Sabatini, P.; Salt, J.; Sanchez Sebastian, V.; Sayago Galvan, I.; Senthilkumar, V.; Soldevila, U.; Sanchez, J.; Torro Pastor, E.; Valero, A.; Valls Ferrer, J.A.; Varriale, L.; Villaplana Perez, M.; Vos, M. |
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Title  |
ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset |
Type |
Journal Article |
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Year |
2023 |
Publication |
European Physical Journal C |
Abbreviated Journal |
Eur. Phys. J. C |
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Volume |
83 |
Issue |
7 |
Pages |
681 - 37pp |
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Abstract |
The flavour-tagging algorithms developed by the AvTLAS Collaboration and used to analyse its dataset of root s = 13 TeV pp collisions from Run 2 of the Large Hadron Collider are presented. These new tagging algorithms are based on recurrent and deep neural networks, and their performance is evaluated in simulated collision events. These developments yield considerable improvements over previous jet-flavour identification strategies. At the 77% b-jet identification efficiency operating point, light-jet (charm-jet) rejection factors of 170 (5) are achieved in a sample of simulated Standard Model t (t) over bar events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained. |
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Address |
[Filmer, E. K.; Jackson, P.; Kong, A. X. Y.; Potti, H.; Ruggeri, T. A.; Ting, E. X. L.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, SA, Australia |
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Springer |
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English |
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ISSN |
1434-6044 |
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Notes |
WOS:001062397400001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5675 |
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Author |
Stoppa, F.; Bhattacharyya, S.; Ruiz de Austri, R.; Vreeswijk, P.; Caron, S.; Zaharijas, G.; Bloemen, S.; Principe, G.; Malyshev, D.; Vodeb, V.; Groot, P.J.; Cator, E.; Nelemans, G. |
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Title |
AutoSourceID-Classifier Star-galaxy classification using a convolutional neural network with spatial information |
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 |
A109 - 16pp |
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Keywords |
methods: data analysis; techniques: image processing; astronomical databases: miscellaneous; stars: imaging; Galaxies: statistics |
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Abstract |
Aims. Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification's reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images.Methods. The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results.Results. We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C's direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy. |
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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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Notes |
WOS:001131898100001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5888 |
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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 |
Place of Publication |
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English |
Summary Language |
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Original 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 |
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Expedition |
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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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Author |
Heavy Flavor Averaging Group (Amhis, Y. et al); Oyanguren, A. |
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Title |
Averages of b-hadron, c-handron, and t-lepton properties as of 2021 |
Type |
Journal Article |
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Year |
2023 |
Publication |
Physical Review D |
Abbreviated Journal |
Phys. Rev. D |
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Volume |
107 |
Issue |
5 |
Pages |
052008 - 365pp |
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Keywords |
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Abstract |
This paper reports world averages of measurements of b-hadron, c-hadron, and τ-lepton properties obtained by the Heavy Flavor Averaging Group using results available before April 2021. In rare cases, significant results obtained several months later are also used. For the averaging, common input parameters used in the various analyses are adjusted (rescaled) to common values, and known correlations are taken into account. The averages include branching fractions, lifetimes, neutral meson mixing parameters, CP violation parameters, parameters of semileptonic decays, and Cabibbo-Kobayashi-Maskawa matrix elements. |
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Address |
[Amhis, Y.] Univ Paris Saclay, CNRS, IN2P3, IJCLab, Orsay, France |
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Publisher |
Amer Physical Soc |
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English |
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Edition |
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ISSN |
2470-0010 |
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Conference |
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Notes |
WOS:000981903600002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5651 |
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Author |
Brandao, P.C.S.; Song, J.; Abreu, L.M.; Oset, E. |
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Title |
B+ decay to K+ ηη with (ηη) from the D bar-D(3720) bound state |
Type |
Journal Article |
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Year |
2023 |
Publication |
Physical Review D |
Abbreviated Journal |
Phys. Rev. D |
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Volume |
108 |
Issue |
5 |
Pages |
054004 - 6pp |
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Keywords |
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Abstract |
We search for a B decay mode where one can find a peak for a DD bound state predicted in effective theories and in lattice QCD calculations, which has also been claimed from some reactions that show an accumulated strength in D D over bar production at threshold. We find a good candidate in the B+-> K+eta eta reaction, by looking at the eta eta mass distribution. The reaction proceeds via a first step in which one has the B+-> D*+ D-0 reaction followed by D*(+) (s) decay to (DK+)-K-0 and a posterior fusion of D-0 over bar D-0 to eta eta, implemented through a triangle diagram that allows the D-0 over bar D-0 to be virtual and to produce the bound state. The choice of eta eta to see the peak is based on results of calculations that find the eta eta among the light pseudoscalar channels with stronger coupling to the D D over bar bound state. We find a neat peak around the predicted mass of that state in the eta eta mass distribution, with an integrated branching ratio for B+-> K+ (D D, bound); (D D, bound) -> eta eta of the order of 1.5 x 10(-4), a large number for hadronic B decays, which should motivate its experimental search. |
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Address |
[Brandao, Pedro C. S.; Abreu, Luciano M.; Oset, E.] Univ Fed Bahia, Inst Fis, Campus Ondina, BR-40170115 Salvador, BA, Brazil, Email: pedro.brandao@ufba.br; |
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Thesis |
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Publisher |
Amer Physical Soc |
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 |
2470-0010 |
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:001085561600001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5765 |
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