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Author |
Kasieczka, G. et al; Sanz, V. |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
The LHC Olympics 2020: a community challenge for anomaly detection in high energy physics |
Type |
Journal Article |
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Year |
2021 |
Publication |
Reports on Progress in Physics |
Abbreviated Journal |
Rep. Prog. Phys. |
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Volume |
84 |
Issue |
12 |
Pages |
124201 - 64pp |
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Keywords |
anomaly detection; machine learning; unsupervised learning; weakly supervised learning; semisupervised learning; beyond the standard model; model-agnostic methods |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
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. |
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Address |
[Kasieczka, Gregor] Univ Hamburg, Inst Expt Phys, Hamburg, Germany, Email: gregor.kasieczka@uni-hamburg.de; |
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Publisher |
IOP Publishing Ltd |
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English |
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ISSN |
0034-4885 |
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Notes |
WOS:000727698500001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5039 |
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Permanent link to this record |
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Author |
Binosi, D.; Chang, L.; Ding, M.H.; Gao, F.; Papavassiliou, J.; Roberts, C.D. |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Distribution amplitudes of heavy-light mesons |
Type |
Journal Article |
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Year |
2019 |
Publication |
Physics Letters B |
Abbreviated Journal |
Phys. Lett. B |
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Volume |
790 |
Issue |
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Pages |
257-262 |
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Keywords |
B-meson decays; Heavy-light mesons; Nonperturbative continuum methods in quantum field theory; Parton distribution amplitudes; Quantum chromodynamics |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
A symmetry-preserving approach to the continuum bound-state problem in quantum field theory is used to calculate the masses, leptonic decay constants and light-front distribution amplitudes of empirically accessible heavy-light mesons. The inverse moment of the B-meson distribution is particularly important in treatments of exclusive B-decays using effective field theory and the factorisation formalism; and its value is therefore computed: lambda(B) = (zeta = 2GeV) = 0.54(3) GeV. As an example and in anticipation of precision measurements at new-generation B-factories, the branching fraction for the rare B -> gamma (E-gamma)l nu(l) radiative decay is also calculated, retaining 1/m(B)(2), and 1/E-gamma(2) corrections to the differential decay width, with the result Gamma(B -> gamma l nu l) /Gamma(B) = 0.47 (15) on E-gamma > 1.5 GeV. |
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Address |
[Binosi, Daniele; Ding, Minghui] European Ctr Theoret Studies Nucl Phys & Related, Str Tabarelle 286, I-38123 Villazzano, TN, Italy, Email: binosi@ectstar.eu; |
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Publisher |
Elsevier Science Bv |
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English |
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ISSN |
0370-2693 |
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Notes |
WOS:000460118200030 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
3934 |
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Permanent link to this record |
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Author |
Pierre Auger Collaboration (Abreu, P. et al); Pastor, S. |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
A search for point sources of EeV neutrons |
Type |
Journal Article |
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Year |
2012 |
Publication |
Astrophysical Journal |
Abbreviated Journal |
Astrophys. J. |
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Volume |
760 |
Issue |
2 |
Pages |
148 - 11pp |
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Keywords |
cosmic rays; Galaxy: disk; methods: data analysis |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
A thorough search of the sky exposed at the Pierre Auger Cosmic Ray Observatory reveals no statistically significant excess of events in any small solid angle that would be indicative of a flux of neutral particles from a discrete source. The search covers from -90 degrees to +15 degrees in declination using four different energy ranges above 1 EeV (10(18) eV). The method used in this search is more sensitive to neutrons than to photons. The upper limit on a neutron flux is derived for a dense grid of directions for each of the four energy ranges. These results constrain scenarios for the production of ultrahigh energy cosmic rays in the Galaxy. |
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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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English |
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ISSN |
0004-637x |
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Notes |
WOS:000311217000052 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
1218 |
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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. |
![goto web page (via DOI) doi](img/doi.gif)
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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 ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
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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ISSN |
0004-6361 |
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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 |
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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. |
![goto web page (via DOI) doi](img/doi.gif)
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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 ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
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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Thesis |
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Publisher |
Edp Sciences S A |
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English |
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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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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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Permanent link to this record |