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Author |
Aarrestad, T. et al; Mamuzic, J.; Ruiz de Austri, R. |
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Title |
Benchmark data and model independent event classification for the large hadron collider |
Type |
Journal Article |
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Year |
2022 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
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Volume |
12 |
Issue  |
1 |
Pages |
043 - 57pp |
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Abstract |
We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of > 1 billion simulated LHC events corresponding to 10 fb(-1) of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge. |
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Address |
[Aarrestad, Thea; Heinrich, Lukas A.; Jawahar, Pratik; Pierini, Maurizio; Touranakou, Mary; Wozniak, Kinga A.] European Org Nucl Res CERN, CH-1211 Geneva 23, Switzerland |
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Scipost Foundation |
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English |
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ISSN |
2542-4653 |
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Notes |
WOS:000807448000038 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5256 |
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Author |
LHCb Collaboration (Aaij, R. et al); Jashal, B.K.; Martinez-Vidal, F.; Oyanguren, A.; Remon Alepuz, C.; Ruiz Vidal, J. |
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Title |
Study of the doubly charmed tetraquark T-cc(+) |
Type |
Journal Article |
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Year |
2022 |
Publication |
Nature Communications |
Abbreviated Journal |
Nat. Commun. |
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Volume |
13 |
Issue |
1 |
Pages |
3351 - 19pp |
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Keywords |
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Abstract |
Quantum chromodynamics, the theory of the strong force, describes interactions of coloured quarks and gluons and the formation of hadronic matter. Conventional hadronic matter consists of baryons and mesons made of three quarks and quark-antiquark pairs, respectively. Particles with an alternative quark content are known as exotic states. Here a study is reported of an exotic narrow state in the (DD0)-D-0 pi(+) mass spectrum just below the D*+D-0 mass threshold produced in proton-proton collisions collected with the LHCb detector at the Large Hadron Collider. The state is consistent with the ground isoscalar T-cc(+), tetraquark with a quark content of cc (u) over bar(d) over bar and spin-parity quantum numbers J(P) =1(+). Study of the DD mass spectra disfavours interpretation of the resonance as the isovector state. The decay structure via intermediate off-shell D*(+) mesons is consistent with the observed D-0 pi(+) mass distribution. To analyse the mass of the resonance and its coupling to the DID system, a dedicated model is developed under the assumption of an isoscalar axial-vector T-cc(+), state decaying to the D*D channel. Using this model, resonance parameters including the pole position, scattering length, effective range and compositeness are determined to reveal important information about the nature of the T-cc(+), state. In addition, an unexpected dependence of the production rate on track multiplicity is observed. |
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Address |
[Aaij, R.; Butter, J. S.; Akiba, K. Carvalho; Sole, S. Ferreres; Gabriel, E.; Geertsema, R. E.; Greeven, L. M.; Heijhoff, K.; Hulsbergen, W.; Hynds, D.; Jans, E.; Ketel, T.; Klaver, S.; Koppenburg, P.; Kostiuk, I; Kuindersma, H. S.; Martinez, M. Lucio; Lukashenko, V; Mauri, A.; Merk, M.; Pellegrino, A.; Raven, G.; Gras, C. Sanchez; Schubiger, M.; Soares, M. Senghi; Snoch, A.; Tuning, N.; Usachov, A.; van Beuzekom, M.; Veronesi, M.] Nikhef Natl Inst Subatom Phys, Amsterdam, Netherlands, Email: Ivan.Belyaev@cern.ch |
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Nature Portfolio |
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English |
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Notes |
WOS:000812556800001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5280 |
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Author |
Albiol, A.; Albiol, F.; Paredes, R.; Plasencia-Martinez, J.M.; Blanco Barrio, A.; Garcia Santos, J.M.; Tortajada, S.; Gonzalez Montano, V.M.; Rodriguez Godoy, C.E.; Fernandez Gomez, S.; Oliver-Garcia, E.; de la Iglesia Vaya, M.; Marquez Perez, F.L.; Rayo Madrid, J.I. |
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Title |
A comparison of Covid-19 early detection between convolutional neural networks and radiologists |
Type |
Journal Article |
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Year |
2022 |
Publication |
Insights into Imaging |
Abbreviated Journal |
Insights Imaging |
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Volume |
13 |
Issue |
1 |
Pages |
122 - 12pp |
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Keywords |
Deep learning; Covid-19; Radiology |
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Abstract |
Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19. |
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Address |
[Albiol, Alberto] Univ Politecn Valencia, iTeam Inst, ETSI Telecomunicac, Camino Vera S-N, Valencia 46022, Spain, Email: alalbiol@iteam.upv.es |
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Publisher |
Springer |
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English |
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Edition |
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ISSN |
1869-4101 |
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Notes |
WOS:000832727200003 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
5302 |
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Author |
Guadilla, V. et al; Algora, A.; Tain, J.L.; Agramunt, J.; Jordan, D.; Monserrate, M.; Montaner-Piza, A.; Nacher, E.; Orrigo, S.E.A.; Rubio, B.; Valencia, E. |
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Title |
Total absorption gamma-ray spectroscopy of the ss decays of Y-96gs,Y-m |
Type |
Journal Article |
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Year |
2022 |
Publication |
Physical Review C |
Abbreviated Journal |
Phys. Rev. C |
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Volume |
106 |
Issue |
1 |
Pages |
014306 - 14pp |
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Abstract |
The ss decays of the ground state (gs) and isomeric state (m) of Y-96 have been studied with the total absorption gamma-ray spectroscopy technique at the Ion Guide Isotope Separator On-Line facility. The separation of the 8(+) isomeric state from the 0(-) ground state was achieved thanks to the purification capabilities of the JYFLTRAP double Penning trap system. The ss-intensity distributions of both decays have been independently determined. In the analyses the deexcitation of the 1581.6 keV level in Zr-96, in which conversion electron emission competes with pair production, has been carefully considered and found to have significant impact on the ss-detector efficiency, influencing the ss-intensity distribution obtained. Our results for Y-96gs (0(-)) confirm the large ground state to ground state ss-intensity probability, although a slightly larger value than reported in previous studies was obtained, amounting to 96.6(-2.1)(+0.3) % of the total ss intensity. Given that the decay of Y-96gs is the second most important contributor to the reactor antineutrino spectrum between 5 and 7 MeV, the impact of the present results on reactor antineutrino summation calculations has been evaluated. In the decay of Y-96m (8(+)), previously undetected ss intensity in transitions to states above 6 MeV has been observed. This shows the importance of total absorption gamma-ray spectroscopy measurements of ss decays with highly fragmented deexcitation patterns. Y-96m (8(+)) is a major contributor to reactor decay heat in uranium-plutonium and thorium-uranium fuels around 10 s after fission pulses, and the newly measured average ss and gamma energies differ significantly from the previous values in evaluated databases. The discrepancy is far above the previously quoted uncertainties. Finally, we also report on the successful implementation of an innovative total absorption gamma-ray spectroscopy analysis of the module-multiplicity gated spectra, as a first proof of principle to distinguish between decaying states with very different spin-parity values. |
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Address |
[Guadilla, V; Le Meur, L.; Fallot, M.; Briz, J. A.; Estienne, M.; Giot, L.; Porta, A.; Cucoanes, A.; Shiba, T.; Zakari-Issoufou, A-A] Univ Nantes, Subatech, IMT Atlantique, CNRS IN2P3, F-44307 Nantes, France, Email: vguadilla@fuw.edu.pl |
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Publisher |
Amer Physical Soc |
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English |
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ISSN |
2469-9985 |
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Notes |
WOS:000832364800001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5313 |
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Author |
Villanueva-Domingo, P.; Villaescusa-Navarro, F.; Angles-Alcazar, D.; Genel, S.; Marinacci, F.; Spergel, D.N.; Hernquist, L.; Vogelsberger, M.; Dave, R.; Narayanan, D. |
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Title |
Inferring Halo Masses with Graph Neural Networks |
Type |
Journal Article |
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Year |
2022 |
Publication |
Astrophysical Journal |
Abbreviated Journal |
Astrophys. J. |
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Volume |
935 |
Issue |
1 |
Pages |
30 - 15pp |
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Keywords |
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Abstract |
Understanding the halo-galaxy connection is fundamental in order to improve our knowledge on the nature and properties of dark matter. In this work, we build a model that infers the mass of a halo given the positions, velocities, stellar masses, and radii of the galaxies it hosts. In order to capture information from correlations among galaxy properties and their phase space, we use Graph Neural Networks (GNNs), which are designed to work with irregular and sparse data. We train our models on galaxies from more than 2000 state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations project. Our model, which accounts for cosmological and astrophysical uncertainties, is able to constrain the masses of the halos with a similar to 0.2 dex accuracy. Furthermore, a GNN trained on a suite of simulations is able to preserve part of its accuracy when tested on simulations run with a different code that utilizes a distinct subgrid physics model, showing the robustness of our method. The PyTorch Geometric implementation of the GNN is publicly available on GitHub (https://github.com/PabloVD/HaloGraphNet). |
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Address |
[Villanueva-Domingo, Pablo] Univ Valencia, Inst Fis Corpuscular IFIC, CSIC, E-46980 Paterna, Spain, Email: pablo.villanueva.domingo@gmail.com; |
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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:000838320900001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5325 |
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