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Herrero-Garcia, J.; Patrick, R.; Scaffidi, A. |
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Title |
A semi-supervised approach to dark matter searches in direct detection data with machine learning |
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Journal Article |
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2022 |
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Journal of Cosmology and Astroparticle Physics |
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J. Cosmol. Astropart. Phys. |
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02 |
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039 - 19pp |
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The dark matter sector remains completely unknown. It is therefore crucial to keep an open mind regarding its nature and possible interactions. Focusing on the case of Weakly Interacting Massive Particles, in this work we make this general philosophy more concrete by applying modern machine learning techniques to dark matter direct detection. We do this by encoding and decoding the graphical representation of background events in the XENONnT experiment with a convolutional variational autoencoder. We describe a methodology that utilizes the `anomaly score' derived from the reconstruction loss of the convolutional variational autoencoder as well as a pre-trained standard convolutional neural network, in a semi-supervised fashion. Indeed, we observe that optimum results are obtained only when both unsupervised and supervised anomaly scores are considered together. A data set that has a higher proportion of anomaly score is deemed anomalous and deserves further investigation. Contrary to classical analyses, in principle all information about the events is used, preventing unnecessary information loss. Lastly, we demonstrate the reach of learning-focused anomaly detection in this context by comparing results with classical inference, observing that, if tuned properly, these techniques have the potential to outperform likelihood-based methods. |
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no |
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yes |
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yes |
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IFIC @ pastor @ |
Serial |
5495 |
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Author |
Liptak, Z. et al; Marinas, C. |
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Title |
Measurements of beam backgrounds in SuperKEKB Phase 2 |
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2022 |
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Nuclear Instruments & Methods in Physics Research A |
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Nucl. Instrum. Methods Phys. Res. A |
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1040 |
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167168 - 19pp |
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The high design luminosity of the SuperKEKB electron–positron collider will result in challenging levels of beam-induced backgrounds in the interaction region. Understanding and mitigating these backgrounds is critical to the success of the Belle II experiment. We report on the first background measurements performed after roll-in of the Belle II detector, a period known as SuperKEKB Phase 2, utilizing both the BEAST II system of dedicated background detectors and the Belle II detector itself. We also report on first revisions to the background simulation made in response to our findings. Backgrounds measured include contributions from synchrotron radiation, beam-gas, Touschek, and injection backgrounds. At the end of Phase 2, single-beam backgrounds originating from the 4 GeV positron Low Energy Ring (LER) agree reasonably well with simulation, while backgrounds from the 7 GeV electron High Energy Ring (HER) are approximately one order of magnitude higher than simulation. We extrapolate these backgrounds forward and conclude it is safe to install the Belle II vertex detector. |
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no |
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yes |
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yes |
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IFIC @ pastor @ |
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5496 |
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Middeldorf-Wygas, M.M.; Oldengott, I.M.; Bödeker, D.; Schwarz, D.J. |
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Title |
Cosmic QCD transition for large lepton flavor asymmetries |
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Journal Article |
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2022 |
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Physical Review D |
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Phys. Rev. D |
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105 |
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123533 - 10pp |
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We study the impact of large lepton flavor asymmetries on the cosmic QCD transition. Scenarios of unequal lepton flavor asymmetries are observationally almost unconstrained and therefore open up a whole new parameter space for the cosmic QCD transition. We find that for large asymmetries, the formation of a Bose-Einstein condensate of pions can occur and identify the corresponding parameter space. In the vicinity of the QCD transition scale, we express the pressure in terms of a Taylor expansion with respect to the complete set of chemical potentials. The Taylor coefficients rely on input from lattice QCD calculations from the literature. The domain of applicability of this method is discussed. |
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no |
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yes |
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yes |
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IFIC @ pastor @ |
Serial |
5497 |
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NA64 Collaboration (Andreev, Y.M. et al); Molina Bueno, L. |
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Title |
Search for a New B-L Z' Gauge Boson with the NA64 Experiment at CERN |
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Journal Article |
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Year |
2022 |
Publication |
Physical Review Letters |
Abbreviated Journal |
Phys. Rev. Lett. |
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Volume |
129 |
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161801 - 6pp |
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A search for a new Z′ gauge boson associated with (un)broken B−L symmetry in the keV–GeV mass range is carried out for the first time using the missing-energy technique in the NA64 experiment at the CERN SPS. From the analysis of the data with 3.22×10^11 electrons on target collected during 2016–2021 runs, no signal events were found. This allows us to derive new constraints on the Z′−e coupling strength, which, for the mass range 0.3≲ mZ′≲ 100 MeV, are more stringent compared to those obtained from the neutrino-electron scattering data. |
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no |
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yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5499 |
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Author |
Garcia Navarro, J.E.; Fernandez-Prieto, L.M.; Villaseñor, A.; Sanz, V.; Ammirati, J.B.; Diaz Suarez, E.A.; Garcia, C. |
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Title |
Performance of Deep Learning Pickers in Routine Network Processing Applications |
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Journal Article |
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Year |
2022 |
Publication |
Seismological Research Letters |
Abbreviated Journal |
Seismol. Res. Lett. |
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Volume |
93 |
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2529-2542 |
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Picking arrival times of P and S phases is a fundamental and time‐consuming task for the routine processing of seismic data acquired by permanent and temporary networks. A large number of automatic pickers have been developed, but to perform well they often require the tuning of multiple parameters to adapt them to each dataset. Despite the great advance in techniques, some problems remain, such as the difficulty to accurately pick S waves and earthquake recordings with a low signal‐to‐noise ratio. Recently, phase pickers based on deep learning (DL) have shown great potential for event identification and arrival‐time picking. However, the general adoption of these methods for the routine processing of monitoring networks has been held back by factors such as the availability of well‐documented software, computational resources, and a gap in knowledge of these methods. In this study, we evaluate recent available DL pickers for earthquake data, comparing the performance of several neural network architectures. We test the selected pickers using three datasets with different characteristics. We found that the analyzed DL pickers (generalized phase detection, PhaseNet, and EQTransformer) perform well in the three tested cases. They are very efficient at ignoring large‐amplitude transient noise and at picking S waves, a task that is often difficult even for experienced analysts. Nevertheless, the performance of the analyzed DL pickers varies widely in terms of sensitivity and false discovery rate, with some pickers missing a significant percentage of true picks and others producing a large number of false positives. There are also variations in run time between DL pickers, with some of them requiring significant resources to process large datasets. In spite of these drawbacks, we show that DL pickers can be used efficiently to process large seismic datasets and obtain results comparable or better than current standard procedures. |
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no |
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yes |
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yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5500 |
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