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Author |
Real, D.; Calvo, D.; Zornoza, J.D.; Manzaneda, M.; Gozzini, R.; Ricolfe-Viala, C.; Lajara, R.; Albiol, F. |
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Title |
Fast Coincidence Filter for Silicon Photomultiplier Dark Count Rate Rejection |
Type |
Journal Article |
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Year |
2024 |
Publication |
Sensors |
Abbreviated Journal  |
Sensors |
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Volume |
24 |
Issue |
7 |
Pages |
2084 - 12pp |
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Keywords |
time-to-digital converters; neutrino telescopes; silicon photomultipliers; dark noise rate filtering |
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Abstract |
Silicon Photomultipliers find applications across various fields. One potential Silicon Photomultiplier application domain is neutrino telescopes, where they may enhance the angular resolution. However, the elevated dark count rate associated with Silicon Photomultipliers represents a significant challenge to their widespread utilization. To address this issue, it is proposed to use Silicon Photomultipliers and Photomultiplier Tubes together. The Photomultiplier Tube signals serve as a trigger to mitigate the dark count rate, thereby preventing undue saturation of the available bandwidth. This paper presents an investigation into a fast and resource-efficient method for filtering the Silicon Photomultiplier dark count rate. A low-resource and fast coincident filter has been developed, which removes the Silicon Photomultiplier dark count rate by using as a trigger the Photomultiplier Tube input signals. The architecture of the coincidence filter, together with the first results obtained, which validate the effectiveness of this method, is presented. |
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[Real, Diego; Calvo, David; Zornoza, Juan de Dios; Manzaneda, Mario; Gozzini, Rebecca; Albiol, Francisco] CSIC Univ Valencia, IFIC Inst Fis Corpuscular, C Catedrat Jose Beltran 2, Paterna 46980, Spain, Email: real@ific.uv.es; |
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Mdpi |
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English |
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WOS:001201226600001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
6063 |
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Author |
Martin-Luna, P.; Esperante, D.; Prieto, A.F.; Fuster-Martinez, N.; Rivas, I.G.; Gimeno, B.; Ginestar, D.; Gonzalez-Iglesias, D.; Hueso, J.L.; Llosa, G.; Martinez-Reviriego, P.; Meneses-Felipe, A.; Riera, J.; Regueiro, P.V.; Hueso-Gonzalez, F. |
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Title |
Simulation of electron transport and secondary emission in a photomultiplier tube and validation |
Type |
Journal Article |
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Year |
2024 |
Publication |
Sensors and Actuators A-Physical |
Abbreviated Journal |
Sens. Actuator A-Phys. |
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Volume |
365 |
Issue |
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Pages |
114859 - 10pp |
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Keywords |
Photomultiplier tube; Photodetector; Proton therapy; Monte Carlo simulation; Measurement |
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Abstract |
The electron amplification and transport within a photomultiplier tube (PMT) has been investigated by developing an in-house Monte Carlo simulation code. The secondary electron emission in the dynodes is implemented via an effective electron model and the Modified Vaughan's model, whereas the transport is computed with the Boris leapfrog algorithm. The PMT gain, rise time and transit time have been studied as a function of supply voltage and external magnetostatic field. A good agreement with experimental measurements using a Hamamatsu R13408-100 PMT was obtained. The simulations have been conducted following different treatments of the underlying geometry: three-dimensional, two-dimensional and intermediate (2.5D). The validity of these approaches is compared. The developed framework will help in understanding the behavior of PMTs under highly intense and irregular illumination or varying external magnetic fields, as in the case of prompt gamma-ray measurements during pencil-beam proton therapy; and aid in optimizing the design of voltage dividers with behavioral circuit models. |
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[Martin-Luna, Pablo; Esperante, Daniel; Fuster-Martinez, Nuria; Gimeno, Benito; Gonzalez-Iglesias, Daniel; Llosa, Gabriela; Martinez-Reviriego, Pablo; Meneses-Felipe, Alba; Hueso-Gonzalez, Fernando] CSIC UV, Inst Fis Corpuscular IFIC, C Catedrat Jose Beltran 2, Paterna 46980, Spain, Email: pablo.martin@uv.es |
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Elsevier Science Sa |
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English |
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0924-4247 |
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WOS:001131902700001 |
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no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
5876 |
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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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Pages |
2529-2542 |
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Abstract |
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 |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5500 |
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Author |
Khosa, C.K.; Sanz, V.; Soughton, M. |
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Title |
A simple guide from machine learning outputs to statistical criteria in particle physics |
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Journal Article |
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Year |
2022 |
Publication |
Scipost Physics Core |
Abbreviated Journal |
SciPost Phys. Core |
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Volume |
5 |
Issue |
4 |
Pages |
050 - 31pp |
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Abstract |
In this paper we propose ways to incorporate Machine Learning training outputs into a study of statistical significance. We describe these methods in supervised classification tasks using a CNN and a DNN output, and unsupervised learning based on a VAE. As use cases, we consider two physical situations where Machine Learning are often used: high-pT hadronic activity, and boosted Higgs in association with a massive vector boson. |
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Address |
[Khosa, Charanjit Kaur] Univ Bristol, HH Wills Phys Lab, Tyndall Ave, Bristol BS8 1TL, Avon, England, Email: Charanjit.Kaur@bristol.ac.uk; |
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Scipost Foundation |
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English |
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WOS:000929724800002 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5475 |
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Author |
van Beekveld, M.; Beenakker, W.; Caron, S.; Kip, J.; Ruiz de Austri, R.; Zhang, Z.Y. |
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Title |
Non-standard neutrino spectra from annihilating neutralino dark matter |
Type |
Journal Article |
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Year |
2023 |
Publication |
Scipost Physics Core |
Abbreviated Journal |
SciPost Phys. Core |
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Volume |
6 |
Issue |
1 |
Pages |
006 - 23pp |
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Abstract |
Neutrino telescope experiments are rapidly becoming more competitive in indirect de-tection searches for dark matter. Neutrino signals arising from dark matter annihilations are typically assumed to originate from the hadronisation and decay of Standard Model particles. Here we showcase a supersymmetric model, the BLSSMIS, that can simulta-neously obey current experimental limits while still providing a potentially observable non-standard neutrino spectrum from dark matter annihilation. |
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Address |
[van Beekveld, Melissa] Univ Oxford, Rudolf Peierls Ctr Theoret Phys, Clarendon Lab, Parks Rd, Oxford OX1 3PU, England, Email: melissa.vanbeekveld@physics.ox.ac.uk; |
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Scipost Foundation |
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WOS:000928492200001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5480 |
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