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Author |
Esteve, R.; Toledo, J.F.; Herrero, V.; Simon, A.; Monrabal, F.; Alvarez, V.; Rodriguez, J.; Querol, M.; Ballester, F. |
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Title |
The Event Detection System in the NEXT-White Detector |
Type |
Journal Article |
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Year |
2021 |
Publication |
Sensors |
Abbreviated Journal  |
Sensors |
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Volume |
21 |
Issue |
2 |
Pages |
673 - 18pp |
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Keywords |
xenon TPC; trigger concepts; data acquisition circuits; FPGA |
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Abstract |
This article describes the event detection system of the NEXT-White detector, a 5 kg high pressure xenon TPC with electroluminescent amplification, located in the Laboratorio Subterraneo de Canfranc (LSC), Spain. The detector is based on a plane of photomultipliers (PMTs) for energy measurements and a silicon photomultiplier (SiPM) tracking plane for offline topological event filtering. The event detection system, based on the SRS-ATCA data acquisition system developed in the framework of the CERN RD51 collaboration, has been designed to detect multiple events based on online PMT signal energy measurements and a coincidence-detection algorithm. Implemented on FPGA, the system has been successfully running and evolving during NEXT-White operation. The event detection system brings some relevant and new functionalities in the field. A distributed double event processor has been implemented to detect simultaneously two different types of events thus allowing simultaneous calibration and physics runs. This special feature provides constant monitoring of the detector conditions, being especially relevant to the lifetime and geometrical map computations which are needed to correct high-energy physics events. Other features, like primary scintillation event rejection, or a double buffer associated with the type of event being searched, help reduce the unnecessary data throughput thus minimizing dead time and improving trigger efficiency. |
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Address |
[Esteve Bosch, Raul; Toledo Alarcon, Jose F.; Herrero Bosch, Vicente; Alvarez Puerta, Vicente; Rodriguez Samaniego, Javier; Ballester Merelo, Francisco] Univ Politecn Valencia, CSIC, Inst Instrumentac Imagen Mol I3M, Ctr Mixto, Camino Vera S-N, Valencia 46022, Spain, Email: rauesbos@eln.upv.es; |
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Mdpi |
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WOS:000611719600001 |
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no |
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yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4693 |
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Author |
Real, D.; Calvo, D.; Diaz, A.; Salesa Greus, F.; Sanchez Losa, A. |
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Title |
A Narrow Optical Pulse Emitter Based on LED: NOPELED |
Type |
Journal Article |
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Year |
2022 |
Publication |
Sensors |
Abbreviated Journal |
Sensors |
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Volume |
22 |
Issue |
19 |
Pages |
7683 - 15pp |
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Keywords |
short optical pulse; optical instrumentation |
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Abstract |
Light sources emitting short pulses are needed in many particle physics experiments using optical sensors as they can replicate the light produced by the particles being detected and are also an important calibration and test element. This work presents NOPELED, a light source based on LEDs emitting short optical pulses with typical rise times of less than 3 ns and Full Width at Half Maximum lower than 7 ns. The emission wavelength depends on the model of LED used. Several LED models have been characterized in the range from 405 to 532 nm, although NOPELED can work with LED emitting wavelengths outside of that region. While the wavelength is fixed for a given LED model, the intensity and the frequency of the optical pulse can be controlled. NOPELED, which also has low cost and simple operation, can be operated remotely, making it appropriate for either different physics experiments needing in-place light sources such as astrophysical neutrino detectors using photo-multipliers or positron emission tomography devices using scintillation counters, or, beyond physics, applications needing short pulses of light such as protein fluorescence or chemodetection of heavy metals. |
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Address |
[Real, Diego; Calvo, David; Salesa Greus, Francisco; Sanchez Losa, Agustin] Univ Valencia, IFIC Inst Fis Corpuscular, CSIC, C Catedrat Jose Beltran 2, Paterna 46980, Spain, Email: real@ific.uv.es; |
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WOS:000867935300001 |
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no |
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yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
5381 |
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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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Address |
[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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Notes |
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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Address |
[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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Notes |
WOS:001131902700001 |
Approved |
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 |
Type |
Journal Article |
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Year |
2022 |
Publication |
Seismological Research Letters |
Abbreviated Journal |
Seismol. Res. Lett. |
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Volume |
93 |
Issue |
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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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Is ISI |
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 |
Type |
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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Notes |
WOS:000929724800002 |
Approved |
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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English |
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Notes |
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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Author |
LHC BSM Reinterpretation Forum (Abdallah, W. et al); Mitsou, V.A.; Sanz, V. |
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Title |
Reinterpretation of LHC results for new physics: status and recommendations after run 2 |
Type |
Journal Article |
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Year |
2020 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
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Volume |
9 |
Issue |
2 |
Pages |
022 - 45pp |
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Abstract |
We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum. We detail current experimental offerings in direct searches for new particles, measurements, technical implementations and Open Data, and provide a set of recommendations for further improving the presentation of LHC results in order to better enable reinterpretation in the future. We also provide a brief description of existing software reinterpretation frameworks and recent global analyses of new physics that make use of the current data. |
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Address |
[Abdallah, Waleed; Dutta, Juhi] Harish Chandra Res Inst HBNI, Allahabad 211019, Uttar Pradesh, India, Email: Andy.Buckley@glasgow.ac.uk; |
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Scipost Foundation |
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English |
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2542-4653 |
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Notes |
WOS:000573102600007 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4547 |
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Author |
Barenboim, G.; Hirn, J.; Sanz, V. |
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Title |
Symmetry meets AI |
Type |
Journal Article |
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Year |
2021 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
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Volume |
11 |
Issue |
1 |
Pages |
014 - 11pp |
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Abstract |
We explore whether Neural Networks (NNs) can discover the presence of symmetries as they learn to perform a task. For this, we train hundreds of NNs on a decoy task based on well-controlled Physics templates, where no information on symmetry is provided. We use the output from the last hidden layer of all these NNs, projected to fewer dimensions, as the input for a symmetry classification task, and show that information on symmetry had indeed been identified by the original NN without guidance. As an interdisciplinary application of this procedure, we identify the presence and level of symmetry in artistic paintings from different styles such as those of Picasso, Pollock and Van Gogh. |
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Address |
[Barenboim, Gabriela; Hirn, Johannes; Sanz, Veronica] Univ Valencia, CSIC, Dept Fis Teor, E-46100 Burjassot, Spain |
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Scipost Foundation |
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English |
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2542-4653 |
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Notes |
WOS:000680039500002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4920 |
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Author |
Khosa, C.K.; Sanz, V.; Soughton, M. |
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Title |
Using machine learning to disentangle LHC signatures of Dark Matter candidates |
Type |
Journal Article |
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Year |
2021 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
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Volume |
10 |
Issue |
6 |
Pages |
151 - 26pp |
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Abstract |
We study the prospects of characterising Dark Matter at colliders using Machine Learning (ML) techniques. We focus on the monojet and missing transverse energy (MET) channel and propose a set of benchmark models for the study: a typical WIMP Dark Matter candidate in the form of a SUSY neutralino, a pseudo-Goldstone impostor in the shape of an Axion-Like Particle, and a light Dark Matter impostor whose interactions are mediated by a heavy particle. All these benchmarks are tensioned against each other, and against the main SM background (Z+jets). Our analysis uses both the leading-order kinematic features as well as the information of an additional hard jet. We explore different representations of the data, from a simple event data sample with values of kinematic variables fed into a Logistic Regression algorithm or a Fully Connected Neural Network, to a transformation of the data into images related to probability distributions, fed to Deep and Convolutional Neural Networks. We also study the robustness of our method against including detector effects, dropping kinematic variables, or changing the number of events per image. In the case of signals with more combinatorial possibilities (events with more than one hard jet), the most crucial data features are selected by performing a Principal Component Analysis. We compare the performance of all these methods, and find that using the 2D images of the combined information of multiple events significantly improves the discrimination performance. |
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Address |
[Khosa, Charanjit Kaur; Sanz, Veronica; Soughton, Michael] Univ Sussex, Dept Phys & Astron, Brighton BN1 9QH, E Sussex, England, Email: Charanjit.Kaur@sussex.ac.uk; |
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Scipost Foundation |
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English |
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2542-4653 |
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Notes |
WOS:000680038800002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4927 |
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