Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/112250
DC Field | Value | Language |
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dc.contributor.author | Cardoso, J. M. R. | - |
dc.contributor.author | Lopes, J. A. M. | - |
dc.contributor.author | Santos, J. M. F. dos | - |
dc.contributor.author | Silva, M. | - |
dc.contributor.author | The Xenon Collaboration | - |
dc.date.accessioned | 2024-01-26T10:50:34Z | - |
dc.date.available | 2024-01-26T10:50:34Z | - |
dc.date.issued | 2023-04-11 | - |
dc.identifier.issn | 2470-0010 | - |
dc.identifier.issn | 2470-0029 | - |
dc.identifier.uri | https://hdl.handle.net/10316/112250 | - |
dc.description | 11 pages, 8 figures | pt |
dc.description.abstract | We developed a detector signal characterization model based on a Bayesian network trained on the waveform attributes generated by a dual-phase xenon time projection chamber. By performing inference on the model, we produced a quantitative metric of signal characterization and demonstrate that this metric can be used to determine whether a detector signal is sourced from a scintillation or an ionization process. We describe the method and its performance on electronic-recoil (ER) data taken during the first science run of the XENONnT dark matter experiment. We demonstrate the first use of a Bayesian network in a waveform-based analysis of detector signals. This method resulted in a 3% increase in ER event-selection efficiency with a simultaneously effective rejection of events outside of the region of interest. The findings of this analysis are consistent with the previous analysis from XENONnT, namely a background-only fit of the ER data. | pt |
dc.description.sponsorship | We gratefully acknowledge support from the National Science Foundation, Swiss National Science Foundation, German Ministry for Education and Research, Max Planck Gesellschaft, Deutsche Forschungsgemeinschaft, Helmholtz Association, Dutch Research Council (NWO), Weizmann Institute of Science, Israeli Science Foundation, Binational Science Foundation, Fundacao para aCiencia e a Tecnologia, R´egion des Pays de la Loire, Knut and Alice Wallenberg Foundation, Kavli Foundation, JSPS Kakenhi and JST FOREST Program in Japan, Tsinghua University Initiative Scientific Research Program, and IstitutoNazionale di Fisica Nucleare. This project has received funding/support fromthe European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 860881-HIDDeN. Data processing was performed using infrastructures from the Open Science Grid, the European Grid Initiative, and the Dutch national e-infrastructure with the support of SURF Cooperative. We are grateful to Laboratori Nazionali del Gran Sasso for hosting and supporting the XENON project. | pt |
dc.language.iso | eng | pt |
dc.publisher | American Physical Society | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.subject | High Energy Physics - Experiment | pt |
dc.title | Detector signal characterization with a Bayesian network in XENONnT | pt |
dc.type | article | pt |
degois.publication.issue | 1 | pt |
degois.publication.title | Physical Review D | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.1103/PhysRevD.108.012016 | - |
degois.publication.volume | 108 | pt |
dc.date.embargo | 2023-04-11 | * |
dc.identifier.url | http://arxiv.org/abs/2304.05428v2 | - |
uc.date.periodoEmbargo | 0 | pt |
item.fulltext | Com Texto completo | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.researchunit | LIBPhys - Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics | - |
crisitem.author.parentresearchunit | University of Coimbra | - |
crisitem.author.orcid | 0000-0002-6366-2963 | - |
crisitem.author.orcid | 0000-0002-8841-6523 | - |
crisitem.author.orcid | 0000-0002-1554-9579 | - |
Appears in Collections: | FCTUC Física - Artigos em Revistas Internacionais LIBPhys - Artigos em Revistas Internacionais |
Files in This Item:
File | Description | Size | Format | |
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Detector-signal-characterization-with-a-Bayesian-network-in-XENONnTPhysical-Review-D.pdf | 829.51 kB | Adobe PDF | View/Open |
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