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Author |
Gomez-Cadenas, J.J.; Benlloch-Rodriguez, J.M.; Ferrario, P. |
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Title |
Application of scintillating properties of liquid xenon and silicon photomultiplier technology to medical imaging |
Type |
Journal Article |
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Year |
2016 |
Publication |
Spectrochimica Acta Part B |
Abbreviated Journal |
Spectroc. Acta Pt. B |
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Volume |
118 |
Issue |
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Pages |
6-13 |
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Keywords |
PET; TOF; Liquid xenon; Energy resolution; High sensitivity; Coincidence resolution time (CRT); SiPMs |
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Abstract |
We describe a new positron emission time-of-flight apparatus using liquid xenon. The detector is based in a liquid xenon scintillating cell. The cell shape and dimensions can be optimized depending on the intended application. In its simplest form, the liquid xenon scintillating cell is a box in which two faces are covered by silicon photomultipliers and the others by a reflecting material such as Teflon. It is a compact, homogenous and highly efficient detector which shares many of the desirable properties of monolithic crystals, with the added advantage of high yield and fast scintillation offered by liquid xenon. Our initial studies suggest that good energy and spatial resolution comparable with that achieved by lutetium oxyorthosilicate crystals can be obtained with a detector based in liquid xenon scintillating cells. In addition, the system can potentially achieve an excellent coincidence resolving time of better than 100 ps. |
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Address |
[Gomez-Cadenas, J. J.; Benlloch-Rodriguez, J. M.; Ferrario, Paola] Univ Valencia, CSIC, IFIC, E-46003 Valencia, Spain, Email: gomez@mail.cern.ch |
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Pergamon-Elsevier Science Ltd |
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English |
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ISSN  |
0584-8547 |
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Notes |
WOS:000374073300002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
2631 |
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Author |
XENON100 Collaboration (Aprile, E. et al); Orrigo, S.E.A. |
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Title |
Observation and applications of single-electron charge signals in the XENON100 experiment |
Type |
Journal Article |
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Year |
2014 |
Publication |
Journal of Physics G |
Abbreviated Journal |
J. Phys. G |
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Volume |
41 |
Issue |
3 |
Pages |
035201 - 13pp |
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Keywords |
xenon; single electron; photoionization; double phase TPC |
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Abstract |
The XENON100 dark matter experiment uses liquid xenon in a time projection chamber (TPC) to measure xenon nuclear recoils resulting from the scattering of dark matter weakly interacting massive particles (WIMPs). In this paper, we report the observation of single-electron charge signals which are not related to WIMP interactions. These signals, which show the excellent sensitivity of the detector to small charge signals, are explained as being due to the photoionization of impurities in the liquid xenon and of the metal components inside the TPC. They are used as a unique calibration source to characterize the detector. We explain how we can infer crucial parameters for the XENON100 experiment: the secondary-scintillation gain, the extraction yield from the liquid to the gas phase and the electron drift velocity. |
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Address |
[Aprile, E.; Budnik, R.; Choi, B.; Contreras, H.; Giboni, K-L; Goetzke, L. W.; Lim, K. E.; Fernandez, A. J. Melgarejo; Messina, M.; Plante, G.; Rizzo, A.] Columbia Univ, Dept Phys, New York, NY 10027 USA, Email: jacob.lamblin@lpsc.in2p3.fr; |
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Publisher |
Iop Publishing Ltd |
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Language |
English |
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ISSN |
0954-3899 |
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Notes |
WOS:000331865800012 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
1959 |
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Author |
Khosa, C.K.; Mars, L.; Richards, J.; Sanz, V. |
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Title |
Convolutional neural networks for direct detection of dark matter |
Type |
Journal Article |
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Year |
2020 |
Publication |
Journal of Physics G |
Abbreviated Journal |
J. Phys. G |
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Volume |
47 |
Issue |
9 |
Pages |
095201 - 20pp |
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Keywords |
dark matter; dark matter detection; neural networks; xenon1T; WIMPs |
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Abstract |
The XENON1T experiment uses a time projection chamber (TPC) with liquid xenon to search for weakly interacting massive particles (WIMPs), a proposed dark matter particle, via direct detection. As this experiment relies on capturing rare events, the focus is on achieving a high recall of WIMP events. Hence the ability to distinguish between WIMP and the background is extremely important. To accomplish this, we suggest using convolutional neural networks (CNNs); a machine learning procedure mainly used in image recognition tasks. To explore this technique we use XENON collaboration open-source software to simulate the TPC graphical output of dark matter signals and main backgrounds. A CNN turns out to be a suitable tool for this purpose, as it can identify features in the images that differentiate the two types of events without the need to manipulate or remove data in order to focus on a particular region of the detector. We find that the CNN can distinguish between the dominant background events (ER) and 500 GeV WIMP events with a recall of 93.4%, precision of 81.2% and an accuracy of 87.2%. |
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Address |
[Khosa, Charanjit K.; Mars, Lucy; Richards, Joel; Sanz, Veronica] Univ Sussex, Dept Phys & Astron, Brighton BN1 9QH, E Sussex, England, Email: charanjit.kaur@sussex.ac.uk; |
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Publisher |
Iop Publishing Ltd |
Place of Publication |
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Editor |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0954-3899 |
ISBN |
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Expedition |
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Conference |
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Notes |
WOS:000555607800001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4485 |
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