dedup_wf_001--cf5b60890259205c7dbdb2a17f93472b

The EXO-200 \mbox{EXO-200} experiment searches for the neutrinoless double beta (0νββ 0\nu\beta\beta ) decay in 136 ^{136} Xe with an ultra-low background single-phase time projection chamber  ~ (TPC) filled with 175 \, kg isotopically enriched liquid xenon  ~ (LXe). The detector has demonstrated good energy resolution and background rejection capabilities by simultaneously collecting scintillation light and ionization charge from the LXe and by a multi-parameter analysis. The combination of both signatures allows for complementary energy estimates and for a full 3D position reconstruction. Advances in computational performance in recent years have made novel Deep Learning techniques applicable to the physics community. This poster will briefly present the concept of the detector, summarize the work on applying Deep Learning methods for EXO-200 \mbox{EXO-200} analyses, and evaluate the potential of Deep Learning based analysis tools towards improving the reconstruction of events in EXO-200 \mbox{EXO-200} .

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PID https://www.doi.org/10.5281/zenodo.1300564
PID https://www.doi.org/10.5281/zenodo.1300563
URL http://dx.doi.org/10.5281/zenodo.1300564
URL https://zenodo.org/record/1300564
URL http://dx.doi.org/10.5281/zenodo.1300563
URL https://figshare.com/articles/Deep_Neural_Networks_for_Energy_and_Position_Reconstruction_in_mbox_EXO-200_/11656302
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Access Right Open Access
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Author ZIEGLER, Tobias
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Collected From ZENODO; figshare; Datacite; FigShare
Hosted By Zenodo; ZENODO; figshare; FigShare
Publication Date 2018-06-29
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Language UNKNOWN
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system:type publication
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Source https://science-innovation-policy.openaire.eu/search/publication?articleId=dedup_wf_001::cf5b60890259205c7dbdb2a17f93472b
Author jsonws_user
Last Updated 27 December 2020, 01:43 (CET)
Created 27 December 2020, 01:43 (CET)