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Extracting low energy signals from raw LArTPC waveforms using deep learning techniques — A proof of concept / Uboldi, Lorenzo (CERN) ; Ruth, David (Unlisted, US, IL) ; Andrews, Michael (Carnegie Mellon U.) ; Wang, Michael H.L.S. (Fermilab) ; Wenzel, Hans Joachim (Fermilab) ; Wu, Wanwei (Fermilab) ; Yang, Tingjun (Fermilab)
We investigate the feasibility of using deep learning techniques, in the form of a one-dimensional convolutional neural network (1D-CNN), for the extraction of signals from the raw waveforms produced by the individual channels of liquid argon time projection chamber (LArTPC) detectors. A minimal generic LArTPC detector model is developed to generate realistic noise and signal waveforms used to train and test the 1D-CNN, and evaluate its performance on low-level signals. [...]
arXiv:2106.09911; FERMILAB-PUB-21-030-ND-SCD.- 2022-04-01 - 9 p. - Published in : Nucl. Instrum. Methods Phys. Res., A 1028 (2022) 166371 Fulltext: fermilab-pub-21-030-nd-scd - PDF; 2106.09911 - PDF; External link: Fermilab Library Server

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Inclusive production of the $P_c$ resonances in $p \overline p $ collisions / D0 Collaboration
We present a study of the inclusive production in $p \overline p $ collisions of the pentaquark states $P_c(4440)$ and $P_c(4457)$ with the decay to the $J/\psi p$ final state previously observed by the LHCb experiment. [...]
arXiv:1910.11767 ; FERMILAB-PUB-19-542-E. - 2019. - 7 p.
Fermilab Library Server (fulltext available) - Fulltext - Fulltext

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