Speaker
Description
The Fermilab Short Baseline Neutrino (SBN) program offers excellent opportunity for new physics searches due to its high intensity of protons on target and the exceptional particle identification and reconstruction capabilities of Liquid Argon Time Projection Chambers (LArTPCs). One such example is the demonstrated sensitivity of the program’s detectors to dilepton pairs originating from exotic Higgs Portal Scalar decays. Columnated showers that come from scalar decays to electron/positron pairs can easily be mistaken for photon pair production or single showers by traditional reconstruction algorithms. In this work, Geant4 is used to generate the distribution of charge deposited by signal events within a box of $^\text{40}{\text{Ar}}$. We apply projections to create two dimensional images of each event similar to those captured by distinct wire planes in operating detectors. We then harness the power of deep neural networks to distinguish images of signal and background events for the Higgs Portal Scalar model at the SBN program, improving upon the projected sensitivity from cut-and-count techniques by 30% in $\sin \theta$ for the benchmark scalar mass of 10 MeV.
