21–26 Jun 2026
U. Ottawa - Learning Crossroads (CRX) Building
America/Toronto timezone
Welcome to the 2026 CAP Congress Program website! / Bienvenue au siteweb du programme du Congrès de l'ACP 2026!

Monte Carlo modeling of pixelated digital SiPMs for the ARGO dark matter experiment

23 Jun 2026, 11:30
15m
U. Ottawa - Learning Crossroads (CRX) Building

U. Ottawa - Learning Crossroads (CRX) Building

100 Louis-Pasteur Private, Ottawa, ON K1N 9N3
Oral (Non-Student) / Orale (non-étudiant(e)) Applied Physics and Instrumentation / Physique appliquée et de l'instrumentation (DAPI / DPAI) (DAPI) T1-1 | (DPAI)

Speaker

Dr Asish Moharana (Carleton University)

Description

ARGO is a future dark matter direct-detection experiment based on a liquid argon (LAr) target proposed to be constructed at SNOLAB in the next decade. ARGO will produce leading sensitivity to heavy dark matter searches above 50 GeV/c2. It will also have excellent sensitivity to detect core-collapse supernova neutrinos and produce high-precision measurements of solar neutrinos at and above the 7Be shoulder. For photodetection in ARGO, we are interested in pixelated silicon photomultiplier (SiPM) photosensors with fast photon timing that will allow good position reconstruction and novel hit-pattern-based event discrimination. However, the dark noise and optical crosstalk (oCT) associated with SiPMs can potentially affect the electron recoil/nuclear recoil pulse-shape discrimination and distort spatial/temporal photon hit patterns, limiting background rejection. We have developed a full Monte Carlo simulation of a pixelated SiPM system, including dark noise and oCT, to evaluate detector performance and determine constraints on SiPMs to achieve that performance. In my talk, I will describe our MC model and present some results about the impact of these SiPM noises on the detector energy threshold and event position reconstruction.

Keyword-1 Liquid argon dark matter detec
Keyword-2 Silicon photomultipliers (SiPM
Keyword-3 Monte Carlo simulation

Author

Dr Asish Moharana (Carleton University)

Presentation materials

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