Speaker
Description
ARGO is a planned large liquid-argon detector for the direct detection of dark matter, using underground argon to reduce intrinsic radioactive backgrounds. A key analysis challenge is to efficiently select nuclear-recoil events from WIMP-like interactions. The analysis focuses on rejecting electronic recoils and surface-alpha backgrounds, while studying neutron-induced nuclear recoils.
This work studies an event-selection strategy for ARGO using simulations. The selection is based on the total detected scintillation light as an energy estimator, pulse-shape discrimination between nuclear and electronic recoils, and three-dimensional position reconstruction to define a fiducial volume inside the detector.
Event position reconstruction is challenging due to the large detector size and the sparse distribution of photon hits. To address this, a machine learning model is trained on simulated ARGO events using the photon charge and timing information. This model reconstructs event positions accurately despite the sparse input, improving the rejection of surface related and external backgrounds.
This work is still in progress, with further developments in the machine-learning reconstruction and event-selection strategy expected to improve performance.
| Keyword-1 | liquid argon |
|---|---|
| Keyword-2 | dark matter |
| Keyword-3 | event reconstruction |