Backgrounds and pulse shape discrimination in the ArDM liquid argon TPC
ArDM Collaboration: J.Calvo (1), C.Cantini (1), P.Crivelli (1),, M.Daniel (2), S. Di Luise (1), A.Gendotti (1), S.Horikawa (1), L.Molina-Bueno, (1), B.Montes (2), W.Mu (1), S.Murphy (1), G.Natterer (1), K.Nguyen (1),, L.Periale (1), Y.Quan (1), B.Radics (1), C.Regenfus (1)

TL;DR
The ArDM liquid argon TPC demonstrated excellent background rejection and detector performance, confirming its suitability for dark matter searches with high electron recoil discrimination and low background levels.
Contribution
This work provides the first detailed analysis of pulse shape discrimination and background levels in the ArDM liquid argon TPC, validating its design for future dark matter detection.
Findings
Electron recoil rejection power exceeds 10^8 at 50% nuclear recoil acceptance.
Measured Rn-222 emanation rate is 65.6 μHz/l in the cryostat.
Ar-39 activity from atmospheric argon is 0.95 Bq/kg.
Abstract
The ArDM experiment completed a single-phase commissioning run in 2015 with an active liquid argon target of nearly one tonne in mass. The analysis of the data and comparison to simulations allowed for a test of the crucial detector properties and confirmed the low background performance of the setup. The statistical rejection power for electron recoil events using the pulse shape discrimination method was estimated using data from a Cf-252 neutron calibration source. Electron and nuclear recoil band profiles were found to be well described by Gaussian distributions. Employing such a model we derive values for the electron recoil statistical rejection power of more than 10 in the tonne-scale liquid argon target for events with more than 50 detected photons at a 50% acceptance for nuclear recoils. The Rn-222 emanation rate of the ArDM cryostat at room temperature was found to be…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
