Measurement of the Longitudinal Diffusion of Ionization Electrons in the MicroBooNE Detector
P. Abratenko, R. An, J. Anthony, J. Asaadi, A. Ashkenazi, S., Balasubramanian, B. Baller, C. Barnes, G. Barr, V. Basque, L. Bathe-Peters,, O. Benevides Rodrigues, S. Berkman, A. Bhanderi, A. Bhat, M. Bishai, A., Blake, T. Bolton, L. Camilleri, D. Caratelli, I. Caro Terrazas

TL;DR
This paper reports the first measurement of the effective longitudinal electron diffusion coefficient in a large-scale liquid argon TPC operating in a neutrino beam, using a large dataset of cosmic muon tracks.
Contribution
It provides the first large-scale measurement of electron diffusion in a neutrino beam environment, improving understanding of electron transport in LArTPCs.
Findings
Measured $D_L$ = 3.74^{+0.28}_{-0.29} cm$^2$/s at 273.9 V/cm
Used ~70,000 cosmic muon tracks for the measurement
Largest dataset ever used for this measurement
Abstract
Accurate knowledge of electron transport properties is vital to understanding the information provided by liquid argon time projection chambers (LArTPCs). Ionization electron drift-lifetime, local electric field distortions caused by positive ion accumulation, and electron diffusion can all significantly impact the measured signal waveforms. This paper presents a measurement of the effective longitudinal electron diffusion coefficient, , in MicroBooNE at the nominal electric field strength of 273.9 V/cm. Historically, this measurement has been made in LArTPC prototype detectors. This represents the first measurement in a large-scale (85 tonne active volume) LArTPC operating in a neutrino beam. This is the largest dataset ever used for this measurement. Using a sample of 70,000 through-going cosmic ray muon tracks tagged with MicroBooNE's cosmic ray tagger system, we measure…
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.
