4-8 November 2019
Adelaide Convention Centre
Australia/Adelaide timezone

Reconstruction for Liquid Argon TPC Neutrino Detectors Using Parallel Architectures

7 Nov 2019, 11:15
15m
Riverbank R6 (Adelaide Convention Centre)

Riverbank R6

Adelaide Convention Centre

Oral Track 2 – Offline Computing Track 2 – Offline Computing

Speaker

Giuseppe Cerati (Fermi National Accelerator Lab. (US))

Description

Neutrinos are particles that interact rarely, so identifying them requires large detectors which produce lots of data. Processing this data with the computing power available is becoming more difficult as the detectors increase in size to reach their physics goals. In liquid argon time projection chambers (TPCs) the charged particles from neutrino interactions produce ionization electrons which drift in an electric field towards a series of collection wires, and the signal on the wires is used to reconstruct the interaction. The MicroBooNE detector currently collecting data at Fermilab has 8000 wires, and planned future experiments like DUNE will have 100 times more, which means that the time required to reconstruct an event will scale accordingly. Modernization of liquid argon TPC reconstruction code, including vectorization, parallelization and code portability to GPUs, will help to mitigate these challenges. The liquid argon TPC hit finding algorithm within the LarSoft framework used across multiple experiments has been vectorized and parallelized. This increases the speed of the algorithm on the order of ten times within a stand alone version on Intel architectures. This new version has been incorporated back into LarSoft so that it can be generally used. These methods will also be applied to other low level reconstruction of the wire signals such as the deconvolution. The applications and performance of this modernized liquid argon TPC wire reconstruction will be presented.

Consider for promotion Yes

Primary authors

Sophie Berkman (Fermi National Accelerator Laboratory) Giuseppe Cerati (Fermi National Accelerator Lab. (US)) Brian Gravelle (University of Oregon) Allison Reinsvold Hall (Fermilab) Boyana Norris Michael Wang (Fermi National Accelerator Lab. (US))

Presentation Materials