Neutrino Physics and Machine Learning 2024

One Neural Network, Two Detector Mediums, Three Detection Regions: Multi-detector Machine Learning with DUNE’s Near Detector Prototype

Jun 26, 2024, 2:45 PM

15m

HCI J4 (ETH Zurich)

Day 2 - Afternoon

Speaker

Jessie Micallef (Institute for AI and Fundamental Interactions (MIT & Tufts))

Description

The DUNE near detector is employing new technologies in Liquid Argon Time Projection Chamber (LArTPC) detection methods, including a 3D charge pixel readout, and is modularized into a 5x7 rectangular grid of TPCs. A smaller 2x2 prototype is nearing testing in the NuMI neutrino beam at Fermilab and we are faced with reconstructing the modularized, 3D LArTPC images. While a chain of machine learning reconstructions have been developed for the clustering charge inputs and identifying particle signatures in the LArTPC, it does not take in any information from the solid scintillator endcap detectors that collect particle spills up and downstream of the 2x2 volume. This work explores combining input from both detector mediums into the machine learning reconstruction to identify particles that cross through both of the multi-detector regions.

Presentation materials

MicallefJ_NPML_Workshop_MLReco3D_2x2.pdf

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