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Title: Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers

Conference · · J.Phys.Conf.Ser.

The Exa.TrkX project presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar reconstruction tasks in the Large Hadron Collider (LHC). Graphs describing particle interactions are formed by treating each detector hit as a node, with edges describing the relationships between hits. We utilise a multi-head attention message passing network which performs graph convolutions in order to label each node with a particle type.We present an updated variant of our GNN architecture, with several improvements. After testing the model on more realistic simulation with regions of unresponsive wires, the target was modified from edge classification to node classification in order to increase robustness. Removing edges as a classification target opens up a broader possibility space for edge-forming techniques; we explore the model’s performance across a variety of approaches, such as Delaunay triangulation, kNN, and radius-based methods. We also extend this model to the 3D context, sharing information between detector views. By using reconstructed 3D spacepoints to map detector hits from each wire plane, the model naively constructs 2D representations that are independent yet fully consistent.

Research Organization:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP)
DOE Contract Number:
AC02-07CH11359
OSTI ID:
1958460
Report Number(s):
FERMILAB-CONF-23-084-PPD; oai:inspirehep.net:2633633
Journal Information:
J.Phys.Conf.Ser., Vol. 2438, Issue 1
Country of Publication:
United States
Language:
English

References (7)

Design, construction and tests of the ICARUS T600 detector
  • Amerio, S.; Amoruso, S.; Antonello, M.
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journal July 2004
Design and construction of the MicroBooNE detector journal February 2017
Construction of precision wire readout planes for the Short-Baseline Near Detector (SBND) journal June 2020
Volume I. Introduction to DUNE journal August 2020
Scalable deep convolutional neural networks for sparse, locally dense liquid argon time projection chamber data journal July 2020
Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers journal January 2021
Performance of a geometric deep learning pipeline for HL-LHC particle tracking journal October 2021

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