Neutrino interaction vertex reconstruction in DUNE with Pandora deep learning

Introduction

The Deep Underground Neutrino Experiment (DUNE) [1], currently under construction, will be a world-leading observatory for the study of neutrinos and nucleon decay. The DUNE far detector modules will be hosted approximately 1.5 km underground at the Sanford Underground Research Facility (SURF), in South Dakota, USA. The liquid argon time projection chambers (LArTPCs) comprising the far detector will contain 70 kt of liquid argon, with a fiducial mass of at least 40 kt.

Among DUNE’s many physics goals are the measurement of the charge-parity violation phase in the lepton sector, determination of the neutrino mass ordering and the octant in which the ${\theta }_{23}$ mixing angle lies, along with a search for supernova neutrino bursts and to test the three-flavour paradigm itself. Physics analyses depend upon determination of event properties such as the flavour of the neutrino interacting or an estimation of the incident neutrino energy. The determination of such quantities depends upon high quality reconstruction of the interactions that will take place inside DUNE’s far detectors. The Pandora Software Development Kit (SDK) acts as one of the main reconstruction tools used by DUNE, providing pattern recognition algorithms to build up a picture of the interactions. This article presents details of the integration of deep learning into the set of algorithms previously described in [2, 3].

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Fig. 1

Schematic of the workspace geometry (red) within the context of the full detector geometry (black). APAs are indicated by gray boxes in the YZ plane of the workspace volume, while CPA walls are indicated in red in the YZ plane. APA walls in the full geometry are indicated by the black boxes in the YZ plane

The Pandora SDK was originally developed to identify the energy deposits of particles in fine-granularity detectors, in particular guiding the design and optimisation of future $e^{+}{e}^{-}$ linear colliders [4, 5]. The multi-algorithm approach to pattern recognition seeks to apply focused, decoupled algorithms to input building blocks. Input is provided into Pandora in the form of a sparse list of hits (localised charge deposits), determined by a low-level hit-finding procedure developed for MicroBooNE [6]. Complex topologies are deferred to later algorithms, when more is understood about the context, in an effort to avoid...