Speakers
Description
SNO+ is an operational multi-purpose neutrino detector located 2km underground at SNOLAB in Sudbury, Ontario, Canada. 780 tonnes of linear alkylbenzene-based liquid scintillator are observed by ~9300 photomultiplier tubes (PMTs) mounted outside the spherical scintillator volume. SNO+ has a broad physics program which will include a search for the neutrinoless double beta decay of $^{130}$Te.
Artificial neural networks are being developed for reconstruction tasks at SNO+, ingesting events which consist of an unordered set of PMT hit information. We present a comparison of these methods applied to the position reconstruction of point-like events, which has traditionally been approached by maximizing a complex likelihood based on PMT hit times. The networks we present include a custom, flexible two-part architecture consisting of a convolutional feature extractor and a simple feedforward network; and a transformer using events tokenised on a hit-by-hit basis. We find some performance gains compared to likelihood optimization while evaluating orders of magnitude faster.
| Type of contribution | Talk: 15 minutes. |
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