Optical photons are used as signal in a wide variety of particle detectors.
Modern neutrino experiments employ hundreds to tens of thousands of photon
detectors to observe signal from millions to billions of scintillation photons
produced from energy deposition of charged particles. These neutrino detectors
are typically large, containing kilotons of target volume, with different
optical properties. Modeling individual photon propagation in form of look-up
table requires huge computational resources. As the size of a table increases
with detector volume for a fixed resolution, this method scales poorly for
future larger detectors. Alternative approaches such as fitting a polynomial to
the model could address the memory issue, but results in poorer performance.
Both look-up table and fitting approaches are prone to discrepancies between
the detector simulation and the data collected. We propose a new approach using
SIREN, an implicit neural representation with periodic activation functions, to
model the look-up table as a 3D scene and reproduces the acceptance map with
high accuracy. The number of parameters in our SIREN model is orders of
magnitude smaller than the number of voxels in the look-up table. As it models
an underlying functional shape, SIREN is scalable to a larger detector.
Furthermore, SIREN can successfully learn the spatial gradients of the photon
library, providing additional information for downstream applications. Finally,
as SIREN is a neural network representation, it is differentiable with respect
to its parameters, and therefore tunable via gradient descent. We demonstrate
the potential of optimizing SIREN directly on real data, which mitigates the
concern of data vs. simulation discrepancies. We further present an application
for data reconstruction where SIREN is used to form a likelihood function for
photon statistics