SPORE: An Event-Level Sampling Pipeline for Multi-Telescope Neutrino Astronomy

Abstract

We present an open-source Python package for simulating neutrino events from astrophysical point and extended sources using tabulated instrument response functions (IRFs). The package encodes three detector response components - effective area, point spread function, and energy resolution - in a selection-agnostic HDF5 format, which can represent a neutrino telescope whose response is supplied in that form, whether from a public release or a private study. Sampling algorithms cover point sources (inverse-CDF with Poisson or fixed-count modes), extended sky distributions (hierarchical inverse-CDF sampling, including full RA- and declination-dependent flux maps), and multi-detector joint analyses. We validate the framework via a round-trip consistency test using the publicly available IceCube 10-year tracks data release: the released IRFs are ingested into the package and used to generate a synthetic event set, whose declination distribution reproduces the observed one to 10-15% across the northern sky. The reconstructed-energy distribution agrees to within about a third over the bulk of the sample but exceeds the data by up to a factor of three below 600 GeV, a discrepancy we trace to the coarse true-energy binning of the public smearing matrix rather than to the sampling: an independent forward fold of the same IRFs reproduces it. We further compare against the IceCube HESE 7.5-year public data release: the sampled deposited-energy spectrum tracks the published best-fit expectation, and the observed data fall within the goodness-of-fit distribution built from 1,000 sampled pseudo-experiments, though on its well-fitting side, as expected for an expectation that was itself fit to those data.