Current neutrino detectors will observe hundreds to thousands of neutrinos from Galactic supernovae, and future detectors will increase this yield by an order of magnitude or more. With such a data set comes the potential for a huge increase in our understanding of the explosions of massive stars, nuclear physics under extreme conditions, and the properties of the neutrino. However, there is currently a large gap between supernova simulations and the corresponding signals in neutrino detectors, which will make any comparison between theory and observation very difficult. SNEWPY is an open-source software package that bridges this gap. The SNEWPY code can interface with supernova simulation data to generate from the model either a time series of neutrino spectral fluences at Earth, or the total time-integrated spectral fluence. Data from several hundred simulations of core-collapse, thermonuclear, and pair-instability supernovae is included in the package. This output may then be used by an event generator such as sntools or an event rate calculator such as the SuperNova Observatories with General Long Baseline Experiment Simulator (SNOwGLoBES). Additional routines in the SNEWPY package automate the processing of the generated data through the SNOwGLoBES software and collate its output into the observable channels of each detector. In this paper we describe the contents of the package, the physics behind SNEWPY, the organization of the code, and provide examples of how to make use of its capabilities.

Current neutrino detectors will observe hundreds to thousands of neutrinos from a Galactic supernova, and future detectors will increase this yield by an order of magnitude or more. With such neutrino data sets, the next Galactic supernova will significantly increase our understanding of the explosions of massive stars, nuclear physics under extreme conditions, and the fundamental properties of neutrinos. However, there is a gulf between supernova simulations and the corresponding signals in detectors, making comparisons between theory and observation, as well as between different detectors, very difficult. SNEWPY offers a unified interface for hundreds of supernova simulations, a large library of flux transformations on the way towards the detector, and an interface to SNOwGLoBES (Scholberg & SNOwGLoBES Contributors, 2021), allowing users to easily calculate and compare expected event rates from many supernova models in many different neutrino detectors.

Current neutrino detectors will observe hundreds to thousands of neutrinos from a Galactic supernovae, and future detectors will increase this yield by an order of magnitude or more. With such a data set comes the potential for a huge increase in our understanding of the explosions of massive stars, nuclear physics under extreme conditions, and the properties of the neutrino. However, there is currently a large gap between

Polarization phase-curve measurements provide a unique constraint on the surface properties of asteroids that are complementary to those from photometry and spectroscopy and have led to the identification of the “Barbarian” asteroids as a class of objects with highly unusual surfaces. We present new near-infrared polarimetric observations of six Barbarian asteroids obtained with the WIRC+Pol instrument on the Palomar Hale telescope. We find a dramatic change in polarimetric behavior from visible to near-infrared for these objects, including a change in the polarimetric inversion angle that is tied to the index of refraction of the surface material. Our observations support a two-phase surface composition consisting of high albedo and high index of refraction inclusions with a small optical size scale embedded in a dark matrix material more closely related to C-complex asteroids. These results are consistent with the interpretation that the Barbarians are remnants of a population of primitive bodies that formed shortly after calcium-aluminum-rich inclusion (CAIs). Near-infrared polarimetry provides a direct test of the constituent grains of asteroid surfaces.