A Deep learning library for neutrino telescopes

Python tools for working with the IceCube public data.

Code for calculating Coherent Elastic Neutrino-Nucleus Scattering (CEvNS) cross sections and recoil spectra. Also includes code for obtaining New Physics constraints from the COHERENT-2017 results.

A lightweight event generator for new physics in neutrino-nucleus scattering.

nuDoBe is a Python tool for neutrinoless double beta decay calculations based on an effective field theory approach.

Bayesian constraints on the astrophysical neutrino population from IceCube data

NuSD is a Geant4-based simulation framework developed to perform simulation studies on various segmented scintillation detectors.

Magnetic moments of astrophysical neutrino (supernova and ultra high-energy neutrinos)

This repository contains the code used to perform the analysis described in the paper "A stacked search for spatial coincidences between IceCube neutrinos and radio pulsars" (https://arxiv.org/abs/2306.03427). The code is written in Python 3.10 and uses the following packages: numpy, scipy, matplotlib, pandas, numba, multiprocessing.

Investigating coincident source-neutrino detections through simulations.

Title: Convolution Neural Networks for the CHIPS Neutrino Detector R&D Project

Steinmetz, A. Modern topics in relativistic spin dynamics and magnetism. PhD dissertation. University of Arizona, 2023.

A Monte Carlo simulation of the electromagnetic cascade to compute the neutrino spectrum from cascade development.

Central repository for scripts handling ZTF Target of Opportunity requests using AMPEL. This includes neutrino, GRB and GW events.

Rafelski, J., Birrell, J., Grayson, C., Steinmetz, A., Yang, C. T. Quarks to Cosmos: Particles and Plasma in Cosmological evolution. In press EPJ ST. arXiv:2409.19031 (2024).

A collection of limits on heavy neutral leptons, including their nuSMEFT interactions

Contribution to the Harald Fritzsch Memorial Volume edited by Gerhard Buchalla, Dieter Lüst and Zhi-Zhong Xing.

A Convolutional Neural Network Implementation for the CHIPS Water Cherenkov R&D Project.

Rafelski, J., Steinmetz, A., & Yang, C. T. Dynamic fermion flavor mixing through transition dipole moments. International Journal of Modern Physics A 38.31 (2023): 2350163.