A Deep learning library for neutrino telescopes

Python tools for working with the IceCube public data.

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

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.

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

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

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

Bayesian constraints on the astrophysical neutrino population from IceCube data

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.

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

Investigating coincident source-neutrino detections through simulations.

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

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

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

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

Rafelski, J., Birrell, J., Steinmetz, A. and Yang, C.T. A short survey of matter-antimatter evolution in the primordial universe. Universe 9.7 (2023): 309.

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. IJMPA (2023).

Classical and Quantum Machine Learning tools for an High Energy Physics task - Final project for Laboratory of Computational Physics module B