Related papers: pynucastro: A Python Library for Nuclear Astrophys…
A large scale collection of both semantic and natural language resources is essential to leverage active Software Engineering research areas such as code reuse and code comprehensibility. Existing machine learning models ingest data from…
In this document drafted by the Neutrino Scattering Theory Experiment Collaboration (NuSTEC), we provide input on the synergies between theoretical and experimental efforts that can provide critical input to the prediction accuracy needed…
We find ourselves on the brink of an exciting era in observational astrophysics, driven by groundbreaking facilities like JWST, Euclid, Rubin, Roman, SKA, or ELT. Simultaneously, computational astrophysics has shown significant strides,…
Neutrino-nucleus reactions as applied to astrophysics are reviewed.
In a previous publication [Atomic Data and Nuclear Data Tables 75, 1 (2000)], we had given seven parameter analytical fits to theoretical reaction rates derived from nuclear cross sections calculated in the statistical model…
Nowadays the analysis of dynamics of and on networks represents a hot topic in the Social Network Analysis playground. To support students, teachers, developers and researchers in this work we introduce a novel framework, namely NDlib, an…
The open source ALPS (Algorithms and Libraries for Physics Simulations) project provides a collection of physics libraries and applications, with a focus on simulations of lattice models and strongly correlated electron systems. The…
This is an overview over the prediction of astrophysical reaction rates in the statistical model, with special emphasis on the parametrization of optical alpha+nucleus potentials.
We present the pulsar_spectra software repository, an open-source pulsar flux density catalogue and automated spectral fitting software that finds the best spectral model and produces publication-quality plots. The Python-based software…
We present an open-source simulation framework for optically detected magnetic resonance, developed in Python. The framework allows users to construct, manipulate, and evolve multipartite quantum systems that consist of spins and electronic…
The sensitivity of underground experiments searching for rare events such as dark matter, neutrino interactions or several beyond the standard model phenomena is often limited by the background caused by neutrons from spontaneous fission…
We analyze in details the standard Primordial Nucleosynthesis scenario. In particular we discuss the key theoretical issues which are involved in a detailed prediction of light nuclide abundances, as the weak reaction rates, neutrino…
We describe a program for computing the abundances of light elements produced during Big Bang Nucleosynthesis which is publicly available at http://parthenope.na.infn.it/. Starting from nuclear statistical equilibrium conditions the program…
Neutrino telescopes are gigaton-scale neutrino detectors comprised of individual light-detection units. Though constructed from simple building blocks, they have opened a new window to the Universe and are able to probe center-of-mass…
In this paper we introduce a new method to design interparticle interactions to target arbitrary crystal structures via the process of self-assembly. We show that it is possible to exploit the curvature of the crystal nucleation free-energy…
Active learning (AL) is a sub-field of ML focused on the development of methods to iteratively and economically acquire data by strategically querying new data points that are the most useful for a particular task. Here, we introduce…
The open source ALPS (Algorithms and Libraries for Physics Simulations) project provides a collection of physics libraries and applications, with a focus on simulations of lattice models and strongly correlated systems. The libraries…
Genarris is an open-source Python package for generating random molecular crystal structures with physical constraints for seeding crystal structure prediction algorithms and training machine learning models. Here we present a new version…
This paper introduces Inferno, a software library built on top of PyTorch that is designed to meet distinctive challenges of using spiking neural networks (SNNs) for machine learning tasks. We describe the architecture of Inferno and key…
Pylearn2 is a machine learning research library. This does not just mean that it is a collection of machine learning algorithms that share a common API; it means that it has been designed for flexibility and extensibility in order to…