skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: νDoBe — A Python tool for neutrinoless double beta decay
A<sc>bstract</sc> We presentνDoBe, a Python tool for the computation of neutrinoless double beta decay (0νββ) rates in terms of lepton-number-violating operators in the Standard Model Effective Field Theory (SMEFT). The tool can be used for automated calculations of 0νββrates, electron spectra and angular correlations for all isotopes of experimental interest, for lepton-number-violating operators up to and including dimension 9. The tool takes care of renormalization-group running to lower energies and provides the matching to the low-energy effective field theory and, at lower scales, to a chiral effective field theory description of 0νββrates. The user can specify different sets of nuclear matrix elements from various many-body methods and hadronic low-energy constants. The tool can be used to quickly generate analytical and numerical expressions for 0νββrates and to generate a large variety of plots. In this work, we provide examples of possible use along with a detailed code documentation. The code can be accessed through: GitHub:https://github.com/OScholer/nudobe Online User-Interface:https://oscholer-nudobe-streamlit-4foz22.streamlit.app/  more » « less
Award ID(s):
2020275
PAR ID:
10508938
Author(s) / Creator(s):
; ;
Corporate Creator(s):
Publisher / Repository:
JHEP
Date Published:
Journal Name:
Journal of High Energy Physics
Edition / Version:
1
Volume:
2023
Issue:
8
ISSN:
1029-8479
Format(s):
Medium: X Other: pdf
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; (, Machine Learning: Science and Technology)
    Abstract We present a critical analysis of physics-informed neural operators (PINOs) to solve partial differential equations (PDEs) that are ubiquitous in the study and modeling of physics phenomena using carefully curated datasets. Further, we provide a benchmarking suite which can be used to evaluate PINOs in solving such problems. We first demonstrate that our methods reproduce the accuracy and performance of other neural operators published elsewhere in the literature to learn the 1D wave equation and the 1D Burgers equation. Thereafter, we apply our PINOs to learn new types of equations, including the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we show that our approach is also applicable to learn the physics of the 2D linear and nonlinear shallow water equations, which involve three coupled PDEs. We release our artificial intelligence surrogates and scientific software to produce initial data and boundary conditions to study a broad range of physically motivated scenarios. We provide thesource code, an interactivewebsiteto visualize the predictions of our PINOs, and a tutorial for their use at theData and Learning Hub for Science. 
    more » « less
  2. ; ; ; (, bioRxiv)
    Abstract Summarydadi is a popular software package for inferring models of demographic history and natural selection from population genomic data. But using dadi requires Python scripting and manual parallelization of optimization jobs. We developed dadi-cli to simplify dadi usage and also enable straighforward distributed computing. Availability and Implementationdadi-cli is implemented in Python and released under the Apache License 2.0. The source code is available athttps://github.com/xin-huang/dadi-cli. dadi-cli can be installed via PyPI and conda, and is also available through Cacao on Jetstream2https://cacao.jetstream-cloud.org/. 
    more » « less
  3. ; (, BMC Bioinformatics)
    Abstract BackgroundThe pan-genome of a species is the union of the genes and non-coding sequences present in all individuals (cultivar, accessions, or strains) within that species. ResultsHere we introduce PGV, a reference-agnostic representation of the pan-genome of a species based on the notion of consensus ordering. Our experimental results demonstrate that PGV enables an intuitive, effective and interactive visualization of a pan-genome by providing a genome browser that can elucidate complex structural genomic variations. ConclusionsThe PGV software can be installed via conda or downloaded fromhttps://github.com/ucrbioinfo/PGV. The companion PGV browser athttp://pgv.cs.ucr.educan be tested using example bed tracks available from the GitHub page. 
    more » « less
  4. ; ; ; ; ; (, The Astrophysical Journal)
    Abstract Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter (DM) components that cannot be directly observed. Galaxy formation simulations can be used to study the relationship between DM density fields and galaxy distributions. However, this relationship can be sensitive to assumptions in cosmology and astrophysical processes embedded in galaxy formation models, which remain uncertain in many aspects. In this work, we develop a diffusion generative model to reconstruct DM fields from galaxies. The diffusion model is trained on the CAMELS simulation suite that contains thousands of state-of-the-art galaxy formation simulations with varying cosmological parameters and subgrid astrophysics. We demonstrate that the diffusion model can predict the unbiased posterior distribution of the underlying DM fields from the given stellar density fields while being able to marginalize over uncertainties in cosmological and astrophysical models. Interestingly, the model generalizes to simulation volumes ≈500 times larger than those it was trained on and across different galaxy formation models. The code for reproducing these results can be found athttps://github.com/victoriaono/variational-diffusion-cdm✎. 
    more » « less
  5. ; ; (, Algorithms for Molecular Biology)
    Abstract We present a new method and software tool called that applies a pangenome index to the problem of inferring genotypes from short-read sequencing data. The method uses a novel indexing structure called the marker array. Using the marker array, we can genotype variants with respect from large panels like the 1000 Genomes Project while reducing the reference bias that results when aligning to a single linear reference. can infer accurate genotypes in less time and memory compared to existing graph-based methods. The method is implemented in the open source software tool available athttps://github.com/alshai/rowbowt. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email