English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
 Local TagsRelease HistoryDetailsSummary
  PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals

Biwer, C. M., Capano, C., De, S., Cabero, M., Brown, D. A., Nitz, A. H., et al. (2019). PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals. Publications of the Astronomical Society of the Pacific, 131(996): 024503. doi:10.1088/1538-3873/aaef0b.

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://hdl.handle.net/21.11116/0000-0002-DEC9-2 Version Permalink: https://hdl.handle.net/21.11116/0000-000A-D829-5
Genre: Journal Article

Files

show Files
hide Files
:
1807.10312.pdf (Preprint), 8MB
View   Save
File Permalink:
https://hdl.handle.net/21.11116/0000-0002-DECB-0
Name:
1807.10312.pdf
Description:
File downloaded from arXiv at 2019-01-29 10:41
OA-Status:
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
View
Copyright Date:
-
Copyright Info:
-
License:
http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Locators

show

Creators

show
hide
 Creators:
Biwer, C. M., Author
Capano, Collin1, Author           
De, Soumi, Author
Cabero, Miriam1, Author           
Brown, Duncan A., Author
Nitz, Alexander H.1, Author           
Raymond, Vivien2, Author           
Affiliations:
1Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, ou_24011              
2Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society, ou_1933290              

Content

show
hide
Free keywords: Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM, Astrophysics, Galaxy Astrophysics, astro-ph.GA,General Relativity and Quantum Cosmology, gr-qc
 Abstract: We introduce new modules in the open-source PyCBC gravitational- wave
astronomy toolkit that implement Bayesian inference for compact-object binary
mergers. We review the Bayesian inference methods implemented and describe the
structure of the modules. We demonstrate that the PyCBC Inference modules
produce unbiased estimates of the parameters of a simulated population of
binary black hole mergers. We show that the posterior parameter distributions
obtained used our new code agree well with the published estimates for binary
black holes in the first LIGO-Virgo observing run.

Details

show
hide
Language(s):
 Dates: 2018-07-262019
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 1807.10312
DOI: 10.1088/1538-3873/aaef0b
URI: http://arxiv.org/abs/1807.10312
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Publications of the Astronomical Society of the Pacific
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 131 (996) Sequence Number: 024503 Start / End Page: - Identifier: -