Use of PyCBC in Scientific Publications¶
If you use any code from PyCBC in a scientific publication, then we ask that you include a citation to the software through its DOI and that you cite the publications relevant to the sections of the code that you are using, as described below.
Citing the PyCBC Software¶
If you do not use a specific release, please cite the DOI for the latest release, or the release closest to the version that you are using.
Citing the scientific publications that describe the algorithms¶
PyCBC implements a large number of data-analysis algorithms and so it is not possible to give one single citation. To give proper scientific credit for the development of PyCBC, in addition to citing the DOI from the software, please cite the appropriate scientific publications below.
If you use the Bayesian inference modules, or code derived from those modules, please cite the paper:
Searches for Compact Binary Coalescence¶
If you use the PyCBC search algorithms, please cite all four of these papers:
- FINDCHIRP: an algorithm for detection of gravitational waves from inspiraling compact binaries. [INSPIRES BibTeX Key] [ADS BibTeX key]
- A chi-squared time-frequency discriminator for gravitational wave detection. [INSPIRES BibTeX Key] [ADS BibTeX key]
- Detecting binary compact-object mergers with gravitational waves: Understanding and Improving the sensitivity of the PyCBC search. [INSPIRES BibTeX Key] [ADS BibTeX key]
- Implementing a search for aligned-spin neutron star – black hole systems with advanced ground based gravitational wave detectors. [INSPIRES BibTeX Key] [ADS BibTeX key]
If you use the offline PyCBC search pipeline, please additionally cite:
- The PyCBC search for gravitational waves from compact binary coalescence. [INSPIRES BibTeX Key] [ADS BibTeX key]
If you use the low-latency PyCBC search pipeline, please additionally cite: