ligo.skymap¶
The ligo.skymap
package provides tools for reading, writing, generating, and
visualizing gravitational-wave probability maps from LIGO and Virgo. Some of
the key features of this package are:
Command line tool bayestar-localize-coincs: BAYESTAR, providing rapid, coherent, Bayesian, 3D position reconstruction for compact binary coalescence events
Command line tool ligo-skymap-from-samples: Create 3D sky maps from posterior sample chains using kernel density estimation
Command line tool ligo-skymap-plot: An everyday tool for plotting HEALPix maps
Module
ligo.skymap.plot.allsky
: Astronomical mapmaking tools for perfectionists and figure connoisseurs
Quick Start, Tutorials¶
Modules¶
Coordinate Frames (ligo.skymap.coordinates
)¶
I/O and Data Format Support (ligo.skymap.io
)¶
Plotting and Visualization (ligo.skymap.plot
)¶
Sky Map Postprocessing (ligo.skymap.postprocess
)¶
- Contouring (
ligo.skymap.postprocess.contour
) - Cosmology (
ligo.skymap.postprocess.cosmology
) - Cross Match Catalogs with HEALPix Sky Maps (
ligo.skymap.postprocess.crossmatch
) - Uncertainty Ellipses (
ligo.skymap.postprocess.ellipse
) - Injection Finding (
ligo.skymap.postprocess.find_injection
) - Postprocessing Utilities
ligo.skymap.postprocess.util
Localization¶
Command Line Tools¶
BAYESTAR Rapid Sky Localization¶
Sky Map Visualization¶
Postprocessing¶
- Joint Sky Localization (
ligo-skymap-combine
) - Easter Egg: List Most Probable Constellations (
ligo-skymap-constellations
) - Posterior Samples to FITS Files (
ligo-skymap-from-samples
) - Plot Summary Statistics (
ligo-skymap-plot-stats
) - Gather Summary Statistics (
ligo-skymap-stats
) - Flatten Multi-Resolution Sky Maps (
ligo-skymap-flatten
) - Unflatten Multi-Resolution Sky Maps (
ligo-skymap-unflatten
)
Index¶
References¶
- 1
Singer, L. P., & Price, L. R. 2016, Phys. Rev. D, 93, 024013. https://doi.org/10.1103/PhysRevD.93.024013
- 2
Singer, L. P., Chen, H.-Y., Holz, D. E., et al. 2016, Astropys. J. Lett., 829, L15. https://doi.org/10.3847/2041-8205/829/1/L15
- 3
Singer, L. P., Chen, H.-Y., Holz, D. E., et al. 2016, Astropys. J. Supp., 226, 10. https://doi.org/10.3847/0067-0049/226/1/10
- 4
Kasliwal, M. M., Nakar, E., Singer, L. P. et al. 2019, Science, 358, 1559. https://10.1126/science.aap9455