Transient Localization

This doc describes the various ways one can define and input the transient localization.

Data Model

The localization is held internally in a dict with keys set by the data model defined in localization.py.

Error Ellipse

The simplest approach is to define the localization as an ellipse on the sky. In this case, one inputs the center of the ellipse, the semi-major axis [“a”, in arcsec], the semi-minor axis [“b”, in arcsec], and the position angle on the sky (“theta”, defined as degrees East of North).

Here is an example:

frb_coord = SkyCoord('21h44m25.255s -40d54m00.10s', frame='icrs')
eellipse = dict(a=0.1, b=0.1, theta=0.)
localiz = dict(type='eellipse', center_coord=frb_coord, eellipse=eellipse)
assert localization.vet_localization(localiz)

The last line of code checks against the data model.

The code then defines the localization PDF as a 2D Gaussian with sigma’s “a” and “b” and orientation given by “theta”.

Healpix

For complex and/or large localizations, HEALPix may offer the best format. Indeed, this is the preferred approach of the Gravitational Wave community.

astropath accommodates two approaches to defining the Healpix localization. We describe each in turn.

Nested

The first is termed “NESTED” and is a full Healpix map of the sky with the PDF defined at every healpix pixel.

Here is an example using the healpix localization for GW170817:

lfile = os.path.join(str(resource_files('astropath').joinpath('data')), 'gw_examples',
                     'GW170817_skymap.fits.gz')
gw170817 = hp.read_map(lfile)
header = fits.open(lfile)[1].header
#
localiz = dict(type='healpix',
               healpix_data=gw170817,
               healpix_nside=header['NSIDE'],
               healpix_ordering='NESTED',
               healpix_coord='C')
assert localization.vet_localization(localiz)

WCS

The localization PDF may be provided as a FITS image using a proper world coordinate system (WCS).

Here is an example:

lfile = os.path.join(str(resource_files('astropath').joinpath('tests')), 'files',
    'mask_frb201123_localization.fits.gz')
hdul = fits.open(lfile)
pdf = hdul[0].data
header = hdul[0].header
wcs = WCS(header)
# Normalize
data /= np.sum(data)
# Define
localiz = dict(type='wcs',
               wcs_data=pdf,
               wcs_WCS=wcs)
assert localization.vet_localization(localiz)

In this example, we have enforced normalization in this image frame. This is not necessary.