FRBHost Class

The FRBHost class represents the host galaxy of an FRB. It inherits from FRBGalaxy and stores photometry, redshifts, morphology, nebular line measurements, and derived physical properties.

Overview

The FRBHost class provides methods to:

Load host galaxy data from the repository
Access photometric measurements and derived quantities
Calculate nebular properties (SFR, extinction)
Interface with SED fitting tools (CIGALE, EAZY)
Retrieve spectra from the specDB archive

Loading Host Galaxies

by_frb

The recommended way to load a host galaxy is through its associated FRB:

from frb.frb import FRB

# Load the FRB first
frb = FRB.by_name('FRB20180924B')

# Get the host galaxy
host = frb.grab_host()

# Print basic info
print(host)
# <FRBHost: 21:44:25.25 -40:54:00.80, FRB=FRB20180924B z=0.3212>

Alternatively, use the FRBHost.by_frb() class method directly:

from frb.frb import FRB
from frb.galaxies.frbgalaxy import FRBHost

frb = FRB.by_name('FRB20180924B')
host = FRBHost.by_frb(frb)

from_json

Load from a specific JSON file:

from frb.frb import FRB
from frb.galaxies.frbgalaxy import FRBHost

frb = FRB.by_name('FRB20180924B')
host = FRBHost.from_json(frb, '/path/to/FRB20180924B_host.json')

Key Attributes

Host galaxies have several attribute dictionaries containing measurements and derived quantities.

Coordinates

# Host galaxy coordinates
host.coord
# <SkyCoord (ICRS): (ra, dec) in deg (321.60521, -40.90022)>

# Associated FRB
host.frb.frb_name
# 'FRB20180924B'

# Host name
host.name
# 'HG20180924B'

Redshift

# Best redshift
host.z
# 0.3212

# Redshift error
host.z_err

# Full redshift dict
host.redshift
# {'z': 0.3212, 'z_spec': 0.3212, 'z_FRB': 0.3212}

Photometry

Photometric measurements are stored in the photom dict:

# All photometry
host.photom
# {'SDSS_u': 21.45, 'SDSS_u_err': 0.15, 'SDSS_g': 20.23, ...}

# Individual bands
host.photom['SDSS_r']
host.photom['SDSS_r_err']

# Fluxes (in mJy) are also stored
host.photom['SDSS_r_flux']
host.photom['SDSS_r_flux_err']

Derived Quantities

Physical properties derived from SED fitting or spectroscopy:

# All derived quantities
host.derived
# {'Mstar': 1.2e10, 'Mstar_err': 2e9, 'SFR_photom': 1.5, ...}

# Stellar mass (solar masses)
host.derived['Mstar']

# Star formation rate (Msun/yr)
host.derived['SFR_photom']    # From SED fitting
host.derived['SFR_nebular']   # From emission lines

# Extinction
host.derived['AV_nebular']
host.derived['EBV_photom']

Nebular Emission Lines

Emission line fluxes (erg/s/cm^2):

# All line measurements
host.neb_lines
# {'Halpha': 1.2e-16, 'Halpha_err': 1e-17, '[OIII] 5007': 5e-17, ...}

# Individual lines
host.neb_lines['Halpha']
host.neb_lines['Hbeta']
host.neb_lines['[NII] 6584']

Morphology

Galaxy structural parameters (typically from GALFIT):

host.morphology
# {'reff_ang': 0.85, 'reff_kpc': 3.2, 'n': 1.5, 'b/a': 0.7, ...}

# Effective radius
host.morphology['reff_ang']  # arcsec
host.morphology['reff_kpc']  # kpc

# Sersic index
host.morphology['n']

Offsets

Angular and physical offsets between FRB and host:

host.offsets
# {'ang_avg': 0.5, 'ang_best': 0.45, 'physical': 2.1, ...}

# Physical offset in kpc
host.offsets['physical']
host.offsets['physical_err']

Calculating Derived Quantities

Nebular SFR

Calculate star formation rate from emission lines:

# First calculate extinction (optional but recommended)
host.calc_nebular_AV(method='Ha/Hb')
print(f"AV = {host.derived['AV_nebular']}")

# Calculate SFR from H-alpha
host.calc_nebular_SFR(method='Ha')
print(f"SFR = {host.derived['SFR_nebular']} Msun/yr")

Line Luminosities

Calculate emission line luminosities:

Lum, Lum_err = host.calc_nebular_lum('Halpha')
print(f"L(Ha) = {Lum}")

Halo DM

Calculate the halo contribution to DM:

DM_halo = host.calc_dm_halo()
print(f"DM_halo = {DM_halo}")

Retrieving Spectra

If spectra are available in the specDB archive:

# Get spectrum and metadata
meta, spec = host.get_metaspec()

# meta is an astropy Table with spectrum info
print(meta)

# spec is an XSpectrum1D object
spec.wavelength  # Wavelength array
spec.flux        # Flux array

# Get all spectra (if multiple exist)
meta, spec = host.get_metaspec(return_all=True)

# Specify instrument
meta, spec = host.get_metaspec(instr='MUSE')

Running SED Fitting

CIGALE

Run CIGALE SED fitting directly:

host.run_cigale(
    data_file='cigale_input.fits',
    config_file='pcigale.ini',
    wait_for_input=False,
    save_sed=True,
    plot=True,
    outdir='cigale_output/'
)

Parse CIGALE results:

host.parse_cigale('cigale_output/results.txt')
print(host.derived['Mstar'])
print(host.derived['SFR_photom'])

Parsing External Results

GALFIT

Parse GALFIT output for morphology:

host.parse_galfit('galfit_output.fits')
print(host.morphology['reff_kpc'])
print(host.morphology['n'])

pPXF

Parse pPXF spectral fitting results:

host.parse_ppxf('ppxf_results.ecsv')
print(host.neb_lines['Halpha'])

Building a Table of Hosts

Create a pandas DataFrame with all host galaxy properties:

from frb.galaxies import utils

# Build the table
host_tbl, tbl_units = utils.build_table_of_hosts()

# View columns
print(host_tbl.columns.tolist())
# ['Host', 'FRBname', 'RA_host', 'DEC_host', 'FRBobj', 'Mstar', 'SFR_photom', ...]

# Filter by stellar mass
massive_hosts = host_tbl[host_tbl['Mstar'] > 1e10]

# Get hosts with spectroscopic redshifts
spec_z = host_tbl[host_tbl['z_spec'].notna()]

# Merge with FRB table
from frb.frb import build_table_of_frbs
frb_tbl, _ = build_table_of_frbs()

import pandas as pd
merged = pd.merge(frb_tbl, host_tbl, left_on='FRB', right_on='FRBname')

Default columns include data from:

derived: Mstar, SFR_photom, SFR_nebular, AV_nebular, M_r, etc.
photom: All photometric bands and fluxes
neb_lines: Emission line fluxes
offsets: Angular and physical offsets
morphology: Structural parameters
redshift: z, z_spec, z_phot, z_FRB

Listing All Hosts

from frb.galaxies.utils import list_of_hosts

# Get all hosts
frbs, hosts = list_of_hosts()

print(f"Number of hosts: {len(hosts)}")

# Iterate
for frb, host in zip(frbs, hosts):
    print(f"{frb.frb_name}: z={host.z}, Mstar={host.derived.get('Mstar', 'N/A')}")

Loading PATH Results

Load probabilistic association results:

from frb.galaxies import utils

path_tbl = utils.load_PATH()
print(path_tbl.columns)
# ['FRB', 'RA', 'Dec', 'P_Ox', 'P_O', 'ang_size', ...]

Writing to JSON

Save a host galaxy object:

host.write_to_json(path='./output/')
# Writes to FRB20180924B_host.json

Setting Redshifts

# Set spectroscopic redshift
host.set_z(0.3212, 'spec', err=0.0001)

# Set photometric redshift
host.set_z(0.35, 'phot', err=0.05)

API Reference

FRBGalaxy (Parent Class)

class frb.galaxies.frbgalaxy.FRBGalaxy(ra, dec, frb, cosmo=None)[source]

Bases: object

Parent class for galaxies in FRB fields

Simple object to hold key observable and derived quantities

Warning: Generating hundreds of these objects will likely be slow. Especially SkyCoord generation. A new class will be warranted for that

Parameters:

ra (float) – RA in deg
dec (float) – DEC in deg
frb (frb.FRB) – FRB object
cosmo (astropy.cosmology) – Cosmology, e.g. Planck18

redshift

Type:: dict

photom

Type:: dict

morphology

Type:: dict

neb_lines

Type:: dict

kinematics

Type:: dict

derived

Type:: dict

classmethod from_dict(frb, idict, override: bool = False, **kwargs)[source]

Instantiate from a dict

Parameters:

frb (frb.FRB)
idict (dict)
override (bool, optional) – Over-ride the cosmology error Not recommended unless you know what you are doing
**kwargs – Passed to the __init__ call

Returns:

classmethod from_json(frb, json_file, verbose: bool = True, **kwargs)[source]

Parameters:

frb (frb.FRB)
json_file
**kwargs

Returns:

FRBGalaxy or None

__init__(ra, dec, frb, cosmo=None)[source]

Parameters:

ra (float)
dec (float)
frb (frb.FRB)
cosmo (astropy.cosmology, optional)

property z

Return the redshift of the galaxy

Returns:: redshift or nadda
Return type:: float or None

property z_err

Return the redshift error of the galaxy

Returns:: redshift or nadda
Return type:: float or None

calc_nebular_lum(line)[source]

Calculate the line luminosity Applies dust extinction if self.derived[‘AV_nebular’] is filled

Mainly a wrapper to nebular.calc_lum()

Parameters:: line (str) – Name of the line

calc_nebular_AV(method='Ha/Hb', min_AV=None, **kwargs)[source]

Calculate an A_V extinction from a pair of Nebular lines

Mainly a wrapper to nebular.calc_dust_extinct

self.derived[‘AV_nebular’] is filled

Parameters:

method (str) – Method to use
min_AV (float) – Minimum A_V value allowed; might set 0. someday
**kwargs – Passed to nebular.calc_dust_extinct

Returns:

calc_nebular_SFR(method='Ha', **kwargs)[source]

Calculate a SFR from a nebular line

Mainly a wrapper to nebular.calc_SFR

self.derived[‘AV_nebular’] is filled with units SFR/yr

Parameters:

method (str) – Method to use, e.g. ‘Ha’ for Halpha
**kwargs – passed to nebular.calc_SFR

Returns:

calc_tot_uncert()[source]

Calculate total uncertainty in arcsec of FRB localization + Host localization in the reference frame of the FRB

Returns:: uncerta, uncertb [arcsec]
Return type:: tuple

parse_photom(phot_tbl, max_off=<Quantity 1. arcsec>, overwrite=True, EBV=None)[source]

Parse photometry from an input table

Fills the self.photom dict

Also fills fluxes in mJy

Parameters:

phot_tbl (astropy.table.Table) – ra, dec entires are required
max_off (Angle, optional)
overwrite (bool, optional)
EBV (float, optional) – Galactic reddening. If included, the photometry has been corrected for this. If not, who knows?! :)

Returns:

run_cigale(data_file='cigale_in.fits', config_file='pcigale.ini', wait_for_input=False, save_sed=True, plot=True, outdir='out', **kwargs)[source]

Generates the input data file for CIGALE given the photometric points and redshift of a galaxy

Parameters:

ID – str, optional An ID for the galaxy. If none, “GalaxyA” is assigned.
data_file (str, optional) – Root name for the photometry data file generated used as input to CIGALE
config_file (str, optional) – Root name for the file where CIGALE’s configuration is generated
wait_for_input (bool, optional) – If true, waits for the user to finish editing the auto-generated config file before running.
save_sed (bool, optional) – Saves the best fit SED if true
plot (bool, optional) – Plots the best fit SED if true
cores (int, optional) – Number of CPU cores to be used. Defaults to all cores on the system.
outdir (str, optional) – Path to the many outputs of CIGALE If not supplied, the outputs will appear in a folder named out/

kwargs: These are passed into gen_cigale_in() and _initialise()

sed_modules (list of ‘str’, optional):: A list of SED modules to be used in the PDF analysis. If this is being input, there should be a corresponding correct dict for sed_modules_params.
sed_module_params (dict, optional):: A dict containing parameter values for the input SED modules. Better not use this unless you know exactly what you’re doing.

get_metaspec(instr=None, return_all=False, specdb_file=None)[source]

Return the meta data and spectra for this FRBGalaxy from the specDB

If there is more than one spectrum, the code returns the first unless return_all=True

Parameters:

instr (str, optional) – Restrict to the input Instrument
return_all (bool, optional) – Return all of the meta, spectra
specdb_file (str, optional) – Path+name of the specDB file to use (over-ride the default)

Returns:

meta data, spectra

Return type:

astropy.table.Table, linetools.spectra.XSpectrum1D

parse_cigale(cigale_file, sfh_file=None, overwrite=True)[source]

Parse the output file from CIGALE

Read into self.derived

Parameters:

cigale_file (str) – Name of the CIGALE results file
sfh_file (str, optional) – Name of the best SFH model file.
overwrite (bool, optional) – Over-write any previous values

Returns:

parse_galfit(galfit_file, overwrite=True, twocomponent=False)[source]

Parse an output GALFIT file: or a gallight JSON file

Loaded into self.morphology

Parameters:

galfit_file (str) – processed ‘out.fits’ file produced by frb.galaxies.galfit.run. Contains a binary table with fit parameters. Or a JSON file from gallight
overwrite (bool, optional) – Need to overwrite the object’s attributes?
twocomponent (bool, optional) – Should the morphology attribute generated contain fit parameters of two components?

parse_ppxf(ppxf_file, overwrite=True, format='ascii.ecsv')[source]

Parse an output pPXF file generated by our custom run

Loaded into self.lines

Parameters:

ppxf_file (str) – pPXF results file
overwrite (bool, optional)
format (str, optional) – Format of the table

Returns:

set_z(z, origin, err=None)[source]

Set the redshift value(s) in self.redshift

Parameters:

z (float) – Redshift value
origin (str) – Origin ‘spec’ for spectroscopic ‘phot’ for photometric
err (float, optional) – Error in the redshift

Returns:

vet_one(attr)[source]

Vette one of the main_attr

Parameters:: attr (str)
Returns:: True = passed
Return type:: bool

vet_all()[source]

Vette all of the main dicts

Parameters:

dict
valid_defs

Returns:

True = passed

Return type:

bool

make_outfile()[source]

Auto-generate an output name for the class

Returns:: Output filename
Return type:: str

write_to_json(outfile=None, path='./', overwrite=True)[source]

Write key aspects of the class to disk in a JSON file

Parameters:

outfile (str, optional) – Output filename If not provided, one will be generated with make_outfile()
path (str, optional) – Path for the output file
overwrite (bool, optional) – Overwrite?

Returns:

FRBHost Class

class frb.galaxies.frbgalaxy.FRBHost(ra, dec, frb, z_frb=None, **kwargs)[source]

Bases: FRBGalaxy

Child of FRBGalaxy specific for an FRB host

Parameters:

ra (float) – RA in deg
dec (float) – DEC in deg
FRB (frb.FRB)

classmethod by_frb(frb, **kwargs)[source]

Parameters:

frb (frb.FRB) – FRB object
**kwargs

Return type:

FRBHost

__init__(ra, dec, frb, z_frb=None, **kwargs)[source]

Parameters:

ra (float)
dec (float)
frb (frb.FRB)
cosmo (astropy.cosmology, optional)

calc_dm_halo(**kwargs)[source]

Calculate the Halo contribution to the host DM given the host stellar mass in its derived properties dict and the FRB coordinates.

Parameters:: **kwargs – Passed to dm_host.dm_host_halo
Returns:: Halo contribution to the DM in pc/cm^3
Return type:: DM_halo (float)

make_outfile()[source]

Overloads the parent method for Host specific naming

Naming is FRBXXXXXX_host.json with XXXXXXX supplied by self.frb

Returns:: Name of the default outfile
Return type:: str

set_z(z, origin, err=None)[source]

Partially overload the main method

The main change is that the input z also sets z_FRB

self.redshift is modified in place

Parameters:

z (float) – Redshift value
origin (str) – Origin ‘spec’ for spectroscopic ‘phot’ for photometric
err (float, optional) – Error in the redshift

Returns:

Utility Functions

frb.galaxies.utils.build_table_of_hosts(attrs: list = None)[source]

Generate a Pandas table of FRB Host galaxy data. These are slurped from the ‘derived’, ‘photom’, and ‘neb_lines’ dicts of each host object

Warning: As standard, missing values are given NaN in the Pandas table: Be careful!

Note

RA, DEC are given as RA_host, DEC_host to avoid conflict with the FRB table

Args:

Returns:: Table of data on FRB host galaxies, dict of their units
Return type:: pd.DataFrame, dict

frb.galaxies.utils.list_of_hosts(skip_bad_hosts=True, verbose: bool = False)[source]

Scan through the Repo and generate a list of FRB Host galaxies

Also returns a list of the FRBs

Parameters:

skip_bad_hosts (bool)
verbose (bool) – If True, print more to the screen

Return type:

list, list

frb.galaxies.utils.load_PATH(PATH_root_file: str = 'adopted.csv')[source]

Load up the PATH table

Parameters:: PATH_root_file (str, optional) – [description]. Defaults to ‘adopted.csv’.
Returns:: Table of galaxy coordinates and PATH results
Return type:: pandas.DataFrame

FRBHost Class

Overview

Loading Host Galaxies

by_frb

from_json

Key Attributes

Coordinates

Redshift

Photometry

Derived Quantities

Nebular Emission Lines

Morphology

Offsets

Calculating Derived Quantities

Nebular SFR

Line Luminosities

Halo DM

Retrieving Spectra

Running SED Fitting

CIGALE

Parsing External Results

GALFIT

pPXF

Building a Table of Hosts

Listing All Hosts

Loading PATH Results

Writing to JSON

Setting Redshifts

API Reference

FRBGalaxy (Parent Class)

FRBHost Class

Utility Functions

See Also