Mock data analysis

Mock data analysis#

This workflow reproduces the analysis bases on the mock O4- and O5-like catalogs in Borghi et al. 2023.

  1. Initialize the Likelihood and Bias terms

from CHIMERA.Likelihood import MockLike
from CHIMERA.Bias import Bias

like = MockLike(model_cosmo, model_mass,  model_rate,
                data_GW, data_GW_names, data_GW_smooth, data_GAL_dir, data_GAL_zerr,
                nside_list, npix_event,sky_conf,
                z_int_H0_prior, z_int_sigma, z_int_res, z_det_range)

bias = Bias(model_cosmo, model_mass, model_rate, file_inj, snr_th)

  1. Define function to compute the full likelihood (likelihood and bias)

def combine_events():

def lnlike(lambda_cosmo, lambda_mass, lambda_rate):
    return like.compute_ln(lambda_cosmo, lambda_mass, lambda_rate) -\
           like.Nevents * bias.compute_ln(lambda_mass, lambda_cosmo, lambda_rate)

LVK data analysis#

Define a function to load the spectra from a catalog folder, (2) load the table of spectral indices to measure, (3) call the Catalog class,

import pylick.io as io
from pylick.indices import IndexLibrary
from pylick.analysis import Catalog

def load_spec(ID):
   ...
   return [wave, flux, ferr, mask]

IDs = [...]
ind_library  = IndexLibrary(index_keys)

ind_measured = Catalog(IDs, load_spec, index_keys, z=zs, do_plot=True, verbose=True)
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