hsc_cosmic_shear

Likelihoods of the HSC Year 3 cosmic shear data

File	likelihood/hsc_cosmic_shear/hsc_cosmic_shear_like.py
Attribution	HSC Collaboration (data)
	Roohi Dalal
	Xiangchong
URL
Citations	https://doi.org/10.48550/arXiv.2304.00701

Dalal et al presented measuremets of the cosmic shear E-mode power spectrum made using three years of data from the Hyper Suprime-Cam (HSC) survey. This module implements the likelihood of that data, using the sacc format. This module is a subclass of the sacc_like likelihood in likelihood/sacc_like. See the documentation for that module for more details.

Note that the analysis presented in Dalal et al used the power spectrum from the linear BACCO emulator and pyhmcode2016, the interfaces for which are not public immediately. They will be released soon.

Assumptions

• HSC measurements of cosmic shear power spectra

Setup Parameters

Name	Type	Default	Description
data_file	str		Filename of the sacc file to use.
data_sets	str	all	Space-separated list of which data sets from within the file to use for likelihoods.
keep_tracers	str		Regular expression to select tracers to use.
angle_range_{dataset}_{i}_{j}	str		Pair of real numbers. If set, for the given data set and pair of bins, cut down the data used to this angular range (min and max)
cut_{dataset}	str		Space-separated list of i,j pairs. (no spaces within the pair, just betwen them, e.g. cut_lss = 1,2 1,1 3,4. Remove this bin from the likelihood.
{name}_section	str	Various depending on name.	For each {name} in the data types used from the file, a cosmosis block section to look for the theory predictions in.
save_theory	str		If set, save the theory predictions used in the likelihood to this sacc file.
save_realization	str		If set, save a simulated data set to this sacc file.
covariance_realizations	int	-1	If >0, assume that the Covariance matrix was estimated from a set of MC simulations and should thus have the Anderson-Hartlap factor applied to increase its size. If zero, assume infinite number of realizations.
sellentin	bool	False	If set, use the Sellentin-Heavens 2016 change to the likelihood to account for this distribution of the covariance estimates. This changes the likelihood to a student’s-t form. Note that this invalidates the simulated data sets used for the ABC sampler.
like_name	str	2pt	The name of the likelihood to save.
likelihood_only	bool	False	Skip saving the covariance, inverse, simulation, etc. Saves some time.
kind	str	cubic	The interpolation to do into the theory splines. See scipy.interpolate.interp1d.

Input values

Section	Name	Type	Default	Description
shear_cl	ell	real 1d		If a Fourier-space measurement is used, the angular wave-number of the predicted theory curves. The name of the section here depends on the data type used from the file. It might be galaxy_cl or shear_cl, for example.
	bin_{i}_{j}	real 1d		For various i,j depending what is found in the file, the theory predictions for this value. For example, C_ell.

Output values

Output values

Section	Name	Type	Description
likelihoods	2pt_like	real	Gaussian likelihood value. Name can be changed in parameter file (see above) for this and the other outputs below.
data_vector	2pt_data	real 1d	The full vector of data points used in the likelihood
	2pt_theory	real 1d	The full vector of theory points used in the likelihood
	2pt_covariance	real 2d	The covariance matrix used
	2pt_inverse_covariance	real 2d	The inverse covariance matrix (precision matrix) used.
	2pt_simulation	real 1d	A simulated data set from the given theory and covariance matrix.
	2pt_angle	real 1d	The angular scale used for each data point.
	2pt_bin1	int 1d	The first bin index used for each data point
	2pt_bin2	int 1d	The second bin index used for each data point