HSC Survey weak lensing data products

These pages present some value added catalogs and analysis files that were produced by members of the HSC survey weak lensing team during analysis of the the HSC Y3 data. We are still working on the corresponding calibrated shear catalog release for HSC Y3 and we will link it here once the data is available. For the HSC Y1 data products, please click here.

• Cosmic shear measurements using two point correlation method and chains (Li et al. 2023)
• Cosmic shear measurements using pseudo-Cl method and chains (Dalal et al. 2023)
• 3x2pt cosmological analysis: measurements and chains (More et al, Sugiyama et al, Miyatake et al. 2023)

The fiducial likelihood for the cosmic shear analyses are available as examples in the CosmoSIS standard library (Fourier space analysis : hsc-y3-shear.ini, real space analysis : hsc-y3-shear-real.ini). Note that the likelihood version made public in CosmoSIS uses the linear matter power spectrum from CAMB. The fiducial likelihood used in our analysis obtains the linear matter power spectrum from the BACCO emulator. While the latter is less computationally expensive, there is no difference in the results between the two approaches.

Cosmic shear measurements were carried out using the real space two point correlation function approach in Li et al. 2023. The corresponding measurements and chains are made available here.

• Data vectors:
  • Two point correlation functions are provided for a 4 bin tomographic analysis (1x1, 1x2, 1x3, 1x4, 2x2, 2x3, 2x4, 3x3, 3x4, 4x4) are in the file \(\xi_+\) and \(\xi_-\).
  • The data format file follows here.
• Data vector covariance:
  • This provides the covariance estimated from mock catalogs.
  • Examples plotting the data vector with error (diagonal terms of the covariance can be found here)
• Source redshift distribution:
  • These provide the source redshift distributions based on the methodology described in Rau et al. (2023).
• PSF systematics data vectors:
  • These provide the PSF systematics based on the methodology described in Zhang et. al (2023).
• Posterior samples from fiducial analysis:
  • Equally weighted posterior samples from samplers with the fiducial setup can be found here.

For questions or requests for other data products, please contact Xiangchong Li (xiangchl@andrew.cmu.edu). When using the above data, please cite the paper by Li et al. 2023, PRD, 108, 12, 123518, (arXiv:2304.00702).

Cosmic shear measurements were carried out using the pseudo-Cl approach in Dalal et al. 2023. The corresponding measurements and chains are made available here.

• Data vectors, covariance and source n(z): This single sacc file contains the following:
  • Data vector - these are the band powers of tomographic lensing spectra, i.e. \(C_{\ell}\)s. We provide the auto and cross-correlation power spectra measured for 4 tomographic redshift bins (10 total spectra), equally spaced between z=0.3 and z=1.5. Each power spectrum is measured in 17 bins, over the \(\ell\) range \(\ell=100\) to \(\ell=15800\), with the following bin edges: \([100, 200, 300, 400, 600, 800, 1000, 1400, 1800, 2200, 3000\), \(3800, 4600, 6200, 7800, 9400, 12600, 15800]\). Our fiducial analysis uses scales between \(\ell = [300, 1800]\), where the large scale cut is due to evidence of systematics leading to the detection of significant B-modes, while the small scale cut is based on the range of scales in which we believe our modeling of intrinsic alignments and baryonic feedback to be reliable and robust. Note that we only include the EE power spectra which contain the cosmological signal, but can make the BB and EB power spectra available upon request.
  • Data vector covariance - as described in Dalal et al. 2023, the covariance is measured using mock catalogs. The full covariance matrix has a size of 170x170, covering the 17 \(\ell\) bins of the 10 power spectra. The covariance matrix should have the same scale cuts applied as the power spectra.
  • Source redshift distribution, N(z) - the measurement of the source redshift distribution combines photometric information with clustering redshifts based on CAMIRA LRGs, as described in detail in Rau et al. (2023).
  • To access data from the sacc file, one can use the script linked here. For further examples of reading and writing information with sacc files, please refer to these example notebooks.
• PSF systematics file:
  • The file contains measurements of the auto- and cross-correlations between the second moment leakage, second moment modeling error, fourth moment leakage and fourth moment modeling error of the point spread function. This is used for computing the additive bias to the Cls from the PSF (see Zhang et. al (2023) and Dalal et al. 2023 for details). The measurements are only non-zero in the range of our fiducial scale cuts \(\ell \in [300, 1800]\).
  • To access the measurements one can use the script linked here.
• Transformation matrix for the PSF parameters:
  • This includes the matrix used to transform the four uncorrelated, normally distributed PSF parameters that we sample into our original definitions of the second and fourth moment PSF leakage and modeling error parameters (see Section V. D. of Dalal et al. 2023).
  • To access the transformation matrix and mean parameter values, one can use the script linked here.
• Posterior samples from fiducial analysis:
  • Equally weighted posterior samples from samplers with the fiducial setup in polychord can be found here.

For questions or requests for other data products, please contact Roohi Dalal (rdalal@astro.princeton.edu). When using the above data, please cite the paper by Dalal et al. 2023, PRD, 108, 12, 123519, (arXiv:2304.00701).

The 3x2 point measurements were carried out in More et al. 2023 and the analysis of the large scale measurements using the minimal bias model was performed in Sugiyama et al. 2023, while the analysis including more non-linear scales was performed in Miyatake et al. 2023. The corresponding measurements, pipeline and chains are made available here.

• Data vectors, fiducial chains:
  • These include the data vectors for the minimal bias model (Minimalbias), the Halo occupation distribution model (Halohod).
  • Each folder contains, fiducial, dempzxwx, mizuki, and dnnz subfolders, which are for fiducial analysis, analysis with dempz x WX photoz, analysis with mizuki photoz, and analysis with dnnz photoz respectively. Each subfolder contains following contents:
    • dataset = all data needed to perform parameter inference.
    • bin_dSigma_logcen.dat = radial bin of g-g lensing = R [Mpc/h]
    • bin_wp_logcen.dat = radial bin of galaxy clustering = R [Mpc/h]
    • bin_xi_logcen.dat = radial bin of cosmic shear = theta [arcmin]
    • covariance.dat = covariance matrix
    • nzl_z[n]_100bin.dat = lens redshift bin for lens redshift bin [n=0,1,2]
    • photoz_bin.dat = bin of photometric redshift distribution
    • stacked_pofz_all.dat = stacked photometric redshift distribution after source sample selection.
    • psf_pp_pq_qq.dat = psf 2pcf for cosmic shear analysis, using all stars
    • psf_pp_pq_qq_reserved.dat = same as above but with reserved starts
    • psf_pp_pq_qq_used.dat = same as above but with used stars
    • signal_dSigma[n].dat = g-g lensing signal for lens redshift bin [n=0,1,2]
    • signal_wp[n]_100RSD.dat = galaxy clustering signal for lens redshift bin [n=0,1,2]
    • signal_xi[pm].dat = cosmic shear signal of plus/minus mode [pm=p, m]
    • sumwlssigcritinvPz_z[n].dat = lens-source pair weight for g-g lensing for each lens redshift bin [n=0,1,2], used for cosmology, photo-z correction (measurement correction).
    • dataset.fits = alternate format of dataset in fits, used for the CosmoSIS likelihood (in progress with Tianqing and Joe)
    • sumwlssigcritinvPz.fits = alternate format of lens-source pair weight in fits, used for cosmCosmoSISosis likelihood
    • analysis_config.yaml = config file for this analysis, passed to the Likelihood code
    • chain_equal_weights.dat = equally weighted chain by multinest
    • param_names.dat = list of names of parameters in the above chain.
• Likelihood pipeline:
  • This pipeline provides the likelihood for both the minimal bias model as well as the HOD model.

For questions or requests for other data products, please contact Surhud More (surhud@iucaa.in), Sunao Sugiyama (ssunao@sas.upenn.edu) and Hironao Miyatake (miyatake@kmi.nagoya-u.ac.jp). When using the above data, please cite all the three papers More et al. 2023, Sugiyama et al. 2023, Miyatake et al. 2023.