CDE Methods Applications: Photo-z, LFI and Spec-Z Estimation

Repository with code for examples (Section 4) of the "Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference" paper.
Folders are arranged in the same way as the example section in the paper:

• photometric_redshift_teddy is the code for photo-z estimation with TEDDY catalogue A and B (section 4.1);
• lfi_cosmological_inference is the code for LFI for \Omega_M and \sigma_8 parameters examples using Galsim toolkit (section 4.2);
• spec_z_estimation is the code for the perturbed SDSS 6 spectra and spec-z classification (section 4.3).

To reproduce the results please see the README in the respective folder.

The methods can be found at the following repositories:

• NNKCDE
• RFCDE
• Flexcode (R) and Flexcode (Python)
• DeepCDE
• cdetools

Citation

@article{dalmasso2020cdetools, author = {{Dalmasso}, N. and {Pospisil}, T. and {Lee}, A.~B. and {Izbicki}, R. and {Freeman}, P.~E. and {Malz}, A.~I.}, title = "{Conditional density estimation tools in python and R with applications to photometric redshifts and likelihood-free cosmological inference}", journal = {Astronomy and Computing}, year = 2020, month = jan, volume = {30}, eid = {100362}, pages = {100362}, doi = {10.1016/j.ascom.2019.100362} }