The Redshift Assessment Infrastructure Layers (RAIL) is an open software package for multiple stages of photometric redshift analysis, developed by DESC. At the current stage, end-to-end testing for RAIL with a realistic dataset is a valuable verification to the pipeline. HSC Y1 is a well-studied dataset for this practice, given its similar depth to LSST, and relatively large number of galaxies. Scientifically, a comparison between the RAIL photo-zs with the HSC photo-zs could shed light on the photo-z systematics issue encountered in HSC Y3.
Contacts: Tianqing Zhang
Day/Time: Monday - Friday
Main communication channel: TBD
GitHub repo: https://github.com/LSSTDESC/rail_pipelines
Zoom room (if applicable): TBD
Goals and deliverable
Continue developing the RAIL pipeline for HSC photo-z training, estimation, summarizing, and evaluation.
Produce p(z) for individual galaxies, and n(z) for ensemble galaxies in tomographic bins.
Comparison between different p(z) and n(z).
Resources and skills needed
python, git, or just passion for learning photo-z estimation
Reprocess HSC photometric redshift with RAIL
The Redshift Assessment Infrastructure Layers (RAIL) is an open software package for multiple stages of photometric redshift analysis, developed by DESC. At the current stage, end-to-end testing for RAIL with a realistic dataset is a valuable verification to the pipeline. HSC Y1 is a well-studied dataset for this practice, given its similar depth to LSST, and relatively large number of galaxies. Scientifically, a comparison between the RAIL photo-zs with the HSC photo-zs could shed light on the photo-z systematics issue encountered in HSC Y3.
Contacts: Tianqing Zhang Day/Time: Monday - Friday Main communication channel: TBD GitHub repo: https://github.com/LSSTDESC/rail_pipelines Zoom room (if applicable): TBD
Goals and deliverable
Continue developing the RAIL pipeline for HSC photo-z training, estimation, summarizing, and evaluation.
Produce p(z) for individual galaxies, and n(z) for ensemble galaxies in tomographic bins.
Comparison between different p(z) and n(z).
Resources and skills needed
python, git, or just passion for learning photo-z estimation
Detailed description