Chairs: Tianqing Zhang, Alex Malz

This session will focus on photometric redshift estimates and quality metrics for LSST objects.

Part of this session will be presented by the Redshift Infrastructure Assessment Layers (RAIL) team, who would like to encourage contributions and to provide a hands-on tutorial session.

Please contact the session chair with any questions, or if you'd like to volunteer to speak or help with this session.

Invited talks in shared slide deck

Rubin Data Management's Photo-z Roadmap (Melissa Graham)

Updates on software development and advanced data products to support Rubin's photo-z production (Julia Gschwend)

DeepDISC photo-z: Using Deep Learning Instance Segmentation models for Image-based Photo-z Estimation (Grant Merz)

Photometric redshifts with RAIL-LePhare (Raphael Shirley & Johann Cohen-Tanugi)

RAIL updates and live demo (Tianqing Zhang)

Contributed talks:

Direct Redshift Calibration of Photometric Galaxy Samples (Noah Weaverdyck)

Photometric redshift estimation from galaxy images with machine learning (Simona Mei)