Spectroscopic Needs for Training of LSST Photometric Redshifts

TL;DR

This paper discusses the importance of extensive spectroscopic data for training LSST photometric redshift algorithms, highlighting potential instruments and strategies to improve redshift accuracy for dark energy research.

Contribution

It identifies key spectroscopic facilities and configurations that can efficiently provide training data to enhance LSST photometric redshift performance.

Findings

Larger training sets reduce photo-z RMS errors.

PFS on Subaru offers rapid baseline training data.

High multiplexing spectrographs improve training efficiency.

Abstract

This white paper summarizes those conclusions of the Snowmass White Paper "Spectroscopic Needs for Imaging Dark Energy Experiments" (arXiv:1309.5384) which are relevant to the training of LSST photometric redshifts; i.e., the use of spectroscopic redshifts to improve algorithms and reduce photo-z errors. The larger and more complete the available training set is, the smaller the RMS error in photo-z estimates should be, increasing LSST's constraining power. Among the better US-based options for this work are the proposed MANIFEST fiber feed for the Giant Magellan Telescope or (with lower survey speed) the WFOS spectrograph on the Thirty Meter Telescope (TMT). Due to its larger field of view and higher multiplexing, the PFS spectrograph on Subaru would be able to obtain a baseline training sample faster than TMT; comparable performance could be achieved with a highly-multiplexed spectrograph on Gemini with at least a 20 arcmin diameter field of view.