SDSS, LSST, and Gaia: Lessons and Synergies

TL;DR

This paper discusses the impact of large-scale surveys like SDSS, Gaia, and LSST on Milky Way studies, emphasizing the importance of early data release and iterative analysis to maximize scientific progress.

Contribution

It provides lessons from SDSS experience and advocates for incremental data releases from surveys to enhance scientific discovery and community engagement.

Findings

Early and frequent data releases accelerate scientific progress.

Incremental improvements in data products benefit the community.

Continuous data updates require adaptable analysis methods.

Abstract

The advent of deep, wide, accurate, digital photometric surveys exemplified by the Sloan Digital Sky Survey (SDSS) has had a profound impact on studies of the Milky Way. In the past decade, we have transitioned from a scarcity to an (over)abundance of precise, well calibrated, observations of stars over a large fraction of the Galaxy. The avalanche of data will continue throughout this decade, culminating with Gaia and LSST. This new reality will necessitate changes in methodology, habits, and expectations both on the side of the large survey projects as well as the astrophysics community at large. We argue, based on the experience with SDSS, that surveys should release data as early and often as possible incorporating incremental improvements in each subsequent release, as opposed to holding off for a single, big, final release. The scientific community will need to reciprocate by performing analyses and (re-analyses) appropriate to the current fidelity of the released data, understanding that these are continually evolving and improving products.