Investing for Discovery in Astronomy
Joan R. Najita

TL;DR
Effective astronomy investment requires a balanced approach supporting diverse team sizes and facilities, fostering both reliable discoveries and disruptive innovations through strategic resource allocation and increased observational opportunities.
Contribution
This paper advocates for a diversified investment strategy in astronomy, emphasizing the importance of supporting both large and small teams and facilities to maximize discovery potential.
Findings
Large teams develop existing ideas, small teams enable disruptive discoveries.
Smaller facilities offer high science return per dollar, acting as growth drivers.
Increasing observing opportunities promotes risk-taking and discovery.
Abstract
How should we invest our available resources to best sustain astronomy's track record of discovery, established over the past few decades? Two strong hints come from (1) our history of astronomical discoveries and (2) literature citation patterns that reveal how discovery and development activities in science are strong functions of team size. These argue that progress in astronomy hinges on support for a diversity of research efforts in terms of team size, research tools and platforms, and investment strategies that encourage risk taking. These ideas also encourage us to examine the implications of the trend toward "big team science" and "survey science" in astronomy over the past few decades, and to reconsider the common assumption that progress in astronomy always means "trading up" to bigger apertures and facilities. Instead, the considerations above argue that we need a balanced…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsHealth and Medical Research Impacts · scientometrics and bibliometrics research · Scientific Computing and Data Management
