Skip to content

Instantly share code, notes, and snippets.

What would you like to do?

Background and Purpose

Over the last four decades, AURA has driven a science model where astronomers and astrophysicists from any University or Institution, through rigorous peer-review, can access forefront facilities without having to be “black-belt” experts in the complex machinery of modern ground or space based telescopes. AURA’s most successful model of this broad engagement has been the Hubble Space Telescope, which, when measured by the metric of number of refereed papers/year integrated over the lifetime of the Observatory, has become the most productive science facility in history. Today, AURA confronts a new challenge: they are building a number of new facilities, which are specifically designed to generate huge data sets, most notably the Large Synoptic Survey Telescope (LSST). LSST will continuously survey the entire Southern sky every three nights, generating over 15 trillion bytes of raw data per night. The over-arching science goals driving this facility are well known: The Nature of Dark Matter and Dark Energy, Cataloging the Solar System, the Structure of the Milky Way, and Exploring the Dynamic Universe. However, the AURA-LSST Team face the enormous challenge of how to turn these petabytes of data into tangible, accessible science results that fulfill the mission of LSST. More importantly, in the spirit of the AURA science model, how do we maximize the science return form LSST by enabling any astronomer, astrophysics, or physicist to extract science from this incredibly large, rich and dynamic data set without also having to be a “black-belt” large data analyst and/or computer scientist? We are also beginning to confront similar challenges in the space arena with the space missions such as WFIRST. How do we create a new generation, data intensive ‘Great Observatory’? AURA proposes a series of tiered workshops entitled “Petabytes to Science” with the goal of enabling the broad Astronomical and Physics communities to:

  • Close the divide first between LSST petabytes and the exciting transformational science the LSST facility promises
  • Expand our discussion to data sets such as those generated by space missions such as WFIRST
  • Establish a viable model for a “data intensive Great Observatory”

Workshop 1 (Kavli Salon style): would bring together a small (7 – 10) expert group from within and outside AURA, across both the ground-based and space-based arenas who can define the technical and operational issues that will challenge a non-expert scientific community when they confront a data set of LSST’s scale. This group will plan a following set of workshops to explore the likely tools and techniques required to enable science to be extracted from the LSST data.

Workshop 2 (Las Vegas workshop): These workshops aim to identify the highest priority technologies and capabilities required for a broad-based user community to take full scientific advantage of the opportunities in data-intensive astronomy being created by the Large Synoptic Survey Telescope (LSST), the Wide Field Infrared Space Telescope (WFIRST), and other massive survey facilities.

Workshop 3: TBD

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment