Center for Astrostatistics

Researchers in observational astronomy often encounter situations where the scientific goals require a statistical interpretation of rather complex data. These problems cover a vast range of statistical issues, such as:

- When does the spatial distribution of galaxies in a cluster indicate subclustering? When do groupings of photons on an X-ray detector indicate a source is extended? These questions lie in the purview of the well-established statistical field of spatial point processes.
- How does one establish luminosity functions, compare samples, or search for correlations in data subject to flux limits? Flux limits cause statistical censoring (i.e. non- detections of known objects) or truncation (i.e. faint objects are missing entirely).
- How can one best discriminate stars from galaxies in digital optical surveys? What is the most valid classification scheme for spiral galaxy morphologies or emission line AGN? These are questions in multivariate analysis, a huge field that is rarely tapped to solve astronomical problems.
- How can one interpret quasi-periodic oscillations in X-ray sources, or best establish periodicities in sparse unevenly- spaced data? These are problems in time series analysis.
- What are the best estimate of Hubble's constant and the age of the Universe derivable from cosmic distance scale studies? This depends on a thorough understanding of linear regression techniques, including those treating measurement errors.

Feigelson and Babu organized international conferences called Statistical Challenges in Modern Astronomy (SCMA) held at Penn State. The purpose is to enhance the dialog between astronomers and statisticians on important research issues. Proceedings of the first (1991), second (1996) and third (2001) SCMA conferences are available. In 1996, we wrioe a brief monograph called Astrostatistics which introduces astronomer and statistician readers to the other field, and provides the first overview of astrostatistical issues. An extensive bibliography and index assists readers in pursuing topics introduced in the book. Drs. Feigelson and Babu have spoken at many meetings in both fields, promoting improvments in statistical methodology for observational astronomy, with an emphasis on time series analysis and multivariate analysis. In the latter context, we published a multivariate study of gamma-ray bursts which reports the discovery of a new class of GRBs.

During the late 1990s, Dr. Feigelson joined with Drs. Akritas in Statistics to form the Statistical Consulting Center for Astronomy. Operating by email and WWW, it gave quick reliable statistical advice for practicing astronomers as they encounter statistical problems. A wide range of problems are addressed such as appropriate treatment of known measurement errors, interpretation of Poisson processes, and parameter estimation in nonlinear regression.

We also created a popular Web metasite called
StatCodes
which provides hypertext links to many statistical codes and services on
the Web. Among many codes available from StatCodes are three developed at
Penn State: ASURV
implementing a suite of survival methods treating censored data (i.e.
with nondetections),
SLOPES
and
BCES
providing several weighted and unweighted linear regressions with analytical
and bootstrap error analysis, and
CENS_TAU
for estimating correlation in multiply-censored multivariate datasets.