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. 
For the past decade, Dr. Babu has worked with astronomers at Penn State and NASA to address such questions. Some efforts have been made to develop new techniques specifically for astrostatistics. But much can be learned by applying methods already known to statisticians to astronomical problems. Dr. Babu and his colleague Dr. E. Feigelson from Astronomy & Astrophysics department have been leaders in the advancement of statistical methodology for astronomical research. 

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 SCMA conference of 1991, the second SCMA conference of 1996 and the third SCMA conference of 2001 are available.  In 1996, they also wrote a 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 improvements in statistical methodology for observational astronomy, with an emphasis on time series analysis and multivariate analysis.  In the latter context, they recently published a multivariate study of gamma-ray bursts  which reports the discovery of a new class of GRBs. 

A popular Web metasite called StatCodes is being maintained 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  providing linear regressions with analytical and bootstrap error analysis, and  CENS_TAU  for estimating correlation in multiply-censored multivariate datasets.