Jogesh Babu, professor of statistics, left, and Eric Feigelson, professor of astronomy and astrophysics, are bringing statistical principles back to astronomy. |

is reacquainting old friends

Astronomers were instrumental in establishing the principles of statistics during the 17th through 19th centuries, but statistics and astronomy diverged in the early 20th century. Now, a team of researchers is bringing statistical principles back to astronomy.

"In the mid-19th century, the focus of statistics shifted to the social
sciences, and that of astronomy moved to quantum mechanics, thermodynamics
and electromagnetism, using such mathematical methods as differential equations,"
said **Eric Feigelson,** professor of astronomy and astrophysics. "Today,
astronomers are not taught the latest statistical methods."

This has not always been the case. Newton's description of the motion of the heavens based on the gravitation laws created a need for statistics, and a variety of statistical practices were developed for astronomy.

"Because Newton made it possible to make repetitive, accurate measurements of planetary characteristics, there were more data available than the astronomers could deal with," the researchers said.

"Astronomers needed a way to reduce the data," said **Jogesh Babu,**
professor of statistics, who is the statistical half of the team.

One attempt that worked was by a French astronomer, Adrien Legendre, who published a new method for determining the orbits of comets in 1805.

"The situation is similar today," the researchers said. "Modern observations produce gigabytes of information everyday. Over a year, terabytes of information are not unusual."

These huge amounts of data pose problems for astronomers not only because of their size, but also because the number of individual properties recorded are large, creating multivariate databases. Modern techniques now also make it possible to record information continuously. These types of databases are best handled with such statistical methods as time series analysis, sampling theory, multivariate analysis and nonlinear regressions. Applying such methods to astronomy forms the basis of the newly named field of astrostatistics.

"The first problem we tackled was a method for dealing with data we know exists but is below our ability to record," Feigelson and Babu said.

That method proved to be survival analysis, the same method used to estimate the lifetime of light bulbs and the survival rate of cancer patients. No one wants to wait around for the last light bulb to sputter out or the last laboratory animal to die to determine their average life spans, so statisticians developed methods to compute the averages before the last subjects expire. This same method works for astronomical objects that are too faint to be detected.

"Astronomy had a need and statistics had an answer," said Babu. "There
may be many other areas where statistics already has the methods and there
may be areas where astronomy can provide new problems for the statisticians
to solve."