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

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, astronomers needed a way to reduce the data. One attempt that worked was by a French astronomer, Adrien Legendre, who published a new method, minimizing the sum of squares of errors, for determining the orbits of comets in 1805.

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 through time-creating time series. 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 modern field of astrostatistics grew in the 1990’s, stimulated by the increasing complexity of astronomical data analysis and interpretation, and by increasing awareness of advances in applied and computational statistics. Cross-disciplinary conferences began to bring astronomers and statisticians together to address statistical challenges in astronomy, astronomical image processing and galaxy clustering — and more recently, those efforts were matched to new textbooks to guide rising astronomers and statisticians.

Astronomers are moving away from observation of single objects to enormous sky surveys at different wave bands (i.e., radio, infrared, visible, X-ray). In addition, efforts such as the discovery of planets orbiting other stars, require detecting extremely weak or rare signals in complex data sets.  And, statistics is guiding progress in understanding the origin of the universe, itself — such as the modeling of faint ripples in cosmic light that suffuses the sky at millimeter wave lengths.

Advanced statistical methods are now deeply embedded in the scientific communities involved in space missions, such as the European Planck survey of the cosmic microwave background and NASA’s Kepler satellite searching for planets orbiting other stars.

And, statistical and informatics innovations are needed for the Big Data projects of wide-field, multi-epoch sky surveys such as the Catalina Real-Time Transient Survey (C.R.T.S.), the Panoramic Survey Telescope & Rapid Response System (Pan-S.T.A.R.R.S.), the Dark Energy Survey, and the forthcoming L.S.S.T..

The scientific insights from these current and future petabyte-scale sky surveys require complex statistical modeling using tools beyond the training of astronomers. Through more collaboration between statisticians and astronomers, as well as increased funding to fertilize these endeavors, such efforts can succeed.

Here’s another interesting link :

For more information, stay tuned!


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