ASTRO Spring Course Prepares Students for Analyzing Astronomical Big Data

Astronomers continue to search the stars in order to archive large amounts of data in hopes of learning the mechanics and wonders of the universe. SAMSI is hosting a Spring Course, “Time Series Methods for Astronomy,” as part of their Program on Statistical, Mathematical and Computational Methods for Astronomy (ASTRO).

The course started in January and runs through April 26th of this year. This is the first time a team of astronomy scholars, is teaching this course. The course is designed by Eric Feigelson, a Distinguished Senior Scholar and Professor from Penn State University’s Department of Astronomy and Astrophysics. Feigelson is accompanied by several SAMSI Astronomy Visiting Scholars from other academic institutions, who made up the remainder of the class instruction.

The class is taught in four phases:

  • Phase I introduces students to a background of astronomical materials, including the introduction to the public domain ‘R’ Statistical Software environment.
  • Phase II reviews classical time series analysis in the time and frequency domain for data analysis.
  • Phase III consists of various expert SAMSI researchers: Ashish Mahabal [Caltech]; Eric Ford [Penn State]; Bekki Dawson [Penn State] and many more, who will be teaching advanced techniques for astronomical time series analysis.
  • Phase IV has students present course projects for class discussion.

Those taking the course will come away with an understanding of how to capture and interpolate data from sometimes problematic astronomical samples. The skills taught during the course are some of the most state-of-the-art techniques in the field. These techniques allow for astrophysicists and astrostatisticians to work together in order to potentially chart the galaxy. Due to the large amount information captured and in some cases, data samples containing uneven dispersion rates, special consideration must be taken in order to ensure all information is measured correctly. Therefore, researchers must know how to interpret the sample information provided and extrapolate accurate data.

For ongoing updates on the course, visit the Time Series Methods for Astronomy page on the SAMSI website. All presentations will be videotaped and posted on the Time Series Methods for Astronomy Video page. For questions please email astro@samsi.info.