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Astrostatistics & Astroinformatics

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  • How can collaborative research spanning astronomy, statistics and/or computer science enhance astronomical and astrophysical research?
  • How can advances in statistical methodology significantly improve the accuracy, precision or robustness of astronomical measurements?
  • How can rapidly evolving computer hardware and innovative algorithms be harnessed to maximally exploit "Big Data" coming from ground-based astronomical observatories such as the Sloan Digital Sky Survey (SDSS), the Atacama Large Millimeter/submillimeter Array (ALMA), the Hobby-Eberly Dark Energy Experiment (HETDEX), the Large Synoptic Survey Telescope (LSST) as well as from current and upcoming space missions like the Kepler/K2 mission, theĀ Transiting Exoplanet Survey Satellite (TESS) and the James Webb Space Telescope (JWST)?
  • How can we accurately characterize astronomical populations, while accounting for selection effects, detection limits and measurement biases? CASt faculty are incorporating a variety of techniques such as survival analysis, hierarchical Bayesian modelling, Approximate Bayesian Computing and emulators for characterizing the luminosity function of young stars, exoplanet populations, black hole populations, and the early universe.
  • How can we accurately quantify the significance of exoplanet detections and the precision of mass measurements given astronomical data with correlated "noise" due to stellar variability? CASt faculty are leading research in time series analysis, Gaussian process models, autoregressive regression, efficient samplers for high-dimensional parameter spaces, and machine learning algorithms for the analysis of photometric time series and high-precision spectra.
  • What is the optimal method of extending the three-dimensional clustering analysis of galaxies (based only on their spatial distribution) incorporating other observed properties such as their magnitudes, colors or spectra?
  • How can we apply rigorous statistical to large numbers of high-precision stellar light curves, such as those from Kepler, K2 and TESS in order to assess and understand the underlying drivers of stellar photometric variability?

Recent Milestones

  • CASt faculty and students have begun parallelizing data analysis codes for large computer clusters and graphical processing units (GPUs). CASt faculty helped lead a successful NSF proposal to establish a CyberLaboratory for Astronomy, Materials & Physics (CyberLAMP). The grant is funding a large hybrid computer cluster, featuring the lasted CPUs and hardware accelerators, including NVIDIA P100 "Pascal" GPUs and "Knight's Landing" Intel Phi processors. The cluster will be deployed at Penn State's new data center in early 2017 and will be administered by the Institute for CyberScience's Advanced CyberInfrastructure group.
  • CASt faculty are program and working group leaders for the 2016-17 SAMSI Program on Statistical, Mathematical and Computational Methods for Astronomy.
  • The Center for Exoplanets and Habitable Worlds has developed a comprehensive research effort involving professors (Ford, Dawson, Feigelson, Bastien), research scientists and students to address statistical challenges in exoplanet characterization and planet formation. Active research projects include characterizing planet host stars via their photometric flickering, detecting transiting planets in the presence of correlated noise, measuring masses of planets from spectroscopic surveys despite astrophysics jitter, characterizing planetary populations from biased astronomical surveys and comparing exoplanet populations to predictions of planet formation models.
  • CASt faculty authored a leading textbook, Modern Statistical Methods for Astronomy with R Applications and lead tutorials in astrostatistics that have trained nearly 2000 astronomers worldwide.
  • The Center for Astrostatistics (CASt) is a leader in cross-disciplinary education and research, promoting the responsible use of modern statistical methodology among astronomers and astrophysicists. CASt faculty teach graduate courses are offered in computational methods (ASTRO 527), high-performance scientific computing (ASTRO 528), and astrostatistics (ASTRO 585). These can be used to fufill requirements for Penn State's Computational Science Graduate Minor.