Big Data - Small Planets

Event date: July 7 - July 11, 2019

    Tsevi Mazeh (Tel Aviv University)



    Astronomy is in the midst of a transformation brought on by the technological advances of the exponentially evolving information age. New detector capabilities, paired with faster computation, have brought the field to a new era, in which the use of advanced data mining and inference methods is both necessary and scientifically rewarding. Consequentially, the field of exoplanet research has gone through an explosion of information in data complexity. From a handful of exoplanets, two decades ago, we now have thousands of candidates of transiting planets coming from ground-based surveys and dedicated space missions. The discovery of these planets emerged from searching through large databases with billions of data points, with search algorithms that have been reasonably effective, but leave plenty of room for improvement with advanced statistical methods. The recently launched Transiting Exoplanet Survey Satellite (TESS) promises to deliver several hundred billions more data points, with the key distinction that the target stars will be much brighter and distributed over the entire sky, thereby making them far more amenable to follow-up observations to characterize planetary mass, size, density, orbital characteristics, and atmospheric composition.

    The field of exoplanets is ripe for a significant jump forward in methodology. Modern algorithms for sequencing, classification, or anomaly detection can provide us with methods to uncover new planetary phenomena. The workshop will bring together experts in the analysis of astronomical time series to discuss machine learning and big data challenges in exoplanet photometry, spectroscopy and population synthesis.