Prof. Dovi Poznanski works on various topics in observational astrophysics and cosmology. There are two main threads in his research, the study of the supernovae arising from the collapse of massive stars, and the use of Machine Learning, and other Big-Data methods. The latter includes the study of distant galaxies, nearby stars, quasars, the interstellar medium, and systematics in cosmological measurements.
Research achievements include: the discovery of a correlation between the explosion energy and stellar progenitor mass for the most common type of supernova, thus putting new constraints on their elusive explosion mechanism; the study of core collapse supernovae as competitive cosmological probes; the development of a machine-learned method to compare spectra, which was then used to find and study further the most unusual galaxies in the largest existing sample, and to triple the known sample of a very rare and poorly understood type of quasar.
Future directions include: with the growth in data quantity and complexity, these methods become essential in order to allow for discovery, as well as a full exploitation of the information they provide. What is now exploratory use of new tools, is soon to become mainstream, essential, and transformative, just as it has become in our everyday (digital) life.
