Giovedi 30 Novembre 2017, h. 11:30 
 Aula L. Rosino Dipartimento di Fisica e Astronomia

Mario Pasquato

  INAF - Osservatorio Astronomico di Padova

 Black hole hunting with artificial intelligence




To make astronomy automatic we must:
1) break down research into a series of simple classification or regression problems;
2) teach computers how to solve these problems without human intervention.
Step 2 is already reality: computers can be taught by example, rather than being explicitly programmed. This is called machine learning, it has countless applications in everyday life and is already changing the face of astronomy.
Step 1 is harder to do, but it ultimately boils down to (realistically) simulating two or more different astrophysical scenarios, producing mock-observations, and comparing them with real data to determine which scenario is actually taking place. My project uses the search for Intermediate Mass Black Holes (IMBHs) in star clusters as a proof-of-concept example and case study in automatic astronomy. I will use a large set of simulations to generate mock observations of clusters that host an IMBH and clusters that do not, train machine learning algorithms on them, and finally classify data from actual clusters into IMBH hosts or non-hosts. In particular, I will discuss the use of photometric data (star counts, surface brightness profiles) and kinematic data (velocities from spectroscopy or proper motions, accelerations and jerks from pulsar timing).





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