Detecting intermediate mass black holes in globular clusters with machine learning

M. Pasquato

    Research output: Contribution to journalConference articlepeer-review

    Abstract

    Mergers of stellar-mass black holes were recently observed in the gravitational wave window opened by LIGO. This puts the spotlight on dense stellar systems and their ability to create intermediate-mass black holes (IMBHs) through repeated merging. Unfortunately, attempts at direct and indirect IMBH detection in star clusters in the nearby universe have proven inconclusive as of now. Indirect detection methods attempt to constrain IMBHs through their effect on star cluster photometric and kinematic observables. They are usually based on looking for a specific, physically motivated signature. While this approach is justified, it may be suboptimal in its usage of the available data. Here I present a new indirect detection method, based on machine learning, that is unaffected by these restrictions. I reduce the scientific question whether a star cluster hosts an IMBH to a classification problem in the machine learning framework. I present preliminary results to illustrate how machine learning models are trained on simulated dataset and measure their performance on previously unseen, simulated data.

    Original languageEnglish (US)
    Pages (from-to)571-574
    Number of pages4
    JournalMemorie della Societa Astronomica Italiana - Journal of the Italian Astronomical Society
    Volume87
    Issue number4
    StatePublished - 2016
    Event2016 Cosmic-Lab Conference: Star Clusters as Cosmic Laboratories for Astrophysics, Dynamics and Fundamental Physics, MODEST 2016 - Bologna, Italy
    Duration: Apr 18 2016Apr 22 2016

    Keywords

    • Galaxy: star clusters
    • Stars: black holes

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Radiology Nuclear Medicine and imaging
    • Astronomy and Astrophysics
    • Instrumentation
    • Atomic and Molecular Physics, and Optics

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