Senior Project Advisor

Brian Hutchinson

Document Type

Project

Publication Date

Spring 2023

Keywords

Machine Learning, Binary Star Systems

Abstract

Eclipsing binaries (EB) are fundamental stellar laboratories that can be detected via long-term photometric monitoring. Analyzing the orbital motion of these EBs offers a unique ability to directly measure the parameters of both stars in the system, including masses, radii, and effective temperatures, without relying on theoretical models. Nonetheless, this process is non-trivial, and arriving to a correct solution for a given system can often take significant time. In the ongoing work, we are developing deep learning models capable of providing fast and accurate predictions of these fundamental parameters in these EBs, which will enable the characterization of an increasingly large number of systems that are now being discovered thanks to large surveys. Accurate predictions of these parameters will provide a better insight into understanding stellar evolution.

(The poster linked to this page is a placeholder for work to be submitted for publication elsewhere and linked to this page after publication.)

Department

Computer Science

Subjects - Topical (LCSH)

Machine learning; Eclipsing binaries; Double stars; Astronomical photometry

Type

Text

Rights

Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this document for commercial purposes, or for financial gain, shall not be allowed without the author’s written permission.

Language

English

Format

application/pdf

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