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Kinematic Planetary Signature Finder (KPSFinder): convolutional neural network-based tool to search for exoplanets in ALMA data

Published online by Cambridge University Press:  01 August 2025

Jaehan Bae Open the ORCID record for Jaehan Bae [Opens in a new window]
Jaehan Bae*
Affiliation:
Department of Astronomy, University of Florida, Gainesville, FL 32611, USA
*
email: jbae@ufl.edu
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Abstract

One of the best and most direct ways to study planet formation processes is to observe young planets while they are forming within their birth protoplanetary disks. As they form, planets tidally interact with their parental disk and produce observable signatures. Recent observations have demonstrated that kinematic planetary signatures (KPS), the perturbed velocity fields of the gas in the protoplanetary disk in the vicinity of the planet, can be observed with the Atacama Large Millimeter/submillimeter Array (ALMA). Here, I introduce a machine learning-based tool KPSFinder (Kinematic Planetary Signature Finder), which aims to find KPS robustly and efficiently.

Keywords

exoplanetsprotoplanetary disksradio interferometrymachine learningconvolutional neural network

Information

Type
Poster Paper
Information
Proceedings of the International Astronomical Union , Volume 19 , Symposium S368: Machine Learning in Astronomy: Possibilities and Pitfalls , August 2023 , pp. 101 - 103
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of International Astronomical Union

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References

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