2022ApJ...941..192T


Query : 2022ApJ...941..192T

2022ApJ...941..192T - Astrophys. J., 941, 192 (2022/December-3)

Locating Hidden Exoplanets in ALMA Data Using Machine Learning.

TERRY J.P., HALL C., ABREAU S. and GLEYZER S.

Abstract (from CDS):

Exoplanets in protoplanetary disks cause localized deviations from Keplerian velocity in channel maps of molecular line emission. Current methods of characterizing these deviations are time consuming,and there is no unified standard approach. We demonstrate that machine learning can quickly and accurately detect the presence of planets. We train our model on synthetic images generated from simulations and apply it to real observations to identify forming planets in real systems. Machine-learning methods, based on computer vision, are not only capable of correctly identifying the presence of one or more planets, but they can also correctly constrain the location of those planets.

Abstract Copyright: © 2022. The Author(s). Published by the American Astronomical Society.

Journal keyword(s): Exoplanet astronomy - Exoplanet detection methods - Hydrodynamical simulations - Protoplanetary disks

Simbad objects: 2

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Number of rows : 2
N Identifier Otype ICRS (J2000)
RA
ICRS (J2000)
DEC
Mag U Mag B Mag V Mag R Mag I Sp type #ref
1850 - 2026
#notes
1 HD 97048 Ae* 11 08 03.3109731720 -77 39 17.490777444 9.03 8.76 9.00   8.64 A0Vep 590 0
2 HD 163296 Ae* 17 56 21.2881851168 -21 57 21.871819008 7.00 6.93 6.85 6.86 6.67 A3VaekA1mA1 1301 0

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