exoALMA. VIII. Probabilistic Moment Maps and Data Products Using Nonparametric Linear Models

exoALMA. VIII. Probabilistic Moment Maps and Data Products Using Nonparametric Linear Models

Extracting robust inferences on physical quantities from disk kinematics measured from Doppler-shifted molecular line emission is challenging due to the data’s size and complexity. In this paper, we develop a flexible linear model of the intensity distribution in each frequency channel, accounting f...

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Main Authors: Thomas Hilder, Andrew R. Casey, Daniel J. Price, Christophe Pinte, Andrés F. Izquierdo, Caitlyn Hardiman, Jaehan Bae, Marcelo Barraza-Alfaro, Myriam Benisty, Gianni Cataldi, Pietro Curone, Ian Czekala, Stefano Facchini, Daniele Fasano, Mario Flock, Misato Fukagawa, Maria Galloway-Sprietsma, Himanshi Garg, Cassandra Hall, Iain Hammond, Jane Huang, John D. Ilee, Kazuhiro Kanagawa, Geoffroy Lesur, Cristiano Longarini, Ryan Loomis, Ryuta Orihara, Giovanni Rosotti, Jochen Stadler, Richard Teague, Hsi-Wei Yen, Gaylor Wafflard, Andrew J. Winter, Lisa Wölfer, Tomohiro C. Yoshida, Brianna Zawadzki
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:The Astrophysical Journal Letters
Subjects:
Astronomy data modeling
Nonparametric inference
Linear regression
Radio interferometry
Protoplanetary disks
Planet formation
Online Access:https://doi.org/10.3847/2041-8213/adc435
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Summary:Extracting robust inferences on physical quantities from disk kinematics measured from Doppler-shifted molecular line emission is challenging due to the data’s size and complexity. In this paper, we develop a flexible linear model of the intensity distribution in each frequency channel, accounting for spatial correlations from the point-spread function. The analytic form of the model’s posterior enables probabilistic data products through sampling. Our method debiases peak intensity, peak velocity, and line width maps, particularly in disk substructures that are only partially resolved. These are needed in order to measure disk mass, turbulence, and pressure gradients and detect embedded planets. We analyze HD 135344B, MWC 758, and CQ Tau, finding velocity substructures 50–200 m s ^−1 greater than with conventional methods. Additionally, we combine our approach with discminer in a case study of J1842. We find that uncertainties in stellar mass and inclination increase by an order of magnitude due to the more realistic noise model. More broadly, our method can be applied to any problem requiring a probabilistic model of an intensity distribution conditioned on a point-spread function.
ISSN:2041-8205

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