exoALMA. VIII. Probabilistic moment maps and data products using nonparametric linear models

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

doi:10.3847/2041-8213/adc435

exoALMA. VIII. Probabilistic moment maps and data products using nonparametric linear models

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 HammondJane 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

^*Corresponding author for this work

School of Physics and Astronomy

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Article number	L13
Number of pages	18
Journal	Astrophysical Journal Letters
Volume	984
Issue number	1
Early online date	28 Apr 2025
DOIs	https://doi.org/10.3847/2041-8213/adc435
Publication status	Published - 1 May 2025

Keywords

Astronomy data modeling
Nonparametric inference
Linear regression
Radio interferometry
Protoplanetary disks
Planet formation
Planetary-disk interactions

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Hilder, T., Casey, A. R., Price, D. J., Pinte, C., Izquierdo, A. F., Hardiman, C., Bae, J., Barraza-Alfaro, M., Benisty, M., Cataldi, G., Curone, P., Czekala, I., Facchini, S., Fasano, D., Flock, M., Fukagawa, M., Galloway-Sprietsma, M., Garg, H., Hall, C., ... Zawadzki, B. (2025). exoALMA. VIII. Probabilistic moment maps and data products using nonparametric linear models. Astrophysical Journal Letters, 984(1), Article L13. https://doi.org/10.3847/2041-8213/adc435

@article{a125bd188e1b4b04a0010ee9d10ae82a,

title = "exoALMA. VIII. Probabilistic moment maps and data products using nonparametric linear models",

abstract = "Extracting robust inferences on physical quantities from disk kinematics measured from Doppler-shifted molecular line emission is challenging due to the data{\textquoteright}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{\textquoteright}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.",

keywords = "Astronomy data modeling, Nonparametric inference, Linear regression, Radio interferometry, Protoplanetary disks, Planet formation, Planetary-disk interactions",

author = "Thomas Hilder and Casey, \{Andrew R.\} and Price, \{Daniel J.\} and Christophe Pinte and Izquierdo, \{Andr{\'e}s F.\} and Caitlyn Hardiman and Jaehan Bae and Marcelo Barraza-Alfaro and Myriam Benisty and Gianni Cataldi and Pietro Curone and Ian Czekala and Stefano Facchini and Daniele Fasano and Mario Flock and Misato Fukagawa and Maria Galloway-Sprietsma and Himanshi Garg and Cassandra Hall and Iain Hammond and Jane Huang and Ilee, \{John D.\} and Kazuhiro Kanagawa and Geoffroy Lesur and Cristiano Longarini and Ryan Loomis and Ryuta Orihara and Giovanni Rosotti and Jochen Stadler and Richard Teague and Hsi-Wei Yen and Gaylor Wafflard and Winter, \{Andrew J.\} and Lisa W{\"o}lfer and Yoshida, \{Tomohiro C.\} and Brianna Zawadzki",

note = "Funding: T.H., C.H., and I.H. are supported by Australian Government Research Training Program (RTP) scholarships. The Flatiron Institute is a division of the Simons Foundation. J.B. acknowledges support from NASA XRP grant No. 80NSSC23K1312. M.B., D.F., and J.S. have received funding from the European Research Council (ERC) under the European Union{\textquoteright}s Horizon 2020 research and innovation program (PROTOPLANETS, grant agreement No. 101002188). Computations by J.S. have been performed on the “Mesocentre SIGAMM” machine, hosted by Observatoire de la Cote d{\textquoteright}Azur. P.C. acknowledges support by the Italian Ministero dell{\textquoteright}Istruzione, Universit{\`a} e Ricerca through the grant Progetti Premiali 2012—iALMA (CUP C52I13000140001) and by the ANID BASAL project FB210003. S.F. is funded by the European Union (ERC, UNVEIL, 101076613) and acknowledges the financial contribution from PRIN-MUR 2022YP5ACE. M.F. is supported by a grant-in-aid from the Japan Society for the Promotion of Science (KAKENHI; No. JP22H01274). J.D.I. acknowledges support from an STFC Ernest Rutherford Fellowship (ST/W004119/1) and a University Academic Fellowship from the University of Leeds. Support for A.F.I. was provided by NASA through the NASA Hubble Fellowship grant No. HST-HF2-51532.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555. C.L. has received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 823823 (DUSTBUSTERS) and by the UK Science and Technology Research Council (STFC) via the consolidated grant ST/W000997/1. C.P. and D.P. acknowledge Australian Research Council funding via FT170100040, DP18010423, DP220103767, and DP240103290. A.C. acknowledges Australian Research Council funding via DP210100018. G.R. acknowledges funding from the Fondazione Cariplo, grant No. 2022-1217, and the European Research Council (ERC) under the European Union{\textquoteright}s Horizon Europe Research \& Innovation Program under grant agreement No. 101039651 (DiscEvol). H.-W.Y. acknowledges support from the National Science and Technology Council (NSTC) in Taiwan through grant NSTC 113-2112-M-001-035- and from the Academia Sinica Career Development Award (AS-CDA-111-M03). G.W.F. acknowledges support from the European Research Council (ERC) under the European Union Horizon 2020 research and innovation program (grant agreement No. 815559, MHDiscs).",

year = "2025",

month = may,

day = "1",

doi = "10.3847/2041-8213/adc435",

language = "English",

volume = "984",

journal = "Astrophysical Journal Letters",

issn = "2041-8205",

publisher = "American Astronomical Society",

number = "1",

}

Hilder, T, Casey, AR, Price, DJ, Pinte, C, Izquierdo, AF, Hardiman, C, Bae, J, Barraza-Alfaro, M, Benisty, M, Cataldi, G, Curone, P, Czekala, I, Facchini, S, Fasano, D, Flock, M, Fukagawa, M, Galloway-Sprietsma, M, Garg, H, Hall, C, Hammond, I, Huang, J, Ilee, JD, Kanagawa, K, Lesur, G, Longarini, C, Loomis, R, Orihara, R, Rosotti, G, Stadler, J, Teague, R, Yen, H-W, Wafflard, G, Winter, AJ, Wölfer, L, Yoshida, TC & Zawadzki, B 2025, 'exoALMA. VIII. Probabilistic moment maps and data products using nonparametric linear models', Astrophysical Journal Letters, vol. 984, no. 1, L13. https://doi.org/10.3847/2041-8213/adc435

TY - JOUR

T1 - exoALMA. VIII. Probabilistic moment maps and data products using nonparametric linear models

AU - Hilder, Thomas

AU - Casey, Andrew R.

AU - Price, Daniel J.

AU - Pinte, Christophe

AU - Izquierdo, Andrés F.

AU - Hardiman, Caitlyn

AU - Bae, Jaehan

AU - Barraza-Alfaro, Marcelo

AU - Benisty, Myriam

AU - Cataldi, Gianni

AU - Curone, Pietro

AU - Czekala, Ian

AU - Facchini, Stefano

AU - Fasano, Daniele

AU - Flock, Mario

AU - Fukagawa, Misato

AU - Galloway-Sprietsma, Maria

AU - Garg, Himanshi

AU - Hall, Cassandra

AU - Hammond, Iain

AU - Huang, Jane

AU - Ilee, John D.

AU - Kanagawa, Kazuhiro

AU - Lesur, Geoffroy

AU - Longarini, Cristiano

AU - Loomis, Ryan

AU - Orihara, Ryuta

AU - Rosotti, Giovanni

AU - Stadler, Jochen

AU - Teague, Richard

AU - Yen, Hsi-Wei

AU - Wafflard, Gaylor

AU - Winter, Andrew J.

AU - Wölfer, Lisa

AU - Yoshida, Tomohiro C.

AU - Zawadzki, Brianna

N1 - Funding: T.H., C.H., and I.H. are supported by Australian Government Research Training Program (RTP) scholarships. The Flatiron Institute is a division of the Simons Foundation. J.B. acknowledges support from NASA XRP grant No. 80NSSC23K1312. M.B., D.F., and J.S. have received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (PROTOPLANETS, grant agreement No. 101002188). Computations by J.S. have been performed on the “Mesocentre SIGAMM” machine, hosted by Observatoire de la Cote d’Azur. P.C. acknowledges support by the Italian Ministero dell’Istruzione, Università e Ricerca through the grant Progetti Premiali 2012—iALMA (CUP C52I13000140001) and by the ANID BASAL project FB210003. S.F. is funded by the European Union (ERC, UNVEIL, 101076613) and acknowledges the financial contribution from PRIN-MUR 2022YP5ACE. M.F. is supported by a grant-in-aid from the Japan Society for the Promotion of Science (KAKENHI; No. JP22H01274). J.D.I. acknowledges support from an STFC Ernest Rutherford Fellowship (ST/W004119/1) and a University Academic Fellowship from the University of Leeds. Support for A.F.I. was provided by NASA through the NASA Hubble Fellowship grant No. HST-HF2-51532.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555. C.L. has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 823823 (DUSTBUSTERS) and by the UK Science and Technology Research Council (STFC) via the consolidated grant ST/W000997/1. C.P. and D.P. acknowledge Australian Research Council funding via FT170100040, DP18010423, DP220103767, and DP240103290. A.C. acknowledges Australian Research Council funding via DP210100018. G.R. acknowledges funding from the Fondazione Cariplo, grant No. 2022-1217, and the European Research Council (ERC) under the European Union’s Horizon Europe Research & Innovation Program under grant agreement No. 101039651 (DiscEvol). H.-W.Y. acknowledges support from the National Science and Technology Council (NSTC) in Taiwan through grant NSTC 113-2112-M-001-035- and from the Academia Sinica Career Development Award (AS-CDA-111-M03). G.W.F. acknowledges support from the European Research Council (ERC) under the European Union Horizon 2020 research and innovation program (grant agreement No. 815559, MHDiscs).

PY - 2025/5/1

Y1 - 2025/5/1

N2 - 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.

AB - 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.

KW - Astronomy data modeling

KW - Nonparametric inference

KW - Linear regression

KW - Radio interferometry

KW - Protoplanetary disks

KW - Planet formation

KW - Planetary-disk interactions

UR - https://www.scopus.com/pages/publications/105004242224

U2 - 10.3847/2041-8213/adc435

DO - 10.3847/2041-8213/adc435

M3 - Article

AN - SCOPUS:105004242224

SN - 2041-8205

VL - 984

JO - Astrophysical Journal Letters

JF - Astrophysical Journal Letters

IS - 1

M1 - L13

ER -

exoALMA. VIII. Probabilistic moment maps and data products using nonparametric linear models

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exoALMA. I. Science goals, project design, and data products

exoALMA. II. Data calibration and imaging pipeline

exoALMA. III. Line-intensity modeling and system property extraction from protoplanetary disks

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