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Fitting pseudo-Sérsic(Spergel) light profiles to galaxies in interferometric data: the excellence of the uv-plane

Fitting pseudo-Sérsic(Spergel) light profiles to galaxies in interferometric data: the excellence of the uv-plane
Fitting pseudo-Sérsic(Spergel) light profiles to galaxies in interferometric data: the excellence of the uv-plane
Modern (sub)millimeter interferometers, such as ALMA and NOEMA, offer high angular resolution and unprecedented sensitivity. This provides the possibility to characterize the morphology of the gas and dust in distant galaxies. To assess the capabilities of current softwares in recovering morphologies and surface brightness profiles in interferometric observations, we test the performance of the Spergel model for fitting in the $uv$-plane, which has been recently implemented in the IRAM software GILDAS (uv$\_$fit). Spergel profiles provide an alternative to the Sersic profile, with the advantage of having an analytical Fourier transform, making them ideal to model visibilities in the $uv$-plane. We provide an approximate conversion between Spergel index and Sersic index, which depends on the ratio of the galaxy size to the angular resolution of the data. We show through extensive simulations that Spergel modeling in the $uv$-plane is a more reliable method for parameter estimation than modeling in the image-plane, as it returns parameters that are less affected by systematic biases and results in a higher effective signal-to-noise ratio (S/N). The better performance in the $uv$-plane is likely driven by the difficulty of accounting for correlated signal in interferometric images. Even in the $uv$-plane, the integrated source flux needs to be at least 50 times larger than the noise per beam to enable a reasonably good measurement of a Spergel index. We characterise the performance of Spergel model fitting in detail by showing that parameters biases are generally low (< 10%) and that uncertainties returned by uv$\_$fit are reliable within a factor of two. Finally, we showcase the power of Spergel fitting by re-examining two claims of extended halos around galaxies from the literature, showing that galaxies and halos can be successfully fitted simultaneously with a single Spergel model.
astro-ph.GA
Tan, Qing-Hua
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Daddi, Emanuele
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Magalhães, Victor de Souza
0117de35-ecc8-47bb-9da0-33270b216882
Gómez-Guijarro, Carlos
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Pety, Jérôme
101849a9-4349-4037-9906-161eb9b6434f
Kalita, Boris S.
aeae5b01-e470-416b-931f-4b84d8bc0375
Elbaz, David
b96eeb7d-5e94-4179-b18e-26c4ee74155d
Liu, Zhaoxuan
78d791ba-c5ee-4848-9e5f-73b2daa756d3
Magnelli, Benjamin
4c8006b2-2911-4153-9425-7269c1f9770a
Puglisi, Annagrazia
97237841-1e6d-48fb-9133-671b6f3af18b
Rujopakarn, Wiphu
f53b4c94-4c07-45a3-b277-c3ad49e2d20c
Silverman, John D.
a1ca3beb-bd5f-4547-9401-edea5433f320
Valentino, Francesco
89d5da01-4e4b-462a-88e0-ea68ce74adc8
Zhang, Shao-Bo
61ceb3a9-a7fa-41df-a792-9abdc9c9e4b0
Tan, Qing-Hua
47ae2ef8-1efb-4e94-b652-c801a028f117
Daddi, Emanuele
8c0071fc-6d92-4e8f-a9ad-42ae5043c497
Magalhães, Victor de Souza
0117de35-ecc8-47bb-9da0-33270b216882
Gómez-Guijarro, Carlos
d678ac86-e177-4b35-95e6-3b56fe7a3cf0
Pety, Jérôme
101849a9-4349-4037-9906-161eb9b6434f
Kalita, Boris S.
aeae5b01-e470-416b-931f-4b84d8bc0375
Elbaz, David
b96eeb7d-5e94-4179-b18e-26c4ee74155d
Liu, Zhaoxuan
78d791ba-c5ee-4848-9e5f-73b2daa756d3
Magnelli, Benjamin
4c8006b2-2911-4153-9425-7269c1f9770a
Puglisi, Annagrazia
97237841-1e6d-48fb-9133-671b6f3af18b
Rujopakarn, Wiphu
f53b4c94-4c07-45a3-b277-c3ad49e2d20c
Silverman, John D.
a1ca3beb-bd5f-4547-9401-edea5433f320
Valentino, Francesco
89d5da01-4e4b-462a-88e0-ea68ce74adc8
Zhang, Shao-Bo
61ceb3a9-a7fa-41df-a792-9abdc9c9e4b0

[Unknown type: UNSPECIFIED]

Record type: UNSPECIFIED

Abstract

Modern (sub)millimeter interferometers, such as ALMA and NOEMA, offer high angular resolution and unprecedented sensitivity. This provides the possibility to characterize the morphology of the gas and dust in distant galaxies. To assess the capabilities of current softwares in recovering morphologies and surface brightness profiles in interferometric observations, we test the performance of the Spergel model for fitting in the $uv$-plane, which has been recently implemented in the IRAM software GILDAS (uv$\_$fit). Spergel profiles provide an alternative to the Sersic profile, with the advantage of having an analytical Fourier transform, making them ideal to model visibilities in the $uv$-plane. We provide an approximate conversion between Spergel index and Sersic index, which depends on the ratio of the galaxy size to the angular resolution of the data. We show through extensive simulations that Spergel modeling in the $uv$-plane is a more reliable method for parameter estimation than modeling in the image-plane, as it returns parameters that are less affected by systematic biases and results in a higher effective signal-to-noise ratio (S/N). The better performance in the $uv$-plane is likely driven by the difficulty of accounting for correlated signal in interferometric images. Even in the $uv$-plane, the integrated source flux needs to be at least 50 times larger than the noise per beam to enable a reasonably good measurement of a Spergel index. We characterise the performance of Spergel model fitting in detail by showing that parameters biases are generally low (< 10%) and that uncertainties returned by uv$\_$fit are reliable within a factor of two. Finally, we showcase the power of Spergel fitting by re-examining two claims of extended halos around galaxies from the literature, showing that galaxies and halos can be successfully fitted simultaneously with a single Spergel model.

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2312.05425v1 - Author's Original
Available under License Creative Commons Attribution.
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e-pub ahead of print date: 9 December 2023
Additional Information: 23 pages, 15 figures, accepted for publication in A&A
Keywords: astro-ph.GA

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Local EPrints ID: 487417
URI: http://eprints.soton.ac.uk/id/eprint/487417
PURE UUID: 7adf6002-c9a2-4902-a84a-c53a3edfe0aa

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Date deposited: 20 Feb 2024 12:48
Last modified: 17 Mar 2024 07:38

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Contributors

Author: Qing-Hua Tan
Author: Emanuele Daddi
Author: Victor de Souza Magalhães
Author: Carlos Gómez-Guijarro
Author: Jérôme Pety
Author: Boris S. Kalita
Author: David Elbaz
Author: Zhaoxuan Liu
Author: Benjamin Magnelli
Author: Annagrazia Puglisi
Author: Wiphu Rujopakarn
Author: John D. Silverman
Author: Francesco Valentino
Author: Shao-Bo Zhang

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