Extended HI disks in nearby spiral galaxies

Bosma, A.

doi:10.1017/s1743921316008887

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…Molecular gas observations (typically of the CO molecule) can provide a complimentary view of these regions at high resolution, revealing the interplay between these gas phases. H is typically more extended than molecular gas, however, allowing it to trace environmental properties such as extended tidal features and the existence of dwarf companions (Hibbard et al 2001;Sancisi et al 2008;Heald et al 2011;Serra et al 2013;Bosma 2016;Koribalski et al 2018).…”

Section: Introductionmentioning

confidence: 99%

A self-supervised, physics-aware, Bayesian neural network architecture for modelling galaxy emission-line kinematics

Dawson

Davis

Gomez

et al. 2021

Monthly Notices of the Royal Astronomical Society

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In the upcoming decades large facilities, such as the SKA, will provide resolved observations of the kinematics of millions of galaxies. In order to assist in the timely exploitation of these vast datasets we blackexplore the use of a self-supervised, physics aware neural network capable of Bayesian kinematic modelling of galaxies. We demonstrate the network’s ability to model the kinematics of cold gas in galaxies with an emphasis on recovering physical parameters and accompanying modelling errors. The model is able to recover rotation curves, inclinations and disc scale lengths for both CO and H i data which match well with those found in the literature. The model is also able to provide modelling errors over learned parameters thanks to the application of quasi-Bayesian Monte-Carlo dropout. This work shows the promising use of machine learning, and in particular self-supervised neural networks, in the context of kinematically modelling galaxies. This work represents the first steps in applying such models for kinematic fitting and we propose that variants of our model would seem especially suitable for enabling emission-line science from upcoming surveys with e.g. the SKA, allowing fast exploitation of these large datasets.

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Section: Introductionmentioning

confidence: 99%

A self-supervised, physics-aware, Bayesian neural network architecture for modelling galaxy emission-line kinematics

Dawson

Davis

Gomez

et al. 2021

Monthly Notices of the Royal Astronomical Society

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Extragalactic H i Survey with FAST: First Look at the Pilot Survey Results

Kang¹,

Zhu²,

Ai³

et al. 2022

Res. Astron. Astrophys.

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We present the first data release of HI sources extracted from a pilot extra- galactic HI survey using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). We extracted sources from three-dimensional(3D) spectral data cubes to perform interactive searching and computing, yielding global HI parameters for each source, extending redshift ranges of HI emission up to z = 0.04. A total of 544 ex- tragalactic HI sources have been detected by the pilot FAST HI drift scan survey covering part of the sky region in right ascension(R.A. or α) and declination(Dec or δ) range 00 h 47 m < R.A.(J2000) < 23 h 22 m and +24 ◦ < Dec.(J2000) < +43 ◦ . Of which, 528 sources are matched with optical counterparts via examination of digi- tal optical survey databases collected from NED ⋆ and Vizier ⋆⋆ data center, and 449 of them have optical velocities. Furthermore, We detect 36 galaxies with HI mass < 10 8 M ⊙ , which is significant for the study of low-mass systems in local universe. We present catalogs for all HI detections with signal to noise ratio(S/N) greater than 5.1. The data are classified into four categories based on their S/N and baseline qualities, which are flagged with code 1 to 4 :(1) 428 sources with signal of S/N > 6.5; (2) 54 sources with signal of 5.1 ≲ S/N ≲ 6.5; (3) 28 sources with relatively poor baselines; (4) 33 sources that are partly masked by strong RFIs. Besides, we find 16 HI sources that have not been matched with any counterparts in the existing galaxy catalogs. This data release can provide guidence for the future extragalactic HI survey with FAST.

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Extended HI disks in nearby spiral galaxies

Cited by 2 publications

References 16 publications

A self-supervised, physics-aware, Bayesian neural network architecture for modelling galaxy emission-line kinematics

A self-supervised, physics-aware, Bayesian neural network architecture for modelling galaxy emission-line kinematics

Extragalactic H i Survey with FAST: First Look at the Pilot Survey Results

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