Retrieving Internal Kinematics of Galaxies with Deep Learning Using Single-band Optical Images

Hansen, Sakina; Conselice, Christopher J.; Fraser-McKelvie, Amelia; Ferreira, Leonardo

doi:10.3847/2515-5172/abc1db

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…This is somehow expected since spectra contain more information than imaging, as previously stated. A similar experiment is performed by Hansen et al (2020) who infer kinematic information of galaxies (velocity dispersion and rotation maps) from single band imaging.…”

Section: Deep Learning Generated Observationsmentioning

confidence: 97%

The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys

Huertas-Company

Lanusse²

2023

Publ. Astron. Soc. Aust.

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The amount and complexity of data delivered by modern galaxy surveys has been steadily increasing over the past years. New facilities will soon provide imaging and spectra of hundreds of millions of galaxies. Extracting coherent scientific information from these large and multi-modal data sets remains an open issue for the community and data-driven approaches such as deep learning have rapidly emerged as a potentially powerful solution to some long lasting challenges. This enthusiasm is reflected in an unprecedented exponential growth of publications using neural networks, which have gone from a handful of works in 2015 to an average of one paper per week in 2021 in the area of galaxy surveys. Half a decade after the first published work in astronomy mentioning deep learning, and shortly before new big data sets such as Euclid and LSST start becoming available, we believe it is timely to review what has been the real impact of this new technology in the field and its potential to solve key challenges raised by the size and complexity of the new datasets. The purpose of this review is thus two-fold. We first aim at summarising, in a common document, the main applications of deep learning for galaxy surveys that have emerged so far. We then extract the major achievements and lessons learned and highlight key open questions and limitations, which in our opinion, will require particular attention in the coming years. Overall, state-of-the-art deep learning methods are rapidly adopted by the astronomical community, reflecting a democratisation of these methods. This review shows that the majority of works using deep learning up to date are oriented to computer vision tasks (e.g. classification, segmentation). This is also the domain of application where deep learning has brought the most important breakthroughs so far. However, we also report that the applications are becoming more diverse and deep learning is used for estimating galaxy properties, identifying outliers or constraining the cosmological model. Most of these works remain at the exploratory level though which could partially explain the limited impact in terms of citations. Some common challenges will most likely need to be addressed before moving to the next phase of massive deployment of deep learning in the processing of future surveys; for example, uncertainty quantification, interpretability, data labelling and domain shift issues from training with simulations, which constitutes a common practice in astronomy.

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Section: Deep Learning Generated Observationsmentioning

confidence: 97%

The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys

Huertas-Company

Lanusse²

2023

Publ. Astron. Soc. Aust.

View full text Add to dashboard Cite

show abstract

“…It should be noted that, because of our choice to use moment maps, the models described in this work are also suitable to analyse optical IFU maps, as they will be handled similarly by the model described in this work and have been shown to encode kinematic information which can be extracted using both analytical and ML approaches (e.g. Stark et al 2018;Hansen et al 2020). This will be explored further in future work (Dawson et al, in prep).…”

Section: Input Datamentioning

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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Evidence of the Great Attractor and Great Repeller from Artificial Neural Network Imputation of Sloan Digital Sky Survey

O’Neill

2024

JHEPGC

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The Sloane Digital Sky Survey (SDSS) has been in the process of creating a 3D digital map of the Universe, since 2000AD. However, it has not been able to map that portion of the sky which is occluded by the dust gas and stars of our own Milkyway Galaxy. This research builds on work from a previous paper that sought to impute this missing galactic information using Inpainting, polar transforms and Linear Regression ANNs. In that paper, the author only attempted to impute the data in the Northern hemisphere using the ANN model, which subsequently confirmed the existence of the Great Attractor and the homogeneity of the Universe. In this paper, the author has imputed the Southern Hemisphere and discovered a region that is mostly devoid of stars. Since this area appears to be the counterpart to the Great Attractor, the author refers to it as the Great Repeller and postulates that it is an area of physical repulsion, inline with the work of GerdPommerenke and others. Finally, the paper investigates large scale structures in the imputed galaxies.

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Retrieving Internal Kinematics of Galaxies with Deep Learning Using Single-band Optical Images

Cited by 3 publications

References 11 publications

The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys

The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys

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

Evidence of the Great Attractor and Great Repeller from Artificial Neural Network Imputation of Sloan Digital Sky Survey

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