Mask galaxy: Morphological segmentation of galaxies

Farías, Humberto; Ortiz, Daniel; Damke, G.; Arancibia, M. Jaque; Solar, Mauricio

doi:10.1016/j.ascom.2020.100420

Cited by 26 publications

(22 citation statements)

References 27 publications

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“…Huertas-Company et al (2020) used a similar type of architecture to study the resolved properties of galaxies by detecting giant star-forming clumps within high redshift galaxies in the CANDELS survey. Burke et al (2019), Farias et al (2020), Tanoglidis et al (2021a) explored an alternative approach based on region based CNN architectures such as Mask RCNNs to perform similar tasks, that is, detection, deblending and classification.…”

Section: Segmentation Deblending and Pixel-level Classificationmentioning

confidence: 99%

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: Segmentation Deblending and Pixel-level Classificationmentioning

confidence: 99%

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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“…Previous efforts at extended-source detection using Voronoi tessellation techniques have been limited by computational cost and the imposition of global thresholding schemes (e.g., vtpdetect, Ebeling & Wiedenmann 1993). Methods akin to seeded region growing (see SrcExtractor, Bertin & Arnouts 1996;NoiseChisel, Akhlaghi & Ichikawa 2015) and machine learning (e.g., Morpheus, Hausen & Robertson 2020; Mask R-CNN, Farias et al 2020;galmask, Gondhalekar et al 2022) have been used for the identification of features in optical images of galaxies. However, the Poisson nature and the sparsity of the X-ray data requires statistically better targeted methods.…”

Section: Introductionmentioning

confidence: 99%

Identifying Diffuse Spatial Structures in High-energy Photon Lists

Fan

Wang

Kashyap

et al. 2023

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Data from high-energy observations are usually obtained as lists of photon events. A common analysis task for such data is to identify whether diffuse emission exists, and to estimate its surface brightness, even in the presence of point sources that may be superposed. We have developed a novel nonparametric event list segmentation algorithm to divide up the field of view into distinct emission components. We use photon location data directly, without binning them into an image. We first construct a graph from the Voronoi tessellation of the observed photon locations and then grow segments using a new adaptation of seeded region growing that we call Seeded Region Growing on Graph, after which the overall method is named SRGonG. Starting with a set of seed locations, this results in an oversegmented data set, which SRGonG then coalesces using a greedy algorithm where adjacent segments are merged to minimize a model comparison statistic; we use the Bayesian Information Criterion. Using SRGonG we are able to identify point-like and diffuse extended sources in the data with equal facility. We validate SRGonG using simulations, demonstrating that it is capable of discerning irregularly shaped low-surface-brightness emission structures as well as point-like sources with strengths comparable to that seen in typical X-ray data. We demonstrate SRGonG’s use on the Chandra data of the Antennae galaxies and show that it segments the complex structures appropriately.

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“…ESO database 2 ; APOGEE, Majewski et al (2017); LAMOST, Zhao et al (2012)) databases, which are also growing in size rapidly. Machine learning has already been applied in various tasks in the field of astronomy; for example, real-time detection of gravitational waves and parameter estimation (George & Huerta 2018), estimation of effective temperatures and metallicities of M-type stars (Antoniadis-Karnavas, A. et al 2020), estimation of initial parameters for asteroseismic modelling (Hendriks & Aerts 2019), classification of diffuse interstellar bands (Hendriks & Aerts 2019), and morphological segmentation of galaxies (Farias et al 2020).…”

Section: Introductionmentioning

confidence: 99%

SUPPNet: Neural network for stellar spectrum normalisation

Różański

Niemczura

Lemiesz

et al. 2022

A&A

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Context. Precise continuum normalisation of merged échelle spectra is a demanding task that is necessary for various detailed spectroscopic analyses. Automatic methods have limited effectiveness due to the variety of features present in the spectra of stars. This complexity often leads to the necessity for manual normalisation which is highly time-consuming. Aims. The aim of this work is to develop a fully automated normalisation tool that works with order-merged spectra and offers flexible manual fine-tuning, if necessary. Methods. The core of the proposed method uses the novel, fully convolutional deep neural network (SUPP Network) that was trained to predict a pseudo-continuum. The post-processing step uses smoothing splines that give access to regressed knots, which are useful for optional manual corrections. The active learning technique was applied to deal with possible biases that may arise from training with synthetic spectra and to extend the applicability of the proposed method to features absent in this kind of spectra. Results. The developed normalisation method was tested with high-resolution spectra of stars with spectral types from O to G, and gives a root mean squared (RMS) error over the set of test stars equal to 0.0128 in the spectral range from 3900 Å to 7000 Å and 0.0081 in the range from 4200 Å to 7000 Å. Experiments with synthetic spectra give a RMS of the order of 0.0050. Conclusions. The proposed method leads to results that are comparable to careful manual normalisation. Additionally, this approach is general and can be used in other fields of astronomy where background modelling or trend removal is a part of data processing.

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Mask galaxy: Morphological segmentation of galaxies

Cited by 26 publications

References 27 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

Identifying Diffuse Spatial Structures in High-energy Photon Lists

SUPPNet: Neural network for stellar spectrum normalisation

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