Pushing automated morphological classifications to their limits with the Dark Energy Survey
J. Vega-Ferrero, H. Domínguez Sánchez, M. Bernardi, M. Huertas-Company, R. Morgan, B. Margalef, M. Aguena, S. Allam, J. Annis, S. Avila, D. Bacon, E. Bertin, D.… 展开作者
J. Vega-Ferrero, H. Domínguez Sánchez, M. Bernardi, M. Huertas-Company, R. Morgan, B. Margalef, M. Aguena, S. Allam, J. Annis, S. Avila, D. Bacon, E. Bertin, D. Brooks, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, A. Choi, C. Conselice, M. Costanzi, L. N. da Costa, M. E. S. Pereira, J. De Vicente, S. Desai, I. Ferrero, P. Fosalba, J. Frieman, J. García-Bellido, D. Gruen, R. A. Gruendl, J. Gschwend, G. Gutierrez, W. G. Hartley, S. R. Hinton, D. L. Hollowood, K. Honscheid, B. Hoyle, M. Jarvis, A. G. Kim, K. Kuehn, N. Kuropatkin, M. Lima, M. A. G. Maia, F. Menanteau, R. Miquel, R. L. C. Ogando, A. Palmese, F. Paz-Chinchón, A. A. Plazas, A. K. Romer, E. Sanchez, V. Scarpine, M. Schubnell, S. Serrano, I. Sevilla-Noarbe, M. Smith, E. Suchyta, M. E. C. Swanson, G. Tarle, F. Tarsitano, C. To, D. L. Tucker, T. N. Varga, R. D. Wilkinson
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Accepted for publication in MNRAS (2021 February 22); 17 pages, 16 figures
We present morphological classifications of $\sim$27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classification scheme separates: (a) early-type galaxies (ETGs) from late-types (LTGs); and (b) face-on galaxies from edge-on. Our Convolutional Neural Networks (CNNs) are trained on a small subset of DES objects with previously known classifications. These typically have $\mathrm{m}_r \lesssim 17.7~\mathrm{mag}$; we model fainter objects to $\mathrm{m}_r < 21.5$ mag by simulating what the brighter objects with well determined classifications would look like if they were at higher redshifts. The CNNs reach 97\% accuracy to $\mathrm{m}_r<21.5$ on their training sets, suggesting that they are able to recover features more accurately than the human eye. We then used the trained CNNs to classify the vast majority of the other DES images. The final catalog comprises five independent CNN predictions for each classification scheme, helping to determine if the CNN predictions are robust or not. We obtain secure classifications for $\sim$ 87\% and 73\% of the catalog for the ETG vs. LTG and edge-on vs. face-on models, respectively. Combining the two classifications (a) and (b) helps to increase the purity of the ETG sample and to identify edge-on lenticular galaxies (as ETGs with high ellipticity). Where a comparison is possible, our classifications correlate very well with Sérsic index (\textit{n}), ellipticity ($ε$) and spectral type, even for the fainter galaxies. This is the largest multi-band catalog of automated galaxy morphologies to date.
[abridged] New near-infrared surveys, using the HST, offer an unprecedented opportunity to study rest-frame optical galaxy morphologies at z>1 and to calibrate automated morphological parameters that will play a key role in classifying future massive datasets like EUCLID or LSST. We study automated parameters (e.g. CAS, Gini, M20) of massive galaxies at 1<z<3, measure their dependence on wavelength and evolution with redshift and quantify the reliability of these parameters in discriminating between visually-determined morphologies, using machine learning algorithms. We find that the relative …
We present a new morphological indicator designed for automated recognition of galaxies with faint asymmetric tidal features suggestive of an ongoing or past merger. We use this new indicator, together with preexisting diagnostics of galaxy structure to study the role of galaxy mergers in inducing (post-)starburst spectral signatures in local galaxies, and investigate whether (post-)starburst galaxies play a role in the build up of the `red sequence'. Our morphological and structural analysis of an evolutionary sample of 335 (post-)starburst galaxies in the SDSS DR7 with starburst ages 0<tSB<0…
Methods. We used different galaxy classification techniques: human labeling, multi-photometry diagrams, Naive Bayes, Logistic Regression, Support Vector Machine, Random Forest, k-Nearest Neighbors, and k-fold validation. Results. We present results of a binary automated morphological classification of galaxies conducted by human labeling, multiphotometry, and supervised Machine Learning methods. We applied its to the sample of galaxies from the SDSS DR9 with 0.02 < z < 0.1 and 24m < Mr < 19.4m. To study the classifier, we used absolute magnitudes: Mu, Mg, Mr , Mi, Mz, Mu-Mr , Mg-Mi, Mu-Mg, Mr-…
We recently developed an automated method, auto-GMM to decompose simulated galaxies. It extracts kinematic structures in an accurate, efficient, and unsupervised way. We use auto-GMM to study the stellar kinematic structures of disk galaxies from the TNG100 run of IllustrisTNG. We identify four to five structures that are commonly present among the diverse galaxy population. Structures having strong to moderate rotation are defined as cold and warm disks, respectively. Spheroidal structures dominated by random motions are classified as bulges or stellar halos, depending on how tightly bound th…
The interstellar medium is highly structured, presenting a range of morphologies across spatial scales. The large data sets resulting from observational surveys and state-of-the-art simulations studying these hierarchical structures means that identification and classification must be done in an automated fashion to be efficient. Here we present RJ-plots, an improved version of the automated morphological classification technique J-plots developed by Jaffa et al. (2018). This method allows clear distinctions between quasi-circular/elongated structures and centrally over/under-dense structures.…
The Galaxy Zoo has provided morphological data on many galaxies. Several biases have been identified in the Galaxy Zoo data. Here we report on a newly discovered selection effect: astronomers interested in studying spiral galaxies may select a set of spiral galaxies based upon a threshold in spirality (the fraction of Galaxy Zoo humans who report seeing spiral structure). SpArcFiRe is an automated tool that decomposes a spiral galaxy into its constituent spiral arms, providing objective, quantitative data on their structure. SpArcFiRe measures the pitch angle of spiral arms. We have observed t…
Galaxy morphology is a key parameter in galaxy evolution studies. The enormous number of galaxies which current and future surveys will observe demand of automated methods for morphological classification. Supervised learning techniques have been successfully used for the morphological classification of galaxies from different datasets, including Sloan Digital Sky Survey (SDSS), Mapping Galaxies with Apache Point Observatory (MaNGA) or Dark Energy Survey (DES). With these proceedings, we release the morphological catalogue for a sample of 670,000 SDSS galaxies based on the deep learning models…
Deep Extragalactic VIsible Legacy Survey (DEVILS): Morphologically-selected galaxy merger fractions and their direct comparison to close-pair samples
The University of Western Australia（西澳大利亚大学）；Monash University（莫纳什大学）；Instituto Astrofisica de Canarias（加那利天体物理研究所）；Institute of Space Sciences (ICE, CSIC)（空间科学研究所）
Classifying the morphologies of galaxies is an important step in understanding their physical properties and evolutionary histories. The advent of large-scale surveys has hastened the need to develop techniques for automated morphological classification. We train and test several convolutional neural network architectures to classify the morphologies of galaxies in both a 3-class (elliptical, lenticular, spiral) and 4-class (+irregular/miscellaneous) schema with a dataset of 14034 visually-classified SDSS images. We develop a new CNN architecture that outperforms existing models in both 3 and …
Extended radio sources are an important minority population in modern deep radio surveys, because they enable detailed investigation of the physics governing radio-emitting regions such as active galaxies and their environments. Cross-identification of radio sources with optical host galaxies is challenging for this extended population, due to their morphological complexity and multiple potential counterparts. In the first data release of the Low-frequency array (LOFAR) Two-metre Sky Survey (LoTSS DR1) the automated likelihood ratio for compact sources was supplemented by a citizen science vis…
Ample evidence exists regarding supernovae being a major contributor to interstellar dust. In this work, the deepest far-infrared observations of the Crab Nebula are used to revisit the estimation of} the dust mass present in this supernova remnant. Images in filters between 70 and 500 $μ$m taken by the PACS and SPIRE instruments on-board of the Herschel Space Observatory are used. With an automated approach we constructed the spectral energy distribution of the Crab nebula to recover the dust mass. This approach makes use of several image processing techniques (thresholding, morphological pro…
There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or a investigation for maximising their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification (Convolutional Neural Network (CNN), K-nearest neighbour, Logistic Regression, Support Vector Machine, Random Forest, and Neural Networks) by using Dark Energy Survey (DES) data combined with visual classification…
Machine learning technique for morphological classification of galaxies from SDSS. IV. Visual inspection vs CNN for merging, irregular, edge-on, barred, ringed, and with dust lanes galaxies at 0.02<z<0.1
Main Astronomical Observatory of the NAS of Ukraine（乌克兰国家科学院主天文台）
Transformer-Based Source Detection and Morphological Classification in LOFAR Deep-Field Continuum Images
University of Oxford（牛津大学）；Instituto de Astrofísica de Canarias（加那利天体物理研究所）；Universidad de La Laguna（拉古纳大学）；University of Malta（马耳他大学）；Anqing Normal University（安庆师范大学）；University of Science and Technology of China（中国科学技术大学）
We present a catalog of 5437 morphologically classified sources in the direction of the Perseus galaxy cluster core, among them 496 early-type low-mass galaxy candidates. The catalog is primarily based on V-band imaging data acquired with the William Herschel Telescope, which we used to conduct automated source detection and to derive photometry. We additionally reduced archival Subaru multiband imaging data in order to measure aperture colors and to perform a morphological classification, benefiting from 0.5 arcsec seeing conditions in the r-band data. Based on morphological and color propert…
We apply four statistical learning methods to a sample of $7941$ galaxies ($z<0.06$) from the Galaxy and Mass Assembly (GAMA) survey to test the feasibility of using automated algorithms to classify galaxies. Using $10$ features measured for each galaxy (sizes, colours, shape parameters \& stellar mass) we apply the techniques of Support Vector Machines (SVM), Classification Trees (CT), Classification Trees with Random Forest (CTRF) and Neural Networks (NN), returning True Prediction Ratios (TPRs) of $75.8\%$, $69.0\%$, $76.2\%$ and $76.0\%$ respectively. Those occasions whereby all four algor…
We have carried a detailed analysis on the impact of cosmological redshift in the non-parametric approach to automated galaxy morphology classification. We artificially redshifted each galaxy from the EFIGI 4458 sample (re-centred at $z \sim 0$) simulating SDSS, DES, LSST and HST instruments set-ups over the range $0 < z < 1.5$. We then traced how the morphometry is degraded in each $z$ using MORFOMETRYKA. In the process, we re-sampled all catalogues to several resolutions and to a diverse signal-to-noise range, allowing us to understand the impact of image sampling and noise on our measuremen…
Comparison and verification methods to trace interaction-driven disturbances in galaxies
School of Physics, University of New South Wales（新南威尔士大学物理学院）；Waterloo Centre for Astrophysics, University of Waterloo（滑铁卢大学天体物理中心）；Department of Physics and Astronomy, University of Waterloo（滑铁卢大学物理与天文系）
We present image-based evolution of galaxy mergers from the Illustris cosmological simulation at 12 time-steps over 0.5 < z < 5. To do so, we created approximately one million synthetic deep Hubble Space Telescope and James Webb Space Telescope images and measured common morphological indicators. Using the merger tree, we assess methods to observationally select mergers with stellar mass ratios as low as 10:1 completing within +/- 250 Myr of the mock observation. We confirm that common one- or two-dimensional statistics select mergers so defined with low purity and completeness, leading to hig…
Upcoming surveys with new radio observatories such as the Square Kilometer Array will generate a wealth of imaging data containing large numbers of radio galaxies. Different classes of radio galaxies can be used as tracers of the cosmic environment, including the dark matter density field, to address key cosmological questions. Classifying these galaxies based on morphology is thus an important step toward achieving the science goals of next generation radio surveys. Radio galaxies have been traditionally been classified as Fanaroff-Riley (FR) I and II, although some exhibit more complex 'bent…
We discuss the properties of H$α$ emission stars across the sample of 22035 spectra from the Gaia-ESO Survey internal data release, observed with the GIRAFFE instrument and largely belonging to stars in young open clusters. Automated fits using two independent Gaussian profiles and a third component that accounts for the nebular emission allow us to discern distinct morphological types of H$α$ line profiles with the introduction of a simplified classification scheme. All in all we find 3765 stars with intrinsic emission and sort their spectra into eight distinct morphological categories: singl…
Robustness Analysis of USmorph: II. Optimizing Feature Extraction, Dimensionality Reduction, and Clustering for Unsupervised Galaxy Morphology Classification
Anqing Normal University（安庆师范大学）；Zhengzhou University（郑州大学）；University of Science and Technology of China（中国科学技术大学）
