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Register a new userGalaxy Morphology Network: A Convolutional Neural Network Used to Study Morphology and Quenching in $sim 100,000$ SDSS and $sim 20,000$ CANDELS Galaxies
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We examine morphology-separated color-mass diagrams to study the quenching of star formation in $sim 100,000$ ($zsim0$) Sloan Digital Sky Survey (SDSS) and $sim 20,000$ ($zsim1$) Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS) galaxies. To classify galaxies morphologically, we developed Galaxy Morphology Network (GaMorNet), a convolutional neural network that classifies galaxies according to their bulge-to-total light ratio. GaMorNet does not need a large training set of real data and can be applied to data sets with a range of signal-to-noise ratios and spatial resolutions. GaMorNets source code as well as the trained models are made public as part of this work ( http://www.astro.yale.edu/aghosh/gamornet.html ). We first trained GaMorNet on simulations of galaxies with a bulge and a disk component and then transfer learned using $sim25%$ of each data set to achieve misclassification rates of $lesssim5%$. The misclassified sample of galaxies is dominated by small galaxies with low signal-to-noise ratios. Using the GaMorNet classifications, we find that bulge- and disk-dominated galaxies have distinct color-mass diagrams, in agreement with previous studies. For both SDSS and CANDELS galaxies, disk-dominated galaxies peak in the blue cloud, across a broad range of masses, consistent with the slow exhaustion of star-forming gas with no rapid quenching. A small population of red disks is found at high mass ($sim14%$ of disks at $zsim0$ and $2%$ of disks at $z sim 1$). In contrast, bulge-dominated galaxies are mostly red, with much smaller numbers down toward the blue cloud, suggesting rapid quenching and fast evolution across the green valley. This inferred difference in quenching mechanism is in agreement with previous studies that used other morphology classification techniques on much smaller samples at $zsim0$ and $zsim1$.
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We present visual-like morphologies over 16 photometric bands, from ultra-violet to near infrared, for 8,412 galaxies in the Cluster Lensing And Supernova survey with Hubble (CLASH) obtained by a convolutional neural network (CNN) model. Our model follows the CANDELS main morphological classification scheme, obtaining the probability for each galaxy at each CLASH band of being spheroid, disk, irregular, point source, or unclassifiable. Our catalog contains morphologies for each galaxy with Hmag < 24.5 in every filter where the galaxy is observed. We trained an initial CNN model using approximately 7,500 expert eyeball labels from The Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS). We created eyeball labels for 100 randomly selected galaxies per each of the 16-filters set of CLASH (1,600 galaxy images in total), where each image was classified by at least five of us. We use these labels to fine-tune the network in order to accurately predict labels for the CLASH data and to evaluate the performance of our model. We achieve a root-mean-square error of 0.0991 on the test set. We show that our proposed fine-tuning technique reduces the number of labeled images needed for training, as compared to directly training over the CLASH data, and achieves a better performance. This approach is very useful to minimize eyeball labeling efforts when classifying unlabeled data from new surveys. This will become particularly useful for massive datasets such as the ones coming from near future surveys such as EUCLID or the LSST. Our catalog consists of prediction of probabilities for each galaxy by morphology in their different bands and is made publicly available at http://www.inf.udec.cl/~guille/data/Deep-CLASH.csv.
The morphology and distribution of microcalcifications in a cluster are the most important characteristics for radiologists to diagnose breast cancer. However, it is time-consuming and difficult for radiologists to identify these characteristics, and there also lacks of effective solutions for automatic characterization. In this study, we proposed a multi-task deep graph convolutional network (GCN) method for the automatic characterization of morphology and distribution of microcalcifications in mammograms. Our proposed method transforms morphology and distribution characterization into node and graph classification problem and learns the representations concurrently. Through extensive experiments, we demonstrate significant improvements with the proposed multi-task GCN comparing to the baselines. Moreover, the achieved improvements can be related to and enhance clinical understandings. We explore, for the first time, the application of GCNs in microcalcification characterization that suggests the potential of graph learning for more robust understanding of medical images.
A fundamental feature of galaxies is their structure, yet we are just now understanding the evolution of structural properties in quantitative ways. As such, we explore the quantitative non-parametric structural evolution of 16,778 galaxies up to $zsim3$ in all five CANDELS fields, the largest collection of high resolution images of distant galaxies to date. Our goal is to investigate how the structure, as opposed to size, surface brightness, or mass, changes with time. In particular, we investigate how the concentration and asymmetry of light evolve in the rest-frame optical. To interpret our galaxy structure measurements, we also run and analyse 300 simulation realisations from IllustrisTNG to determine the timescale of mergers for the CAS system. We measure that from $z=0-3$, the median asymmetry merger timescale is $0.56^{+0.23}_{-0.18}$Gyr, and find it does not vary with redshift. Using this data, we find that galaxies become progressively asymmetric at a given mass at higher redshifts and we derive merger rates which scale as $sim(1+z)^{1.87pm0.04}$Gyr$^{-1}$, which agrees well with recent machine learning and galaxy pair approaches, removing previous inconsistencies. We also show that far-infrared selected galaxies that are invisible to textit{HST} have a negligible effect on our measurements. We also find that galaxies are more concentrated at higher redshifts. We interpret this as a sign of how their formation occurs from a smaller initial galaxy that later grows into a larger one through mergers, consistent with the size growth of galaxies from `inside-out, suggesting that the centres are the oldest parts of most galaxies.
We present reduced data and data products from the 3D-HST survey, a 248-orbit HST Treasury program. The survey obtained WFC3 G141 grism spectroscopy in four of the five CANDELS fields: AEGIS, COSMOS, GOODS-S, and UDS, along with WFC3 $H_{140}$ imaging, parallel ACS G800L spectroscopy, and parallel $I_{814}$ imaging. In a previous paper (Skelton et al. 2014) we presented photometric catalogs in these four fields and in GOODS-N, the fifth CANDELS field. Here we describe and present the WFC3 G141 spectroscopic data, again augmented with data from GO-1600 in GOODS-N. The data analysis is complicated by the fact that no slits are used: all objects in the WFC3 field are dispersed, and many spectra overlap. We developed software to automatically and optimally extract interlaced 2D and 1D spectra for all objects in the Skelton et al. (2014) photometric catalogs. The 2D spectra and the multi-band photometry were fit simultaneously to determine redshifts and emission line strengths, taking the morphology of the galaxies explicitly into account. The resulting catalog has 98,663 measured redshifts and line strengths down to $JH_{IR}leq 26$ and 22,548 with $JH_{IR}leq 24$, where we comfortably detect continuum emission. Of this sample 5,459 galaxies are at $z>1.5$ and 9,621 are at $0.7<z<1.5$, where H$alpha$ falls in the G141 wavelength coverage. Based on comparisons with ground-based spectroscopic redshifts, and on analyses of paired galaxies and repeat observations, the typical redshift error for $JH_{IR}leq 24$ galaxies in our catalog is $sigma_z approx 0.003 times (1+z)$, i.e., one native WFC3 pixel. The $3sigma$ limit for emission line fluxes of point sources is $1.5times10^{-17}$ ergs s$^{-1}$ cm$^{-2}$. We show various representations of the full dataset, as well as individual examples that highlight the range of spectra that we find in the survey.
We introduce a novel method for reconstructing the projected matter distributions of galaxy clusters with weak-lensing (WL) data based on convolutional neural network (CNN). Training datasets are generated with ray-tracing through cosmological simulations. We control the noise level of the galaxy shear catalog such that it mimics the typical properties of the existing ground-based WL observations of galaxy clusters. We find that the mass reconstruction by our multi-layered CNN with the architecture of alternating convolution and trans-convolution filters significantly outperforms the traditional reconstruction methods. The CNN method provides better pixel-to-pixel correlations with the truth, restores more accurate positions of the mass peaks, and more efficiently suppresses artifacts near the field edges. In addition, the CNN mass reconstruction lifts the mass-sheet degeneracy when applied to sufficiently large fields. This implies that this CNN algorithm can be used to measure cluster masses in a model independent way for future wide-field WL surveys.
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