Multiband Galaxy Morphologies for CLASH: A Convolutional Neural Network Transferred from CANDELS

We present visual-like morphologies over 16 photometric bands, from ultraviolet to near-infrared, for 8412 galaxies in the Cluster Lensing And Supernova survey with Hubble (CLASH) obtained using a convolutional neural network (ConvNet) model. Our model follows the Cosmic Assembly Near-IR Deep Extrag...

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Bibliographic Details
Published in:Publications of the Astronomical Society of the Pacific 2019-10, Vol.131 (1004), p.108002
Main Authors: Pérez-Carrasco, M., Cabrera-Vives, G., Martinez-Marin, M., Cerulo, P., Demarco, R., Protopapas, P., Godoy, J., Huertas-Company, M.
Format: Article
Language:English
Subjects:
Astrophysics
Classification
Galaxies
Morphology
Neural networks
Performance evaluation
Physics
Polls & surveys
Stars & galaxies
Supernova
Training
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Summary:We present visual-like morphologies over 16 photometric bands, from ultraviolet to near-infrared, for 8412 galaxies in the Cluster Lensing And Supernova survey with Hubble (CLASH) obtained using a convolutional neural network (ConvNet) model. Our model follows the Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (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 ConvNet model using approximately 7500 expert eyeball labels from CANDELS. We created eyeball labels for 100 randomly selected galaxies per each of the 16-filter set of CLASH (1600 galaxy images in total), where each image was classified by at least five of us. We use these labels to fine-tune the network 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 data sets such as those 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.
ISSN:0004-6280
1538-3873
DOI:10.1088/1538-3873/aaeeb4