REVIEW 5 cited by
Improving galaxy morphologies for SDSS with Deep Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Improving galaxy morphologies for SDSS with Deep Learning
read the original abstract
We present a morphological catalogue for $\sim$ 670,000 galaxies in the Sloan Digital Sky Survey in two flavours: T-Type, related to the Hubble sequence, and Galaxy Zoo 2 (GZ2 hereafter) classification scheme. By combining accurate existing visual classification catalogues with machine learning, we provide the largest and most accurate morphological catalogue up to date. The classifications are obtained with Deep Learning algorithms using Convolutional Neural Networks (CNNs). We use two visual classification catalogues, GZ2 and Nair & Abraham (2010), for training CNNs with colour images in order to obtain T-Types and a series of GZ2 type questions (disk/features, edge-on galaxies, bar signature, bulge prominence, roundness and mergers). We also provide an additional probability enabling a separation between pure elliptical (E) from S0, where the T-Type model is not so efficient. For the T-Type, our results show smaller offset and scatter than previous models trained with support vector machines. For the GZ2 type questions, our models have large accuracy (> 97\%), precision and recall values (> 90\%) when applied to a test sample with the same characteristics as the one used for training. The catalogue is publicly released with the paper.
Forward citations
Cited by 5 Pith papers
-
Identifying backsplash galaxies using machine learning
Machine learning trained on The Three Hundred simulations identifies backsplash galaxies in cluster outskirts with ~75% purity/completeness, and has been applied to HI-tail galaxies in Virgo.
-
Too many quiescent late-type galaxies or a matter of misclassification?
Quiescent late-type galaxies in SDSS morphological catalogues are overestimated by a factor of ~2 due to misclassified edge-on S0 galaxies.
-
ALMA CO-CAVITY II. Resolved Scaling Relations in Void Galaxies
In void galaxies, the resolved relation between molecular gas and stellar mass is the tightest of the three star-formation scaling relations, with 0.16 dex scatter.
-
A machine learning approach to estimating HI deficiency in galaxies
A random forest model trained on isolated ALFALFA-SDSS galaxies predicts HI mass from optical properties with RMSE≈0.22 dex, revealing a 0.15 dex median HI deficiency increase in dense environments.
-
COSMOS-Web: does halo mass alone shape the clustering of star-forming and quiescent galaxies?
Quiescent galaxies cluster more strongly than star-forming ones by 0.5-1 dex after halo-mass matching, with one-halo conformity up to z~2 that disappears at higher redshifts.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.