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1. Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks

   https://doi.org/10.1093/mnras/stac3228  

   Cheng, T-Y ; Domínguez Sánchez, H. ; Vega-Ferrero, J. ; Conselice, C. J. ; Siudek, M. ; Aragón-Salamanca, A. ; Bernardi, M. ; Cooke, R. ; Ferreira, L. ; Huertas-Company, M. ; et al ( November 2022 , Monthly Notices of the Royal Astronomical Society)

   We compare the two largest galaxy morphology catalogues, which separate early- and late-type galaxies at intermediate redshift. The two catalogues were built by applying supervised deep learning (convolutional neural networks, CNNs) to the Dark Energy Survey data down to a magnitude limit of ∼21 mag. The methodologies used for the construction of the catalogues include differences such as the cutout sizes, the labels used for training, and the input to the CNN – monochromatic images versus gri-band normalized images. In addition, one catalogue is trained using bright galaxies observed with DES (i < 18), while the other is trained with bright galaxies (r < 17.5) and ‘emulated’ galaxies up to r-band magnitude 22.5. Despite the different approaches, the agreement between the two catalogues is excellent up to i < 19, demonstrating that CNN predictions are reliable for samples at least one magnitude fainter than the training sample limit. It also shows that morphological classifications based on monochromatic images are comparable to those based on gri-band images, at least in the bright regime. At fainter magnitudes, i > 19, the overall agreement is good (∼95 per cent), but is mostly driven by the large spiral fraction in the two catalogues. In contrast, the agreement within the elliptical population is not as good, especially at faint magnitudes. By studying the mismatched cases, we are able to identify lenticular galaxies (at least up to i < 19), which are difficult to distinguish using standard classification approaches. The synergy of both catalogues provides an unique opportunity to select a population of unusual galaxies.

    

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2. Knee Osteoarthritis Classification Using 3D CNN and MRI

   https://doi.org/10.3390/app11115196  

   Guida, Carmine ; Zhang, Ming ; Shan, Juan ( June 2021 , Applied Sciences)

   null (Ed.)

   Osteoarthritis (OA) is the most common form of arthritis and can often occur in the knee. While convolutional neural networks (CNNs) have been widely used to study medical images, the application of a 3-dimensional (3D) CNN in knee OA diagnosis is limited. This study utilizes a 3D CNN model to analyze sequences of knee magnetic resonance (MR) images to perform knee OA classification. An advantage of using 3D CNNs is the ability to analyze the whole sequence of 3D MR images as a single unit as opposed to a traditional 2D CNN, which examines one image at a time. Therefore, 3D features could be extracted from adjacent slices, which may not be detectable from a single 2D image. The input data for each knee were a sequence of double-echo steady-state (DESS) MR images, and each knee was labeled by the Kellgren and Lawrence (KL) grade of severity at levels 0–4. In addition to the 5-category KL grade classification, we further examined a 2-category classification that distinguishes non-OA (KL ≤ 1) from OA (KL ≥ 2) knees. Clinically, diagnosing a patient with knee OA is the ultimate goal of assigning a KL grade. On a dataset with 1100 knees, the 3D CNN model that classifies knees with and without OA achieved an accuracy of 86.5% on the validation set and 83.0% on the testing set. We further conducted a comparative study between MRI and X-ray. Compared with a CNN model using X-ray images trained from the same group of patients, the proposed 3D model with MR images achieved higher accuracy in both the 5-category classification (54.0% vs. 50.0%) and the 2-category classification (83.0% vs. 77.0%). The result indicates that MRI, with the application of a 3D CNN model, has greater potential to improve diagnosis accuracy for knee OA clinically than the currently used X-ray methods. 

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3. A Southern Photometric Quasar Catalog from the Dark Energy Survey Data Release 2

   https://doi.org/10.3847/1538-4365/ac9ea8  

   Yang, Qian ; Shen, Yue ( December 2022 , The Astrophysical Journal Supplement Series)

   We present a catalog of 1.4 million photometrically selected quasar candidates in the southern hemisphere over the ∼5000 deg²Dark Energy Survey (DES) wide survey area. We combine optical photometry from the DES second data release (DR2) with available near-infrared (NIR) and the all-sky unWISE mid-infrared photometry in the selection. We build models of quasars, galaxies, and stars with multivariate skew-tdistributions in the multidimensional space of relative fluxes as functions of redshift (or color for stars) and magnitude. Our selection algorithm assigns probabilities for quasars, galaxies, and stars and simultaneously calculates photometric redshifts (photo-z) for quasar and galaxy candidates. Benchmarking on spectroscopically confirmed objects, we successfully classify (with photometry) 94.7% of quasars, 99.3% of galaxies, and 96.3% of stars when all IR bands (NIRYJHKand WISE W1W2) are available. The classification and photo-zregression success rates decrease when fewer bands are available. Our quasar (galaxy) photo-zquality, defined as the fraction of objects with the difference between the photo-z z_pand the spectroscopic redshiftz_s, ∣Δz∣ ≡ ∣z_s−z_p∣/(1 +z_s) ≤ 0.1, is 92.2% (98.1%) when all IR bands are available, decreasing to 72.2% (90.0%) using optical DES data only. Our photometric quasar catalog achieves an estimated completeness of 89% and purity of 79% atr< 21.5 (0.68 million quasar candidates), with reduced completeness and purity at 21.5 <r≲ 24. Among the 1.4 million quasar candidates, 87,857 have existing spectra, and 84,978 (96.7%) of them are spectroscopically confirmed quasars. Finally, we provide quasar, galaxy, and star probabilities for all (0.69 billion) photometric sources in the DES DR2 coadded photometric catalog.

    

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4. Transfer learning for galaxy feature detection: Finding giant star-forming clumps in low-redshift galaxies using Faster Region-based Convolutional Neural Network

   https://doi.org/10.1093/rasti/rzae013  

   Popp, Jürgen_J ; Dickinson, Hugh ; Serjeant, Stephen ; Walmsley, Mike ; Adams, Dominic ; Fortson, Lucy ; Mantha, Kameswara ; Mehta, Vihang ; Dawson, James_M ; Kruk, Sandor ; et al ( April 2024 , RAS Techniques and Instruments)

   Giant star-forming clumps (GSFCs) are areas of intensive star-formation that are commonly observed in high-redshift (z ≳ 1) galaxies but their formation and role in galaxy evolution remain unclear. Observations of low-redshift clumpy galaxy analogues are rare but the availability of wide-field galaxy survey data makes the detection of large clumpy galaxy samples much more feasible. Deep Learning (DL), and in particular Convolutional Neural Networks (CNNs), have been successfully applied to image classification tasks in astrophysical data analysis. However, one application of DL that remains relatively unexplored is that of automatically identifying and localizing specific objects or features in astrophysical imaging data. In this paper, we demonstrate the use of DL-based object detection models to localize GSFCs in astrophysical imaging data. We apply the Faster Region-based Convolutional Neural Network object detection framework (FRCNN) to identify GSFCs in low-redshift (z ≲ 0.3) galaxies. Unlike other studies, we train different FRCNN models on observational data that was collected by the Sloan Digital Sky Survey and labelled by volunteers from the citizen science project ‘Galaxy Zoo: Clump Scout’. The FRCNN model relies on a CNN component as a ‘backbone’ feature extractor. We show that CNNs, that have been pre-trained for image classification using astrophysical images, outperform those that have been pre-trained on terrestrial images. In particular, we compare a domain-specific CNN – ‘Zoobot’ – with a generic classification backbone and find that Zoobot achieves higher detection performance. Our final model is capable of producing GSFC detections with a completeness and purity of ≥0.8 while only being trained on ∼5000 galaxy images.

    

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5. An ALMA CO(1-0) survey of the 2Jy sample: large and massive molecular discs in radio AGN host galaxies

   https://doi.org/10.1093/mnras/stae1745  

   Tadhunter, C. ; Oosterloo, T. ; Morganti, R. ; Almeida, C_Ramos ; Martín, M_Villar ; Emonts, B. ; Dicken, D. ( July 2024 , Monthly Notices of the Royal Astronomical Society)

   The jets of radio AGN provide one of the most important forms of active galactic nuclei (AGN) feedback, yet considerable uncertainties remain about how they are triggered. Since the molecular gas reservoirs of the host galaxies can supply key information about the dominant triggering mechanism(s), here we present Atacama Large Millimeter/sub-millimeter Array CO(1-0) observations of a complete sample of 29 powerful radio AGN ($P_{1.4\,{\rm GHz}} \gt 10^{25}$ W Hz$^{-1}$ and $0.05 \lt z \lt 0.3$) with an angular resolution of about 2–3 arcsec (corresponding to 2–8 kpc). We detect molecular gas with masses in the range $10^{8.9} \lt M_{{\rm H}_2} \lt 10^{10.2}$ M$_\odot$ in the early-type host galaxies of ten targets, while for the other 19 sources, we derive upper limits. The detection rate of objects with such large molecular masses – $34\pm 9$ per cent – is higher than in the general population of non-active early-type galaxies (ETGs: $\lt $10 per cent). The kinematics of the molecular gas are dominated in most cases by rotating disc-like structures, with diameters up to 25 kpc. Compared with the results for samples of quiescent ETG in the literature, we find a larger fraction of more massive, more extended and less settled molecular gas structures. In most of the CO-detected sources, the results are consistent with triggering of the AGN as the gas settles following a merger or close encounter with a gas-rich companion. However, in a minority of objects at the centres of rich clusters of galaxies, the accretion of gas cooling from the hot X-ray haloes is a plausible alternative to galaxy interactions as a triggering mechanism.

    

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