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Morphology is a fundamental property of any galaxy population. It is a major indicator of the physical processes that drive galaxy evolution and in turn the evolution of the entire Universe. Historically, galaxy images were visually classified by trained experts. However, in the era of big data, more efficient techniques are required. In this work, we present a k -nearest neighbours based approach that utilizes non-parametric morphological quantities to classify galaxy morphology in Sloan Digital Sky Survey images. Most previous studies used only a handful of morphological parameters to identify galaxy types. In contrast, we explore 1023 morphological spaces (defined by up to 10 non-parametric statistics) to find the best combination of morphological parameters. Additionally, while most previous studies broadly classified galaxies into early types and late types or ellipticals, spirals, and irregular galaxies, we classify galaxies into 11 morphological types with an average accuracy of |${\sim} 80\!-\!90 \, {{\rm per\, cent}}$| per T-type. Our method is simple, easy to implement, and is robust to varying sizes and compositions of the training and test samples. Preliminary results on the performance of our technique on deeper images from the Hyper Suprime-Cam Subaru Strategic Survey reveal that an extension of our method to modern surveys with better imaging capabilities might be possible. [ABSTRACT FROM AUTHOR]

We investigate the influence of star formation and instantaneous active galactic nuclei (AGN) feedback processes on the ionized gas velocity dispersion in a sample of 1285 emission-line galaxies with stellar masses |$\log \, (M_*/\mathrm{ M}_{\odot }) \ge 9$| from the integral-field spectroscopy Sydney-AAO Multi-object Integral-field Galaxy Survey. We fit both narrow- and broad-emission-line components using aperture spectra integrated within one effective radius, while ensuring the elimination of velocity differences between the spectra of individual spaxels. Our analysis reveals that 386 (30 per cent) galaxies can be adequately described using a single-emission component while 356 (28 per cent) galaxies require two (broad and narrow) components. Galaxies characterized by high-mass, elevated star formation rate surface density, or type-2 AGN-like emissions tend to feature an additional broad-emission-line component, leading to their classification as double-component galaxies. We explore the correlations between M * and gas velocity dispersions, highlighting that the prominence of the broad component significantly contributes to elevating the gas velocity dispersion. Galaxies displaying AGN-like emission based on optical definitions show enhanced gas velocity dispersions. In star-forming galaxies, both stellar mass and star-formation rate surface density substantially contribute to the velocity dispersion of the narrow component. Increased star-forming activity appears to elevate the velocity dispersion of the narrow component. The broad component exhibits a weaker dependence on stellar mass and is primarily driven by galactic outflows. We suggest that strong star-forming activity leads to the formation of a broad-emission-line component, but the impact on inflating gas velocity dispersion is moderate. On the other hand, AGN-driven outflows appear to be a more important contributor to the elevated velocity dispersion of the ionized gas. [ABSTRACT FROM AUTHOR]

The spiral structure is an important morphology within galaxies, providing information on the formation, evolution, and environment of spiral galaxies. The number of spiral arms is one of the important parameters to describe the morphology of spiral galaxies. In this project, we study the classification of spiral galaxies by the number of spiral arms based on deep learning algorithms. The data set for this project consists of eligible samples from Galaxy Zoo 2 and Galaxy Zoo DECaLS. To better identify the texture features of the spiral arms, we designed a convolutional neural network model incorporating Gabor filter (Gabor Residual Filtering Convolutional Net, GReFC-Net), and used other networks for 3 and 4-way classifications. In the 3-way case, the GReFC-Net algorithm achieves the highest precision, recall, F1-score, and AUC value, which are 96.25%, 96.23%, 96.21%, and 0.9937. In the 4-way case, the GReFC-Net algorithm has the highest recall, F1-score and AUC value, which are 95.57%, 95.42% and 0.9957. The interpretability of GReFC-Net is analyzed by the SHAP method, and the results show that the network can identify the spiral arm structure of spiral galaxies well. It can be seen that the GReFC-Net algorithm can be effectively applied to the automatic measurement task of spiral arm structure in a large number of spiral galaxies. [ABSTRACT FROM AUTHOR]

Galaxy morphologies and their relation with physical properties have been a relevant subject of study in the past. Most galaxy morphology catalogues have been labelled by human annotators or by machine learning models trained on human-labelled data. Human-generated labels have been shown to contain biases in terms of the observational properties of the data, such as image resolution. These biases are independent of the annotators, that is, are present even in catalogues labelled by experts. In this work, we demonstrate that training deep learning models on biased galaxy data produces biased models, meaning that the biases in the training data are transferred to the predictions of the new models. We also propose a method to train deep learning models that considers this inherent labelling bias, to obtain a de-biased model even when training on biased data. We show that models trained using our deep de-biasing method are capable of reducing the bias of human-labelled data sets. [ABSTRACT FROM AUTHOR]

The increased volume of images and galaxies surveyed by recent and upcoming projects consolidates the need for accurate and scalable automated AI-driven classification methods. This paper proposes a new algorithm based on a custom neural network architecture for classifying galaxies from deep space surveys. The convolutional neural network (CNN) presented is trained using 10,000 galaxy images obtained from the Galaxy Zoo 2 dataset. It is designed to categorize galaxies into five distinct classes: completely round smooth, in-between smooth (falling between completely round and cigar-shaped), cigar-shaped smooth, edge-on, and spiral. The performance of the proposed CNN is assessed using a set of metrics such as accuracy, precision, recall, F1 score, and area under the curve. We compare our solution with well-known architectures like ResNet-50, DenseNet, EfficientNet, Inception, MobileNet, and one proposed model for galaxy classification found in the recent literature. The results show an accuracy rate of 96.83%, outperforming existing algorithms. [ABSTRACT FROM AUTHOR]

Radio galaxies exhibit a rich diversity of morphological characteristics, which make their classification into distinct types a complex challenge. To address this challenge effectively, we introduce an innovative approach for radio galaxy classification using COSFIRE filters. These filters possess the ability to adapt to both the shape and orientation of prototype patterns within images. The COSFIRE approach is explainable, learning-free, rotation-tolerant, efficient, and does not require a large training set. To assess the efficacy of our method, we conducted experiments on a benchmark radio galaxy data set comprising of 1180 training samples and 404 test samples. Notably, our approach achieved an average accuracy rate of 93.36 per cent. This achievement outperforms contemporary deep learning models, and it is the best result ever achieved on this data set. Additionally, COSFIRE filters offer better computational performance, ∼20 × fewer operations than the DenseNet-based competing method (when comparing at the same accuracy). Our findings underscore the effectiveness of the COSFIRE filter-based approach in addressing the complexities associated with radio galaxy classification. This research contributes to advancing the field by offering a robust solution that transcends the orientation challenges intrinsic to radio galaxy observations. Our method is versatile in that it is applicable to various image classification approaches. [ABSTRACT FROM AUTHOR]

Astronomy and astrophysics data sets have been increasing over the last decade as many new telescopes and detectors have been launched. High-redshift radio galaxies are powerful radio sources that are the ideal targets to discuss the evolution of Hi; thus, they are one of the key points to understand the universe evolution and formation. The cloud computing systems are applicable with IoT considering the processing time over training data using ANN. Machine learning is a subfield of AI and it is used by scientists for prediction or classification purposes considering the input data. Machine learning algorithms have become increasingly popular among astronomers and are now used for a wide range of astrophysical calculations and fields. This paper proposes five types of machine learning algorithms, namely back-propagation neural networks (BPNN), decision tree algorithm (DT), gradient boosting classifier algorithm (GBA), radial basis function neural network (RBFNN), and support vector machine (SVM). The machine learning models are implemented to classify and compare the results of high-redshift radio galaxies by their location in the sky in ELAIS-N1, ELAIS-N2, the Lockman hole, VIMOS fields, in order to increase the performance efficiency, accuracy and improve our confidence considering the critical nature of the calculations in redshift galaxies. When 100 instances were considered, back-propagation neural networks achieved an accuracy rate of 70%; however, when 200 instances were considered, radial basis function neural networks achieved an accuracy rate of 88.2%. [ABSTRACT FROM AUTHOR]

This paper presents a morphology classification catalog of spiral and ring features of 59854 magnitude-limited galaxies (r < 17.8 mag, and additional 628005 subsamples down to r = 20 mag) at z = 0.01–0.3 based on the Third Public Data Release of the Hyper Suprime-Cam Subaru Strategic Program. We employ two deep-learning classifiers to determine the spiral and ring structures separately based on GALAXY CRUISE Data Release 1, which is dedicated to Hyper Suprime-Cam data. The number of spiral and ring galaxies contain 31864 and 8808 sources, respectively, which constitute 53% and 15% of the sample. A notable result of this study is the construction of a large sample of ring galaxies utilizing high-quality imaging data delivered by the Subaru Hyper Suprime-Cam. However, the accurate identification of ring galaxies remains difficult at a limited seeing resolution. Additionally, we confirm that most spiral galaxies are located on the star-forming main sequence, whereas ring galaxies preferentially reside in the green valley at stellar masses of 1010.5–1011 solar mass. Furthermore, decreasing fractions of spiral and ring galaxies are observed toward the centers of the galaxy clusters. The obtained morphology catalog is publicly available on the GALAXY CRUISE website. [ABSTRACT FROM AUTHOR]

There will be an unprecedented increase in the number of galaxies observed as a result of the current and upcoming surveys. Consequently, data-driven approaches have become the main tools for deciphering and evaluating this massive volume of data. Computer vision combined with deep learning has proven most effective for recognizing galaxy morphology but most of the conventional deep learning models are large in terms of parameters due to which computational cost, risk of overfitting increases. In this paper, we proposed a lightweight convolutional neural network (CNN) model using separable convolution which helps to reduce trainable parameters of the model. Further, Efficient Channel Attention (ECA) mechanism is used to focus on important features. ECA focuses on features channel wise without dimensionality reduction which reduces the computational overhead. Performance of proposed model named as "DIAT-DSCNN-ECA-Net" is evaluated on two datasets such as Galaxy Zoo 2, Galaxy Zoo DECaLS, each having seven different types of galaxies, achieved an accuracy of 90.81% and 94.17% respectively at the cost of 1.8 Mega-Byte model size, 0.13 million parameters, 1.04 Floating Point Operations (FLOPs). The outcomes of the experiments demonstrate that the proposed approach can outperform the existing CNN models. [ABSTRACT FROM AUTHOR]

We investigate whether barred galaxies are statistically more likely to harbour radial molecular gas flows and what effect those flows have on their global properties. Using 46 galaxies from the ALMA-MaNGA QUEnching and STar formation (ALMaQUEST) survey, we identify galaxies hosting optical bars using a combination of the morphological classifications in Galaxy Zoo 2 and HyperLEDA. In order to detect radial molecular gas flows, we employ full 3D kinematic modelling of the ALMaQUEST 12CO(1–0) data cubes. By combining our bar classifications with our radial bar-driven flow detections, we find that galaxies classed as barred are statistically more likely to host large-scale radial gas motions compared to their un-barred and edge-on galaxy counterparts. Moreover, the majority of barred galaxies require multicomponent surface brightness profiles in their best-fitting models, indicative of the presence of resonance systems. We find that galaxies classed as barred with radial bar-driven flows ('barred + radial flow' subset) have significantly suppressed global star-formation efficiencies compared to barred galaxies without radial bar-driven flows and galaxies in the other morphological sub-samples. Our 'barred + radial flow' subset galaxies also possess consistently centrally concentrated molecular gas distributions, with no indication of depleted gas mass fractions, suggesting that gas exhaustion is not the cause of their suppressed star formation. Furthermore, these objects have higher median gas mass surface densities in their central 1 kpc, implying that central gas enhancements do not fuel central starbursts in these objects. We propose that dynamical effects, such as shear caused by large-scale inflows of gas, act to gravitationally stabilize the inner gas reservoirs. [ABSTRACT FROM AUTHOR]

The morphological diversity of galaxies is a relevant probe of galaxy evolution and cosmological structure formation. However, in large sky surveys, even the morphological classification of galaxies into two classes, like late-type (LT) and early-type (ET), still represents a significant challenge. In this work, we present a Deep Learning (DL) based morphological catalogue built from images obtained by the Southern Photometric Local Universe Survey (S-PLUS) Data Release 3 (DR3). Our DL method achieves a purity rate of 98.5 per cent in accurately distinguishing between spiral, as part of the larger category of LT galaxies, and elliptical, belonging to ET galaxies. Additionally, we have implemented a secondary classifier that evaluates the quality of each galaxy stamp, which allows to select only high-quality images when studying properties of galaxies on the basis of their DL morphology. From our LT/ET catalogue of galaxies, we recover the expected colour–magnitude diagram in which LT galaxies display bluer colours than ET ones. Furthermore, we also investigate the clustering of galaxies based on their morphology, along with their relationship to the surrounding environment. As a result, we deliver a full morphological catalogue with 164 314 objects complete up to r petro < 18, covering ∼1800 deg2, from which ∼55 000 are classified as high reliability, including a significant area of the Southern hemisphere that was not covered by previous morphology catalogues. [ABSTRACT FROM AUTHOR]

Subjective classification of spiral galaxies is not sufficient for studying the effect of bars on their physical characteristics. In reality the problem is to comprehend the complex correlations in a multivariate parametric space. Multivariate tools are the best ones for understanding this complex correlation. In this work an objective classification of a large set (26,089) of spiral galaxies was compiled as a value added galaxy catalogue from sdss DR 15 virtual data archive. Initially for dimensionality reduction, Independent Component Analysis is performed to determine a set of Independent Components that are linear combinations of 48 observed features (namely ionized lines, Lick indices, photometric and morphological properties). Subsequently a K-means cluster analysis is carried out on the basis of the 14 best chosen Independent Components to obtain 12 distinct homogeneous groups of spiral galaxies. Amongst these, 3 groups are the oldest ones (1.6 Gyr − 5.9 Gyr), while 5 groups fall in the medium aged category (1.4 Gyr − 1.6 Gyr), 2 groups consist of only unbarred spirals, 1 group is the youngest one and the remaining one is an outlier. In many groups there are clear indication of recurrent bar formation phenomena which is consistent with few previous simulation works. In order to study the robustness of the clusters with respect to the method of clustering, a second method of clustering by Gaussian Mixture Modeling Method (GMMBC) is applied. [ABSTRACT FROM AUTHOR]

The Chinese Space Station Telescope (abbreviated as CSST) is a future advanced space telescope. Real-time identification of galaxy and nebula/star cluster (abbreviated as NSC) images is of great value during CSST survey. While recent research on celestial object recognition has progressed, the rapid and efficient identification of high-resolution local celestial images remains challenging. In this study, we conducted galaxy and NSC image classification research using deep learning methods based on data from the Hubble Space Telescope. We built a local celestial image data set and designed a deep learning model named HR-CelestialNet for classifying images of the galaxy and NSC. HR-CelestialNet achieved an accuracy of 89.09 per cent on the testing set, outperforming models such as AlexNet, VGGNet, and ResNet, while demonstrating faster recognition speeds. Furthermore, we investigated the factors influencing CSST image quality and evaluated the generalization ability of HR-CelestialNet on the blurry image data set, demonstrating its robustness to low image quality. The proposed method can enable real-time identification of celestial images during CSST survey mission. [ABSTRACT FROM AUTHOR]

This paper explores the application of machine learning methods for classifying astronomical sources using photometric data, including normal and emission line galaxies (ELGs; starforming, starburst, AGN, broad-line), quasars, and stars. We utilized samples from Sloan Digital Sky Survey (SDSS) Data Release 17 (DR17) and the ALLWISE catalogue, which contain spectroscopically labelled sources from SDSS. Our methodology comprises two parts. First, we conducted experiments, including three-class, four-class, and seven-class classifications, employing the Random Forest (RF) algorithm. This phase aimed to achieve optimal performance with balanced data sets. In the second part, we trained various machine learning methods, such as k -nearest neighbours (KNN), RF, XGBoost (XGB), voting, and artificial neural network (ANN), using all available data based on promising results from the first phase. Our results highlight the effectiveness of combining optical and infrared features, yielding the best performance across all classifiers. Specifically, in the three-class experiment, RF and XGB algorithms achieved identical average F1 scores of 98.93 per cent on both balanced and unbalanced data sets. In the seven-class experiment, our average F1 score was 73.57 per cent. Using the XGB method in the four-class experiment, we achieved F1 scores of 87.9 per cent for normal galaxies (NGs), 81.5 per cent for ELGs, 99.1 per cent for stars, and 98.5 per cent for quasars (QSOs). Unlike classical methods based on time-consuming spectroscopy, our experiments demonstrate the feasibility of using automated algorithms on carefully classified photometric data. With more data and ample training samples, detailed photometric classification becomes possible, aiding in the selection of follow-up observation candidates. [ABSTRACT FROM AUTHOR]

Context. Markarian (Mrk) galaxies, revealed due to their ultraviolet excess are very famous; they play a significant role in many astro-physical problems and come in a variety of types: Seyferts (Sys), low-ionization narrow emission-line regions (LINERs), starbursts (SBs), blue compact dwarf galaxies (BCDGs), as well as some quasars (QSOs) and blazars. They also appear as sources of non-optical radiation, such as gamma-ray, X-ray, ultraviolet (UV), infrared (IR), and radio, including some extremely high-energy gamma-ray sources (Mrk 421, Mrk 501) and very high-luminosity IR galaxies (Mrk 231). Aims. The classifications of Mrk galaxies for activity types have been carried out based on old mostly poor-quality and inhomoge-neous spectra (relatively low-resolution photographic spectra and low S/N spectra). The Sloan Digital Sky Surveys (SDSS) provides ~90% of the existing medium-resolution spectra, and is a relevant homogeneous database for classification of galaxies. Our aim was to obtain homogeneous classifications of Mrk galaxies by means of the SDSS spectra. Methods. Based on our work on the classification of newly revealed objects from various recent catalogs, we developed a fine classification scheme for activity types based on the homogeneous database of the SDSS spectroscopy. This scheme was applied to Mrk galaxies having spectra in the SDSS (779 out of 1544, 50.45%). Results. As a result, 779 Mrk galaxies now have homogeneous optical spectral classification that can be used for further studies and statistics of their physical and spatial properties. We revealed 2 QSOs, 49 classical Seyferts (broad-line Seyferts 1, BLS1s), 17 narrow-line Seyferts 1 (NLS1s), 4 Sy2s, 12 LINERs, 11 active galactic nuclei (AGN) without a definite type, 31 Composite spectrum objects, and 533 HII/Starbursts. Due to low-quality spectra, some objects were left without definite activity types and were classified as emission or absorption galaxies. [ABSTRACT FROM AUTHOR]

Emission-line galaxy classification plays an important role in comprehending the formation and evolution of galaxies. The widely used optical spectral classification method for galaxies is the BPT diagram, which classifies emission-line galaxies on the basis of precise spectral line measurements. Various classical machine learning methods have been utilized to classify galaxy spectra. Deep learning (DL) is more feasible for a huge amount of data, as it can learn patterns autonomously from the original data. This study aims to explore the possibility of applying DL to classify galaxy spectra and improve classification efficiency. A one-dimensional convolutional neural network model called GalSpecNet was constructed to classify emission-line galaxy spectra, which recognizes star-forming, composite, active galactic nucleus (AGN), and normal galaxies with an accuracy of over 93 per cent. This study employs the Gradient-weighted Class Activation Mapping to elucidate the decision-making process of the model by inspecting spectral features that the model prioritizes for each type of galaxy. The findings suggest that the model considers features highly consistent with the conventional BPT method. Subsequently, we applied the model to the cross-matched galaxies of Sloan Digital Sky Survey Data Release 16 (DR16) and Large Sky Area Multi-Object Fiber Spectroscopic Telescope DR8 and present a catalogue comprising of 41 699 star-forming candidates and 55 103 AGN candidates. The catalogue is publicly available. [ABSTRACT FROM AUTHOR]

Citizen science is gaining popularity as a valuable tool for labelling large collections of astronomical images by the general public. This is often achieved at the cost of poorer quality classifications made by amateur participants, which are usually verified by employing smaller data sets labelled by professional astronomers. Despite its success, citizen science alone will not be able to handle the classification of current and upcoming surveys. To alleviate this issue, citizen science projects have been coupled with machine learning techniques in pursuit of a more robust automated classification. However, existing approaches have neglected the fact that, apart from the data labelled by amateurs, (limited) expert knowledge of the problem is also available along with vast amounts of unlabelled data that have not yet been exploited within a unified learning framework. This paper presents an innovative learning methodology for citizen science capable of taking advantage of expert- and amateur-labelled data, featuring a transfer of labels between experts and amateurs. The proposed approach first learns from unlabelled data with a convolutional auto-encoder and then exploits amateur and expert labels via the pre-training and fine-tuning of a convolutional neural network, respectively. We focus on the classification of galaxy images from the Galaxy Zoo project, from which we test binary, multiclass, and imbalanced classification scenarios. The results demonstrate that our solution is able to improve classification performance compared to a set of baseline approaches, deploying a promising methodology for learning from different confidence levels in data labelling. [ABSTRACT FROM AUTHOR]

We present the first results from GALAXY CRUISE, a community (or citizen) science project based on data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). The current paradigm of galaxy evolution suggests that galaxies grow hierarchically via mergers, but our observational understanding of the role of mergers is still limited. The data from HSC-SSP are ideally suited to improve our understanding with improved identifications of interacting galaxies thanks to the superb depth and image quality of HSC-SSP. We launched a community science project, GALAXY CRUISE, in 2019 and have collected over two million independent classifications of 20686 galaxies at z  < 0.2. We first characterize the accuracy of the participants' classifications and demonstrate that it surpasses previous studies based on shallower imaging data. We then investigate various aspects of interacting galaxies in detail. We show that there is a clear sign of enhanced activities of super-massive black holes and star formation in interacting galaxies compared to those in isolated galaxies. The enhancement seems particularly strong for galaxies undergoing violent mergers. We also show that the mass growth rate inferred from our results is roughly consistent with the observed evolution of the stellar mass function. The second season of GALAXY CRUISE is currently underway and we conclude with future prospects. We make the morphological classification catalog used in this paper publicly available at the GALAXY CRUISE website, which will be particularly useful for machine-learning applications. [ABSTRACT FROM AUTHOR]

We present results on the morphological and structural evolution of a total of 3956 galaxies observed with JWST at 1.5 < z < 6.5 in the JWST CEERS observations that overlap with the CANDELS EGS field. This is the biggest visually classified sample observed with JWST yet, ∼20 times larger than previous studies, and allows us to examine in detail how galaxy structure has changed over this critical epoch. All sources were classified by six individual classifiers using a simple classification scheme aimed at producing disk/spheroid/peculiar classifications, whereby we determine how the relative number of these morphologies has evolved since the Universe's first billion years. Additionally, we explore structural and quantitative morphology measurements using Morfometryka, and show that galaxies with M * > 109 M ⊙ at z > 3 are not dominated by irregular and peculiar structures, either visually or quantitatively, as previously thought. We find a strong dominance of morphologically selected disk galaxies up to z = 6 in this mass range. We also find that the stellar mass and star formation rate densities are dominated by disk galaxies up to z ∼ 6, demonstrating that most stars in the Universe were likely formed in a disk galaxy. We compare our results to theory to show that the fraction of types we find is predicted by cosmological simulations, and that the Hubble Sequence was already in place as early as one billion years after the Big Bang. Additionally, we make our visual classifications public for the community. [ABSTRACT FROM AUTHOR]

The LOFAR (Low Frequency Array) Two-metre Sky Survey (LoTSS) is an invaluable new tool for investigating the properties of sources at low frequencies and has helped to open up the study of galaxy populations in this regime. In this work, we perform a search for host galaxies of gamma-ray bursts (GRBs). We use the relative density of sources in Data Release 2 (DR2) of LoTSS to define the probability of a chance alignment, P chance, and find 18 sources corresponding to 17 GRBs that meet a P chance < 1 per cent criterion. We examine the nature and properties of these radio sources using both LOFAR data and broad-band information, including their radio spectral index, star formation rate estimates, and any contributions from active galactic nucleus emission. Assuming that the radio emission is dominated by star formation, we find that our sources show high star formation rates (101–103 M⊙ yr−1) compared with both a field galaxy sample and a sample of core-collapse supernova hosts, and the majority of putative hosts are consistent with ultra-luminous infrared galaxy classifications. As a result of our analyses, we define a final sample of eight likely GRB host candidates in the LoTSS DR2 survey. [ABSTRACT FROM AUTHOR]

Galaxy morphology is one of the most fundamental ways to describe galaxy properties, but the morphology we observe may be affected by wavelength and spatial resolution, which may introduce systematic bias when comparing galaxies at different redshift. Taking advantage of the broad wavelength coverage from optical to near-IR and the high-resolution NIRCam instrument of the JWST, we measure the nonparametric morphological parameters of a total of 1376 galaxies at z ≃ 0.8–3.0 in the CEERS field through an optimized code called statmorph_csst. We divide our sample into three redshift intervals and investigate the wavelength and redshift dependence of the morphological parameters. We also explore how the widely used galaxy type classification methods based on the morphological parameters depend on wavelength and spatial resolution. We find that there are variations in all morphological parameters with rest-frame wavelength (λ rf), especially at the short-wavelength end, and that λ rf mainly affects the classification between late- and early-type galaxies. As λ rf increases, the galaxies on the G – M 20 diagram move to the upper left with a slope of −0.23 ± 0.03 on average. We find that spatial resolution mainly affects the merger identification. The merger fraction in F200W resolution can be ≳2 times larger than that in F444W resolution. Furthermore, we compare the morphological parameter evolution of galaxies with different stellar masses. We find that there are differences in the morphological evolution of high- and low-mass (log M * ≥ 10 and 9 < log M * < 10) galaxies in the studied redshift range, which may be caused by their different evolutionary paths. [ABSTRACT FROM AUTHOR]

The automated identification of extragalactic objects in large surveys provides reliable and reproducible samples of galaxies in less time than procedures involving human interaction. However, regions near the Galactic disc are more challenging due to the dust extinction. We present the methodology for the automatic classification of galaxies and non-galaxies at low Galactic latitude regions using both images and photometric and morphological near-IR data from the VISTA Variables in the Vía Láctea eXtended (VVVX) survey. Using the VVV NIR Galaxy Catalogue (VVV NIRGC), we analyse by statistical methods the most relevant features for galaxy identification. This catalogue was used to train a convolutional neural network with image data and an XGBoost model with both photometric and morphological data and then to generate a data set of extragalactic candidates. This allows us to derive probability catalogues used to analyse the completeness and purity as a function of the configuration parameters and to explore the best combinations of the models. As a test case, we apply this methodology to the Northern disc region of the VVVX survey, obtaining 172 396 extragalactic candidates with probabilities of being galaxies. We analyse the performance of our methodology in the VVV disc, reaching an F1-score of 0.67, a 65 per cent purity, and a 69 per cent completeness. We present the VVV NIRGC: Northern part of the Galactic disc comprising 1003 new galaxies, with probabilities greater than 0.6 for either model, with visual inspection and with only two previously identified galaxies. In the future, we intend to apply this methodology to other areas of the VVVX survey. [ABSTRACT FROM AUTHOR]

Classification of galaxies is traditionally associated with their morphologies through visual inspection of images. The amount of data to come render this task, inhuman and Machine Learning (mainly Deep Learning) has been called to the rescue for more than a decade. However, the results look mitigate and there seems to be a shift away from the paradigm of the traditional morphological classification of galaxies. In this paper, I want to show that the algorithms indeed are very sensitive to the features present in images, features that do not necessarily correspond to the Hubble or de Vaucouleurs vision of a galaxy. However, this does not preclude to get the correct insights into the physics of galaxies. I have applied a state-of-the-art 'traditional' Machine Learning clustering tool, called Fisher-EM, a latent discriminant subspace Gaussian mixture model algorithm to 4458 galaxies carefully classified into 18 types by the EFIGI project. The optimum number of clusters given by the integrated complete likelihood criterion is 47. The correspondence with the EFIGI classification is correct, but it appears that the Fisher-EM algorithm gives a great importance to the distribution of light which translates to characteristics such as the bulge to disc ratio, the inclination or the presence of foreground stars. The discrimination of some physical parameters (bulge-to-total luminosity ratio, (B–V) T , intrinsic diameter, presence of flocculence or dust, and arm strength) is very comparable in the two classifications. [ABSTRACT FROM AUTHOR]

We report the discovery of six ultra-faint Milky Way satellites identified through matched-filter searches conducted using Dark Energy Camera (DECam) data processed as part of the second data release of the DECam Local Volume Exploration (DELVE) survey. Leveraging deep Gemini/GMOS-N imaging (for four candidates) as well as follow-up DECam imaging (for two candidates), we characterize the morphologies and stellar populations of these systems. We find that these candidates all share faint absolute magnitudes (M V ≥ −3.2 mag) and old, metal-poor stellar populations (τ > 10 Gyr, [Fe/H] < −1.4 dex). Three of these systems are more extended (r 1/2 > 15 pc), while the other three are compact (r 1/2 < 10 pc). From these properties, we infer that the former three systems (Boötes V, Leo Minor I, and Virgo II) are consistent with ultra-faint dwarf galaxy classifications, whereas the latter three (DELVE 3, DELVE 4, and DELVE 5) are likely ultra-faint star clusters. Using data from the Gaia satellite, we confidently measure the proper motion of Boötes V, Leo Minor I, and DELVE 4, and tentatively detect a proper-motion signal from DELVE 3 and DELVE 5; no signal is detected for Virgo II. We use these measurements to explore possible associations between the newly discovered systems and the Sagittarius dwarf spheroidal, the Magellanic Clouds, and the Vast Polar Structure, finding several plausible associations. Our results offer a preview of the numerous ultra-faint stellar systems that will soon be discovered by the Vera C. Rubin Observatory and highlight the challenges of classifying the faintest stellar systems. [ABSTRACT FROM AUTHOR]

Classification of galaxy morphology is a hot issue in astronomical research. Although significant progress has been made in the last decade in classifying galaxy morphology using deep learning technology, there are still some deficiencies in spatial feature representation and classification accuracy. In this study, we present a multiscale convolutional capsule network (MSCCN) model for the classification of galaxy morphology. First, this model improves the convolutional layers using a multibranch structure to extract the multiscale hidden features of galaxy images. In order to further explore the hidden information in the features, the multiscale features are encapsulated and fed into the capsule layer. Second, we use a sigmoid function to replace the softmax function in dynamic routing, which can enhance the robustness of MSCCN. Finally, the classification model achieves 97 per cent accuracy, 96 per cent precision, 98 per cent recall, and 97 per cent F1-score under macroscopic averaging. In addition, a more comprehensive model evaluation was accomplished in this study. We visualized the morphological features for the part of sample set that used the t-distributed stochastic neighbour embedding (t-SNE) algorithm. The results show that the model has a better generalization ability and robustness, and it can be effectively used in the galaxy morphological classification. [ABSTRACT FROM AUTHOR]

We investigate the role of the close environment on the nuclear activity of a sample of 436 nearby (z < 0.3) QSO 2's – selected from SDSS-III spectra, via comparison of their environment and interaction parameters with those of a control sample of 1308 galaxies. We have used the corresponding SDSS images to obtain the number of neighbour galaxies N , tidal strength parameter Q and asymmetry parameters. We find a small excess of N in the QSOs compared to its three controls, and no difference in Q. The main difference is an excess of asymmetry in the QSOs hosts, which is almost twice that of the control galaxies. This difference is not due to the hosts' morphology, since there is no difference in their Galaxy Zoo classifications. HST images of two highly asymmetric QSO 2 hosts of our sample show that both sources have a close companion (at projected separations ∼ 5 kpc), which we thus conclude is the cause of the observed asymmetry in the lower resolution SDSS images. The mean projected radius of the controls is 〈 r 〉 = 8.53 ± 0.06 kpc, while that of the QSO hosts is 〈 r 〉 = 9.39 ± 0.12 kpc, supporting the presence of interaction signatures in the outer regions of the QSO hosts. Our results favour a scenario in which nuclear activity in QSO 2's is triggered by close galaxy interactions – when the distance between the host and companion is of the order of the galaxy radius, implying that they are already in the process of merger. [ABSTRACT FROM AUTHOR]

We present a novel natural language processing (NLP) approach to deriving plain English descriptors for science cases otherwise restricted by obfuscating technical terminology. We address the limitations of common radio galaxy morphology classifications by applying this approach. We experimentally derive a set of semantic tags for the Radio Galaxy Zoo EMU (Evolutionary Map of the Universe) project and the wider astronomical community. We collect 8486 plain English annotations of radio galaxy morphology, from which we derive a taxonomy of tags. The tags are plain English. The result is an extensible framework, which is more flexible, more easily communicated, and more sensitive to rare feature combinations, which are indescribable using the current framework of radio astronomy classifications. [ABSTRACT FROM AUTHOR]

State-of-the-art radio observatories produce large amounts of data which can be used to study the properties of radio galaxies. However, with this rapid increase in data volume, it has become unrealistic to manually process all of the incoming data, which in turn led to the development of automated approaches for data processing tasks, such as morphological classification. Deep learning plays a crucial role in this automation process and it has been shown that convolutional neural networks (CNNs) can deliver good performance in the morphological classification of radio galaxies. This paper investigates two adaptations to the application of these CNNs for radio galaxy classification. The first adaptation consists of using principal component analysis (PCA) during pre-processing to align the galaxies' principal components with the axes of the coordinate system, which will normalize the orientation of the galaxies. This adaptation led to a significant improvement in the classification accuracy of the CNNs and decreased the average time required to train the models. The second adaptation consists of guiding the CNN to look for specific features within the samples in an attempt to utilize domain knowledge to improve the training process. It was found that this adaptation generally leads to a stabler training process and in certain instances reduced overfitting within the network, as well as the number of epochs required for training. [ABSTRACT FROM AUTHOR]

A small subset of extragalactic double radio sources, termed HYbrid MOrpholgy Radio Sources (HYMORS), is distinguished by a very unusual, hybrid morphology in terms of Fanaroff–Riley (FR) classification. In HYMORS, one radio lobe appears edge-darkened (FR I), while the other shows a well-defined emission peak near its outer edge (edge-brightened, FR II). Such sources are rare, but critical for constraining the mechanism responsible for FR dichotomy, a widely debated issue in extragalactic astrophysics. Here, we highlight the need for caution in assigning FR type, in view of some upcoming observational campaigns to confirm HYMORS among the candidates. To illustrate this, we highlight the cases of three radio sources, which have been perceived to be HYMORS, including the radio galaxy 0500 + 630 (4C + 63.07), which has been claimed to be a good, original example of a HYMORS, with a FR I western lobe and a FR II eastern lobe marked by a prominent terminal hot spot. However, its recent VLASS map at 3 GHz has revealed that the western lobe actually extends much farther out than reported and terminates in a well-defined emission peak. This implies that the source is a regular FR II radio galaxy and not a HYMORS. We also provide a brief perspective of the HYMORS phenomenon and underscore the need to confirm a FR I classification by ruling out additional FR II characteristics, such as an inward lobe-widening and spectral steepening, as well as a lack of prominent radio jet within the lobe. [ABSTRACT FROM AUTHOR]

We search for signatures of recent galaxy close interactions and mergers in a sample of 202 early-type galaxies in the local universe from the public SDSS Stripe82 deep images (μ r ∼ 28.5 mag arcsec−2). Using two different methods to remove galaxies' smooth and symmetric light distribution, we identify and characterize 11 distinct types of merger remnants embedded in the diffuse light of these early-type galaxies. We discuss how the morphology of merger remnants can result from different kinds of minor and major mergers, and estimate the fraction of early-type galaxies in the local universe with evidence of recent major (27 per cent) and minor (57 per cent) mergers. The merger fractions deduced are higher than in several earlier surveys. Among remnants, we find that shells are the dominant merger debris (54 per cent) associated with early-type galaxies, resulting from both major and minor mergers, with those characteristics of major mergers being significant (24 per cent of shell host galaxies). The most uncommon merger-related structures are boxy isophotes of the stellar distribution and the presence of disc fragments near the cores of galaxies. We develop a classification scheme for these fine structures that may be used to infer their likely genesis histories. The classification is primarily based on the mass ratios of the merged galaxies. This work, when combined with predictions from numerical simulations, indicates that most (if not all) early-type galaxies in the local Universe are continually evolving as a result of (minor) merger activities. [ABSTRACT FROM AUTHOR]

Photometric techniques are one of the fundamentals and of great importance in the study of astronomical phenomena, including galaxies, and have witnessed a wide development during the last 100 years in equipment, sensitivity and accuracy in data analysis, especially after the direction toward space telescopes and the widespread use of a CCD camera. Therefore, in this research, an analytical study is made to compare two types of galaxies, which are spiral and lenticular galaxies, using photometric techniques and compare the photometric parameters of each type with tables and illustrations. An analysis of the morphological of the two galaxies is done by using the Least Square Fitting Method, and it is fully explained in the research. The results show the clear difference between the structure of spiral and lenticular galaxies. [ABSTRACT FROM AUTHOR]

This work deals with the classification of galaxies based on the evolutionary and hierarchical development of their parameters. We apply the astrocladistic approach to galaxies of the M81 group. Physical parameters such as magnitudes and rotation velocities, among others, were selected and described for 28 galaxies of the M81 group. We carried out a Maximum Parsimony analysis on this dataset with equally weighted characters (parameters), followed by an implied weights analysis. As a result of the first analysis, we obtained 100 equally parsimonious trees, each with 64 evolutionary steps. With the implicit weights analysis we reduced the number of most parsimonious trees to four. We observed the existence of hierarchical organizations with well-defined evolutionary groups, despite the small number of parameters available. According to our results the basal galaxies within the M81 group are objects with spiral structures, low magnitudes and high rotation velocities. The clades containing the derived galaxies are composed mainly of irregular dwarf galaxies with little interstellar material. [ABSTRACT FROM AUTHOR]

We present a catalog of about 25,000 images of massive (M ⋆ ≥ 109 M ⊙) galaxies at redshifts 3 ≤ z ≤ 6 from the TNG50 cosmological simulation, tailored for observations at multiple wavelengths carried out with JWST. The synthetic images were created with the SKIRT radiative transfer code, including the effects of dust attenuation and scattering. The noiseless images were processed with the mirage simulator to mimic the Near Infrared Camera (NIRCam) observational strategy (e.g., noise, dithering pattern, etc.) of the Cosmic Evolution Early Release Science (CEERS) survey. In this paper, we analyse the predictions of the TNG50 simulation for the size evolution of galaxies at 3 ≤ z ≤ 6 and the expectations for CEERS to probe that evolution. In particular, we investigate how sizes depend on the wavelength, redshift, mass, and angular resolution of the images. We find that the effective radius accurately describes the three-dimensional half-mass–radius of the TNG50 galaxies. Sizes observed at 2 μ m are consistent with those measured at 3.56 μ m at all redshifts and masses. At all masses, the population of higher- z galaxies is more compact than their lower- z counterparts. However, the intrinsic sizes are smaller than the mock observed sizes for the most massive galaxies, especially at z ≲ 4. This discrepancy between the mass and light distributions may point to a transition in the galaxy morphology at z = 4–5, where massive compact systems start to develop more extended stellar structures. 2222 Data publicly released at https://www.tng-project.org/costantin22. [ABSTRACT FROM AUTHOR]

Out of the estimated few trillion galaxies, only around a million have been detected through radio frequencies, and only a tiny fraction, approximately a thousand, have been manually classified. We have addressed this disparity between labeled and unlabeled images of radio galaxies by employing a semi-supervised learning approach to classify them into the known Fanaroff-Riley Type I (FRI) and Type II (FRII) categories. A Group Equivariant Convolutional Neural Network (G-CNN) was used as an encoder of the state-of-the-art self-supervised methods SimCLR (A Simple Framework for Contrastive Learning of Visual Representations) and BYOL (Bootstrap Your Own Latent). The G-CNN preserves the equivariance for the Euclidean Group E(2), enabling it to effectively learn the representation of globally oriented feature maps. After representation learning, we trained a fully-connected classifier and fine-tuned the trained encoder with labeled data. Our findings demonstrate that our semi-supervised approach outperforms existing state-of-the-art methods across several metrics, including cluster quality, convergence rate, accuracy, precision, recall, and the F1-score. Moreover, statistical significance testing via a t-test revealed that our method surpasses the performance of a fully supervised G-CNN. This study emphasizes the importance of semi-supervised learning in radio galaxy classification, where labeled data are still scarce, but the prospects for discovery are immense. [ABSTRACT FROM AUTHOR]

In the nearby universe, jets from active galactic nuclei (AGN) are observed to have a dramatic impact on their surrounding extragalactic environment. The effect of jets at high redshift (z > 1:5) is instead much more poorly constrained. However, studying the jet impact at cosmic noon, the epoch in which both star formation and AGN activity peak, is crucial for fully understanding galaxy evolution. Here we present a study of the giant (~750 kpc) radio galaxy 103025+052430 located at the centre of a protocluster at redshift z = 1:7, with a focus on its interaction with the external medium. We present new LOFAR observations at 144 MHz, which we combine with VLA 1.4 GHz data and 0.5-7 keV Chandra archival data. The new radio map at 144MHz confirms that the source has a complex morphology, which can possibly fit the hybrid morphology radio galaxy classification. The large size of the source enabled us to perform a resolved radio spectral index analysis, a very unique opportunity for a source at this high redshift. This revealed a tentative unexpected flattening of the radio spectral index at the edge of the backflow in the western lobe, which might be indicating plasma compression. The spatial coincidence between this region and the thermal X-ray bubble C suggests a causal connection between the two. In contrast to previous estimates for the bright X-ray component A, we find that inverse Compton scattering between the radio-emitting plasma of the eastern lobe and cosmic microwave background photons can account for a large fraction (~45%-80%) of its total 0.5-7 keV measured flux. Finally, the X-ray bubble C, which is consistent with a thermal origin, is found to be significantly overpressurised with respect to the ambient medium. This suggests that it will tend to expand and release its energy into the surroundings, contributing to the overall intracluster medium heating. Overall, 103025+052430 enables us to investigate the interaction between AGN jets and the surrounding medium in a system that is likely the predecessor of the rich galaxy clusters we all know well at z = 0. [ABSTRACT FROM AUTHOR]

Context. The morphological classification of galaxies is considered a relevant issue and can be approached from different points of view. The increasing growth in the size and accuracy of astronomical data sets brings with it the need for the use of automatic methods to perform these classifications. Aims. The aim of this work is to propose and evaluate a method for the automatic unsupervised classification of kinematic morphologies of galaxies that yields a meaningful clustering and captures the variations of the fundamental properties of galaxies. Methods.We obtained kinematic maps for a sample of 2064 galaxies from the largest simulation of the EAGLE project that mimics integral field spectroscopy images. These maps are the input of a dimensionality reduction algorithm followed by a clustering algorithm. We analysed the variation of physical and observational parameters among the clusters obtained from the application of this procedure to different inputs. The inputs studied in this paper are (a) line-of-sight velocity maps for the whole sample of galaxies observed at fixed inclinations; (b) line-of-sight velocity, dispersion, and flux maps together for the whole sample of galaxies observed at fixed inclinations; (c) line-of-sight velocity, dispersion, and flux maps together for two separate subsamples of edge-on galaxies with similar amount of rotation; and (d) line-of-sight velocity, dispersion, and flux maps together for galaxies from different observation angles mixed. Results. The application of the method to solely line-of-sight velocity maps achieves a clear division between slow rotators (SRs) and fast rotators (FRs) and can differentiate rotation orientation. By adding the dispersion and flux information at the input, low-rotation edge-on galaxies are separated according to their shapes and, at lower inclinations, the clustering using the three types of maps maintains the overall information obtained using only the line-of-sight velocity maps. This method still produces meaningful groups when applied to SRs and FRs separately, but in the first case the division into clusters is less clear than when the input includes a variety of morphologies. When applying the method to a mixture of galaxies observed from different inclinations, we obtain results that are similar to those in our previous experiments with the advantage that in this case the input is more realistic. In addition, our method has proven to be robust: it consistently classifies the same galaxies viewed from different inclinations. [ABSTRACT FROM AUTHOR]

With the development of a series of Galaxy sky surveys in recent years, the observations increased rapidly, which makes the research of machine learning methods for galaxy image recognition a hot topic. Available automatic galaxy image recognition researches are plagued by the large differences in similarity between categories, the imbalance of data between different classes, and the discrepancy between the discrete representation of Galaxy classes (DDRGC) and the essentially gradual changes from one morphological class to the adjacent class. These limitations have motivated several astronomers and machine learning experts to design projects with improved galaxy image recognition capabilities. Therefore, this paper proposes a novel learning method, 'Hierarchical Imbalanced data learning with Weighted sampling and Label smoothing' (HIWL). The HIWL consists of three key techniques respectively dealing with the above-mentioned three problems: (1) designed a hierarchical galaxy classification model based on an efficient backbone network; (2) utilized a weighted sampling scheme to deal with the imbalance problem; and (3) adopted a label smoothing technique to alleviate the DDRGC problem. We applied this method to galaxy photometric images from the Galaxy Zoo-The Galaxy Challenge, exploring the recognition of completely round smooth, in between smooth, cigar-shaped, edge-on, and spiral. The overall classification accuracy is 96.32 per cent, and some superiorities of the HIWL are shown based on recall, precision, and F1-Score in comparing with some related works. In addition, we also explored the visualization of the galaxy image features and model attention to understand the foundations of the proposed scheme. [ABSTRACT FROM AUTHOR]

The distribution of the spin directions of spiral galaxies in the Sloan Digital Sky Survey has been a topic of debate in the past two decades, with conflicting conclusions reported even in cases where the same data were used. Here, we follow one of the previous experiments by applying the SpArcFiRe algorithm to annotate the spin directions in an original dataset of Galaxy Zoo 1. The annotation of the galaxy spin directions is carried out after the first step of selecting the spiral galaxies in three different manners: manual analysis by Galaxy Zoo classifications, by a model-driven computer analysis, and with no selection of spiral galaxies. The results show that when spiral galaxies are selected by Galaxy Zoo volunteers, the distribution of their spin directions as determined by SpArcFiRe is not random, which agrees with previous reports. When selecting the spiral galaxies using a model-driven computer analysis or without selecting the spiral galaxies at all, the distribution is also not random. Simple binomial distribution analysis shows that the probability of the parity violation to occur by chance is lower than 0.01. Fitting the spin directions as observed from the Earth to cosine dependence exhibits a dipole axis with statistical strength of 2.33 σ to 3.97 σ. These experiments show that regardless of the selection mechanism and the analysis method, all experiments show similar conclusions. These results are aligned with previous reports using other methods and telescopes, suggesting that the spin directions of spiral galaxies as observed from the Earth exhibit a dipole axis formed by their spin directions. Possible explanations can be related to the large-scale structure of the universe or to the internal structure of galaxies. The catalogs of annotated galaxies generated as part of this study are available. [ABSTRACT FROM AUTHOR]

We present a machine-learning framework to accurately characterize the morphologies of active galactic nucleus (AGN) host galaxies within z < 1. We first use PSFGAN to decouple host galaxy light from the central point source, then we invoke the Galaxy Morphology Network (G a M or N et) to estimate whether the host galaxy is disk-dominated, bulge-dominated, or indeterminate. Using optical images from five bands of the HSC Wide Survey, we build models independently in three redshift bins: low (0 < z < 0.25), mid (0.25 < z < 0.5), and high (0.5 < z < 1.0). By first training on a large number of simulated galaxies, then fine-tuning using far fewer classified real galaxies, our framework predicts the actual morphology for ∼60%–70% of the host galaxies from test sets, with a classification precision of ∼80%–95%, depending on the redshift bin. Specifically, our models achieve a disk precision of 96%/82%/79% and bulge precision of 90%/90%/80% (for the three redshift bins) at thresholds corresponding to indeterminate fractions of 30%/43%/42%. The classification precision of our models has a noticeable dependency on host galaxy radius and magnitude. No strong dependency is observed on contrast ratio. Comparing classifications of real AGNs, our models agree well with traditional 2D fitting with GALFIT. The PSFGAN+G a M or N et framework does not depend on the choice of fitting functions or galaxy-related input parameters, runs orders of magnitude faster than GALFIT, and is easily generalizable via transfer learning, making it an ideal tool for studying AGN host galaxy morphology in forthcoming large imaging surveys. [ABSTRACT FROM AUTHOR]

The optical morphology of galaxies is strongly related to galactic environment, with the fraction of early-type galaxies increasing with local galaxy density. In this work, we present the first analysis of the galaxy morphology–density relation in a cosmological hydrodynamical simulation. We use a convolutional neural network, trained on observed galaxies, to perform visual morphological classification of galaxies with stellar masses |$M_\ast \gt 10^{10} \, \rm {M}_{\odot }$| in the EAGLE simulation into elliptical, lenticular and late-type (spiral/irregular) classes. We find that EAGLE reproduces both the galaxy morphology–density and morphology–mass relations. Using the simulations, we find three key processes that result in the observed morphology–density relation: (i) transformation of disc-dominated galaxies from late-type (spiral) to lenticular galaxies through gas stripping in high-density environments, (ii) formation of lenticular galaxies by merger-induced black hole feedback in low-density environments, and (iii) an increasing fraction of high-mass galaxies, which are more often elliptical galaxies, at higher galactic densities. [ABSTRACT FROM AUTHOR]

We propose a Multimodal Machine Learning method for estimating the Photometric Redshifts of quasars (PhotoRedshift-MML for short), which has long been the subject of many investigations. Our method includes two main models, i.e. the feature transformation model by multimodal representation learning, and the photometric redshift estimation model by multimodal transfer learning. The prediction accuracy of the photometric redshift was significantly improved owing to the large amount of information offered by the generated spectral features learned from photometric data via the MML. A total of 415 930 quasars from Sloan Digital Sky Survey (SDSS) Data Release 17, with redshifts between 1 and 5, were screened for our experiments. We used |Δ z | = |(z phot − z spec)/(1 + z spec)| to evaluate the redshift prediction and demonstrated a |$4.04{{\ \rm per\ cent}}$| increase in accuracy. With the help of the generated spectral features, the proportion of data with |Δ z | < 0.1 can reach |$84.45{{\ \rm per\ cent}}$| of the total test samples, whereas it reaches |$80.41{{\ \rm per\ cent}}$| for single-modal photometric data. Moreover, the Root Mean Square (RMS) of |Δ z | is shown to decrease from 0.1332 to 0.1235. Our method has the potential to be generalized to other astronomical data analyses such as galaxy classification and redshift prediction. [ABSTRACT FROM AUTHOR]

In this study, the morphological galaxy classification process was carried out with a hybrid approach. Since the Galaxy classification process may contain detailed information about the universe's formation, it remains the current research topic. Researchers divided more than 100 billion galaxies into ten different classes. It is not always possible to understand which class the galaxy types belong. However, Artificial Intelligence (AI) can be used for successful classification. There are studies on the automatic classification of galaxies into a small number of classes. As the number of classes increases, the success of the used methods decreases. Based on the literature, the classification using Convolutional Neural Network (CNN) is better. Three metaheuristic algorithms are used to obtain the optimum architecture of CNN. These are Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms. A CNN architecture with nine hidden layers and two full connected layers was used. The number of neurons in the hidden layers and the fully connected layers, the learning coefficient and the batch size values were optimized. The classification accuracy of my model was 85%. The best results were obtained usingGWO. Manual optimization of CNN is difficult. It was carried out with the help of the GWO meta-heuristic algorithm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

It is often stated that the removal of gas by ram-pressure stripping of a galaxy disc is not a common process in galaxy groups. In this study, with the aid of an observational classification of galaxies and a simple physical model, we show that this may not be true. We examined and identified 45 ram-pressure-stripped galaxy candidates from a sample of 1311 galaxy group members within 125 spectroscopically selected galaxy groups. Of these, 13 galaxies are the most secure candidates with multiple distinct features. These candidate ram-pressure-stripped galaxies have similar properties to those found in clusters – they occur at a range of stellar masses, are largely blue and star-forming, and have phase-space distributions consistent with being first infallers into their groups. The only stand-out feature of these candidates is they exist not in clusters, but in groups, with a median halo mass of 1013.5 M⊙. Although this may seem surprising, we employ an analytic model of the expected ram-pressure stripping force in groups and find that reasonable estimates of the relevant infall speeds and intragroup medium content would result in ram-pressure-stripped galaxies at these halo masses. Finally, given the considerable uncertainty on the lifetime of the ram-pressure phase, this physical mechanism could be the dominant quenching mechanism in galaxy groups, if our ram-pressure-stripped candidates can be confirmed. [ABSTRACT FROM AUTHOR]

The extent of black hole growth during different galaxy evolution phases and the connection between galaxy compactness and active galactic nucleus (AGN) activity remain poorly understood. We use Hubble Space Telescope imaging of the CANDELS fields to identify star-forming and quiescent galaxies at z  = 0.5–3 in both compact and extended phases and use Chandra X-ray imaging to measure the distribution of AGN accretion rates and track black hole growth within these galaxies. We show that accounting for the impact of AGN light changes ∼20 per cent of the X-ray sources from compact to extended galaxy classifications. We find that ∼10–25 per cent of compact star-forming galaxies host an AGN, a mild enhancement (by a factor ∼2) compared to extended star-forming galaxies or compact quiescent galaxies of equivalent stellar mass and redshift. However, AGNs are not ubiquitous in compact star-forming galaxies and this is not the evolutionary phase, given its relatively short time-scale, where the bulk of black hole mass growth takes place. Conversely, we measure the highest AGN fractions (∼10–30 per cent) within the relatively rare population of extended quiescent galaxies. For massive galaxies that quench at early cosmic epochs, substantial black hole growth in this extended phase is crucial to produce the elevated black hole mass-to-galaxy stellar mass scaling relation observed for quiescent galaxies at z ∼ 0. We also show that AGN fraction increases with compactness in star-forming galaxies and decreases in quiescent galaxies within both the compact and extended subpopulations, demonstrating that AGN activity depends closely on the structural properties of galaxies. [ABSTRACT FROM AUTHOR]

We cross-match between the WISE All-Sky and PS1 3π DR2 source catalogues. The resulting catalogue has 354 590 570 objects, significantly fewer than the parent PS1 catalogue, but its combination of optical and infrared colours facilitate both better source classification and photometric redshift estimation. We perform a neural network-based classification of the objects into galaxies, quasars, and stars, then run neural network-based photometric redshift estimation for the galaxies. The star sample purity and quasar sample completeness measures improve substantially, and the resulting photo- z 's are significantly more accurate in terms of statistical scatter and bias than those calculated from PS1 properties alone. The catalogue will be a basis for future large-scale structure studies, and will be made available as a high-level science product via the Mikulski Archive for Space Telescopes. [ABSTRACT FROM AUTHOR]