Sloan Digital Sky Survey Imaging Research Articles

Edge-on Low-surface-brightness Galaxy Candidates Detected from SDSS Images Using YOLO

Low-surface-brightness galaxies (LSBGs), fainter members of the galaxy population, are thought to be numerous. However, due to their low surface brightness, the search for a wide-area sample of LSBGs is difficult, which in turn limits our ability to fully understand the formation and evolution of galaxies as well as galaxy relationships. Edge-on LSBGs, due to their unique orientation, offer an excellent opportunity to study galaxy structure and galaxy components. In this work, we utilize the You Only Look Once object detection algorithm to construct an edge-on LSBG detection model by training on 281 edge-on LSBGs in Sloan Digital Sky Survey (SDSS) gri-band composite images. This model achieved a recall of 94.64% and a purity of 95.38% on the test set. We searched across 938,046 gri-band images from SDSS Data Release 16 and found 52,293 candidate LSBGs. To enhance the purity of the candidate LSBGs and reduce contamination, we employed the Deep Support Vector Data Description algorithm to identify anomalies within the candidate samples. Ultimately, we compiled a catalog containing 40,759 edge-on LSBG candidates. This sample has similar characteristics to the training data set, mainly composed of blue edge-on LSBG candidates. The catalog is available online at https://github.com/worldoutside/Edge-on_LSBG.

The New Swift/UVOT+MaNGA (SwiM) Value-added Catalog

We present the the new Swift/UVOT+MaNGA (SwiM) catalog (SwiM_v4.1). SwiM_v4.1 is designed to study star formation and dust attenuation within nearby galaxies given the unique overlap of Swift/UVOT near-ultraviolet (NUV) imaging and MaNGA integral field optical spectroscopy. SwiM_v4.1 comprises 559 objects, ∼4 times more than the original SwiM catalog (SwiM_v3.1), spans the redshift range z ≈ 0.0002–0.1482, and provides a more diverse and rich sample. Approximately 5% of the final MaNGA sample is included in SwiM_v4.1, and 42% of the SwiM_v4.1 galaxies are cross-listed with other well-known catalogs. We present the same data as SwiM_v3.1, including UVOT images, Sloan Digital Sky Survey (SDSS) images, and MaNGA emission-line and spectral index maps with the same pixel size and angular resolution for each galaxy, and a file containing galaxy and observational properties. We designed SwiM_v4.1 to be unbiased, which resulted in some objects having low signal-to-noise ratios in their MaNGA or Swift data. We addressed this by providing a new file containing the fraction of science-ready pixels in each MaNGA emission-line map, and the integrated flux and inverse variance for all three NUV filters. The uniform angular resolution and sampling in SwiM_v4.1 will help answer a number of scientific questions, including constraining quenching and attenuation in the local Universe and studying the effects of black hole feedback. The galaxy maps, catalog files, and their associated data models are publicly released on the SDSS website (a description of the SwiM VAC is provided at https://www.sdss4.org/dr17/data_access/value-added-catalogs/?vac_id=swift-manga-value-added-catalog, and the data are stored on the SDSS Science Archive Server at https://data.sdss.org/sas/dr17/manga/swim/v4.1/).

Deep optical imaging of star-forming blue early-type galaxies

Blue early-type galaxies with galaxy-scale ongoing star formation are interesting targets in order to understand the stellar mass buildup in elliptical and S0 galaxies in the local Universe. We study the star-forming population of blue early-type galaxies to understand the origin of star formation in these otherwise red and dead stellar systems. The legacy survey imaging data taken with the dark energy camera in the g, r, and z bands for 55 star-forming blue early-type galaxies were examined, and g − r color maps were created. We identified low surface brightness features near 37 galaxies, faint-level interaction signatures near 15 galaxies, and structures indicative of recent merger activity in the optical color maps of all 55 galaxies. These features are not visible in the shallow Sloan Digital Sky Survey imaging data in which these galaxies were originally identified. Low surface brightness features found around galaxies could be remnants of recent merger events. The star-forming population of blue early-type galaxies could be post-merger systems that are expected to be the pathway for the formation of elliptical galaxies. We hypothesize that the star-forming population of blue early-type galaxies is a stage in the evolution of early-type galaxies. The merger features will eventually disappear, fuel for star formation will cease, and the galaxy will move to the passive population of normal early-type galaxies.

Directly Deriving Parameters from SDSS Photometric Images

Stellar atmospheric parameters (effective temperature, surface gravity, and metallicity) are fundamental for understanding the formation and evolution of stars and galaxies. Photometric data can provide a low-cost way to estimate these parameters, but traditional methods based on photometric magnitudes have many limitations. In this paper, we propose a novel model called Bayesian Convit, which combines an approximate Bayesian framework with a deep-learning method, namely Convit, to derive stellar atmospheric parameters from Sloan Digital Sky Survey images of stars and effectively provide corresponding confidence levels for all the predictions. We achieve high accuracy for T eff and [Fe/H], with σ(T eff) = 172.37 K and σ([Fe/H]) = 0.23 dex. For , which is more challenging to estimate from image data, we propose a two-stage approach: (1) classify stars into two categories based on their values (>4 dex or <4 dex) and (2) regress separately these two subsets. We improve the estimation accuracy of stars with dex significantly to dex, which are comparable to those based on spectral data. The final joint result is dex. Our method can be applied to large photometric surveys like Chinese Space Station Telescope and Large Synoptic Survey Telescope.

L-dwarf Detection from SDSS Images using Improved Faster R-CNN

We present a data-driven approach to automatically detect L dwarfs from Sloan Digital Sky Survey (SDSS) images using an improved Faster R-CNN framework based on deep learning. The established L-dwarf automatic detection (LDAD) model distinguishes L dwarfs from other celestial objects and backgrounds in SDSS field images by learning the features of 387 SDSS images containing L dwarfs. Applying the LDAD model to the SDSS images containing 93 labeled L dwarfs in the test set, we successfully detected 83 known L dwarfs with a recall rate of 89.25% for known L dwarfs. Several techniques are implemented in the LDAD model to improve its detection performance for L dwarfs, including the deep residual network and the feature pyramid network. As a result, the LDAD model outperforms the model of the original Faster R-CNN, whose recall rate of known L dwarfs is 80.65% for the same test set. The LDAD model was applied to detect L dwarfs from a larger validation set including 843 labeled L dwarfs, resulting in a recall rate of 94.42% for known L dwarfs. The newly identified candidates include L dwarfs, late M and T dwarfs, which were estimated from color (i − z) and spectral type relation. The contamination rates for the test candidates and validation candidates are 8.60% and 9.27%, respectively. The detection results indicate that our model is effective to search for L dwarfs from astronomical images.

The Discovery of Blue-cored Dwarf Early-type Galaxies in Isolated Environments

The presence of blue-cored dwarf early-type galaxies (dE(bc)s) in high-density environments supports the scenario of the transformation of infalling late-type galaxies into quiescent dwarf early-type galaxies by environmental effects. While low-density environments lacking environmental processes could not be relevant to the formation of dE(bc)s, we discovered a large sample of rare dE(bc)s in isolated environments at z < 0.01 using the NASA–Sloan Atlas catalog. Thirty-two isolated dE(bc)s were identified by visual inspection of the Sloan Digital Sky Survey images and g − r color profiles. We found that (1) isolated dE(bc)s exhibit similar structural parameters to dE(bc)s in the Virgo cluster; (2) based on the color–magnitude relation, color gradients, and optical emission lines of dE(bc)s, isolated dE(bc)s show more vigorous, centrally concentrated star formation (SF) compared to their counterparts in the Virgo cluster; (3) at a given stellar mass, isolated dE(bc)s tend to have a larger fraction of gas mass than their Virgo counterparts. We discuss a scenario of episodic SF sustained by gas accretion, suggested by Sánchez Almeida et al., in which the star-bursting blue compact dwarf galaxy (BCD)−quiescent BCD (QBCD) cycle can be repeated during the Hubble time. We suggest that, in this cadence, isolated dE(bc)s might be QBCDs at pre- or post-BCD stages. Our results imply that dE(bc)s comprise a mixture of objects with two types of origins, nature or nurture, depending on their environment.

The redshift evolution of the S0 fraction for z &amp;lt; 1 in COSMOS

ABSTRACT Lenticular (S0) galaxies are galaxies that exhibit a bulge and disc component, yet lack any clear spiral features. With features considered intermediary between spirals and ellipticals, S0s have been proposed to be a transitional morphology, however their exact origin and nature is still debated. In this work, we study the redshift evolution of the S0 fraction out to z ∼ 1 using deep learning to classify F814W (i band) Hubble Space Telescope-Advanced Camera for Surveys (HST-ACS) images of 85 378 galaxies in the Cosmic Evolution Survey (COSMOS). We classify galaxies into four morphological categories: elliptical (E), S0, spiral (Sp), and irregular/miscellaneous (IrrM). Our deep learning models, initially trained to classify Sloan Digital Sky Survey (SDSS) images with known morphologies, have been successfully adapted to classify high-redshift COSMOS images via transfer learning and data augmentation, enabling us to classify S0s with superior accuracy. We find that there is an increase in the fraction of S0 galaxies with decreasing redshift, along with a corresponding reduction in the fraction of spirals. We find a bimodality in the mass distribution of our classified S0s, from which we find two separate S0s populations: high-mass S0s, which are mostly red and quiescent; and low-mass S0s, which are generally bluer and include both passive and star-forming S0s, the latter of which cannot solely be explained via the faded spiral formation pathway. We also find that the S0 fraction in high-mass galaxies begins rising at higher z than in low-mass galaxies, implying that high-mass S0s evolved earlier.

Automatic detection of cataclysmic variables from SDSS images

Abstract Investigating rare and new objects have always been an important direction in astronomy. Cataclysmic variables (CVs) are ideal and natural celestial bodies for studying the accretion process of semi-detached binaries with accretion processes. However, the sample size of CVs must increase because a lager gap exists between the observational and the theoretical expanding CVs. Astronomy has entered the big data era and can provide massive images containing CV candidates. CVs as a type of faint celestial objects, are highly challenging to be identified directly from images using automatic manners. Deep learning has rapidly developed in intelligent image processing and has been widely applied in some astronomical fields with excellent detection results. YOLOX, as the latest YOLO framework, is advantageous in detecting small and dark targets. This work proposes an improved YOLOX-based framework according to the characteristics of CVs and Sloan Digital Sky Survey (SDSS) photometric images to train and verify the model to realise CV detection. We use the Convolutional Block Attention Module to increase the number of output features with the feature extraction network and adjust the feature fusion network to obtain fused features. Accordingly, the loss function is modified. Experimental results demonstrate that the improved model produces satisfactory results, with average accuracy (mean average Precision at 0.5) of 92.0%, Precision of 92.9%, Recall of 94.3%, and $F1-score$ of 93.6% on the test set. The proposed method can efficiently achieve the identification of CVs in test samples and search for CV candidates in unlabeled images. The image data vastly outnumber the spectra in the SDSS-released data. With supplementary follow-up observations or spectra, the proposed model can help astronomers in seeking and detecting CVs in a new manner to ensure that a more extensive CV catalog can be built. The proposed model may also be applied to the detection of other kinds of celestial objects.

Galaxy Spin Classification. I. Z-wise versus S-wise Spirals with the Chirality Equivariant Residual Network

The angular momentum of galaxies (galaxy spin) contains rich information about the initial condition of the universe, yet it is challenging to efficiently measure the spin direction for the tremendous amount of galaxies that are being mapped by ongoing and forthcoming cosmological surveys. We present a machine-learning-based classifier for the Z-wise versus S-wise spirals, which can help to break the degeneracy in the galaxy spin direction measurement. The proposed chirality equivariant residual network (CE-ResNet) is manifestly equivariant under a reflection of the input image, which guarantees that there is no inherent asymmetry between the Z-wise and S-wise probability estimators. We train the model with Sloan Digital Sky Survey images, with the training labels given by the Galaxy Zoo 1 project. A combination of data augmentation techniques is used during the training, making the model more robust to be applied to other surveys. We find an ∼30% increase in both types of spirals when Dark Energy Spectroscopic Instrument (DESI) images are used for classification, due to the better imaging quality of DESI. We verify that the ∼7σ difference between the numbers of Z-wise and S-wise spirals is due to human bias, since the discrepancy drops to <1.8σ with our CE-ResNet classification results. We discuss the potential systematics relevant to future cosmological applications.

The Effect of Environment on Galaxy Spiral Arms, Bars, Concentration, and Quenching

For a sample of 4378 nearby spiral and S0 galaxies, Yu & Ho used Fourier analysis of Sloan Digital Sky Survey images to show that the strengths of the spiral arms and the pitch angles of the arms are inversely correlated with central concentration. In the current study, we search for trends in the Yu & Ho spiral arm parameters with environment and specific star formation rate (sSFR). When comparing galaxies with similar concentrations, we do not find a significant difference in the arm strengths or pitch angles of spiral galaxies in clusters compared to field galaxies. When differences in concentration are taken into account, we also find no significant difference in the parameter f3 for cluster spirals compared to field spirals, where f3 is the normalized m = 3 Fourier amplitude. When concentration is held fixed, both arm strength and pitch angle are correlated with sSFR, but f3 is not. These relations support the suggestion by Davis et al. of a “fundamental plane” of spiral structure involving pitch angle, bulge stellar mass, and gas surface density. We discuss these results in terms of theories of spiral arm production and quenching in galaxies. To aid comparison with earlier studies based on Galaxy Zoo, we explore how the Yu & Ho parameters relate to similar parameters measured by Galaxy Zoo (i.e., f3 versus the number of arms, pitch angle versus winding parameter, and concentration versus bulge class).

A photometry pipeline for SDSS images based on convolutional neural networks

ABSTRACT In this paper, we propose a convolutional neural network (CNN)-based photometric pipeline for the Sloan Digital Sky Survey (SDSS) images. The pipeline includes three main parts: the target source detection, the target source classification, and the photometric parameter measurement. The last part is completed using traditional methods. The paper mainly focuses on the first two parts and does not present the last. In the 1st part, a network named TSD-YOLOv4 is proposed to detect new sources missed by the SDSS photometric pipeline according to the PhotoObjAll catalogue of SDSS. In the second part, a target source classification network named TSCNet is constructed to classify sources into galaxies, quasars, and stars directly from photometric images. Experiments show that TSD-YOLOv4 outperforms other networks (Faster-RCNN, YOLOv4, YOLOX, etc.) in all metrics, with an accuracy of 0.988, a recall of 0.997, and an F1-score of 0.992, and TSCNet has good performance with a classification accuracy of 0.944 on the test set with 23 265 sources, and precision rates of 0.98, 0.908, and 0.918 for galaxies, quasars, and stars, respectively. On the other hand, the recall rates are 0.982, 0.903, and 0.921 for galaxies, quasars, and stars, respectively. The TSCNet has higher accuracy, fewer parameters, and faster inference speed than the leading astronomical photometric source classification network, the APSCNet model. In addition, the effect of magnitude distribution on the classification results is discussed in the experiments. The experiments prove that the proposed pipeline can be used as a powerful tool to supplement the SDSS photometric catalogue.

Morphology, colour–magnitude, and scaling relations of galaxies in Abell 426

ABSTRACT We present photometric properties of 183 member galaxies in the Abell 426 cluster using the Sloan Digital Sky Survey (SDSS) imaging and spectroscopic observation. Detailed morphology based on visual classification followed by multicomponent image decomposition of 179 galaxies is presented in the SDSS g, r, i bands. More than 80 per cent of the members are early-type galaxies (ETGs), with elliptical, dwarf elliptical (dE), and lenticular morphology and follow the red-sequence in the colour–magnitude diagram (CMD). With a few dEs and spirals in the blue cloud, the cluster CMD is nearly unimodal. The dEs are ∼2-mag fainter and follow a different Sersic index and central velocity dispersion distribution than their bright counterparts. Further, we establish the Kormendy relation (KR) and the fundamental plane relation (FPR) for five different samples of ETGs constructed based on derived physical parameters such as Sersic index, concentration, central velocity dispersion in g, r, i bands. The mean r-band slope and zero-point of the KR are 3.02 ± 0.1 and 18.65 ± 0.03 in close agreement to other cluster ellipticals in the local and higher redshift. Kinematics-based ETG sample produces the least scatter in KR with zero-point getting brighter by ∼1.3 mag from g to i band. The dEs and other low-mass ETGs follow the KR with a similar slope but with ∼1.3 mag fainter zero-point and form a parallel KR. The bright ellipticals follow an FPR with a = 1.37 ± 0.003, b = 0.35 ± 0.05, and c = −9.37 ± 0.02 in the r band; galaxies tend to deviate from this relation at the low-mass end. A catalogue with morphology and 2D structural analysis is available online.

Quasar UV Luminosity Function at 3.5 &lt; z &lt; 5.0 from SDSS Deep Imaging Data

Abstract We present a well-designed sample of more than 1000 type 1 quasars at 3.5 &lt; z &lt; 5 and derive UV quasar luminosity functions (QLFs) in this redshift range. These quasars were selected using the Sloan Digital Sky Survey (SDSS) imaging data in the Stripe 82 and overlap regions with repeat imaging observations that are about 1 mag fainter than the SDSS single-epoch data. The follow-up spectroscopic observations were conducted by the SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS) as one of the BOSS ancillary programs. Reaching i ∼ 21.5 mag, our sample bridges previous samples from brighter and deeper surveys. We use a 1/V a method to derive binned QLFs at 3.6 &lt; z &lt; 4.0, 4.0 &lt; z &lt; 4.5, and 4.5 &lt; z &lt; 4.9 and then use a double power-law model to parameterize the QLFs. We also combine our data with literature QLFs to better constrain the QLFs across a much wider luminosity baseline. The faint- and bright-end slopes of the QLFs in this redshift range are around −1.7 and −3.7, respectively, with uncertainties from 0.2 to 0.3 to &gt;0.5. The evolution of the QLFs from z ∼ 5 to 3.5 can be described by a pure density evolution model (∝10 kz ) with a parameter k similar to that at 5 &lt; z &lt; 7, suggesting a nearly uniform evolution of the quasar density at z = 3.5–7.

Automatic detection of low surface brightness galaxies from Sloan Digital Sky Survey images

ABSTRACTLow surface brightness (LSB) galaxies are galaxies with central surface brightness fainter than the night sky. Due to the faint nature of LSB galaxies and the comparable sky background, it is difficult to search LSB galaxies automatically and efficiently from large sky survey. In this study, we established the low surface brightness galaxies autodetect (LSBG-AD) model, which is a data-driven model for end-to-end detection of LSB galaxies from Sloan Digital Sky Survey (SDSS) images. Object-detection techniques based on deep learning are applied to the SDSS field images to identify LSB galaxies and estimate their coordinates at the same time. Applying LSBG-AD to 1120 SDSS images, we detected 1197 LSB galaxy candidates, of which 1081 samples are already known and 116 samples are newly found candidates. The B-band central surface brightness of the candidates searched by the model ranges from 22 to 24 mag arcsec−2, quite consistent with the surface brightness distribution of the standard sample. A total of 96.46 per cent of LSB galaxy candidates have an axial ratio (b/a) greater than 0.3, and 92.04 per cent of them have $fracDev\_r$ &amp;lt; 0.4, which is also consistent with the standard sample. The results show that the LSBG-AD model learns the features of LSB galaxies of the training samples well, and can be used to search LSB galaxies without using photometric parameters. Next, this method will be used to develop efficient algorithms to detect LSB galaxies from massive images of the next-generation observatories.

Low-metallicity globular clusters in the low-mass isolated spiral galaxy NGC 2403

ABSTRACTThe globular cluster (GC) systems of low-mass late-type galaxies, such as NGC 2403, have been poorly studied to date. As a low mass galaxy (M*  = 7 × 109 M⊙), cosmological simulations predict NGC 2403 to contain few, if any, accreted GCs. It is also isolated, with a remarkably undisturbed HI disc. Based on candidates from the literature, Sloan Digital Sky Survey and Hyper Suprime-Cam imaging, we selected several GCs for follow-up spectroscopy using the Keck Cosmic Web Imager. From their radial velocities and other properties, we identify eight bona-fide GCs associated with either the inner halo or the disc of this bulgeless galaxy. A stellar population analysis suggests a wide range of GC ages from shortly after the big bang until the present day. We find all of the old GCs to be metal-poor with [Fe/H] ≤ −1. The age–metallicity relation for the observed GCs suggests that they were formed over many Gyr from gas with a low effective yield, similar to that observed in the SMC. Outflows of enriched material may have contributed to the low yield. With a total system of ∼50 GCs expected, our study is the first step in fully mapping the star cluster history of NGC 2403 in both space and time.

Galaxy classification: a deep learning approach for classifying Sloan Digital Sky Survey images

ABSTRACTIn recent decades, large-scale sky surveys such as Sloan Digital Sky Survey (SDSS) have resulted in generation of tremendous amount of data. The classification of this enormous amount of data by astronomers is time consuming. To simplify this process, in 2007 a volunteer-based citizen science project called ‘Galaxy Zoo’ was introduced, which has reduced the time for classification by a good extent. However, in this modern era of deep learning, automating this classification task is highly beneficial as it reduces the time for classification. For the last few years, many algorithms have been proposed which happen to do a phenomenal job in classifying galaxies into multiple classes. But all these algorithms tend to classify galaxies into less than six classes. However, after considering the minute information which we know about galaxies, it is necessary to classify galaxies into more than eight classes. In this study, a neural network model is proposed so as to classify SDSS data into 10 classes from an extended Hubble Tuning Fork. Great care is given to disc edge and disc face galaxies, distinguishing between a variety of substructures and minute features which are associated with each class. The proposed model consists of convolution layers to extract features making this method fully automatic. The achieved test accuracy is 84.73  per cent which happens to be promising after considering such minute details in classes. Along with convolution layers, the proposed model has three more layers responsible for classification, which makes the algorithm consume less time.

A Detailed Morphological and Spectroscopic Study of Merging Dwarf Galaxy PGC 030133

We present a detailed morphology and spectroscopic study of a merging dwarf galaxy PGC 030133. Using the publicly available fiber spectroscopic data from the Sloan Digital Sky Survey (SDSS), we analyzed nine-strong emission lines of wavelength range 4336 Å to 6739 Å. We find that the strongest emission line is OIII5007, with an intensity of 146.32 × 10-17 erg/s/cm2 /Å. The observed emission lines are well fitted with a Gaussian profile with a coefficient of regression greater than 96%, and the derived full-width half maximum (FWHM) is less than 4.2Å. The Balmer decrement, characterized by the line ratio between Hα and Hβ is 3.07, suggesting a presence of dust at the center of PGC 030133. We derived the star-formation rate and emission line metallicity of PGC 030133 using extinction corrected emission line fluxes. SFR derived from Hα emission line flux is 0.0033 Mʘyear-1 and emission line metallicity derived from flux ratio between NII and Hα is 8.13 dex. Using the SDSS, g, and z-band imaging data, we derived morphological parameters. PGC 030133 has a half-light radius of 3.38 arc second and 3.05 arc second in g-band and z-band respectively and observed one-dimensional light profile is well fitted with a Sersic function with near exponential Sersic index ~ 0.9.

Are All Post-starbursts Mergers? HST Reveals Hidden Disturbances in the Majority of PSBs

Abstract How do galaxies transform from blue, star-forming spirals to red, quiescent early-type galaxies? To answer this question, we analyzed a set of 26 gas-rich, shocked post-starburst galaxies with Hubble Space Telescope (HST) imaging in B, I, and H bands and Sloan Digital Sky Survey (SDSS) i-band imaging of similar depth but lower resolution. We found that post-starbursts in our sample have intermediate morphologies between disk- and bulge-dominated (Sérsic n = 1.7 − 0.0 + 0.3 ) and have red bulges, likely due to dust obscuration in the cores. A majority of galaxies in our sample are more morphologically disturbed than regular galaxies (88%, corresponding to &gt;3σ significance) when observed with HST, with asymmetry and Sérsic residual flux fraction being the most successful measures of disturbance. Most disturbances are undetected at the lower resolution of SDSS imaging. Although ∼27% galaxies are clear merger remnants, we found that disturbances in another ∼30% of the sample are internal, caused by small-scale perturbations or dust substructures rather than tidal features, and require high-resolution imaging to detect. We found 2.8σ evidence that asymmetry features fade on timescales ∼200 Myr, and may vanish entirely after ∼750 Myr, so we do not rule out a possible merger origin of all post-starbursts given that asymmetric features may have already faded. This work highlights the importance of small-scale disturbances, detected only in high-resolution imaging, in understanding structural evolution of transitioning galaxies.

Survey2Survey: a deep learning generative model approach for cross-survey image mapping

ABSTRACT During the last decade, there has been an explosive growth in survey data and deep learning techniques, both of which have enabled great advances for astronomy. The amount of data from various surveys from multiple epochs with a wide range of wavelengths, albeit with varying brightness and quality, is overwhelming, and leveraging information from overlapping observations from different surveys has limitless potential in understanding galaxy formation and evolution. Synthetic galaxy image generation using physical models has been an important tool for survey data analysis, while deep learning generative models show great promise. In this paper, we present a novel approach for robustly expanding and improving survey data through cross survey feature translation. We trained two types of neural networks to map images from the Sloan Digital Sky Survey (SDSS) to corresponding images from the Dark Energy Survey (DES). This map was used to generate false DES representations of SDSS images, increasing the brightness and S/N while retaining important morphological information. We substantiate the robustness of our method by generating DES representations of SDSS images from outside the overlapping region, showing that the brightness and quality are improved even when the source images are of lower quality than the training images. Finally, we highlight images in which the reconstruction process appears to have removed large artefacts from SDSS images. While only an initial application, our method shows promise as a method for robustly expanding and improving the quality of optical survey data and provides a potential avenue for cross-band reconstruction.

The origin of low-surface-brightness galaxies in the dwarf regime

ABSTRACT Low-surface-brightness galaxies (LSBGs) – defined as systems that are fainter than the surface-brightness limits of past wide-area surveys – form the overwhelming majority of galaxies in the dwarf regime (M⋆ &amp;lt; 109 M⊙). Using NewHorizon, a high-resolution cosmological simulation, we study the origin of LSBGs and explain why LSBGs at similar stellar mass show the large observed spread in surface brightness. NewHorizon galaxies populate a well-defined locus in the surface brightness–stellar mass plane, with a spread of ∼3 mag arcsec−2, in agreement with deep Sloan Digital Sky Survey (SDSS) Stripe 82 data. Galaxies with fainter surface brightnesses today are born in regions of higher dark matter density. This results in faster gas accretion and more intense star formation at early epochs. The stronger resultant supernova feedback flattens gas profiles at a faster rate, which, in turn, creates shallower stellar profiles (i.e. more diffuse systems) more rapidly. As star formation declines towards late epochs ( z &amp;lt; 1), the larger tidal perturbations and ram pressure experienced by these systems (due to their denser local environments) accelerate the divergence in surface brightness, by increasing their effective radii and reducing star formation, respectively. A small minority of dwarfs depart from the main locus towards high surface brightnesses, making them detectable in past wide surveys (e.g. standard-depth SDSS images). These systems have anomalously high star formation rates, triggered by recent fly-by or merger-driven starbursts. We note that objects considered extreme or anomalous at the depth of current data sets, e.g. ‘ultra-diffuse galaxies’, actually dominate the predicted dwarf population and will be routinely visible in future surveys like the Legacy Survey of Space and Time (LSST).