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DOE OSTI2022

HIPASS study of southern ultradiffuse galaxies and low surface brightness galaxies

ABSTRACT We present results from an H i counterpart search using the HI Parkes All Sky Survey (HIPASS) for a sample of low surface brightness galaxies (LSBGs) and ultradiffuse galaxies (UDGs) identified from the Dark Energy Survey (DES). We aimed to establish the redshifts of the DES LSBGs to determine the UDG fraction and understand their properties. Out of 409 galaxies investigated, none were unambiguously detected in H i. Our study was significantly hampered by the high spectral rms of HIPASS and thus in this paper we do not make any strong conclusive claims but discuss the main trends and possible scenarios our results reflect. The overwhelming number of non-detections suggest that (a) Either all the LSBGs in the groups, blue or red, have undergone environment aided pre-processing and are H i deficient or the majority of them are distant galaxies, beyond the HIPASS detection threshold. (b) The sample investigated is most likely dominated by galaxies with H i masses typical of dwarf galaxies. Had there been Milky Way (MW) size (Re) galaxies in our sample, with proportionate H i content, they would have been detected, even with the limitations imposed by the HIPASS spectral quality. This leads us to infer that if some of the LSBGs have MW-size optical diameters, their H i content is possibly in the dwarf range. More sensitive observations using the SKA precursors in future may resolve these questions.

Zhou, Yun-Fan↗

DOE OSTI2021

Detecting Low Surface Brightness Galaxies with Mask R-CNN

Low surface brightness galaxies (LSBGs), galaxies that are fainter than the dark night sky, are famously difficult to detect. However, studies of these galaxies are essential to improve our understanding of the formation and evolution of low-mass galaxies. In this work, we train a deep learning model using the Mask R-CNN framework on a set of simulated LSBGs inserted into images from the Dark Energy Survey (DES) Data Release 2 (DR2). This deep learning model is combined with several conventional image pre-processing steps to develop a pipeline for the detection of LSBGs. We apply this pipeline to the full DES DR2 coadd image dataset, and preliminary results show the detection of 22 large, high-quality LSBG candidates that went undetected by conventional algorithms. Furthermore, we find that Galactic cirrus represents the largest contaminant in our resulting candidate list.

Levy, Caleb↗

DOE OSTI2021

The Intrinsic Shapes of Low Surface Brightness Galaxies (LSBGs): A Discriminant of LSBG Galaxy Formation Mechanisms

We use the low surface brightness galaxy (LSBG) samples created from the Hyper Suprime-Cam Subaru Strategic Program (781 galaxies), the Dark Energy Survey (20977 galaxies), and the Legacy Survey (selected via H ι detection in the Arecibo Legacy Fast ALFA Survey, 188 galaxies) to infer the intrinsic shape distribution of the LSBG population. To take into account the effect of the surface brightness cuts employed when constructing LSBG samples, we simultaneously model both the projected ellipticity and the apparent surface brightness in our shape inference. We find that the LSBG samples are well characterized by oblate spheroids, with no significant difference between red and blue LSBGs. This inferred shape distribution is in good agreement with similar inferences made for ultra-diffuse cluster galaxy samples, indicating that environment does not play a key role in determining the intrinsic shape of LSBGs. In this study, we also find some evidence that LSBGs are more thickened than similarly massive high surface brightness dwarfs. We compare our results to intrinsic shape measures from contemporary cosmological simulations, and find that the observed LSBG intrinsic shapes place considerable constraints on the formation path of such galaxies. In particular, LSBG production via the migration of star formation to large radii produces intrinsic shapes in good agreement with our observational findings.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

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

ABSTRACT Low 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 $fracDev\_r$ < 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.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

DeepShadows: Separating low surface brightness galaxies from artifacts using deep learning

Searches for low-surface-brightness galaxies (LSBGs) in galaxy surveys are plagued by the presence of a large number of artifacts (e.g., objects blended in the diffuse light from stars and galaxies, Galactic cirrus, star-forming regions in the arms of spiral galaxies, etc.) that have to be rejected through time consuming visual inspection. In future surveys, which are expected to collect hundreds of petabytes of data and detect billions of objects, such an approach will not be feasible. We investigate the use of convolutional neural networks (CNNs) for the problem of separating LSBGs from artifacts in survey images. We take advantage of the fact that we have available a large number of labeled LSBGs and artifacts from the Dark Energy Survey, that we use to train, validate, and test a CNN model. That model, which we call DeepShadows , achieves a test accuracy of 92.0%, a significant improvement relative to feature-based machine learning models. We also study the ability to use transfer learning to adapt this model to classify objects from the deeper Hyper-Suprime-Cam survey, and we show that after the model is retrained on a very small sample from the new survey, it can reach an accuracy of 87.6%. Finally, these results demonstrate that CNNs offer a very promising path in the quest to study the low-surface-brightness universe.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Environmental Quenching of Low-surface-brightness Galaxies Near Hosts from Large Magellanic Cloud to Milky Way Mass Scales

Low-surface-brightness galaxies (LSBGs) are excellent probes of quenching and other environmental processes near massive galaxies. We study an extensive sample of LSBGs near massive hosts in the local universe that are distributed across a diverse range of environments. The LSBGs with surface-brightness ${\mu }_{\mathrm{eff},{g}}\gt 24.2\,\mathrm{mag}\,{\mathrm{arcsec}}^{-2}$ are drawn from the Dark Energy Survey Year 3 catalog while the hosts with masses $9.0\lt \mathrm{log}({{ \mathcal M }}_{\star }/{M}_{\odot })\lt 11.0$ comparable to the Milky Way and the Large Magellanic Cloud are selected from the z0MGS sample. We study the projected radial density profiles of LSBGs as a function of their color and surface brightness around hosts in both the rich Fornax–Eridanus cluster environment and the low-density field. We detect an overdensity with respect to the background density, out to 2.5 times the virial radius for both hosts in the cluster environment and the isolated field galaxies. When the LSBG sample is split by g − i color or surface brightness μ eff, g , we find the LSBGs closer to their hosts are significantly redder and brighter, like their high-surface-brightness counterparts. The LSBGs form a clear “red sequence” in both the cluster and isolated environments that is visible beyond the virial radius of the hosts. This suggests preprocessing of infalling LSBGs and a quenched backsplash population around both host samples. More so, the relative prominence of the “blue cloud” feature implies that preprocessing is ongoing near the isolated hosts compared to the cluster environment where the LSBGs are already well processed.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Beyond Mass and Multiscale Environments: What Shapes Low Surface Brightness Galaxies? Evidence from MaNGA

The origin of low surface brightness (LSB) galaxies remains a key open question in galaxy formation, reflecting the balance between internal mechanisms and environmental influence. Using MaNGA integral-field spectroscopy, we investigate whether LSB and high surface brightness (HSB) galaxies of comparable stellar mass (9 < logM * < 10) occupy distinct environments or differ primarily through internal evolution. Our late-type sample comprises 113 central and 29 satellite LSB galaxies, and 374 central and 142 satellite HSB galaxies. We characterize environments on scales from ∼100 kpc to 10 Mpc, analyzing radial profiles of stellar mass surface density (Σ * ), star formation activity, and gas-phase metallicity. Central LSB and HSB galaxies inhabit similarly low-density large-scale (>200 kpc) environments, but LSB galaxies are more isolated on small scales (∼100 kpc). Even after matching in stellar mass and environment, LSB galaxies show systematically lower Σ * , Σ SFR , and metallicities, often hosting diffuse, weakly star-forming bulges embedded in extended disks. These results indicate that LSB structure and star formation are not primarily governed by a large-scale environment or halo mass. While secondary halo properties such as spin, concentration, or gas accretion history are often invoked, their environmental dependence appears weak. Instead, LSB–HSB differences for centrals likely reflect divergent assembly or interaction histories and internal processes—such as angular momentum-driven disk evolution or inefficient gas conversion—largely decoupled from a large-scale environment. Nonetheless, the environment still influences the observed star formation and chemical differences between central and satellite LSB galaxies.

Shen, Mengting [Xiamen University (China); SDSS Co↗

DOE OSTI2024

Weak Gravitational Lensing around Low Surface Brightness Galaxies in the DES Year 3 Data

We present galaxy-galaxy lensing measurements using a sample of low surface brightness galaxies (LSBGs) drawn from the Dark Energy Survey Year 3 (Y3) data as lenses. LSBGs are diffuse galaxies with a surface brightness dimmer than the ambient night sky. These dark-matter-dominated objects are intriguing due to potentially unusual formation channels that lead to their diffuse stellar component. Given the faintness of LSBGs, using standard observational techniques to characterize their total masses proves challenging. Weak gravitational lensing, which is less sensitive to the stellar component of galaxies, could be a promising avenue to estimate the masses of LSBGs. Our LSBG sample consists of 23,790 galaxies separated into red and blue color types at g - i ≥ 0.60 and g - i < 0.60 , respectively. Combined with the DES Y3 shear catalog, we measure the tangential shear around these LSBGs and find signal-to-noise ratios of 6.67 for the red sample, 2.17 for the blue sample, and 5.30 for the full sample. We use the clustering redshifts method to obtain redshift distributions for the red and blue LSBG samples. Assuming all red LSBGs are satellites, we fit a simple model to the measurements and estimate the host halo mass of these LSBGs to be log(M host /M ⊙ ) = $12.98^{+0.10}_{-0.11}$. We place a 95% upper bound on the subhalo mass at log(M sub /M ⊙ ) < 11.51. By contrast, we assume the blue LSBGs are centrals, and place a 95% upper bound on the halo mass at log(M host /M ⊙ ) < 11.84. We find that the stellar-to-halo mass ratio of the LSBG samples is consistent with that of the general galaxy population. This work illustrates the viability of using weak gravitational lensing to constrain the halo masses of LSBGs.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Probabilistic Inference of Low-Surface-Brightness Galaxy Morphological Parameters Using Simulation-Based Inference

Low-surface-brightness galaxies (LSBGs) are diffuse, often dark-matter-dominated systems whose faintness makes their structural parameters difficult to measure reliably in wide-field imaging surveys. Robust parameter inference, including uncertainty quantification, is important for population studies and for comparisons with models of galaxy formation, as future surveys are expected to produce increasingly large samples of diffuse galaxies. In practice, LSBG profile modeling is sensitive to sky- background errors, masking choices, contaminating background sources, and the computational cost of obtaining posterior-level uncertainties for large samples. Motivated by these questions, we develop a simulation-based inference (SBI) framework for estimating posterior distributions of LSBG morphological parameters from simulated galaxy images. Using PyImfit, we generate DES-like single-Sersic profile LSBG images with known position angle, ellipticity, Sersic index, effective surface brightness, and effective radius. We then train a normalizing-flow-based neural posterior estimator using the sbi package to infer these parameters from the simulated images. For isolated simulated galaxies, the SBI posterior recovers the true input parameters, produces posterior predictive residuals consistent with the assumed noise model, and shows good empirical calibration in a DES-motivated test regime. We also compare SBI with PyImfit-based MCMC inference and find broadly comparable posterior constraints, while SBI enables substantially faster posterior sampling after training. Finally, we test robustness to compact background contaminants. A model trained only on isolated galaxies produces undercovered posteriors on contaminated images, whereas training on simulations with variable contaminant positions and fluxes improves calibration across contaminated test sets. These results demonstrate the promise of SBI for scalable, uncertainty-aware LSBG morphology inference, while emphasizing that posterior reliability strongly depends on whether training simulations include relevant observational complications.

Batbayar, Bilguun [U. Chicago (main)]↗

DOE OSTI2021

Shadows in the Dark: Low-surface-brightness Galaxies Discovered in the Dark Energy Survey

We present a catalog of 23,790 extended low-surface-brightness galaxies (LSBGs) identified in $\sim 5000\,{\deg }^{2}$ from the first three years of imaging data from the Dark Energy Survey (DES). Based on a single-component Sérsic model fit, we define extended LSBGs as galaxies with g-band effective radii ${R}_{\mathrm{eff}}(g)\gt 2\buildrel{\prime\prime}\over{.} 5$ and mean surface brightness ${\bar{\mu }}_{\mathrm{eff}}(g)\gt 24.2\,\mathrm{mag}\,{\mathrm{arcsec}}^{-2}$. We find that the distribution of LSBGs is strongly bimodal in (g-r) versus (g-i) color space. We divide our sample into red (g- i ≥ 0.60) and blue (g- i < 0.60) galaxies and study the properties of the two populations. Redder LSBGs are more clustered than their blue counterparts and are correlated with the distribution of nearby (z < 0.10) bright galaxies. Red LSBGs constitute ~33% of our LSBG sample, and $\sim 30 \% $ of these are located within 1° of low-redshift galaxy groups and clusters (compared to ~8% of the blue LSBGs). For nine of the most prominent galaxy groups and clusters, we calculate the physical properties of associated LSBGs assuming a redshift derived from the host system. In these systems, we identify 41 objects that can be classified as ultradiffuse galaxies, defined as LSBGs with projected physical effective radii ${R}_{\mathrm{eff}}\gt 1.5\,\mathrm{kpc}$ and central surface brightness ${\mu }_{0}(g)\gt 24.0\,\mathrm{mag}\,{\mathrm{arcsec}}^{-2}$. The wide-area sample of LSBGs in DES can be used to test the role of environment on models of LSBG formation and evolution.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Weak Gravitational Lensing of Low Surface Brightness Galaxies in the DES Year 3 Catalog

We present galaxy-galaxy lensing measurements of a sample of low surface brightness galaxies (LSBGs) drawn from the Dark Energy Survey Year 3 (Y3) data. LSBGs are diffuse galaxies with a surface brightness dimmer than the ambient night sky. Given their faintness, the use of standard observational techniques proves challenging. Weak gravitational lensing probes both the baryonic and dark matter content of galaxies, rendering it a powerful technique to estimate LSBG masses. The LSBG lens sample consists of 23, 790 total extended galaxies separated into red and blue color types at $g-i\ge 0.60$ and $g-i\le 0.60$, respectively. We use the Y3 \metacal\ shape catalog as the source sample, with a number density of {\color{red}$5.59 \mathrm{gal}/\mathrm{arcmin}^2$}. We measure the tangential shear around the lens galaxies across angular scales of $0.25-400$ arcmin and find a signal-to-noise of 6.67 for red galaxies, 2.17 for blue galaxies, and 5.30 for the total sample. W e fit a model built from two NFW profiles corresponding to the LSBG dark matter subhalo and host halo to the red LSBG shear measurements with an MCMC. We estimate the host halo mass at $7.3 ^{+2.0}_{-1.6}\times 10^{12} M_\mathrm{\odot}$. We place a 95\% upper bound on the subhalo mass at $2.8\times 10^{11} M_\mathrm{\odot}$. We utilize the lens sample photometry to obtain an estimate of the red LSBG stellar mass distribution. We compare the ratio between the stellar mass and the subhalo mass to the parameterized, satellite-specific SHMR \citep{Moster2010}. This work represents the first example of an attempted constraint on the masses of LSBGs using weak gravitational lensing.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Weak Gravitational Lensing of Low Surface Brightness Galaxies in the Dark Energy Year 3 Catalog

We present galaxy-galaxy lensing measurements of a sample of low surface brightness galaxies (LSBGs) drawn from the Dark Energy Survey Year 3 (Y3) data. LSBGs are diffuse galaxies with a surface brightness dimmer than the ambient night sky. Given their faintness, the use of standard observational techniques proves challenging. Weak gravitational lensing probes both the baryonic and dark matter content of galaxies, rendering it a powerful technique to estimate LSBG masses. The LSBG lens sample consists of 23, 790 total extended galaxies separated into red and blue color types at $g-i\ge 0.60$ and $g-i\le 0.60$, respectively. We use the Y3 \sc{metacalibration} shape catalog as the source sample, with a number density of $5.59 \mathrm{gal}/\mathrm{arcmin}^2$. We measure the tangential shear around the lens galaxies across angular scales of $0.25-400$ arcmin and find a signal-to-noise of 6.67 for red galaxies, 2.17 for blue galaxies, and 5.30 for the total sample. We fit a model built from two NFW profiles corresponding to the LSBG dark matter subhalo and host halo to the red LSBG shear measurements with an MCMC. We estimate the host halo mass at $7.3 ^{+2.0}_{-1.6}\times 10^{12} M_\mathrm{\odot}$. We place a 95\% upper bound on the subhalo mass at $3\times 10^{11} M_\mathrm{\odot}$. We utilize the lens sample photometry to obtain an estimate of the red LSBG stellar mass distribution. We compare the ratio between the stellar mass and the subhalo mass to the parameterized, satellite-specific SHMR. This work represents the first example of an attempted constraint on the masses of LSBGs using weak gravitational lensing.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Anomalous Stellar Populations in Low-surface-brightness Galaxies

We present new Hubble Space Telescope WFC3 near-IR observations of the color–magnitude diagrams (CMDs) in two low-surface-brightness galaxies, F575-3 and F615-1, notable for having no current star formation based on a lack of Hα emission. Key features of the near-IR CMDs are resolved, such as the red giant branch (RGB), the asymptotic giant branch (AGB) region, and the top of the blue main sequence. F575-3 has the bluest RGB of any CMD in the literature, indicating an extremely low mean metallicity. F615-1 has unusually wide RGB and AGB sequences, suggesting multiple episodes of star formation from metal-poor gas, and possibly infalling material. Both galaxies have an unusual population of stars to the red of the RGB and lower in luminosity than typical AGB stars. These stars have normal optical colors but abnormal near-IR colors. We suggest that this population of stars might be analogous to local peculiar stars like Be stars with strong near-IR excesses owing to a surrounding disk of hot gas.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Searching in H I for Massive Low Surface Brightness Galaxies: Samples from HyperLeda and the UGC

A search has been made for 21 cm H I line emission in a total of 350 unique galaxies from two samples whose optical properties indicate they may be massive. The first consists of 241 low surface brightness (LSB) galaxies of morphological type Sb and later selected from the HyperLeda database and the second consists of 119 LSB galaxies from the UGC with morphological types Sd-m and later. Of the 350 unique galaxies, 239 were observed at the Nançay Radio Telescope, 161 at the Green Bank Telescope, and 66 at the Arecibo telescope. A total of 295 (84.3%) were detected, of which 253 (72.3%) appear to be uncontaminated by any other galaxies within the telescope beam. Finally, of the total detected, uncontaminated galaxies, at least 31 appear to be massive LSB galaxies, with a total H I mass ≥ 10 10 M ⊙ , for H 0 = 70 kms -1 Mpc -1 . If we expand the definition to also include galaxies with significant total (rather than just gas) mass, i.e., those with an inclination-corrected H I line width W 50,cor > 500 km s -1 , this brings the total number of massive LSB galaxies to 41. There are no obvious trends between the various measured global galaxy properties, particularly between mean surface brightness and galaxy mass.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Discovery and analysis of low-surface-brightness galaxies in the environment of NGC 1052

The environment of NGC 1052 has recently attracted much attention because of the presence of low-surface-brightness galaxies (LSBGs) with apparently “exotic” properties, making it a region of high interest for the detection of new objects. Here, we used public deep photometric data from the Dark Energy Camera Legacy Survey to carry out a comprehensive search for LSBGs over a wide region of 6 × 6 degrees, equivalent to 2 × 2 Mpc at the distance of NGC 1052. We detected 42 LSBGs with r eff > 5 arcsec and μ g (0) > 24 mag arcsec -2 , of which 20 are previously undetected objects. Among all the newly detected objects, RCP 32 stands out with extreme properties: r eff = 23.0 arcsec and $\langle$μ g $\rangle$ eff = 28.6 mag arcsec -2 . This makes RCP 32 one of the lowest surface brightness galaxies ever detected through integrated photometry, located at just 10 arcmin from the extensively studied NGC 1052-DF2. We explored the presence of globular clusters (GCs) in the LSBGs. We marginally detected a GC system in RCP 32, and argue that this LSBG is of great interest for follow-up observations given its extremely low baryon density. After analyzing the distribution of galaxies with available spectroscopy, we identified a large-scale structure of approximately 1 Mpc that is well isolated in redshift space and centered on NGC 1052. The spatial correlation analysis between the LSBGs and this large-scale structure suggests their association. However, when exploring the distribution of effective radius, we find an overpopulation of large LSBGs (r eff > 15 arcsec) located close to the line of sight of NGC 1052. We argue that this is suggestive of a substructure with similar radial velocity in sight projection, but at a closer distance, to which some of these apparently larger LSBGs could be associated. However, possible effects derived from tidal interactions are worthy of further study. Our work expands the catalog of LSBGs with new interesting objects and provides a detailed environmental context for the study of LSBGs in this region.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

LSBGnet: an improved detection model for low-surface brightness galaxies

ABSTRACT The Chinese Space Station Telescope (CSST) is scheduled to launch soon, which is expected to provide a vast amount of image potentially containing low-surface brightness galaxies (LSBGs). However, detecting and characterizing LSBGs is known to be challenging due to their faint surface brightness, posing a significant hurdle for traditional detection methods. In this paper, we propose LSBGnet, a deep neural network specifically designed for automatic detection of LSBGs. We established LSBGnet-SDSS model using data set from the Sloan Digital Sky Survey (SDSS). The results demonstrate a significant improvement compared to our previous work, achieving a recall of 97.22 per cent and a precision of 97.27 per cent on the SDSS test set. Furthermore, we use the LSBGnet-SDSS model as a pre-training model, employing transfer learning to retrain the model with LSBGs from Dark Energy Survey (DES), and establish the LSBGnet-DES model. Remarkably, after retraining the model on a small DES sample, it achieves over 90 per cent precision and recall. To validate the model’s capabilities, we utilize the trained LSBGnet-DES model to detect LSBG candidates within a selected 5 sq. deg area in the DES footprint. Our analysis reveals the detection of 204 LSBG candidates, characterized by a mean surface brightness range of $23.5\ \mathrm{ mag}\ \mathrm{ arcsec}^{-2}\le \bar{\mu }_{\text{eff}}(g)\le 26.8\ \mathrm{ mag}\ \mathrm{ arcsec}^{-2}$ and a half-light radius range of 1.4 arcsec ≤ r1/2 ≤ 8.3 arcsec. Notably, 116 LSBG candidates exhibit a half-light radius ≥2.5 arcsec. These results affirm the remarkable performance of our model in detecting LSBGs, making it a promising tool for the upcoming CSST.

Su, Hao (ORCID:0009000778888301)↗

DOE OSTI2022

Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural Networks

Measuring the structural parameters (size, total brightness, light concentration, etc.) of galaxies is a significant first step towards a quantitative description of different galaxy populations. In this work, we demonstrate that a Bayesian Neural Network (BNN) can be used for the inference, with uncertainty quantification, of such morphological parameters from simulated low-surface-brightness galaxy images. Compared to traditional profile-fitting methods, we show that the uncertainties obtained using BNNs are comparable in magnitude, well-calibrated, and the point estimates of the parameters are closer to the true values. Our method is also significantly faster, which is very important with the advent of the era of large galaxy surveys and big data in astrophysics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Self-consistent Color–Stellar Mass-to-light Ratio Relations for Low Surface Brightness Galaxies

The color–stellar mass-to-light ratio relation (CMLR) is a widely accepted tool for estimating the stellar mass (M {sub *}) of a galaxy. However, an individual CMLR tends to give distinct M {sub *} for a same galaxy when it is applied in different bands. Examining five representative CMLRs from the literature, we find that the difference in M {sub *} predicted in different bands from optical to near-infrared by a CMLR is 0.1 ∼ 0.3 dex. Based on a sample of low surface brightness galaxies that covers a wide range of color and luminosity, we therefore recalibrated each original CMLR in r, i, z, J, H, and K bands to give internally self-consistent M {sub *} for a same galaxy. The g–r is the primary color indicator in the recalibrated relations, which show little dependence on red (r–z) or near-infrared (J–K) colors. Additionally, the external discrepancies in the originally predicted γ {sub *} by the five independent CMLRs have been greatly reduced after recalibration, especially in the near-infrared bands, implying that the near-infrared luminosities are more robust in predicting γ {sub *}. For each CMLR, the recalibrated relations provided in this work could produce internally self-consistent M {sub *} from divergent photometric bands, and are extensions of the recalibrations from the Johnson–Cousin filter system by the pioneering work of McGaugh & Schombert to the filter system of the Sloan Digital Sky Survey.

79 ASTRONOMY AND ASTROPHYSICS↗