Sloan Digital Sky Survey Galaxies Research Articles

Star Formation Rates in [Ne V] 3426 Å Selected Active Galactic Nuclei: Evidence for a Decrease along the Main Sequence?

Studying the behavior along the galaxy main sequence is key in furthering our understanding of the possible connection between active galactic nucleus (AGN) activity and star formation. We select a sample of 1215 AGN from the catalog of Sloan Digital Sky Survey galaxy properties from the Portsmouth group by detection of the high-ionization [Ne v] 3426 Å emission line. Our sample extends from 1040 to 1042.5 erg s−1 in [Ne v] luminosity in a redshift range z = 0.17 to 0.57. We compare the specific star formation rates (sSFRs; SFR scaled by galaxy mass) obtained from the corrected [O ii] and Hα luminosities, and the spectral energy distribution (SED)–determined values from Portsmouth. We find that the emission-line-based sSFR values are unreliable for the [Ne v] sample due to the AGN contribution, and proceed with the SED sSFRs for our study of the main sequence. We find evidence for a decrease in sSFR along the main sequence in the [Ne v] sample, which is consistent with results from the hard X-ray Burst Alert Telescope AGN sample, which extends to lower redshifts than our [Ne v] sample. Although we do not find evidence that the concurrent AGN activity is suppressing star formation, our results are consistent with a lower gas fraction in the host galaxies of the AGN as compared to that of the star-forming galaxies. If the evacuation of gas, and therefore suppression of star formation, is due to AGN activity, it must have occurred in a previous epoch.

Classifying Intermediate-redshift Galaxies in SDSS: Alternative Diagnostic Diagrams

We select a sample of 1437 active galactic nuclei (AGN) from the catalog of the Sloan Digital Sky Survey galaxy properties from the Portsmouth group by detection of the high-ionization [Ne v] 3426 Å emission line. We compare the fluxes of [Ne III] 3869 Å, [O III] 5007 Å, [O II] 3726, 3728 Å, and [O i] 6300 Å to that of [Ne v]. All four lines show a strong linear correlation with [Ne v], although lines from ions with lower ionization potentials have a lower correlation coefficient. We investigate the use of two forbidden line ratio diagnostic diagrams that do not rely on Hα in order to classify high-redshift galaxies. These use the [Ne III]/[O II] line ratio plotted against [O III]/[O i] and [O III]/[O II], respectively. We use photoionization modeling to characterize the behavior of the narrow-line region in AGN and star-forming regions and test the validity of our diagnostic diagrams. We also use a luminosity cutoff of log L [O III] [erg s−1] = 42, which lowers the contamination of the AGN region by star-forming galaxies down to 10% but does not remove green pea and purple grape galaxies from the AGN region. We also investigate the OHNO diagram, which uses [Ne III]/[O II] plotted against [O III]/Hβ. Using our new diagnostic diagrams, we are able to reliably classify AGN up to a redshift of z ≤ 1.06 and add more than 822 new AGN to the [Ne v]-selected AGN sample.

Connection between SDSS galaxies and ELUCID subhaloes in the eye of machine learning

ABSTRACT We explore the feasibility of learning the connection between Sloan Digital Sky Survey (SDSS) galaxies and ELUCID subhaloes with random forest (RF). ELUCID is a constrained N-body simulation constructed using the matter density field of SDSS. Based on a SDSS-ELUCID matched catalogue, we build RF models that predict Mr magnitude, colour, stellar mass M*, and specific star formation rate (sSFR) with several subhalo properties. While the RF can predict Mr and M* with reasonable accuracy, the prediction accuracy of colour and sSFR is low, which could be due to the mismatch between galaxies and subhaloes. To test this, we shuffle the galaxies in subhaloes of narrow mass bins in the local neighbourhood using galaxies of a semi-analytic model (SAM) and the TNG hydrodynamic simulation. We find that the shuffling only slightly reduces the colour prediction accuracy in SAM and TNG, which is still considerably higher than that of the SDSS. This suggests that the true connection between SDSS colour and subhalo properties could be weaker than that in the SAM and TNG without the mismatch effect. We also measure the Pearson correlation coefficient between the galaxy and subhalo properties in SDSS, SAM, and TNG. Similar to the RF results, we find that the colour–subhalo correlation in SDSS is lower than in both SAM and TNG. We also show that the galaxy–subhalo correlations depend on subhalo mass in the galaxy formation models. Advanced surveys with fainter galaxies will provide new insights into the galaxy–subhalo relation in the real Universe.

Estimating stellar population and emission-line properties in S-PLUS galaxies

ABSTRACT We present tests of a new method to simultaneously estimate stellar population and emission-line (EL) properties of galaxies out of the Southern Photometric Local Universe Survey (S-PLUS) photometry. The technique uses the AlStar code, updated with an empirical prior that greatly improves its ability to estimate ELs using only the survey’s 12 bands. The tests compare the output of (noise-perturbed) synthetic photometry of the Sloan Digital Sky Survey (SDSS) galaxies to properties derived from previous full spectral fitting and detailed EL analysis. For realistic signal-to-noise ratios, stellar population properties are recovered to better than 0.2 dex in masses, mean ages, metallicities, and ±0.2 mag for the extinction. More importantly, ELs are recovered remarkably well for a photometric survey. We obtain input − output dispersions of 0.05–0.2 dex for the equivalent widths of [O ii], [O iii], Hβ, Hα, [N ii], and [S ii], and even better for lines stronger than ∼5 Å. These excellent results are achieved by combining two empirical facts into a prior that restricts the EL space available for the fits. (1) Because, for the redshifts explored here, Hα and [N ii] fall in a single narrow band (J0660), their combined equivalent width is always well recovered, even when [N ii]/Hα is not. (2) We know from SDSS that $W_{\mathrm{H}\alpha +[\mathrm{N}\,\small {\rm II}] }$ correlates with [N ii]/Hα, which can be used to tell if a galaxy belongs to the left or right wings in the classical Baldwin–Phillips–Telervich (BPT) diagnostic diagram. Example applications to integrated light and spatially resolved data are also presented, including a comparison with independent results obtained with the Multi-Unit Spectroscopic Explorer (MUSE)-based integral field spectroscopy.

Improved GALEX UV Photometry for 700,000 SDSS Galaxies

The Galaxy Evolution Explorer (GALEX) satellite performed the first and only large-area UV survey, which in tandem with the Sloan Digital Sky Survey (SDSS) has facilitated modeling of the spectral energy distributions of low-redshift galaxies and the determination of various galaxy properties, in particular the star formation rate. However, the relatively crude angular resolution of GALEX (5″) made its images susceptible to blending of sources, resulting in potentially biased far-UV and near-UV (NUV) pipeline photometry. To remedy this issue and take advantage of model-fit photometry, we use the EMphot software to obtain forced GALEX photometry for ∼700,000 SDSS galaxies at z < 0.3. Positional priors of target galaxies and potentially contaminating neighbors were taken from SDSS. New photometry is based on the best-fitting of three model profiles: optical-like, exponential, and flat. New photometry mitigates blending present in the original pipeline catalogs, which affected 16% of galaxies at a level of >0.2 mag and 2% at a level of >1 mag. Pipeline NUV magnitudes are severely affected (≳1 mag) when the neighbor is brighter than the target galaxy and within 10″, or when the neighbor is fainter and within ∼3″ of the target. New photometry fixes edge-of-detector bias, which affected pipeline photometry by up to 0.1 mag in NUV. We present catalogs with new photometry for GALEX observations of different depths, corresponding to the all-sky imaging survey (AIS), medium imaging survey, and deep imaging survey. Catalogs feature combined magnitudes for multiple detections of the same galaxy in a survey.

Similar Image Retrieval using Autoencoder. I. Automatic Morphology Classification of Galaxies

We present the construction of an image similarity retrieval engine for the morphological classification of galaxies using the Convolutional AutoEncoder (CAE). The CAE is trained on 90,370 preprocessed Sloan Digital Sky Survey galaxy images listed in the Galaxy Zoo 2 (GZ2) catalog. The visually similar output images returned by the trained CAE suggest that the encoder efficiently compresses input images into latent features, which are then used to calculate similarity parameters. Our Tool for Searching a similar Galaxy Image based on a Convolutional Autoencoder using Similarity (TSGICAS) leverages this similarity parameter to classify galaxies’ morphological types, enabling the identification of a wider range of classes with high accuracy compared to traditional supervised ML techniques. This approach streamlines the researcher’s work by allowing quick prioritization of the most relevant images from the latent feature database. We investigate the accuracy of our automatic morphological classifications using three galaxy catalogs: GZ2, Extraction de Formes Idéalisées de Galaxies en Imagerie (EFIGI), and Nair & Abraham (NA10). The correlation coefficients between the morphological types of input and retrieved galaxy images were found to be 0.735, 0.811, and 0.815 for GZ2, EFIGI, and NA10 catalogs, respectively. Despite differences in morphology tags between input and retrieved galaxy images, visual inspection showed that the two galaxies were very similar, highlighting TSGICAS’s superior performance in image similarity search. We propose that morphological classifications of galaxies using TSGICAS are fast and efficient, making it a valuable tool for detailed galaxy morphological classifications in other imaging surveys.

Metallicity beats sSFR: the connection between superluminous supernova host galaxy environments and the importance of metallicity for their production

ABSTRACT We analyse 33 Type I superluminous supernovae (SLSNe) taken from Zwicky Transient Facility (ZTF)’s Bright Transient Survey to investigate the local environments of their host galaxies. We use a spectroscopic sample of galaxies from the Sloan Digital Sky Survey (SDSS) to determine the large-scale environmental density of the host galaxy. Noting that SLSNe are generally found in galaxies with low stellar masses, high star formation rates (SFRs), and low metallicities, we find that SLSN hosts are also rarely found within high-density environments. Only $3\substack{+9 \\ -1}$ per cent of SLSN hosts were found in regions with two or more bright galaxies within 2 Mpc. For comparison, we generate a sample of 662 SDSS galaxies matched to the photometric properties of the SLSN hosts. This sample is also rarely found within high-density environments, suggesting that galaxies with properties required for SLSN production favour more isolated environments. Furthermore, we select galaxies within the IllustrisTNG simulation to match SLSN host galaxy properties in colour and stellar mass. We find that the fraction of simulated galaxies in high-density environments quantitatively match the observed SLSN hosts only if we restrict to simulated galaxies with metallicity 12 + log (O/H) ≤ 8.12. In contrast, limiting to only the highest specific star formation rate (sSFR) galaxies in the sample leads to an overabundance of SLSN hosts in high-density environments. Thus, our measurement of the environmental density of SLSN host galaxies appears to break the degeneracy between low metallicity and high sSFR as the driver for SLSN hosts and provides evidence that the most constraining factor on SLSN production is low metallicity.

Searching for Spectroscopic Signatures of Ongoing Quenching in SDSS Galaxies

In this paper, we estimate the “star formation change parameter,” SFR79, which characterizes the current SFR relative to the average during the last 800 Myr, for ∼300,000 galaxies selected from the Sloan Digital Sky Survey (SDSS). The goals are to examine, in a much larger and independent sample, the trends previously reported in a sample of star-forming (SF) MaNGA galaxies and also to search for spectroscopic signatures of ongoing quenching in the so-called “Green Valley,” which is generally believed to contain galaxies that are migrating from the SF population to the quenched population of galaxies. Measuring SFR79 for our large sample of SDSS galaxies, we first confirm the basic results of SF galaxies published by Wang & Lilly. We then discuss in detail the calibration and meaning of SFR79 for galaxies that are well below the SFMS and establish the expected statistical signature of systematic ongoing quenching from modeling the z ∼ 0 quenching rate of the SF population. We conclude that it is not possible at present to establish unambiguous observational evidence for systematic ongoing quenching processes, due to limitations both in the noise of the observational data, in particular in the measurements of Hδ absorption, and in the calibration of SFR79, as well as biases introduced by the necessity of selecting objects with significant Hα emission. We do however see plausible indications of ongoing quenching, which are quantitatively consistent with expectations from “growth+quenching” models of galaxy evolution and a typical e-folding timescale for quenching of ∼500 Myr.

First Constraints on Growth Rate from Redshift-space Ellipticity Correlations of SDSS Galaxies at 0.16 < z < 0.70

We report the first constraints on the growth rate of the universe, f(z)σ 8(z), with intrinsic alignments (IAs) of galaxies. We measure the galaxy density-intrinsic ellipticity cross-correlation and intrinsic ellipticity autocorrelation functions over 0.16 < z < 0.7 from luminous red galaxies (LRGs) and LOWZ and CMASS galaxy samples in the Sloan Digital Sky Survey (SDSS) and SDSS-III BOSS survey. We detect clear anisotropic signals of IA due to redshift-space distortions. By combining measured IA statistics with the conventional galaxy clustering statistics, we obtain tighter constraints on the growth rate. The improvement is particularly prominent for the LRG, which is the brightest galaxy sample and known to be strongly aligned with underlying dark matter distribution; using the measurements on scales above 10 h −1 Mpc, we obtain (68% confidence level) from the clustering-only analysis and with clustering and IA, meaning 19% improvement. The constraint is in good agreement with the prediction of general relativity, f σ 8 = 0.4937 at z = 0.34. For LOWZ and CMASS samples, the improvement of constraints on f σ 8 is found to be 10% and 3.5%, respectively. Our results indicate that the contribution from IA statistics for cosmological constraints can be further enhanced by carefully selecting galaxies for a shape sample.

Chemically peculiar stars in integrated light stellar population models and local group galaxies

ABSTRACT Integrated light models that incorporate common types of chemically peculiar (CP) stars are assembled using synthetic spectra. Selected spectral features encode significant age information for populations with ages ∼50 Myr &amp;lt; age &amp;lt; ∼2 Gyr. Due to the alleviation of template mismatch, the inclusion of CP star features in model spectra improves the accuracy of recovered stellar population parameters, but we are not able to show that new or unique age information can be extracted from the weak CP features compared to continuum fitting and strong-feature strengths, at least at the present state of the art. An age-extraction routine that recovers two- and three-burst age structures is employed to analyse the spectra of local group galaxies. NGC 224 (M 31) has a stellar population too old for the types of CP stars we examine. NGC 221 (M 32) also shows no CP spectral features. It appears to contain a component at age ∼1 Gyr at 1 per cent by mass in addition to its dominant 4.7 Gyr population. Unlike Sloan Digital Sky Survey galaxy spectrum averages, NGC 205 (M 110) contains no features due to mercury–manganese (HgMn) stars. This excludes the age range associated with HgMn production, and its near-nuclear spectrum is best fitted by a 68 ± 2 Myr population superimposed on an older population with a 1.85 ± 0.1 Gyr component. Both NGC 205 and NGC 221 have an ancient component whose mass is not easy to constrain given the overwhelming light dominance of the younger populations.

Cold gas mass measurements for the era of large optical spectroscopic surveys

ABSTRACT Gas plays an important role in many processes in galaxy formation and evolution, but quantifying the importance of gas has been hindered by the challenge to measure gas masses for large samples of galaxies. Data sets of direct atomic and molecular gas measurements are sufficient to establish simple scaling relations, but often not large enough to quantify three-parameter relations, or second-order dependences. As an alternative approach, we derive here indirect cold gas measurements from optical emission lines using photoionization models for galaxies in the Sloan Digital Sky Survey (SDSS) main galaxy sample and the PHANGS-MUSE survey. We calibrate the gas surface density measurements using xCOLD GASS and PHANGS-ALMA molecular gas measurements to ensure that our measurements are reliable. We demonstrate the importance of taking into account the scale dependence of the relation between optical depth (τV) and gas surface density (Σgas) and provide a general prescription to estimate Σgas from τV, metallicity, and the dust-to-metal ratio, at any arbitrary physical resolution. To demonstrate that the indirect cold gas masses are accurate enough to quantify the role of gas in galaxy evolution, we study the mass–metallicity relation of SDSS galaxies and show that as a third parameter gas mass is better than star formation rate at reducing the scatter of the relation, as predicted by models and simulations.

Cosmological inference from an emulator based halo model. II. Joint analysis of galaxy-galaxy weak lensing and galaxy clustering from HSC-Y1 and SDSS

We present high-fidelity cosmology results from a blinded joint analysis of galaxy-galaxy weak lensing (ΔΣ) and projected galaxy clustering (wp) measured from the Hyper Suprime-Cam Year-1 (HSC-Y1) data and spectroscopic Sloan Digital Sky Survey (SDSS) galaxy catalogs in the redshift range 0.15<z<0.7. We define luminosity-limited samples of SDSS galaxies to serve as the tracers of wp in three spectroscopic redshift bins, and as the lens samples for ΔΣ. For the ΔΣ measurements, we select a single sample of 4×106 source galaxies over 140 deg2 from HSC-Y1 with photometric redshifts (photo z) greater than 0.75, enabling a better handle of photo-z errors by comparing the ΔΣ amplitudes for the three lens redshift bins. The deep, high-quality HSC-Y1 data enable significant detections of the ΔΣ signals, with integrated signal-to-noise ratio S/N∼15 in the range 3≤R/[h−1 Mpc]≤30 for the three lens samples, despite the small area coverage. For cosmological parameter inference, we use an input galaxy-halo connection model built on the dark emulator package (which uses an ensemble set of high-resolution N-body simulations and enables fast, accurate computation of the clustering observables) with a halo occupation distribution that includes nuisance parameters to marginalize over modeling uncertainties. We model the ΔΣ and wp measurements on scales from R≃3 and 2 h−1 Mpc, respectively, up to 30 h−1 Mpc (therefore excluding the baryon acoustic oscillations information) assuming a flat ΛCDM cosmology, marginalizing over about 20 nuisance parameters and demonstrating the robustness of our results to them. With various tests using mock catalogs described in Miyatake et al. [preceding paper, Phys. Rev. D 106, 083519 (2022)], we show that any bias in the clustering amplitude S8≡σ8(Ωm/0.3)0.5 due to uncertainties in the galaxy-halo connection is less than ∼50% of the statistical uncertainty of S8, unless the assembly biaseffect is unexpectedly large. Our best-fit models have S8=0.795−0.042+0.049 (mode and 68% credible interval) for the flat ΛCDM model; we find tighter constraints on the quantity S8(α=0.17)≡σ8(Ωm/0.3)0.17=0.745−0.031+0.039.23 MoreReceived 29 November 2021Accepted 22 August 2022DOI:https://doi.org/10.1103/PhysRevD.106.083520© 2022 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasCosmologyDark energyGravitational lensesGravitation, Cosmology & Astrophysics

Ulisse: A tool for one-shot sky exploration and its application for detection of active galactic nuclei

Context.Modern sky surveys are producing ever larger amounts of observational data, which makes the application of classical approaches for the classification and analysis of objects challenging and time consuming. However, this issue may be significantly mitigated by the application of automatic machine and deep learning methods.Aims.We proposeulisse, a new deep learning tool that, starting from a single prototype object, is capable of identifying objects that share common morphological and photometric properties, and hence of creating a list of candidate lookalikes. In this work, we focus on applying our method to the detection of active galactic nuclei (AGN) candidates in a Sloan Digital Sky Survey galaxy sample, because the identification and classification of AGN in the optical band still remains a challenging task in extragalactic astronomy.Methods.Intended for the initial exploration of large sky surveys,ulissedirectly uses features extracted from the ImageNet dataset to perform a similarity search. The method is capable of rapidly identifying a list of candidates, starting from only a single image of a given prototype, without the need for any time-consuming neural network training.Results.Our experiments showulisseis able to identify AGN candidates based on a combination of host galaxy morphology, color, and the presence of a central nuclear source, with a retrieval efficiency ranging from 21% to 65% (including composite sources) depending on the prototype, where the random guess baseline is 12%. We findulisseto be most effective in retrieving AGN in early-type host galaxies, as opposed to prototypes with spiral- or late-type properties.Conclusions.Based on the results described in this work,ulissecould be a promising tool for selecting different types of astro-physical objects in current and future wide-field surveys (e.g.,Euclid, LSST etc.) that target millions of sources every single night.

Unsupervised clustering and analysis of WISE spiral galaxies

ABSTRACT The Wide-Field Infrared Explorer (WISE) survey captured massive amounts of infrared astronomical object data, and different astronomical objects will show different characteristics. Especially spiral galaxies that are richer in colour than other astronomical objects. In addition, the Sloan Digital Sky Survey (SDSS) has obtained a large number of visible light images and their parameter. So this paper mainly explores the colour characteristics of WISE spiral galaxies, and uses SDSS, 2MASS, WISE, and MANGA parameters to analyse the differences between different classes and the commonality of the same class, so that provides help for astronomers to study infrared spiral galaxies. First, the RA and Dec. of the spiral galaxy samples were crossed in GalaxyZoo2. The flux data of w1, w2, and w3 bands in WISE were crossed for image synthesis. The Bootstrap Your Own Latent contrastive learning framework and K-means clustering were used to unsupervised classify the infrared spiral galaxy images. After multiple experiments, five classes of images were selected by referring to the best clustering results. Finally, the parameter of galaxies in SDSS, 2MASS, WISE, and MANGA catalogues were crossed, including redshift, 12 magnitudes that from visible light band to mid-infrared band, stellar formation rate, stellar metallicity, stellar velocity dispersion, etc. These parameters were qualitatively and quantitatively analyzed. The effectiveness of unsupervised clustering algorithms for handling unlabeled data is demonstrated and two special classes of galaxies are found. The analysis result shows that the distribution characteristics of different parameters of different classes of infrared spiral galaxies are different.

Photometric redshifts from SDSS images with an interpretable deep capsule network

ABSTRACT Studies of cosmology, galaxy evolution, and astronomical transients with current and next-generation wide-field imaging surveys like the Rubin Observatory Legacy Survey of Space and Time are all critically dependent on estimates of photometric redshifts. Capsule networks are a new type of neural network architecture that is better suited for identifying morphological features of the input images than traditional convolutional neural networks. We use a deep capsule network trained on ugriz images, spectroscopic redshifts, and Galaxy Zoo spiral/elliptical classifications of ∼400 000 Sloan Digital Sky Survey galaxies to do photometric redshift estimation. We achieve a photometric redshift prediction accuracy and a fraction of catastrophic outliers that are comparable to or better than current methods for SDSS main galaxy sample-like data sets (r ≤ 17.8 and zspec ≤ 0.4) while requiring less data and fewer trainable parameters. Furthermore, the decision-making of our capsule network is much more easily interpretable as capsules act as a low-dimensional encoding of the image. When the capsules are projected on a two-dimensional manifold, they form a single redshift sequence with the fraction of spirals in a region exhibiting a gradient roughly perpendicular to the redshift sequence. We perturb encodings of real galaxy images in this low-dimensional space to create synthetic galaxy images that demonstrate the image properties (e.g. size, orientation, and surface brightness) encoded by each dimension. We also measure correlations between galaxy properties (e.g. magnitudes, colours, and stellar mass) and each capsule dimension. We publicly release our code, estimated redshifts, and additional catalogues at https://biprateep.github.io/encapZulate-1.

Convolutional neural network-reconstructed velocity for kinetic SZ detection

We report the detection of the kinetic Sunyaev-Zel’dovich (kSZ) effect in galaxy clusters with a 4.9σ significance using the latest 217 GHz Planck map from data release 4. For the detection, we stacked the Planck map at the positions of 30 431 galaxy clusters from the Wen-Han-Liu (WHL) catalog. To align the sign of the kSZ signals, the line-of-sight velocities of galaxy clusters were estimated with a machine-learning approach, in which the relation between the galaxy distribution around a cluster and its line-of-sight velocity was trained through a convolutional neural network. To train our network, we used the simulated galaxies and galaxy clusters in the Magneticum cosmological hydrodynamic simulations. The trained model was applied to the large-scale distribution of the Sloan Digital Sky Survey galaxies to derive the line-of-sight velocities of the WHL galaxy clusters. Assuming a standard β-model for the intracluster medium, we obtained the gas mass fraction in R500 to be fgas, 500 = 0.09 ± 0.02 within the galaxy clusters with the average mass of M500 ∼ 1.0 × 1014 h−1 M⊙.

Galaxy Zoo: Clump Scout: Surveying the Local Universe for Giant Star-forming Clumps

Massive, star-forming clumps are a common feature of high-redshift star-forming galaxies. How they formed, and why they are so rare at low redshift, remains unclear. In this paper we identify the largest sample yet of clumpy galaxies (7050) at low redshift using data from the citizen science project Galaxy Zoo: Clump Scout, in which volunteers classified 58,550 Sloan Digital Sky Survey (SDSS) galaxies spanning redshift 0.02 < z < 0.15. We apply a robust completeness correction by comparing with simulated clumps identified by the same method. Requiring that the ratio of clump to galaxy flux in the SDSS u band be greater than 8% (similar to clump definitions used by other works), we estimate the fraction of local star-forming galaxies hosting at least one clump (f clumpy) to be . We also compute the same fraction with a less stringent relative flux cut of 3% (), as the higher number count and lower statistical noise of this fraction permit finer comparison with future low-redshift clumpy galaxy studies. Our results reveal a sharp decline in f clumpy over 0 < z < 0.5. The minor merger rate remains roughly constant over the same span, so we suggest that minor mergers are unlikely to be the primary driver of clump formation. Instead, the rate of galaxy turbulence is a better tracer for f clumpy over 0 < z < 1.5 for galaxies of all masses, which supports the idea that clump formation is primarily driven by violent disk instability for all galaxy populations during this period.

IQ Collaboratory. III. The Empirical Dust Attenuation Framework—Taking Hydrodynamical Simulations with a Grain of Dust

Abstract We present the empirical dust attenuation (EDA) framework—a flexible prescription for assigning realistic dust attenuation to simulated galaxies based on their physical properties. We use the EDA to forward model synthetic observations for three state-of-the-art large-scale cosmological hydrodynamical simulations: SIMBA, IllustrisTNG, and EAGLE. We then compare the optical and UV color–magnitude relations, (g − r) − M r and (far-UV −near-UV) − M r , of the simulations to a M r &lt; − 20 and UV complete Sloan Digital Sky Survey galaxy sample using likelihood-free inference. Without dust, none of the simulations match observations, as expected. With the EDA, however, we can reproduce the observed color–magnitude with all three simulations. Furthermore, the attenuation curves predicted by our dust prescription are in good agreement with the observed attenuation–slope relations and attenuation curves of star-forming galaxies. However, the EDA does not predict star-forming galaxies with low A V since simulated star-forming galaxies are intrinsically much brighter than observations. Additionally, the EDA provides, for the first time, predictions on the attenuation curves of quiescent galaxies, which are challenging to measure observationally. Simulated quiescent galaxies require shallower attenuation curves with lower amplitude than star-forming galaxies. The EDA, combined with forward modeling, provides an effective approach for shedding light on dust in galaxies and probing hydrodynamical simulations. This work also illustrates a major limitation in comparing galaxy formation models: by adjusting dust attenuation, simulations that predict significantly different galaxy populations can reproduce the same UV and optical observations.

The large-scale environment of thermonuclear and core-collapse supernovae

ABSTRACT The new generation of wide-field time-domain surveys has made it feasible to study the clustering of supernova (SN) host galaxies in the large-scale structure (LSS) for the first time. We investigate the LSS environment of SN populations, using 106 dark matter density realisations with a resolution of ∼3.8 Mpc, constrained by the 2M+ + galaxy survey. We limit our analysis to redshift z &amp;lt; 0.036, using samples of 498 thermonuclear and 782 core-collapse SNe from the Zwicky Transient Facility’s Bright Transient Survey and Census of the Local Universe catalogues. We detect clustering of SNe with high significance; the observed clustering of the two SNe populations is consistent with each other. Further, the clustering of SN hosts is consistent with that of the Sloan Digital Sky Survey (SDSS) Baryon Oscillation Spectroscopic Survey DR12 spectroscopic galaxy sample in the same redshift range. Using a tidal shear classifier, we classify the LSS into voids, sheets, filaments, and knots. We find that both SNe and SDSS galaxies are predominantly found in sheets and filaments. SNe are significantly under-represented in voids and over-represented in knots compared to the volume fraction in these structures. This work opens the potential for using forthcoming wide-field deep SN surveys as a complementary LSS probe.

Star-forming Dwarf Galaxies in Filamentary Structures around the Virgo Cluster: Probing Chemical Pre-processing in Filament Environments

Abstract It has been proposed that the filament environment is closely connected to the pre-processing of galaxies, where their properties may have been changed by environmental effects in the filament before they fell into the galaxy cluster. We present the chemical properties of star-forming dwarf galaxies (SFDGs) in five filamentary structures (Virgo III, Leo Minor, Leo II A, Leo II B, and Canes Venatici) around the Virgo cluster using the Sloan Digital Sky Survey optical spectroscopic data and Galaxy Evolution Explorer ultraviolet photometric data. We investigate the relationship between stellar mass, gas-phase metallicity, and specific star formation rate (sSFR) of the SFDGs in the Virgo filaments in comparison to those in the Virgo cluster and field. We find that, at a given stellar mass, SFDGs in the Virgo filaments show lower metallicity and higher sSFR than those in the Virgo cluster on average. We observe that SFDGs in the Virgo III filament show enhanced metallicities and suppressed star formation activities comparable to those in the Virgo cluster, whereas SFDGs in the other four filaments exhibit similar properties to the field counterparts. Moreover, about half of the galaxies in the Virgo III filament are found to be morphologically transitional dwarf galaxies that are supposed to be on the way to transforming into quiescent dwarf early-type galaxies. Based on the analysis of the galaxy perturbation parameter, we propose that the local environment represented by the galaxy interactions might be responsible for the contrasting features in chemical pre-processing found in the Virgo filaments.