SDSS Galaxies Research Articles

An investigation of the star-forming main sequence considering the nebular continuum emission at low-z

Context. Galaxy evolution has been studied by interpreting the spectral energy distribution of galaxies using spectral synthesis codes. This method has been crucial in discovering different pillars of modern galaxy evolution theories. However, this analysis was mostly carried out using spectral synthesis codes that are purely stellar, that is, they assume that the nebular contribution to the total continuum is negligible. The code FADO is the first publicly available population spectral synthesis tool that treats the contribution from ionised gas to the observed emission self-consistently. This is expected to have a particularly strong effect in star-forming (SF) galaxies. Aims. We study the impact of the nebular contribution on the determination of the star formation rate (SFR), stellar mass, and consequent effect on the star-forming main sequence (SFMS) at low redshift. Methods. We applied FADO to the spectral database of the SDSS to derive the physical properties of galaxies. As a comparison, we used the data in the MPA-JHU catalogue, which contains the properties of SDSS galaxies derived without the nebular contribution. We selected a sample of SF galaxies with Hα and Hβ flux measurements, and we corrected the fluxes for the nebular extinction through the Balmer decrement. We then calculated the Hα luminosity to estimate the SFR. Then, by combining the stellar mass and SFR estimates from FADO and MPA-JHU, the SFMS was obtained. Results. The Hα flux estimates are similar between FADO and MPA-JHU. Because the Hα flux was used as tracer of the SFR, FADO and MPA-JHU agree in their SFR. The stellar mass estimates are slightly higher for FADO than for MPA-JHU on average. However, considering the uncertainties, the differences are negligible. With similar SFR and stellar mass estimates, the derived SFMS is also similar between FADO and MPA-JHU. Conclusions. Our results show that for SDSS normal SF galaxies, the additional modelling of the nebular contribution does not affect the retrieved fluxes and consequentially also does not influence SFR estimators based on the extinction-corrected Hα luminosity. For the stellar masses, the results point to the same conclusion. These results are a consequence of the fact that the vast majority of normal SF galaxies in the SDSS have a low nebular contribution. However, the obtained agreement might only hold for local SF galaxies, but higher-redshift galaxies might show different physical properties when FADO is used. This would then be an effect of the expected increased nebular contribution.

Machine learning technique for morphological classification of galaxies from the SDSS. III. The CNN image-based inference of detailed features

This paper follows a series of our works on the applicability of various machine learning methods to morphological galaxy classification (Vavilova et al., 2021, 2022). We exploited the sample of ~315800 low-redshift SDSS DR9 galaxies with absolute stellar magnitudes of −24m < Mr < −19.4m at 0.003 < z < 0.1 redshifts as a target data set for the CNN classifier. Because it is tightly overlapped with the Galaxy Zoo 2 (GZ2) sample, we use these annotated data as the training data set to classify galaxies into 34 detailed features. In the presence of a pronounced difference in visual parameters between galaxies from the GZ2 training data set and galaxies without known morphological parameters, we applied novel procedures, which allowed us for the first time to get rid of this difference for smaller and fainter SDSS galaxies with mr < 17.7. We describe in detail the adversarial validation technique as well as how we managed the optimal train-test split of galaxies from the training data set to verify our CNN model based on the DenseNet-201 realistically. We have also found optimal galaxy image transformations, which help increase the classifier’s generalization ability. We demonstrate for the first time that implication of the CNN model with a train-test split of data sets and size-changing function simulating a decrease in magnitude and size (data augmentation) significantly improves the classification of smaller and fainter SDSS galaxies. It can be considered as another way to improve the human bias for those galaxy images that had a poor vote classification in the GZ project. Such an approach, like autoimmunization, when the CNN classifier, trained on very good galaxy images, is able to retrain bad images from the same homogeneous sample, can be considered co-planar to other methods of combating such a human bias. The most promising result is related to the CNN prediction probability in the classification of detailed features. The accuracy of the CNN classifier is in the range of 83.3—99.4 % depending on 32 features (exception is for “disturbed” (68.55 %) and “arms winding medium” (77.39 %) features). As a result, for the first time, we assigned the detailed morphological classification for more than 140000 low-redshift galaxies, especially at the fainter end. A visual inspection of the samples of galaxies with certain morphological features allowed us to reveal typical problem points of galaxy image classification by shape and features from the astronomical point of view. The morphological catalogs of low-redshift SDSS galaxies with the most interesting features are available through the UkrVO website (http://ukr-vo.org/galaxies/) and VizieR.

Cosmological inference from an emulator based halo model. I. Validation tests with HSC and SDSS mock catalogs

We present validation tests of emulator-based halo model method for cosmological parameter inference, assuming hypothetical measurements of the projected correlation function of galaxies, $w_{\rm p}(R)$, and the galaxy-galaxy weak lensing, $\Delta\!\Sigma(R)$, from the spectroscopic SDSS galaxies and the Hyper Suprime-Cam Year1 (HSC-Y1) galaxies. To do this, we use \textsc{Dark Emulator} developed in Nishimichi et al. based on an ensemble of $N$-body simulations, which is an emulation package enabling a fast, accurate computation of halo clustering quantities for flat-geometry $w$CDM cosmologies. Adopting the halo occupation distribution, the emulator allows us to obtain model predictions of $\Delta\!\Sigma$ and $w_{\rm p}$ for the SDSS-like galaxies at a few CPU seconds for an input set of parameters. We present performance and validation of the method by carrying out Markov Chain Monte Carlo analyses of the mock signals measured from a variety of mock catalogs that mimic the SDSS and HSC-Y1 galaxies. We show that the halo model method can recover the underlying true cosmological parameters to within the 68\% credible interval, except for the mocks including the assembly bias effect (although we consider the unrealistically-large amplitude of assembly bias effect). Even for the assembly bias mock, we demonstrate that the cosmological parameters can be recovered {\it if} the analysis is restricted to scales $R\gtrsim 10~h^{-1}{\rm Mpc}$. We also show that, by using a single population of source galaxies to infer the relative strengths of $\Delta\!\Sigma$ for multiple lens samples at different redshifts, the joint probes method allows for self-calibration of photometric redshift errors and multiplicative shear bias. Thus we conclude that the emulator-based halo model method can be safely applied to the HSC-Y1 dataset, achieving a precision of $\sigma(S_8)\simeq 0.04$.

Sconce: a cosmic web finder for spherical and conic geometries.

The latticework structure known as the cosmic web provides a valuable insight into the assembly history of large-scale structures. Despite the variety of methods to identify the cosmic web structures, they mostly rely on the assumption that galaxies are embedded in a Euclidean geometric space. Here, we present a novel cosmic web identifier called sconce (Spherical and CONic Cosmic wEb finder) that inherently considers the 2D (RA, DEC) spherical or the 3D (RA, DEC, z) conic geometry. The proposed algorithms in sconce generalize the well-known subspace constrained mean shift (scms) method and primarily address the predominant filament detection problem. They are intrinsic to the spherical/conic geometry and invariant to data rotations. We further test the efficacy of our method with an artificial cross-shaped filament example and apply it to the SDSS galaxy catalogue, revealing that the 2D spherical version of our algorithms is robust even in regions of high declination. Finally, using N-body simulations from Illustris, we show that the 3D conic version of our algorithms is more robust in detecting filaments than the standard scms method under the redshift distortions caused by the peculiar velocities of haloes. Our cosmic web finder is packaged in python as sconce-scms and has been made publicly available.

Study preferred alignments of angular momentum vector of i – magnitude SDSS galaxies having redshift 0.54 – 0.60

We present a study of the three-dimensional orientation of spin vector of i – magnitude having higher redshift ranging from 0.54 to 0.60 with respect to the equilateral co-ordinate system. The main aim of this work is to know the orientation and projection of the spin vector of distant galaxies. The database is taken from SDSS 14th data release (DR-14) and this redshift range contains 239,209 galaxies. SDSS provides two – dimensional observed data, we convert it into three-dimension rotation axes of the galaxy with the help of the ‘position angle inclination method’. The expected isotropy distribution curves are obtained by removing the selection effects and performing a random simulation generating 107 virtual galaxies. To check, isotropy or anisotropy we have to carry out four statistical tests: chi-square, auto-correlation coefficient, first-order Fourier series, and First-order Fourier probability. In the study, we observed random orientation of spin vector of galaxies in all twenty samples and supported a strongly Hierarchy model of galaxy evolution. Also, the projection of spin vector is found to be isotropic in samples i01, i02, i03, i16, i17, i18, i19, and i20 and the remaining samples i04 to i15 are seen as local anisotropic due to the merging phenomenon of galaxies and tidal effect. This shows the problem in a coordinate system. BIBECHANA 19 (2022) 68-74

EMPRESS. V. Metallicity Diagnostics of Galaxies over 12 + log(O/H) ≃ 6.9–8.9 Established by a Local Galaxy Census: Preparing for JWST Spectroscopy

We present optical-line gas metallicity diagnostics established by the combination of local SDSS galaxies and the largest compilation of extremely metal-poor galaxies (EMPGs) including new EMPGs identified by the Subaru EMPRESS survey. A total of 103 EMPGs are included, covering a large parameter space of magnitude (M i = −19 to −7) and Hβ equivalent width (10–600 Å), i.e., wide ranges of stellar mass and star formation rate. Using reliable metallicity measurements from the direct method for these galaxies, we derive the relationships between strong optical-line ratios and gas-phase metallicity over the range of ≃ 6.9–8.9, corresponding to 0.02–2 solar metallicity Z ⊙. We confirm that the R23 index, ([O iii]+[O ii])/Hβ, is the most accurate metallicity indicator with a metallicity uncertainty of 0.14 dex over the range among various popular metallicity indicators. The other metallicity indicators show large scatters in the metal-poor range (≲0.1 Z ⊙). It is explained by our CLOUDY photoionization modeling that, unlike the R23 index, the other metallicity indicators do not use a sum of singly and doubly ionized lines and cannot trace both low- and high-ionization gas. We find that the accuracy of the metallicity indicators is significantly improved if one uses Hβ equivalent width measurements that tightly correlate with ionization states. In this work, we also present the relation of physical properties with the UV-continuum slope β and ionization production rate ξ ion derived with GALEX data for the EMPGs and provide local anchors of galaxy properties together with the optical-line metallicity indicators that are available in the form of a machine-readable table and useful for forthcoming JWST spectroscopic studies.

Using 3D and 2D analysis for analyzing large-scale asymmetry in galaxy spin directions

Abstract The nature of galaxy spin is still not fully known. Iye, Yagi, and Fukumoto (2021, AJ, 907, 123) applied a 3D analysis to a dataset of bright SDSS galaxies that was used in the past for photometric analysis. They showed that the distribution of spin directions of spiral galaxies is random, providing a dipole axis with low statistical significance of 0.29σ. However, to show random distribution, two decisions were made, each of which can lead to random distribution regardless of the real distribution of the spin direction of galaxies. The first decision was to limit the dataset arbitrarily to z < 0.1, which is a redshift range in which previous literature already showed that random distribution is expected. More importantly, while the 3D analysis requires the redshift of each galaxy, the analysis was done with the photometric redshift. If the asymmetry existed, its signal is expected to be an order of magnitude weaker than the error of the photometric redshift, and therefore a low statistical signal under these conditions is expected. When using the exact same data without limiting to zphot < 0.1 and without using the photometric redshift, the distribution of the spin directions in that dataset shows a statistical signal of >2σ. Code and data for reproducing the analysis are publicly available. These results are in agreement with other experiments with SDSS, Pan-STARRS, HST, and the DESI Legacy Survey. The paper also examines other previous studies that showed random distribution in galaxy spin directions. While further research will be required, the current evidence suggests that large-scale asymmetry between the number of clockwise and counterclockwise galaxies cannot be ruled out.

Classification of cosmic structures for galaxies with deep learning: connecting cosmological simulations with observations

ABSTRACT We explore the capability of deep learning to classify cosmic structures. In cosmological simulations, cosmic volumes are segmented into voids, sheets, filaments, and knots, according to distribution and kinematics of dark matter (DM), and galaxies are also classified according to the segmentation. However, observational studies cannot adopt this classification method using DM. In this study, we demonstrate that deep learning can bridge the gap between the simulations and observations. Our models are based on 3D convolutional neural networks and trained with data of distribution of galaxies in a simulation to deduce the structure classes from the galaxies rather than DM. Our model can predict the class labels as accurate as a previous study using DM distribution for the training and prediction. This means that galaxy distribution can be a substitution for DM for the cosmic-structure classification, and our models using galaxies can be directly applied to wide-field survey observations. When observational restrictions are ignored, our model can classify simulated galaxies into the four classes with an accuracy (macro-averaged F1-score) of 64 per cent. If restrictions such as limiting magnitude are considered, our model can classify SDSS galaxies at ∼100 Mpc with an accuracy of 60 per cent. In the binary classification distinguishing void galaxies from the others, our model can achieve an accuracy of 88 per cent.

Red Dragon: a redshift-evolving Gaussian mixture model for galaxies

ABSTRACT Precision-era optical cluster cosmology calls for a precise definition of the red sequence (RS), consistent across redshift. To this end, we present the Red Dragon algorithm: an error-corrected multivariate Gaussian mixture model (GMM). Simultaneous use of multiple colours and smooth evolution of GMM parameters result in a continuous RS and blue cloud (BC) characterization across redshift, avoiding the discontinuities of red fraction inherent in swapping RS selection colours. Based on a mid-redshift spectroscopic sample of SDSS galaxies, an RS defined by Red Dragon selects quiescent galaxies (low specific star formation rate) with a balanced accuracy of over $90{{\ \rm per\ cent}}$. This approach to galaxy population assignment gives more natural separations between RS and BC galaxies than hard cuts in colour–magnitude or colour–colour spaces. The Red Dragon algorithm is publicly available at bitbucket.org/wkblack/red-dragon-gamma/.

Impact of thermal Sunyaev–Zeldovich effect on cross-correlations between Planck cosmic microwave background lensing and SDSS galaxy density fields

ABSTRACT Residual foreground contamination by thermal Sunyaev–Zeldovich (tSZ) effect from galaxy clusters in cosmic microwave background (CMB) maps propagates into the reconstructed CMB lensing field, and thus biases the intrinsic cross-correlation between CMB lensing and large-scale structure (LSS). Through stacking analysis, we show that residual tSZ contamination causes an increment of lensing convergence in the central part of the clusters and a decrement of lensing convergence in the cluster outskirts. We quantify the impact of residual tSZ contamination on cross-correlations between the Planck 2018 CMB lensing convergence maps and the Sloan Digital Sky Survey-IV galaxy density data through cross-power spectrum computation. In contrast with the Planck 2018 tSZ-deprojected smica lensing map, our analysis using the tSZ-contaminated smica lensing map measures an $\sim\!2.5{{\ \rm per\,cent}}$ negative bias at multipoles ℓ ≲ 500 and transits to an $\sim\!9{{\ \rm per\,cent}}$ positive bias at ℓ ≳ 1500, which validates earlier theoretical predictions of the overall shape of such tSZ-induced spurious cross-correlation. The tSZ-induced lensing convergence field in Planck CMB data is detected with more than 1σ significance at ℓ ≲ 500 and more than 14σ significance at ℓ ≳ 1500, yielding an overall 14.8σ detection. We also show that masking galaxy clusters in CMB data is not sufficient to eliminate the spurious lensing signal, still detecting a non-negligible bias with 5.5σ significance on cross-correlations with galaxy density fields. Our results emphasize how essential it is to deproject the tSZ effect from CMB maps at the component separation stage and adopt tSZ-free CMB lensing maps for cross-correlations with LSS data.

The average dust attenuation curve at z ∼ 1.3 based on HST grism surveys

ABSTRACT We present the first characterization of the average dust attenuation curve at z ∼ 1.3 by combining rest-frame ultraviolet through near-IR photometry with Balmer decrement (H α/H β) constraints for ∼900 galaxies with 8 ≲ log (M⋆/M⊙) < 10.2 at 0.75 < z < 1.5 in the HST WFC3 IR Spectroscopic Parallel and 3D-HST grism surveys. Using galaxies in SDSS, we establish that the (H α + [N ii])/[O iii] line ratio and stellar mass are good proxies for the Balmer decrement in low-spectral resolution grism data when only upper limits on H β are available and/or H α is blended with [N ii]. The slope of the z ∼ 1.3 attenuation curve ($A(0.15\, \rm{\mu m})/A(V)=3.15$) and its normalization (RV = 3.26) lie in-between the values found for z = 0 and z ∼ 2 dust attenuation curves derived with similar methods. These provide supporting evidence that the average dust attenuation curve of star-forming galaxies evolves continuously with redshift. The z ∼ 1.3 curve has a mild 2175 Å feature (bump amplitude, Eb = 0.83; ∼25 per cent that of the MW extinction curve), which is comparable to several other studies at 0 < z ≲ 3, and suggests that the average strength of this feature may not evolve significantly with redshift. The methods we develop to constrain dust attenuation from HST grism data can be applied to future grism surveys with JWST, Euclid, and RST. These new facilities will detect millions of emission line galaxies and offer the opportunity to significantly improve our understanding of how and why dust attenuation curves evolve.

Machine learning technique for morphological classification of galaxies from SDSS. II. The image-based morphological catalogs of galaxies at 0.02<z<0.1

We applied the image-based approach with a convolutional neural network (CNN) model to the sample of low-redshift galaxies with –24m<Mr<–19.4m from the SDSS DR9. We divided it into two subsamples, SDSS DR9 galaxy dataset and Galaxy Zoo 2 (GZ2) dataset, considering them as the inference and training datasets, respectively. To determine the principal parameters of galaxy morphology defined within the GZ2 project, we classified the galaxies into five visual types and 34 morphological features of galaxies from the inference dataset, which do not match with GZ2 training dataset. As a result, we created the morphological catalog of 315782 galaxies at 0.02<z<0.1, where these classes and features were defined for the first time for 216148 galaxies by image-based CNN classifier. For the rest of galaxies the initial morphological classification was re-assigned as in the GZ2 project. Main results are presented in the catalog of 19468 completely rounded, 27321 rounded in-between, 3235 cigar-shaped, 4099 edge-on, 18615 spiral, and 72738 general low-redshift galaxies of the studied SDSS sample. Our method shows the promising performance of morphological classification attaining >93 % of accuracy for five classes morphology prediction except the cigar-shaped (~75 %) and completely rounded (~83 %) galaxies. As for the classification of galaxies by their detailed structural morphological features, our CNN model gives the accuracy in the range of 92–99 % depending on features, a number of galaxies with the given feature in the inference dataset, and the galaxy image quality. As a result, for the first time we assigned 34 morphological detailed features (bar, rings, number of spiral arms, mergers, etc.) for more than 160000 low-redshift galaxies from the SDSS DR9. We demonstrate that implication of the CNN model with adversarial validation and adversarial image data augmentation improves classification of smaller and fainter SDSS galaxies with mr <17.7.

LoTSS jellyfish galaxies

Context. The cluster environment has a strong impact on galaxy star formation, as seen by the fact that clusters host proportionally more red, passive galaxies relative to the field. Ram pressure stripping may drive this environmental quenching by directly stripping cold gas from galactic disks. In some cases, ram pressure stripping gives rise to ‘jellyfish galaxies’, observed with clear ‘tentacles’ of stripped gas extending beyond the optical extent of the galaxy. Aims. In this paper we present a large sample of jellyfish galaxies in low redshift clusters (z < 0.05), identified through 120−168 MHz radio continuum from the LOFAR Two-metre Sky Survey (LoTSS). Methods. From a parent sample of 29 X-ray-detected SDSS galaxy clusters and their spectroscopic members, we visually identify 95 star-forming, LoTSS jellyfish galaxies with 144 MHz radio tails. Star formation rates (SFRs) and stellar masses are obtained for all galaxies from SED fits. For each jellyfish galaxy we determine the tail orientation with respect to the cluster centre and quantify the prominence of the radio tails with the 144 MHz shape asymmetry. Results. After carefully accounting for redshift-dependent selection effects, we find that the frequency of jellyfish galaxies is relatively constant from cluster to cluster. LoTSS jellyfish galaxies are preferentially found at small clustercentric radius and large velocity offsets within their host clusters and have radio tails that are oriented away from the cluster centre. These galaxies also show enhanced star formation, relative to both ‘normal’ cluster galaxies and isolated field galaxies, but generally fall within the scatter of the L144 MHz − SFR relation. Conclusions. The properties of the LoTSS jellyfish galaxies identified in this work are fully consistent with expectations from ram pressure stripping. This large sample of jellyfish galaxies will be valuable for further constraining ram pressure stripping and star formation quenching in nearby galaxy clusters. We show that LOFAR is a powerful instrument for identifying ram pressure stripped galaxies across extremely wide fields. Moving forward, we will push the search for jellyfish galaxies beyond this initial cluster sample, including a comprehensive survey of the galaxy group regime.

Hosts and triggers of AGNs in the Local Universe

Based on the spectroscopic and shear catalogs for SDSS galaxies in the local Universe, we compared optically selected active galactic nuclei (AGNs) with control star-forming and quiescent galaxies on galactic and inter-halo scales, and larger. We find that AGNs are preferentially found in two specific stages of galaxy evolution: in the starburst and ‘green valley’ phases. We also find that the stellar population of their host galaxies is quite independent of stellar mass, which is not the case for more typical galaxies. Combining galaxy-galaxy lensing and galaxy clustering on large scales, we measured the mass of AGN host halos. The typical halo mass is about 1012 h−1 M⊙, similar to the characteristic mass in the stellar mass-halo mass relation (SHMR). For a given stellar mass, AGN host galaxies and star-forming galaxies share the same SHMR, while quiescent galaxies have more massive halos. Clustering analyses on halo scales reveals that AGNs are surrounded by a larger number of satellites (with stellar mass down to 1/1000 of the mass of the central galaxy) than star-forming galaxies and that galaxies with a greater stellar velocity dispersion have a greater number of satellites. The number of satellites also increase with halo mass, reaching unity around 1012 h−1 M⊙. Our results suggest a scenario in which the interaction of the central galaxy with the satellites triggers an early episode of starburst and AGN activity, followed by multiple AGN cycles driven by the non-axisymmetric structure produced by the interaction. The feedback from the starburst and AGN reduces the amount of cold gas for fueling the central black hole, producing a characteristic halo mass scale, that is, ∼1012 h−1 M⊙, where the AGN fraction peaks.

J-PAS: Measuring emission lines with artificial neural networks

In the years to come, the Javalambre-Physics of the Accelerated Universe Astrophysical Survey (J-PAS) will observe 8000 deg2 of the northern sky with 56 photometric bands. J-PAS is ideal for the detection of nebular emission objects. This paper presents a new method based on artificial neural networks (ANNs) that is aimed at measuring and detecting emission lines in galaxies up to z = 0.35. These lines are essential diagnostics for understanding the evolution of galaxies through cosmic time. We trained and tested ANNs with synthetic J-PAS photometry from CALIFA, MaNGA, and SDSS spectra. To this aim, we carried out two tasks. First, we clustered galaxies in two groups according to the values of the equivalent width (EW) of Hα, Hβ, [N II], and [O III] lines measured in the spectra. Then we trained an ANN to assign a group to each galaxy. We were able to classify them with the uncertainties typical of the photometric redshift measurable in J-PAS. Second, we utilized another ANN to determine the values of those EWs. Subsequently, we obtained the [N II]/Hα, [O III]/Hβ, and O 3N 2 ratios, recovering the BPT diagram ([O III]/Hβ versus [N II]/Hα). We studied the performance of the ANN in two training samples: one is only composed of synthetic J-PAS photo-spectra (J-spectra) from MaNGA and CALIFA (CALMa set) and the other one is composed of SDSS galaxies. We were able to fully reproduce the main sequence of star-forming galaxies from the determination of the EWs. With the CALMa training set, we reached a precision of 0.092 and 0.078 dex for the [N II]/Hα and [O III]/Hβ ratios in the SDSS testing sample. Nevertheless, we find an underestimation of those ratios at high values in galaxies hosting an active galactic nuclei. We also show the importance of the dataset used for both training and testing the model. Such ANNs are extremely useful for overcoming the limitations previously expected concerning the detection and measurements of the emission lines in such surveys as J-PAS. Furthermore, we show the capability of the method to measure a EW of 10 Å in Hα, Hβ, [N II] and [O III] lines with a signal-to-noise ratio (S/N) of 5, 1.5, 3.5, and 10, respectively, in the photometry. Finally, we compare the properties of emission lines in galaxies observed with miniJPAS and SDSS. Despite the limitation of such a comparison, we find a remarkable correlation in their EWs.

Compact groups from semi-analytical models of galaxy formation – II. Different assembly channels

ABSTRACT We study the formation of over 6000 compact groups (CGs) of galaxies identified in mock redshift-space galaxy catalogues built from semi-analytical models of galaxy formation (SAMs) run on the Millennium Simulations. We select CGs of four members in our mock SDSS galaxy catalogues and, for each CG, we trace back in time the real-space positions of the most massive progenitors of their four galaxies. By analysing the evolution of the distance of the galaxy members to the centre of mass of the group, we identify four channels of CG formation. The classification of these assembly channels is performed with an automatic recipe inferred from a preliminary visual inspection and based on the orbit of the galaxy with the fewest number of orbits. Most CGs show late assembly, with the last galaxy arriving on its first or second passage, while only 10–20 per cent form by the gradual contraction of their orbits by dynamical friction, and only a few per cent forming early with little subsequent contraction. However, a SAM from a higher resolution simulation leads to earlier assembly. Assembly histories of CGs also depend on cosmological parameters. At similar resolution, CGs assemble later in SAMs built on parent cosmological simulations of high density parameter. Several observed properties of mock CGs correlate with their assembly history: early-assembling CGs are smaller, with shorter crossing times, and greater magnitude gaps between their brightest two members, and their brightest galaxies have smaller spatial offsets and are more passive.

Cosmological constraints on the magnification bias on sub-millimetre galaxies after large-scale bias corrections

Context. The study of the magnification bias produced on high-redshift sub-millimetre galaxies by foreground galaxies through the analysis of the cross-correlation function was recently demonstrated as an interesting independent alternative to the weak-lensing shear as a cosmological probe. Aims. In the case of the proposed observable, most of the cosmological constraints mainly depend on the largest angular separation measurements. Therefore, we aim to study and correct the main large-scale biases that affect foreground and background galaxy samples to produce a robust estimation of the cross-correlation function. Then we analyse the corrected signal to derive updated cosmological constraints. Methods. We measured the large-scale, bias-corrected cross-correlation functions using a background sample of H-ATLAS galaxies with photometric redshifts > 1.2 and two different foreground samples (GAMA galaxies with spectroscopic redshifts or SDSS galaxies with photometric ones, both in the range 0.2 < z < 0.8). These measurements are modelled using the traditional halo model description that depends on both halo occupation distribution and cosmological parameters. We then estimated these parameters by performing a Markov chain Monte Carlo under multiple scenarios to study the performance of this observable and how to improve its results. Results. After the large-scale bias corrections, we obtain only minor improvements with respect to the previous magnification bias results, mainly confirming their conclusions: a lower bound on Ωm > 0.22 at 95% CL and an upper bound σ8 < 0.97 at 95% CL (results from the zspec sample). Neither the much higher surface density of the foreground photometric sample nor the assumption of Gaussian priors for the remaining unconstrained parameters significantly improve the derived constraints. However, by combining both foreground samples into a simplified tomographic analysis, we were able to obtain interesting constraints on the Ωm − σ8 plane as follows: Ωm = 0.50−0.20+0.14 and σ8 = 0.75−0.10+0.07 at 68% CL.

Main-sequence Scatter is Real: The Joint Dependence of Galaxy Clustering on Star Formation and Stellar Mass

Abstract We present new measurements of the clustering of stellar-mass-complete samples of ∼40,000 SDSS galaxies at z ∼ 0.03 as a joint function of stellar mass and specific star formation rate (sSFR). Our results confirm what Coil et al. find at z ∼ 0.7: galaxy clustering is a stronger function of sSFR at fixed stellar mass than of stellar mass at fixed sSFR. We also find that galaxies above the star-forming main sequence (SFMS) with higher sSFR are less clustered than galaxies below the SFMS with lower sSFR, at a given stellar mass. A similar trend is present for quiescent galaxies. This confirms that main-sequence scatter, and scatter within the quiescent sequence, is physically connected to the large-scale cosmic density field. We compare the resulting galaxy bias versus sSFR, and relative bias versus sSFR ratio, for different galaxy samples across 0 < z < 1.2 to mock galaxy catalogs based on the empirical galaxy evolution model of Behroozi et al. This model fits PRIMUS and DEEP2 clustering data well at intermediate redshift, but agreement with SDSS is not as strong. We show that increasing the correlation between galaxy SFR and halo accretion rate at z ∼ 0 in the model substantially improves agreement with SDSS data. Mock catalogs suggest that central galaxies contribute substantially to the dependence of clustering on sSFR at a given stellar mass and that the signal is not simply an effect of satellite galaxy fraction differences with sSFR. Our results are highly constraining for galaxy evolution models and show that the stellar-to-halo mass relation depends on sSFR.

An observed correlation between galaxy spins and initial conditions

Galaxy spins can be predicted from the initial conditions in the early Universe through the tidal tensor twist. In simulations, their directions are well preserved through cosmic time, consistent with expectations of angular momentum conservation. We report a $\sim 3 \sigma$ detection of correlation between observed oriented directions of galaxy angular momenta and their predictions based on the initial density field reconstructed from the positions of SDSS galaxies. The detection is driven by a group of spiral galaxies classified by the Galaxy Zoo as (anti-)clockwise, with a modest improvement from adding galaxies from MaNGA and SAMI surveys. This is the first such detection of the oriented galaxy spin direction, which opens a way to use measurements of galaxy spins to probe fundamental physics in the early Universe.

Satellites and central galaxies in SDSS: the influence of interactions on their properties

ABSTRACT We use SDSS-DR14 to construct a sample of galaxy systems consisting of a central object and two satellites. We adopt projected distance and radial velocity difference criteria and impose an isolation criterion to avoid membership in larger structures. We also classify the interaction between the members of each system through a visual inspection of galaxy images, finding ${\sim}80{{\ \rm per\ cent}}$ of the systems lack evidence of interactions whilst the remaining ${\sim}20{{\ \rm per\ cent}}$ involve some kind of interaction, as inferred from their observed distorted morphology. We have considered separately, samples of satellites and central galaxies, and each of these samples were tested against suitable control sets to analyse the results. We find that central galaxies showing signs of interactions present evidence of enhanced star formation activity and younger stellar populations. As a counterpart, satellite samples show these galaxies presenting older stellar populations with a lower star formation rate than the control sample. The observed trends correlate with the stellar mass content of the galaxies and with the projected distance between the members involved in the interaction. The most massive systems are less affected since they show no star formation excess, possibly due to their more evolved stage and less gas available to form new stars. Our results suggest that it is arguably a transfer of material during interactions, with satellites acting as donors to the central galaxy. As a consequence of the interactions, satellite stellar population ages rapidly and new bursts of star formation may frequently occur in the central galaxy.