Redshift Survey Research Articles

CosmoMIA: cosmic web-based redshift space halo distribution

Modern galaxy surveys demand extensive survey volumes and resolutions surpassing current dark matter-only simulations' capabilities. To address this, many methods employ effective bias models on the dark matter field to approximate object counts on a grid. However, realistic catalogs necessitate specific coordinates and velocities for a comprehensive understanding of the Universe.In this research, we explore sub-grid modeling to create accurate catalogs, beginning with coarse grid number counts at resolutions of approximately 5.5 h -1 Mpc per side. These resolutions strike a balance between modeling nonlinear damping of baryon acoustic oscillations and facilitating large-volume simulations. Augmented Lagrangian Perturbation Theory (ALPT) is utilized to model the dark matter field and motions, replicating the clustering of a halo catalog derived from a massive simulation at z = 1.1.Our approach involves four key stages: Tracer Assignment: Allocating dark matter particles to tracers based on grid cell counts, generating additional particles to address discrepancies. Attractor Identification: Defining attractors based on particle cosmic web environments, acting as gravitational focal points. Tracer Collapse: Guiding tracers towards attractors, simulating structure collapse. Redshift Space Distortions: Introducing redshift space distortions to simulated catalogs using ALPT and a random dispersion term. Results demonstrate accurate reproduction of monopoles and quadrupoles up to wave numbers of approximately k = 0.6 h Mpc-1. This method holds significant promise for galaxy surveys like DESI, EUCLID, and LSST, enhancing our understanding of the cosmos across scales.

Constraining ultra slow roll inflation using cosmological datasets

In recent years, the detection of gravitational waves by LIGO and PTA collaborationshave raised the intriguing possibility of excess matter power at small scales. Such anincrease can be achieved by ultra slow roll (USR) phase during inflationary epoch.We constrain excess power over small scales within the framework of such models usingcosmological datasets, particularly of CMB anisotropies and Lyman-α.We parameterize the USR phase in terms of thee-fold at the onset of USR (counted from the end of inflation) N̅1 andthe duration of USR phase Δ N.The former dictates the scale of enhancement in the primordial power spectrum, while the latter determines the amplitude of such anenhancement.From a joint dataset of CMB and galaxy surveys, we obtainN̅1 ≲ 45 with no bound on Δ N. This in turn implies thatthe scales over which the power spectrum can deviate significantly from thenearly scale invariant behavior of a typical slow-roll model isk ≳1 Mpc-1.On the other hand, the Lyman-α data is sensitive to baryonic powerspectrum along the line of sight. We consider a semi-analytic theoretical methodand high spectral-resolution Lyman-α data to constrain the model.The Lyman-α data limits both the USR parameters:N̅1 ≲ 41 and Δ N ≲ 0.4.This constrains the amplitude of the power spectrum enhancement to be less thana factor of hundred over scales 1 ≲ k/ Mpc-1≲ 100,thereby considerably improving the constraint on power over these scales ascompared to the bounds arrived at from CMB spectral distortion.

CLASS-OneLoop: accurate and unbiased inference from spectroscopic galaxy surveys

The power spectrum is the most commonly applied summary statistics to extract cosmological information from the observed three-dimensional distribution of galaxies in spectroscopic surveys. We present CLASS-OneLoop, a new numerical tool, fully integrated into the Boltzmann code CLASS, enabling the calculation of the one-loop power spectrum of biased tracers in spectroscopic surveys. Built upon the Eulerian moment expansion framework for redshift-space distortions, the implemented model incorporates a complete set of nonlinear biases, counterterms, and stochastic contributions, and includes the infrared resummation and the Alcock-Paczynski effect. The code features an evaluation of the loops by either direct numerical integration or Fast Fourier Transform, and employs a fast-slow parameter decomposition, which is essential for accelerating MCMC runs. After presenting performance and validation tests, as an illustration of the capabilities of the code, we apply it to fit the measured redshift-space halo power spectrum wedges on a ΛCDM subset of the AbacusSummit simulation suite and considering scales up to kmax = 0.3 h/Mpc. We find that the one-loop model adeptly recovers the fiducial cosmology of the simulation, while a simplified model commonly used in the literature for sensitivity forecasts yields significantly biased results. Furthermore, we conduct Monte Carlo Markov Chain (MCMC) forecasts for a DESI-like survey, considering a model with a dynamical dark energy component. Our results demonstrate the ability to independently constrain cosmological and nuisance parameters, even in the presence of a large parameter space with twenty-nine variables.

The quiescent population at 0.5 ≤ z ≤ 0.9: Environmental impact on the mass–size relation

Context. How the quiescent galaxies evolve with redshift and the factors that impact their evolution are still debated. It is still unclear what the dominant mechanisms of passive galaxy growth are and what role is played by the environment in shaping their evolutionary paths over cosmic time. Aims. The population of quiescent galaxies is altered over time by several processes that can affect their mean properties. Our aim is to study the mass–size relation (MSR) of the quiescent population and to understand how the environment shapes the MSR at intermediate redshift. Methods. We used the VIMOS Public Extragalactic Redshift Survey (VIPERS), a large spectroscopic survey of ∼90 000 galaxies in the redshift range 0.5 ≤ z ≤ 1.2. We selected a mass-complete sample of 4786 passive galaxies based on the NUVrK diagram and refined it using the Dn4000 spectral index to study the MSR of the passive population over 0.5 ≤ z ≤ 0.9. The impact of the environment on the MSR and on the growth of the quiescent population is studied through the density contrast. Results. The slope and the intercept of the MSR, α = 0.62 ± 0.04 and log(A) = 0.52 ± 0.01, agree well with values from the literature at the same redshift. The intercept decreases with redshift, Re(z) = 8.20 × (1 + z)−1.70, while the slope remains roughly constant, and the same trend is observed in the low-density (LD) and high-density (HD) environments. Thanks to the largest spectroscopic sample at 0.5 ≤ z ≤ 0.9, these results are not prone to redshift uncertainties from photometric measurements. We find that the average size of the quiescent population in the LD and HD environments are identical within 3σ and this result is robust against a change in the definition of the LD and HD environments or a change in the selection of quiescent galaxies. In the LD and HD environments, ∼30 and ∼40% of the population have experienced a minor merger process between 0.5 ≤ z ≤ 0.9. However, minor mergers account only for 30–40% of the size evolution in this redshift range, the remaining evolution likely being due to the progenitor bias.

The hierarchical cosmic web and assembly bias

Accurate modeling of galaxy distributions is paramount for cosmological analysis using galaxy redshift surveys. However, this endeavor is often hindered by the computational complexity of resolving the dark matter halos that host these galaxies. To address this challenge, we propose the development of effective assembly bias models down to small scales, i.e., going beyond the local density dependence capturing non-local cosmic evolution. We introduce a hierarchical cosmic web classification that indirectly captures up to third-order long- and short-range non-local bias terms. This classification system also enables us to maintain positive definite parametric bias expansions. Specifically, we subdivide the traditional cosmic web classification, which is based on the eigenvalues of the tidal field tensor, with an additional classification based on the Hessian matrix of the negative density contrast. We obtain the large-scale dark matter field on a mesh with ~3.9 h -1 Mpc cell side resolution through Augmented Lagrangian Perturbation Theory. To assess the effectiveness of our model, we conduct tests using a reference halo catalogue extracted from the UNIT project simulation, which was run within a cubical volume of 1 h -1 Gpc side. The resulting mock halo catalogs, generated through our approach, exhibit a high level of accuracy in terms of the one-, two- and three-point statistics. They reproduce the reference power-spectrum within better than 2 percent accuracy up to wavenumbers k ~ 0.8 h Mpc-1 and provide accurate bispectra within the scales that are crucial for cosmological analysis. This effective bias approach provides a forward model appropriate for field-level cosmological inference and holds significant potential for facilitating cosmological analysis of galaxy redshift surveys, particularly in the context of projects such as DESI, EUCLID, and LSST.

From VIPERS to SDSS: Unveiling galaxy spectra evolution over 9 Gyr through unsupervised machine learning

Aims. This study aims to trace the chronological evolution of galaxy spectra over cosmic time. Focusing on the VIPERS dataset, we seek to understand the diverse population of galaxies within narrow redshift bins, comparing our findings with the previously mapped diversity of SDSS galaxies. Methods. We used Fisher-EM, an unsupervised sub-space model-based classification algorithm to classify a dataset of 79 224 galaxy spectra from the VIMOS Public Extragalactic Redshift Survey (VIPERS). The dataset was divided into 26 samples by bins of redshift ranging from z ∼ 0.4 to z ∼ 1.2, which were classified independently. Classes of subsequent bins were linked through the k-nearest neighbours method to create a chronological tree of classes at different epochs. Results. Based on the optical spectra, three main chronological galaxy branches emerged: (i) red passive, (ii) blue star forming, and (iii) very blue, possibly associated with AGN activity. Each of the branches differentiates into sub-branches, discriminating finer properties such as D4000 break, colour, star-formation rate, and stellar masses, and/or disappear with cosmic time. Notably, these classes align remarkably well with the branches identified in a previous SDSS analyses, indicating a robust and consistent classification across datasets. The chronological ‘tree’ constructed from VIPERS data provides valuable insights into the temporal evolution of these spectral classes. Conclusions. The synergy between VIPERS and SDSS datasets enhances our understanding of the evolutionary pathways of galaxy spectra. The remarkable correspondence between independently derived branches in both datasets underscores the reliability of our unsupervised machine-learning approach. The three sub-trees show complex branching structures that highlight different physical and evolutionary behaviours. This study contributes to the broader comprehension of galaxy evolution by providing a chronologically organised framework for interpreting optical spectra within specific redshift ranges.

Cosmological forecast of the void size function measurement from the CSST spectroscopic survey

ABSTRACT Void size function (VSF) contains the information of the cosmic large-scale structure (LSS), and can be used to derive the properties of dark energy and dark matter. We predict the VSFs measured from the spectroscopic galaxy survey operated by China’s Space Survey Telescope (CSST), and study the strength of cosmological constraint. We employ a high-resolution Jiutian simulation to get CSST galaxy mock samples based on an improved semi-analytical model. We identify voids from this galaxy catalogue using the watershed algorithm without assuming a spherical shape, and estimate the VSFs at different redshift bins from $z=0.5$ to 1.1. We propose a void selection method based on the ellipticity, and assume the void linear underdensity threshold $\delta _{\rm v}$ in the theoretical model is redshift-dependent and set it as a free parameter in each redshift bin. The Markov Chain Monte Carlo method is adopted to implement the constraints on the cosmological and void parameters. We find that the CSST VSF measurement can constrain the cosmological parameters to a few per cent level. The best-fitting values of $\delta _{\rm v}$ are ranging from $\sim -0.4$ to $-0.1$ as the redshift increases from 0.5 to 1.1, which has a distinct difference from the theoretical calculation with $\delta _{\rm v}\simeq -2.7$ assuming the spherical evolution and using particles as tracer. Our method can provide a good reference for the void identification and selection in the VSF analysis of the spectroscopic galaxy surveys.

The hyperplane of early-type galaxies: using stellar population properties to increase the precision and accuracy of the fundamental plane as a distance indicator

ABSTRACT We use deep spectroscopy from the SAMI (Sydney-AAO Multi-object Integral) Galaxy Survey to explore the precision of the fundamental plane (FP) of early-type galaxies as a distance indicator for future single-fibre spectroscopy surveys. We study the optimal trade-off between sample size and signal-to-noise ratio (SNR), and investigate which additional observables can be used to construct hyperplanes with smaller intrinsic scatter than the FP. We add increasing levels of random noise (parametrized as effective exposure time) to the SAMI spectra to study the effect of increasing measurement uncertainties on the FP- and hyperplane-inferred distances. We find that, using direct-fit methods, the values of the FP and hyperplane best-fitting coefficients depend on the spectral SNR, and reach asymptotic values for a mean $\langle \mathrm{ SNR} \rangle =40\, \mathrm{\mathring{\rm A}}^{-1}$. As additional variables for the FP we consider three stellar-population observables: light-weighted age, stellar mass-to-light ratio, and a novel combination of Lick indices ($I_\mathrm{age}$). For an $\langle \mathrm{ SNR} \rangle =45~\mathrm{\mathring{\rm A}}^{-1}$ (equivalent to 1-h exposure on a 4-m telescope), all three hyperplanes outperform the FP as distance indicators. Being an empirical spectral index, $I_\mathrm{age}$ avoids the model-dependent uncertainties and bias underlying age and mass-to-light ratio measurements, yet yields a 10 per cent reduction of the median distance uncertainty compared to the FP. We also find that, as a by-product, the $I_\mathrm{age}$ hyperplane removes most of the reported environment bias of the FP. After accounting for the different SNR, these conclusions also apply to a 50 times larger sample from SDSS-III (Sloan Digital Sky Survey). However, in this case, only $\mathrm{ age}$ removes the environment bias.

The Lockman-SpReSO project

Aims. In this work, we carry out a comprehensive study using galaxy data from the Lockman Spectroscopic Redshift Survey using Osiris (Lockman-SpReSO) project, a far-infrared (FIR) selected sample of galaxies observed using optical spectroscopy. We analyse a sub-sample of star-forming galaxies (SFGs) with secure spectroscopic redshifts, mostly in the luminous infrared (IR) galaxy domain. From these galaxies, parameters such as the extinction, star formation rate (SFR), and metallicity have been derived. The present paper examines how these properties evolve in relation to each other and in comparison with low-redshift FIR and non-FIR-selected samples of galaxies. Methods. We applied distinct selection criteria to attain an SFG sample with minimal AGN contamination. Multiple approaches were used to estimate the intrinsic extinction, SFR, and gas-phase metallicity for the SFGs. In conjunction with findings in the literature, we examined the correlation between SFRs and stellar masses (M*), as well as the metallicity evolution depending on M*. Finally, we also studied the 3D relationship between M*, SFR, and metallicity. Results. From the initial spectroscopic sample of 409 FIR-selected objects from the Lockman-SpReSO catalogue, 69 active galactic nuclei (AGNs) were identified and excluded (17%), which is nearly double the percentage found in local studies, leaving us with a sample of 340 SFGs. The analysis of the M*–SFR relationship revealed that Lockman-SpReSO IR-selected SFGs show signs of evolution at redshifts z > 0.4, shifting above the main sequence (MS), with a mean value of ∼0.4 dex. They are located within the starburst galaxy region since 78% of the galaxies fall into this category. In addition, no evident flattening was found in the relation to specific SFR with redshift for log M* (M⊙)≳10.5. In line with the M*–metallicity relation (MZR) outcomes published in previous studies for optically selected SFGs; however, during the analysis of the MZR, it was found that IR-selected SFGs exhibit lower metallicities than the values anticipated on the basis of their M* and redshift. During the investigation of the 3D M*–SFR–metallicity relation (FP), it was established that the research sample is consistent with relations published in the existing literature, with an average scatter of ∼0.2 dex. However, a re-calibration of the FP when using the SFR obtained from the IR luminosity is required and, in this case, no attenuation in the correlation for log M* (M⊙)≳10.5 was observed. This result points to a possible evolution of the more massive fraction of the sample in the sense that the present-day star formation rate is lower with respect to the average rate of star formation in the past.

A novel analysis of contamination in Lyman-break galaxy samples at z ∼ 6-8: spatial correlation with intermediate-redshift galaxies at z ∼ 1.3-2

ABSTRACT Potential contamination from low/intermediate-redshift galaxies, such as objects with a prominent Balmer break, affects the photometric selection of high-redshift galaxies through identification of a Lyman break. Traditionally, contamination is estimated from spectroscopic follow-up and/or simulations. Here, we introduce a novel approach to estimating contamination for Lyman-break galaxy (LBG) samples based on measuring spatial correlation with the parent population of lower redshift interlopers. We propose two conceptual approaches applicable to different survey strategies: a single large contiguous field and a survey consisting of multiple independent lines of sight. For a large single field, we compute the cross-correlation function between galaxies at redshift $z \sim 6$ and intermediate-redshift galaxies at $z \sim 1.3$. We apply the method to the CANDELS GOODS-S and XDF surveys and compare the measurement with simulated mock observations, finding that the contamination level in both cases is not measurable and lies below 5.5 per cent (at 90 per cent confidence). For random-pointing multiple field surveys, we measure instead the number count correlation between high-redshift galaxies and interlopers, as a two-point correlation analysis is not generally feasible. We show an application to the LBG samples at redshift $z \sim 8$ and the possible interloper population at $z \sim 2$ in the Brightest of Reionizing Galaxies (BoRG) survey. By comparing the Pearson correlation coefficient with the result from Monte Carlo simulations, we estimate a contamination fraction of $62^{+13}_{-39} \, \mathrm{ per}\, \mathrm{ cent}$, consistent with previous estimates in the literature. These results validate the proposed approach and demonstrate its utility as an independent check of contamination in photometrically selected samples of high-redshift galaxies.

Euclid: Testing photometric selection of emission-line galaxy targets

Multi-object spectroscopic galaxy surveys typically make use of photometric and colour criteria to select their targets. That is not the case of which will use the NISP slitless spectrograph to record spectra for every source over its field of view. Slitless spectroscopy has the advantage of avoiding defining a priori a specific galaxy sample, but at the price of making the selection function harder to quantify. In its Wide Survey was designed to build robust statistical samples of emission-line galaxies with fluxes brighter than $ 2e-16 erg s cm $, using the Halpha -$ N ii right $ complex to measure redshifts within the range $ $. Given the expected signal-to-noise ratio of NISP spectra, at such faint fluxes a significant contamination by incorrectly measured redshifts is expected, either due to misidentification of other emission lines, or to noise fluctuations mistaken as such, with the consequence of reducing the purity of the final samples. This can be significantly ameliorated by exploiting the extensive photometric information to identify emission-line galaxies over the redshift range of interest. Beyond classical multi-band selections in colour space, machine learning techniques provide novel tools to perform this task. Here, we compare and quantify the performance of six such classification algorithms in achieving this goal. We consider the case when only the photometric and morphological measurements are used, and when these are supplemented by the extensive set of ancillary ground-based photometric data, which are part of the overall scientific strategy to perform lensing tomography. The classifiers are trained and tested on two mock galaxy samples, the EL-COSMOS and Euclid Flagship2 catalogues. The best performance is obtained from either a dense neural network or a support vector classifier, with comparable results in terms of the adopted metrics. When training on on-board photometry alone, these are able to remove $87<!PCT!>$ of the sources that are fainter than the nominal flux limit or lie outside the $0.9<z<1.8$ redshift range, a figure that increases to $97<!PCT!>$ when ground-based photometry is included. These results show how by using the photometric information available to it will be possible to efficiently identify and discard spurious interlopers, allowing us to build robust spectroscopic samples for cosmological investigations.

Point cloud approach to generative modeling for galaxy surveys at the field level

Point cloud approach to generative modeling for galaxy surveys at the field level

MHONGOOSE discovery of a gas-rich low surface brightness galaxy in the Dorado group

We present the discovery of a low-mass, gas-rich low surface brightness galaxy in the Dorado group, at a distance of 17.7 Mpc. Combining deep MeerKAT 21-cm observations from the MeerKAT Observations of Nearby Galactic Objects: Observing Southern Emitters (MHONGOOSE) survey with deep photometric images from the VST Early-type Galaxy Survey (VEGAS) we find a stellar and neutral atomic hydrogen ( gas mass of $M_ and respectively. This low surface brightness galaxy is the lowest-mass detection found in a group beyond the local Universe ($D 10$ Mpc). The dwarf galaxy has the typical overall properties of gas-rich low surface brightness galaxies in the Local group, but with some striking differences. Namely, the MHONGOOSE observations reveal a very low column density ($ disk with asymmetrical morphology possibly supported by rotation and higher velocity dispersion in the centre. There, deep optical photometry and UV observations suggest a recent enhancement of the star formation. Found at galactocentric distances where in the Local Group dwarf galaxies are depleted of cold gas (at a projected distance of $390$ kpc from the group centre), this galaxy is likely on its first orbit within the Dorado group. We discuss the possible environmental effects that may have caused the formation of the disk and the enhancement of star formation (SF), highlighting the short-lived phase (a few hundreds million years) of the gaseous disk, before either SF or hydrodynamical forces will deplete the gas of the galaxy.

Generating mock galaxy catalogues for flux-limited samples like the DESI Bright Galaxy Survey

ABSTRACT Accurate mock galaxy catalogues are crucial to validate analysis pipelines used to constrain dark energy models. We present a fast HOD-fitting method which we apply to the AbacusSummit simulations to create a set of mock catalogues for the DESI Bright Galaxy Survey, which contain r-band magnitudes and $(g-r)$ colours. The halo tabulation method fits HODs for different absolute magnitude threshold samples simultaneously, preventing unphysical HOD crossing between samples. We validate the HOD fitting procedure by fitting to real-space clustering measurements and galaxy number densities from the MXXL BGS mock, which was tuned to the SDSS and GAMA surveys. The best-fitting clustering measurements and number densities are mostly within the assumed errors, but the clustering for the faint samples is low on large scales. The best-fitting HOD parameters are robust when fitting to simulations with different realizations of the initial conditions. When varying the cosmology, trends are seen as a function of each cosmological parameter. We use the best-fitting HOD parameters to create cubic box and cut sky mocks from the AbacusSummit simulations, in a range of cosmologies. As an illustration, we compare the ${}^{0.1}M_r\lt -20$ sample of galaxies in the mock with BGS measurements from the DESI one-percent survey. We find good agreement in the number densities, and the projected correlation function is reasonable, with differences that can be improved in the future by fitting directly to BGS clustering measurements. The cubic box and cut-sky mocks in different cosmologies are made publicly available.

WISE2MBH: a scaling-based algorithm for probing supermassive black hole masses through WISE catalogues

ABSTRACT Supermassive Black Holes (SMBHs) are commonly found at the centres of massive galaxies. Estimating their masses (MBH) is crucial for understanding galaxy-SMBH co-evolution. We present WISE2MBH, an efficient algorithm that uses cataloged Wide-field Infrared Survey Explorer (WISE) magnitudes to estimate total stellar mass (M*) and scale this to bulge mass (MBulge), and MBH, estimating the morphological type (TType) and bulge fraction (B/T) in the process. WISE2MBH uses scaling relations from the literature or developed in this work, providing a streamlined approach to derive these parameters. It also distinguishes QSOs from galaxies and estimates the galaxy TType using WISE colours with a relation trained with galaxies from the 2MASS Redshift Survey. WISE2MBH performs well up to z ∼ 0.5 thanks to K-corrections in magnitudes and colours. WISE2MBH MBH estimates agree very well with those of a selected sample of local galaxies with MBH measurements or reliable estimates: a Spearman score of ∼0.8 and a RMSE of ∼0.63 were obtained. When applied to the ETHER sample at z ≤ 0.5, WISE2MBH provides ∼1.9 million MBH estimates (78.5 per cent new) and ∼100 thousand upper limits. The derived local black hole mass function (BHMF) is in good agreement with existing literature BHMFs. Galaxy demographic projects, including target selection for the Event Horizon Telescope, can benefit from WISE2MBH for up-to-date galaxy parameters and MBH estimates. The WISE2MBH algorithm is publicly available on GitHub.

A mock redshift catalogue of the dusty star-forming galaxy population with intrinsic clustering and lensing for deep millimetre surveys

ABSTRACT We present a new cosmologically motivated mock redshift survey of the dusty star-forming galaxy population. Our mock survey is based on the Bolshoi–Planck dark matter halo simulation and covers an area of 5.3 deg2. Using a semi-empirical approach, we generate a light-cone and populate the dark matter haloes with galaxies. Infrared properties are assigned to the galaxies based on theoretical and empirical relations from the literature. Additionally, background galaxies are gravitationally lensed by dark matter haloes along the line of sight assuming a point-mass model approximation. We characterize the mock survey by measuring the star formation rate density, integrated number counts, redshift distribution, and infrared luminosity function. When compared with single-dish and interferometric observations, the predictions from our mock survey closely follow the compiled results from the literature. We have also directed this study towards characterizing one of the extragalactic legacy surveys to be observed with the TolTEC camera at the Large Millimeter Telescope: the 0.8 sq. degree Ultra Deep Survey, with expected depths of 0.025, 0.018, and 0.012 mJy beam−1 at 1.1, 1.4, and 2.0 mm. Exploiting the clustering information in our mock survey, we investigate its impact on the effect of flux boosting by the fainter population of dusty galaxies, finding that clustering can increase the median boosting by 0.5 per cent at 1.1 mm, 0.8 per cent at 1.4 mm, and 2.0 per cent at 2.0 mm, and with higher dispersion.

AGNfitter-rx : Modeling the radio-to-X-ray spectral energy distributions of AGNs

We present new advancements in the modeling of the spectral energy distributions (SEDs) of active galaxies by introducing the radio-to-X-ray fitting capabilities of the publicly available Bayesian code AGNfitter . The new code release, called AGNfitter-rx models the broad-band photometry covering the radio, infrared (IR), optical, ultraviolet (UV), and X-ray bands consistently using a combination of theoretical and semi-empirical models of the active galactic nucleus (AGN) and host-galaxy emission. This framework enables the detailed characterization of four physical components of the active nuclei, namely the accretion disk, the hot dusty torus, the relativistic jets and core radio emission, and the hot corona, and can be used to model three components within the host galaxy: stellar populations, cold dust, and the radio emission from the star-forming regions. Applying AGNfitter-rx to a diverse sample of 36 AGN SEDs at $z 0.7$ from the AGN SED ATLAS, we investigated and compared the performance of state-of-the-art torus and accretion disk emission models in terms of fit quality and inferred physical parameters. We find that clumpy torus models that include polar winds and semi-empirical accretion disk templates including emission-line features significantly increase the fit quality in 67<!PCT!> of the sources by reducing by $2 fit residuals in the $1.5-5 1mm m$ and $0.7 1mm m$ regimes. We demonstrate that, by applying AGNfitter-rx to photometric data, we are able to estimate the inclination and opening angles of the torus, consistent with spectroscopic classifications within the AGN unified model, as well as black hole masses congruent with virial estimates based on Halpha . We investigate wavelength-dependent AGN fractions across the spectrum for Type 1 and Type 2 AGNs, finding dominant AGN fractions in radio, mid-infrared, and X-ray bands, which are in agreement with the findings from empirical methods for AGN selection. The wavelength coverage and the flexibility for the inclusion of state-of-the-art theoretical models make AGNfitter-rx a unique tool for the further development of SED modeling for AGNs in present and future radio-to-X-ray galaxy surveys.

Convolutional and hybrid neural network for cluster membership

We conduct investigations of the membership of galaxies inside a cluster using a machine-learning Convolutional Neural Network (CNN), and a hybrid of CNN with Multi-Layer Perceptron (MLP) neural network. These networks are trained using optical images of each galaxy in I and V bands as well as using various photometric and morphological parameters of galaxies extracted and measured from these images. The optical images are observed with Subaru Suprime-Cam for 16 clusters at redshift ∼ 0.15–0.3. This dataset enables a wide and faint investigation of cluster membership that is essential for the investigations of dwarf galaxies, but it is often difficult to achieve both simultaneously. We optimize the network structure to directly determine the membership classification, rather than estimating the galaxy redshift first as an intermediate step and then determining the membership from the redshifts. We find: 1. The CNN model can be useful for the application of identifying the cluster membership of galaxies, similar to the MLP model, implying that the CNN model can use information beyond galaxy SED. 2. A simple CNN model appears to predict the cluster membership less accurately than the 17-measure MLP model. 3. For better performance of CNN, the masking of the object is important, as well as scaling based on the photometric calibration. 4. The hybrid model of CNN and MLP combined can produce a better prediction than the single MLP (or CNN) model; however, the improvement is small at best implying that the information extracted by 17 measures for the MLP model is fairly comprehensive in sense of the total information contained in pixels of the image. 5. All models appear to predict the membership more accurately inside a smaller radius closer to the cluster center. 6. Faint galaxies are harder to assign their memberships even if using our models, though the performance is more robust than other photometric methods.Artificial intelligence (AI) can help us effectively combine various conventional methods of finding cluster membership. By making it inherit advantages of each of these conventional methods, The AI membership can achieve overall good performance in various performance statistics simultaneously, such as purity, completeness, F1 score, AUC, and accuracy. The overall good performance of the AI membership is vital to cluster studies in the era of faint and data-intensive galaxy survey s in which the complete spectroscopic observation of all galaxies is out of reach.

The SAMI Galaxy Survey: impact of star formation and AGN feedback processes on the ionized gas velocity dispersion

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

The informativeness of [C ii] line-intensity mapping as a probe of the H i content and metallicity of galaxies at the end of reionization

ABSTRACT Line-intensity mapping (LIM) experiments coming online now will survey fluctuations in aggregate emission in the [C ii] ionized carbon line from galaxies at the end of reionization. Experimental progress must be matched by theoretical reassessments of approaches to modelling and the information content of the signal. We present a new model for the halo–[C ii] connection, building upon results from the Feedback In Realistic Environments simulations suggesting that gas mass and metallicity most directly determine [C ii] luminosity. Applying our new model to an ensemble of peak-patch halo light-cones, we generate new predictions for the [C ii] LIM signal at z ≳ 6. We expect a baseline 4000-h LIM survey from the Cerro Chajnantor Atacama Telescope facility to have the fundamental sensitivity to detect the [C ii] power spectrum at a significance of 5σ at z ∼ 6, with an extended or successor Stage 2 experiment improving significance to 48σ at z ∼ 6 and achieving 11σ at z ∼ 7.5. Cross-correlation through stacking, simulated against a mock narrow-band Lyman-break galaxy survey, would yield a strong detection of the radial profile of cosmological [C ii] emission surrounding star-forming galaxies. We also analyse the role of a few of our model’s parameters through the pointwise relative entropy (PRE) of the distribution of [C ii] intensities. While the PRE signature of different model parameters can become degenerate or diminished after factoring in observational distortions, various parameters do imprint themselves differently on the one-point statistics of the intrinsic signal. Further work can pave the way to access this information and distinguish different sources of non-Gaussianity in the [C ii] LIM observation.