Parameters Of Galaxies Research Articles

Parameter measurement based on photometric images I: The method and the gas-phase metallicity of spiral galaxies

The gas-phase metallicity is a crucial parameter for understanding the evolution of galaxies. Considering that the number of multiband galaxy images can typically reach tens of millions, using these images as input data to predict gas-phase metallicity has become a feasible method. However, the accuracy of metallicity estimates from images is relatively limited. To solve this problem, we propose the galaxy parameter measurement residual network (GPM-ResNet), a deep learning method designed to predict gas-phase metallicity from photometric images of DESI. The parameters of photometric images are labeled with gas-phase metallicity values, which were obtained through spectroscopic methods with a high accuracy. These labeled images serve as the training dataset for the GPM-ResNet method. GPM-ResNet mainly consists of two modules: a multi-order feature extractor and a parameter generator, enhancing the ability to effectively extract features related to gas-phase metallicity from photometric images. The σ of Z_ pred -Z_ true is 0.12 dex, which significantly outperforms the predicted results of the second-order polynomial (σ=0.16 dex) and the third-order polynomial (σ=0.16 dex) fit using the color-metallicity relation on the same dataset. To further emphasize the superiority of GPM-ResNet, we analyzed the predicted results on various network architectures, galaxy sizes, image resolutions, and wavelength bands of images. Moreover, we explored the mass-metallicity relation and recovered the relation successfully by utilizing the predicted values, Z_ pred . Finally, we applied GPM-ResNet to predict the gas-phase metallicity of spiral (EXP) galaxies observed by DESI, resulting in a comprehensive catalog containing 5,095,815 pieces of data.

How complex are galaxies? A non-parametric estimation of the intrinsic dimensionality of wide-band photometric data

Abstract Galaxies are complex objects, yet the number of independent parameters to describe them remains unknown. We present here a non-parametric method to estimate the intrinsic dimensionality of large datasets. We apply it to wide-band photometric data drawn from the COSMOS2020 catalogue and a comparable mock catalogue from the Horizon-AGN simulation. Our galaxy catalogues are limited in signal-to-noise ratio in all optical and NIR bands. Our results reveal that most of the variance in the wide-band photometry of this galaxy sample can be described with at most 4.3 ± 0.5 independent parameters for star-forming galaxies and 2.9 ± 0.2 for passive ones, both in the observed and simulated catalogues. We identify one of these parameters to be noise-driven, and recover that stellar mass and redshift are two key independent parameters driving the magnitudes. Our findings support the idea that wide-band photometry does not provide more than one additional independent parameter for star-forming galaxies. Although our sample is not mass-limited and may miss some passive galaxies due to our cut in SNR, our work suggests that dimensionality reduction techniques may be effectively used to explore and analyse wide-band photometric data, provided the used latent space is at least four-dimensional.

Morphological Classification of Galaxies Through Structural and Star Formation Parameters Using Machine Learning

Abstract We employ the XGBoost machine learning (ML) method for the morphological classification of galaxies into two (early-type, late-type) and five (E, S0–S0a, Sa–Sb, Sbc–Scd, Sd–Irr) classes, using a combination of non-parametric (C, A, S, AS, Gini, M20, c5090), parametric (Sérsic index, n), geometric (axial ratio, BA), global colour (g − i, u − r, u − i), colour gradient (Δ(g − i)), and asymmetry gradient (ΔA9050) information, all estimated for a local galaxy sample (z < 0.15) compiled from the Sloan Digital Sky Survey (SDSS) imaging data. We train the XGBoost model and evaluate its performance through multiple standard metrics. Our findings reveal better performance when utilizing all fourteen parameters, achieving accuracies of 88% and 65% for the two-class and five-class classification tasks, respectively. In addition, we investigate a hierarchical classification approach for the five-class scenario, combining three XGBoost classifiers. We observe comparable performance to the “direct” five-class classification, with discrepancies of only up to 3%. Using SHAP (an advanced interpretation tool), we analyse how galaxy parameters impact the model’s classifications, providing valuable insights into the influence of these features on classification outcomes. Finally, we compare our results with previous studies and find them consistently aligned.

Multi-layer Perceptron for Predicting Galaxy Parameters (MLP-GaP): Stellar Masses and Star Formation Rates

Abstract The large-scale imaging survey will produce massive photometric data in multi-bands for billions of galaxies. Defining strategies to quickly and efficiently extract useful physical information from this data is mandatory. Among the stellar population parameters for galaxies, their stellar masses and star formation rates (SFRs) are the most fundamental. We develop a novel tool, Multi-Layer Perceptron for Predicting Galaxy Parameters (MLP-GaP), that uses a machine learning (ML) algorithm to accurately and efficiently derive the stellar masses and SFRs from multi-band catalogs. We first adopt a mock data set generated by the Code Investigating GALaxy Emission (CIGALE) for training and testing data sets. Subsequently, we used a multi-layer perceptron model to build MLP-GaP and effectively trained it with the training data set. The results of the test performed on the mock data set show that MLP-GaP can accurately predict the reference values. Besides MLP-GaP has a significantly faster processing speed than CIGALE. To demonstrate the science-readiness of the MLP-GaP, we also apply it to a real data sample and compare the stellar masses and SFRs with CIGALE. Overall, the predicted values of MLP-GaP show a very good consistency with the estimated values derived from spectral energy distribution fitting. Therefore, the capability of MLP-GaP to rapidly and accurately predict stellar masses and SFRs makes it particularly well-suited for analyzing huge amounts of galaxies in the era of large sky surveys.

Investigating the Correlations between Physical Parameters of Elliptical and Lenticular Galaxies

This study aims to investigate the features of stellar-gaseous kinematics and dynamics mass using scaling coefficient relationships (such as the Faber–Jackson relation (FJR)) of two samples of elliptical and lenticular galaxies. These two samples of 80 ellipticals and 97 lenticulars were selected from previous literature works. The Statistical Package for Social Sciences )SPSS( and Matrix Laboratory (MATLAB) program were used to find out the associations of multiple factors under investigation such as main kinematic properties of the gaseous-stellar (effective radius Re, surface density within the effective radius (Ʃeff) , stellar mass in the blue band (Mstar (B)), gas mass ( Mgas), dynamic (Mdyn) and baryonic (Mbar), supermassive black holes masses (MBH) in the elliptical and S0 galaxies. We concluded that the experimental relations between (Log Mbar (B) - Log Mdyn (B), Log Mbar (B) - Log MBH) have a robust correlation of ~1, and the slope appears to be more linearly (Slope ≥1) for both types of galaxies (Ellipticals and Lenticulars). This paper also noted that the empirical convergences between Log Σeff (B) and Log Mdyn have a strong high regression relationship of ~1. The slope appears to be approximately linear ~1 in the lenticular galaxies, but there is no relationship between Log Σeff (B) and Log Mdyn in the elliptical galaxies. Due to the strong impact of dynamical rotation on elliptical galaxies, it is interestingly believed that they are objects under turbulence and originate from interactions and mergers with galaxies in a spiral shape.

Euclid preparation

LENSMC is a weak lensing shear measurement method developed for Euclid and Stage-IV surveys. It is based on forward modelling in order to deal with convolution by a point spread function (PSF) with comparable size to many galaxies, sampling the posterior distribution of galaxy parameters via Markov chain Monte Carlo, and marginalisation over nuisance parameters for each of the 1.5 billion galaxies observed by Euclid. We quantified the scientific performance through high-fidelity images based on the Euclid Flagship simulations and emulation of the Euclid VIS images, realistic clustering with a mean surface number density of 250 arcmin−2 (IE < 29.5) for galaxies, and 6 arcmin−2 (IE < 26) for stars, and a diffraction-limited chromatic PSF with a full width at half maximum of 0′.′2 and spatial variation across the field of view. LENSMC measured objects with a density of 90 arcmin−2 (IE < 26.5) in 4500 deg2. The total shear bias was broken down into measurement (our main focus here) and selection effects (which will be addressed in future work). We found measurement multiplicative and additive biases of m1 = (−3.6 ± 0.2) × 10−3, m2 = (−4.3 ± 0.2) × 10−3, c1 = (−1.78 ± 0.03) × 10−4, and c2 = (0.09 ± 0.03) × 10−4; a large detection bias with a multiplicative component of 1.2 × 10−2 and an additive component of −3 × 10−4; and a measurement PSF leakage of α1 = (−9 ± 3) × 10−4 and α2 = (2 ± 3) × 10−4. When model bias is suppressed, the obtained measurement biases are close to Euclid requirement and largely dominated by undetected faint galaxies (−5 × 10−3). Although significant, model bias will be straightforward to calibrate given its weak sensitivity on galaxy morphology parameters. LENSMC is publicly available at gitlab.com/gcongedo/LensMC.

The Lyman alpha reference sample. XV. Relating ionised gas kinematics with Lyman-alpha observables

Gas kinematics affect the radiative transfer and escape of hydrogen Lyman-alpha (Lyalpha ) emission from galaxies. We investigate this interplay empirically by relating the ionised gas kinematics of 42 galaxies in the extended Lyalpha Reference Sample (eLARS) with their Lyalpha escape fractions, $f_ esc Ly alpha $, Lyalpha equivalent widths, $ EW Ly alpha $, and Lyalpha luminosities, $L_ Ly alpha $. To this aim we use PMAS integral-field spectroscopic observations of the Balmer-alpha line. Our sample contains 18 rotating discs, 13 perturbed rotators, and 13 galaxies with more complex kinematics. The distributions of $f_ esc Ly alpha EW Ly alpha $, and $L_ Ly alpha $ do not differ significantly between these kinematical classes, but the largest Lyalpha observables are found amongst the kinematically complex systems. We find no trends between either $f_ esc Ly alpha $ or $ EW Ly alpha $ and kinematic or photometric inclinations. We calculate shearing velocities, $v_ shear $, and intrinsic velocity dispersions, $ obs obs $) and more than half of the sources show dispersion-dominated kinematics. We uncover clear trends between Lyalpha observables and global kinematical statistics: $ EW Ly alpha $ and $L_ Ly alpha $ correlate with $ obs $, while $f_ esc Ly alpha $ anti-correlates with $v_ shear $ and $v_ shear obs $. Moreover, we find, that galaxies with $ EW Ly alpha are characterised by higher $ and lower $v_ shear obs $ than galaxies below this threshold. We discuss the statistical importance of $v_ shear obs $, and $v_ shear obs $ for regulating the Lyalpha observables in comparison to other galaxy parameters. It emerges that $ obs $ is the dominating parameter for regulating $ EW Ly alpha $ and that is as important as nebular extinction, gas covering fraction, and ionising photon production efficiency in regulating $f_ esc Ly alpha $. A simple scenario where the starburst age is simultaneously regulating turbulence, $ EW Ly alpha $, and $f_ esc esc $ is not supported by our observations. However, we show that the small-scale distribution of dust appears to be influenced by turbulence in some galaxies. In support of our observational result, we discuss how turbulence is theoretically expected to play a significant role in modulating $f_ esc Ly alpha

Retrieval of the physical parameters of galaxies from WEAVE-StePS-like data using machine learning

Context. The William Herschel Telescope Enhanced Area Velocity Explorer (WEAVE) is a new, massively multiplexing spectrograph that allows us to collect about one thousand spectra over a 3 square degree field in one observation. The WEAVE Stellar Population Survey (WEAVE-StePS) in the next 5 years will exploit this new instrument to obtain high-S/N spectra for a magnitude-limited (IAB = 20.5) sample of ∼25 000 galaxies at moderate redshifts (z ≥ 0.3), providing insights into galaxy evolution in this as yet unexplored redshift range. Aims. We aim to test novel techniques for retrieving the key physical parameters of galaxies from WEAVE-StePS spectra using both photometric and spectroscopic (spectral indices) information for a range of noise levels and redshift values. Methods. We simulated ∼105 000 galaxy spectra assuming star formation histories with an exponentially declining star formation rate, covering a wide range of ages, stellar metallicities, specific star formation rates (sSFRs), and dust extinction values. We considered three redshifts (i.e. z = 0.3, 0.55, and 0.7), covering the redshift range that WEAVE-StePS will observe. We then evaluated the ability of the random forest and K-nearest neighbour algorithms to correctly predict the average age, metallicity, sSFR, dust attenuation, and time since the bulk of formation, assuming no measurement errors. We also checked how much the predictive ability deteriorates for different noise levels, with S/NI,obs = 10, 20, and 30, and at different redshifts. Finally, the retrieved sSFR was used to classify galaxies as part of the blue cloud, green valley, or red sequence. Results. We find that both the random forest and K-nearest neighbour algorithms accurately estimate the mass-weighted ages, u-band-weighted ages, and metallicities with low bias. The dispersion varies from 0.08–0.16 dex for age and 0.11–0.25 dex for metallicity, depending on the redshift and noise level. For dust attenuation, we find a similarly low bias and dispersion. For the sSFR, we find a very good constraining power for star-forming galaxies, log sSFR ≳ −11, where the bias is ∼0.01 dex and the dispersion is ∼0.10 dex. However, for more quiescent galaxies, with log sSFR ≲ −11, we find a higher bias, ranging from 0.61 to 0.86 dex, and a higher dispersion, ∼0.4 dex, depending on the noise level and redshift. In general, we find that the random forest algorithm outperforms the K-nearest neighbours. Finally, we find that the classification of galaxies as members of the green valley is successful across the different redshifts and S/Ns. Conclusions. We demonstrate that machine learning algorithms can accurately estimate the physical parameters of simulated galaxies for a WEAVE-StePS-like dataset, even at relatively low S/NI, obs = 10 per Å spectra with available ancillary photometric information. A more traditional approach, Bayesian inference, yields comparable results. The main advantage of using a machine learning algorithm is that, once trained, it requires considerably less time than other methods.

Uncovering the first-infall history of the LMC through its dynamical impact in the Milky Way halo

ABSTRACT The gravitational interactions between the LMC and the Milky Way cause dynamical perturbations in the MW halo, leading to biased distributions of stellar density and kinematics. We run 50 high-resolution N-body simulations exploring varying masses and halo shapes of the MW and LMC to study the evolution of LMC-induced perturbations. By measuring mean velocities of simulated halo stars, we identify a discontinuity between the first-infall and second-passage scenarios of the LMC’s orbital history. In the first-infall, the Galactocentric latitudinal velocity hovers around 16 km s$^{-1}$ for stars at 50–100 kpc, while it subsides to about 8 km s$^{-1}$ in the second-passage scenario. We demonstrate that, this reduced perturbation magnitude in the second-passage scenario is mainly due to the short dynamical times of the Galactic inner halo and the lower velocity of the LMC during its second infall into the MW. Using a subset of $\sim 1100$ RR Lyrae stars located in the outer halo (50 kpc $\le R_{\mathrm{GC}}\lt $ 100 kpc) with precise distance estimates from Gaia, we find the mean latitudinal velocity ($v_{b}$) in the Galactocentric frame to be $\langle v_{b} \rangle =18.1 \pm 4.1$ km s$^{-1}$. The observation supports the first-infall scenario with a massive LMC ($\sim$$2.1 \times 10^{11} \, \mathrm{M}_{\odot }$) at infall, an oblate MW halo with a virial mass $M_{200}\lt 1.4\times 10^{12}\,\mathrm{M}_{\odot }$ and a flattening parameter $q\gt 0.7$. Our study indicates that LMC-induced kinematic disturbances can reveal its orbital history and key characteristics, as well as those of the MW. This approach shows promise in helping determine fundamental parameters of both galaxies.

Black holes regulate cool gas accretion in massive galaxies

The nucleus of almost all massive galaxies contains a supermassive black hole (BH)1. The feedback from the accretion of these BHs is often considered to have crucial roles in establishing the quiescence of massive galaxies2–14, although some recent studies show that even galaxies hosting the most active BHs do not exhibit a reduction in their molecular gas reservoirs or star formation rates15–17. Therefore, the influence of BHs on galaxy star formation remains highly debated and lacks direct evidence. Here, based on a large sample of nearby galaxies with measurements of masses of both BHs and atomic hydrogen (HI), the main component of the interstellar medium18, we show that the HI gas mass to stellar masses ratio (μHI = MHI/M⋆) is more strongly correlated with BH masses (MBH) than with any other galaxy parameters, including stellar mass, stellar mass surface density and bulge masses. Moreover, once the μHI–MBH correlation is considered, μHI loses dependence on other galactic parameters, demonstrating that MBH serves as the primary driver of μHI. These findings provide important evidence for how the accumulated energy from BH accretion regulates the cool gas content in galaxies, by ejecting interstellar medium gas and/or suppressing gas cooling from the circumgalactic medium.

Constraining the Clustering and 21 cm Signature of Radio Galaxies at Cosmic Dawn

The efficiency of radio emission is an important unknown parameter of early galaxies at cosmic dawn, as models with high efficiency have been shown to modify the cosmological 21 cm signal substantially, deepening the absorption trough and boosting the 21 cm power spectrum. Such models have been previously directly constrained by the overall extragalactic radio background, as observed by Absolute Radiometer for Cosmology, Astrophysics, and Diffuse Emission 2 and Long Wavelength Array. In this work, we constrain the clustering of high-redshift radio sources by utilizing the observed upper limits on arcminute-scale anisotropy from the Very Large Array at 4.9 GHz and Australia Telescope Compact Array at 8.7 GHz. Using a seminumerical simulation of a plausible astrophysical model for illustration, we show that the clustering constraints on the radio efficiency are much stronger than those from the overall background intensity by a factor that varies between 18 and 55 in the redshift range of 7–22. As a result, the predicted maximum depth of the global 21 cm signal is lowered by a factor of 6 (to 1400 mK), and the maximum 21 cm power spectrum peak at cosmic dawn is lowered by a factor of 45 (to 1.3 × 105 mK2). We conclude that the observed clustering is the strongest current direct constraint on such models, but strong early radio emission from galaxies remains viable for producing a strongly enhanced 21 cm signal from cosmic dawn.

Stochastic prior for non-parametric star-formation histories

ABSTRACT The amount of power contained in the variations in galaxy star-formation histories (SFHs) across a range of time-scales encodes key information about the physical processes which modulate star formation. Modelling the SFHs of galaxies as stochastic processes allows the relative importance of different time-scales to be quantified via the power spectral density (PSD). In this paper, we build upon the PSD framework and develop a physically motivated, ‘stochastic’ prior for non-parametric SFHs in the spectral energy distribution (SED)-modelling code prospector. We test this prior in two different regimes: (1) massive, $z = 0.7$ galaxies with both photometry and spectra, analogous to those observed with the LEGA-C survey, and (2) $z = 8$ galaxies with photometry only, analogous to those observed with NIRCam on JWST. We find that it is able to recover key galaxy parameters (e.g. stellar mass, stellar metallicity) to the same level of fidelity as the commonly used continuity prior. Furthermore, the realistic variability information incorporated by the stochastic SFH model allows it to fit the SFHs of galaxies more accurately and precisely than traditional non-parametric models. In fact, the stochastic prior is $\gtrsim 2\times$ more accurate than the continuity prior in measuring the recent star-formation rates (log SFR$_{100}$ and log SFR$_{10}$) of both the $z = 0.7$ and $z = 8$ mock systems. While the PSD parameters of individual galaxies are difficult to constrain, the stochastic prior implementation presented in this work allows for the development of hierarchical models in the future, i.e. simultaneous SED-modelling of an ensemble of galaxies to measure their underlying PSD.

Strong nebular emissions associated with Mg ii absorptions detected in the SDSS spectra of background quasars

ABSTRACT We present long-slit spectroscopic observations of 40 Galaxy On Top of Quasars (GOTOQs) at ${0.37 \leqslant z \leqslant 1.01}$ using the South African Large Telescope. Using this and available photometric data, we measure the impact parameters of the foreground galaxies to be in the range of 3–16 kpc with a median value of 8.6 kpc. This is the largest sample of galaxies producing Mg ii absorption at such low impact parameters. These quasar–galaxy pairs are ideal for probing the disc–halo interface. At such impact parameters, we do not find any anticorrelation between rest equivalent width (REW) of Ca ii, Mn ii, Fe ii, Mg ii, and Mg i absorption and impact parameter. These sight lines are typically redder than those of strong Mg ii absorbers, with the colour excess, E(B − V) for our sample ranging from −0.191 to 0.422, with a median value of 0.058. In the E(B − V) versus W3935 plane, GOTOQs occupy the same region as Ca ii absorbers. For a given E(B − V), we find larger W3935 than what has been found in the Milky Way, probably due to a smaller dust-to-gas ratio in GOTOQs. Galaxy parameters could be measured for twelve cases, and their properties seem to follow the trends found for strong Mg ii absorbers. Measuring the host galaxy properties for the full sample using HST photometry or AO-assisted ground-based imaging is important to gain insights into the relationship between the stellar mass of galaxies and the metal line REW distributions at low impact parameters.

Galaxies’ properties in the Fundamental Plane across time

Context. Using the Illustris-1 and IllustrisTNG-100 simulations, we investigate the properties of the Fundamental Plane (FP), which is the correlation between the effective radius Re, the effective surface intensity Ie, and the central stellar velocity dispersion σ of galaxies, at different cosmic epochs. Aims. Our aim is to study the properties of galaxies in the FP and its projections across time, adopting samples covering different intervals of mass. We would like to demonstrate that the position of a galaxy in the FP space strongly depends on its degree of evolution, which might be represented by the β and $ L^\prime_0 $ parameters entering the L = $ L^\prime_0 $ (t)σβ(t) law. Methods. Starting from the comparison of the basic relations among the structural parameters of artificial and real galaxies at low redshift, we obtain the fit of the FP and its coefficients at different cosmic epochs for samples of different mass limits. Then, we analyze the dependence of the galaxy position in the FP space as a function of the β parameter and the star formation rate (SFR). Results. We find that: (1) the coefficients of the FP change with the mass range of the galaxy sample; (2) the low luminous and less massive galaxies do not share the same FP of the bright massive galaxies; (3) the scatter around the fitted FP is quite small at any epoch and increases when the mass interval increases; (4) the distribution of galaxies in the FP space strongly depends on the β values (i.e., on the degree of virialization and the star formation rate). Conclusions. The FP is a complex surface that is well approximated by a plane only when galaxies share similar masses and condition of virialization.

1991T-like Supernovae* *This paper includes data gathered with the 6.5 m Magellan telescopes at Las Campanas Observatory, Chile.

Understanding the nature of the luminous 1991T-like supernovae (SNe) is of great importance to SN cosmology as they are likely to have been more common in the early Universe. In this paper, we explore the observational properties of 1991T-like SNe to study their relationship to other luminous, slow-declining Type Ia supernovae (SNe Ia). From the spectroscopic and photometric criteria defined in Phillips et al., we identify 17 1991T-like SNe from the literature. Combining these objects with 10 1991T-like SNe from the Carnegie Supernova Project-II, the spectra, light curves, and colors of these events, along with their host galaxy properties, are examined in detail. We conclude that 1991T-like SNe are closely related in essentially all of their UV, optical, and near-infrared properties—as well as their host galaxy parameters—to the slow-declining subset of Branch core-normal SNe and to the intermediate 1999aa-like events, forming a continuum of luminous SNe Ia. The overriding difference between these three subgroups appears to be the extent to which 56Ni mixes into the ejecta, producing the premaximum spectra dominated by Fe iii absorption, the broader UV light curves, and the higher luminosities that characterize the 1991T-like events. Nevertheless, the association of 1991T-like SNe with the rare Type Ia circumstellar material SNe would seem to run counter to this hypothesis, in which case 1991T-like events may form a separate subclass of SNe Ia, possibly arising from single-degenerate progenitor systems.

Eccentricity evolution of PTA sources from cosmological initial conditions

ABSTRACT Recent results from pulsar timing arrays (PTAs) show evidence for a gravitational wave background (GWB) consistent with a population of unresolved supermassive black hole (SMBH) binaries (BHBs). While the data do not yet constrain the slope of the spectrum, this appears to flatten at the lowest frequencies, deviating from the power-law shape expected for circular binaries evolving solely due to gravitational wave (GW) emission. Interestingly, such flattening can be explained with a population of eccentric rather than circular binaries. The eccentricity of BHBs is notoriously difficult to predict based simply on the parameters of the host galaxies and the initial galactic orbit, as it is subject to stochastic effects. We study the evolution of the eccentricity of BHBs formed in galactic mergers with cosmological initial conditions from pairing to coalescence, with a focus on potential PTA sources. We select galactic mergers from the IllustrisTNG100-1 simulation and re-simulate them at high resolution with the N-body code griffin down to binary separations of the order of a parsec. We then estimate coalescence time-scales with a semi-analytical model of the evolution under the effects of GW emission and stellar hardening. We find that most mergers in IllustrisTNG100-1 occur on highly eccentric orbits, and that the eccentricity of BHBs at binary formation correlates with the initial eccentricity of the merger, if this is no larger than approximately 0.9. For extremely eccentric mergers, the binaries tend to form with modest eccentricities. We discuss the implications of these results on the interpretation of the observed GWB.

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.

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 PSF smoothing effect on concentration-related parameters of high-redshift galaxies in HST and JWST

Aims. We performed a comprehensive investigation of the PSF smoothing effect on the measurement of concentration-related parameters (C, Gini, and M20) of high-redshift galaxies in the HST and JWST surveys. Methods. Our sample contains massive galaxies (109.5 M⊙ ≤ M* ≤ 1011.5 M⊙) from the CANDELS/EGS survey (at redshift 0 < z < 2), and the CEERS survey (at redshift 1 < z < 3). The non-parametric concentration-related parameters (R20, R80, C, Gini, and M20) and the model-dependent parameters (n and Re) of these galaxies were derived from Statmorph and GALFIT, respectively. The best-fit Sérsic index (n) derived from image modelling is generally robust against the PSF smoothing effect and can be used to describe the intrinsic light distribution of galaxies. On the other hand, the concentration-related parameters are significantly affected by the PSF smoothing effect since they are directly calculated from the pixels of galaxy images. We tried to evaluate the PSF smoothing effect by comparing the concentration-related parameters to the Sérsic index in both observations and mock images. Results. We find that the concentration index is generally underestimated, especially for smaller galaxies with a higher Sérsic index (eventually converging to the concentration index of the PSF). However, galaxies with a lower Sérsic index (n ≤ 1) or larger relative size (Re/FWHM > 3) are less affected by the PSF smoothing effect. Tests with idealised mock images reveal that overestimating the measured R20/Re ratio leads to underestimating the concentration index C. Another commonly used concentration index C59, derived from R50 and R90 values, is less affected by the PSF. The Gini coefficient and the absolute M20 statistic also show a similar behaviour as the concentration index. Caution should be taken for the possible correction of the concentration-related parameters, where both the relative size and the Sérsic index of the galaxy are important. We also generated high-redshift artificial images from the low-redshift HST observations and confirm that the traditional correction method that simply adds a single term to the non-parametric indicators of galaxies at higher redshifts is unable to reliably recover the true distribution of the structural parameters. Compared to the HST images, the PSF smoothing is much less severe for images in the CEERS survey (for the short-wavelength filters) due to the much higher spatial resolution. In fact, it is better to use the Sérsic index rather than the non-parametric morphology indicators to trace the light concentration for galaxies at high redshifts. From the single Sérsic modelling of the HST and JWST images, we also confirm that galaxies at higher redshifts are more compact with smaller Re. The low-mass galaxies are more disc-like (n ∼ 1) compared to the high-mass galaxies that are more spheroid dominated (n ∼ 3).

Estimating Galaxy Parameters with Self-organizing Maps and the Effect of Missing Data

The current and upcoming large data volume galaxy surveys require the use of machine-learning techniques to maximize their scientific return. This study explores the use of Self-Organizing Maps (SOMs) to estimate galaxy parameters with a focus on handling cases of missing data and providing realistic probability distribution functions for the parameters. We train an SOM with a simulated mass-limited lightcone assuming a ugrizY JHK s +IRAC data set, mimicking the Hyper Suprime-Cam Deep joint data set. For parameter estimation, we derive SOM likelihood surfaces considering photometric errors to derive total (statistical and systematic) uncertainties. We explore the effects of missing data, including which bands are particularly critical to the accuracy of the derived parameters. We demonstrate that the parameter recovery is significantly better when the missing bands are “filled in” rather than if they are completely omitted. We propose a practical method for such recovery of missing data.