Hyper Suprime-Cam Subaru Research Articles

Dark Matter Halos of Luminous Active Galactic Nuclei from Galaxy–Galaxy Lensing with the HSC Subaru Strategic Program

We assess the dark matter halo masses of luminous active galactic nuclei (AGNs) over the redshift range 0.2–1.2 using galaxy–galaxy lensing based on imaging data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). We measure the weak lensing signal of a sample of 48,907 AGNs constructed using HSC and Wide-field Infrared Survey Explorer photometry. As expected, we find that the lensing mass profile of total AGN sample is consistent with that of massive galaxies ( log(M*/h−2M⊙)∼ 10.61). Surprisingly, the lensing signal remains unchanged when the AGN sample is split into four host galaxy stellar mass bins. Specifically, we find that the excess surface density of AGNs residing in galaxies with high stellar masses significantly differs from that of the control sample. We further fit a halo occupation distribution model to the data to infer the posterior distribution of parameters including the average halo mass. We find that the characteristic halo mass of the full AGN population lies near the knee ( log(Mh/h−1M⊙)=12.0 ) of the stellar-to-halo mass relation (SHMR). Illustrative of the results given above, the halo masses of AGNs residing in host galaxies with high stellar masses (i.e., above the knee of the SHMR) fall below the calibrated SHMR while the halo masses of the low stellar mass sample are more consistent with the established SHMR. These results indicate that massive halos with a higher clustering bias tend to suppress AGN activity, probably due to the lack of available gas.

Cross-correlation of Luminous Red Galaxies with Machine Learning Selected Active Galactic Nuclei in HSC-SSP: Unobscured AGN Residing in More Massive Halos

Active galactic nuclei (AGN) are the signposts of black hole growth, and likely play an important role in galaxy evolution. An outstanding question is whether AGN of different spectral types indicate different evolutionary stages in the coevolution of black holes and galaxies. We present the angular correlation function between an AGN sample selected from Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) optical photometry and Wide-field Infrared Survey Explorer mid-IR photometry and a luminous red galaxy (LRG) sample from HSC-SSP. We investigate AGN clustering strength as a function of luminosity and spectral features across three independent HSC fields totaling ∼600 deg2, for z ∈ 0.6 −1.2 and AGN with L 6 μm > 3 × 1044 erg s−1. There are ∼28,500 AGN and ∼1.5 million LRGs in our primary analysis. We determine the average halo mass for the full AGN sample (M h ≈ 1012.9 h −1 M ⊙), and note that it does not evolve significantly as a function of redshift (over this narrow range) or luminosity. We find that, on average, unobscured AGN (M h ≈ 1013.3 h −1 M ⊙) occupy ∼4.5× more massive halos than obscured AGN (M h ≈ 1012.6 h −1 M ⊙), at 5σ statistical significance using 1D uncertainties, and at 3σ using the full covariance matrix, suggesting a physical difference between unobscured and obscured AGN, beyond the line-of-sight viewing angle. Furthermore, we find evidence for a halo mass dependence on reddening level within the Type I AGN population, which could support the existence of a dust-obscured phase. However, we also find that quite small systematic shifts in the redshift distributions of the AGN sample could explain current and previously observed differences in M h .

A Systematic Search for Rapid Transients in the Subaru HSC-SSP Transient Survey

Recent high-cadence transient surveys have discovered rapid transients whose light-curve timescales are shorter than those of typical supernovae (SNe). In this paper, we present a systematic search for rapid transients at medium-high redshifts among 3381 SNe candidates obtained from the Hyper Suprime-Cam Subaru Strategic Program transient survey. We developed a machine learning classifier to classify the SN candidates into four types (Type Ia, Ibc, II SNe and rapid transients) based on the features derived from the light curves. By applying this classifier to the 3381 SN candidates and by further applying the quality cut, we selected 14 rapid transient samples. They are located at a wide range of redshifts (0.34 ≤ z ≤ 1.85) and show a wide range of the peak absolute magnitude (−17 ≥ M ≥ −22). The event rate of the rapid transients is estimated to be ∼6 × 103 events yr−1 Gpc−3 at z ∼ 0.74, which corresponds to about 2% of the event rate of normal core-collapse SNe at a similar redshift. Based on the luminosity and color evolution, we selected two candidates of Type Ibn SNe at z ∼ 0.75. The event rate of Type Ibn SN candidates is more than 1% of Type Ib SN rate at the same redshift, suggesting that this fraction of massive stars at this redshift range eruptively ejects their He-rich envelope just before the explosions. Also, two objects at z = 1.37 and 1.85 show high luminosities comparable to superluminous SNe. Their event rate is about 10%–25% of superluminous SNe at z ∼ 2.

Survey of gravitationally lensed objects in HSC imaging (SuGOHI) – X. Strong lens finding in the HSC-SSP using convolutional neural networks

ABSTRACT We apply a novel model based on convolutional neural networks (CNN) to identify gravitationally lensed galaxies in multiband imaging of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) Survey. The trained model is applied to a parent sample of 2350 061 galaxies selected from the $\sim$ 800 deg$^2$ Wide area of the HSC-SSP Public Data Release 2. The galaxies in HSC Wide are selected based on stringent pre-selection criteria, such as multiband magnitudes, stellar mass, star formation rate, extendedness limit, photometric redshift range, etc. The trained CNN assigns a score from 0 to 1, with 1 representing lenses and 0 representing non-lenses. Initially, the CNN selects a total of 20 241 cutouts with a score greater than 0.9, but this number is subsequently reduced to 1522 cutouts after removing definite non-lenses for further visual inspection. We discover 43 grade A (definite) and 269 grade B (probable) strong lens candidates, of which 97 are completely new. In addition, we also discover 880 grade C (possible) lens candidates, 289 of which are known systems in the literature. We identify 143 candidates from the known systems of grade C that had higher confidence in previous searches. Our model can also recover 285 candidate galaxy-scale lenses from the Survey of Gravitationally lensed Objects in HSC Imaging (SuGOHI), where a single foreground galaxy acts as the deflector. Even though group-scale and cluster-scale lens systems are not included in the training, a sample of 32 SuGOHI-c (i.e. group/cluster-scale systems) lens candidates is retrieved. Our discoveries will be useful for ongoing and planned spectroscopic surveys, such as the Subaru Prime Focus Spectrograph project, to measure lens and source redshifts in order to enable detailed lens modelling.

Weak-Lensing Shear-Selected Galaxy Clusters from the Hyper Suprime-Cam Subaru Strategic Program: II. Cosmological Constraints from the Cluster Abundance

We present cosmological constraints using the abundance of weak-lensing shear-selected galaxy clusters in the Hyper Suprime-Cam (HSC) Subaru Strategic Program. The clusters are selected on the aperture-mass maps constructed using the three-year (Y3) weak-lensing data with an area of ≈500deg 2, resulting in a sample size of 129 clusters with high signal-to-noise ratios ν≥4.7. Owing to the deep, wide-field, and uniform imaging of the HSC survey, this is by far the largest sample of shear-selected clusters, for which the selection solely depends on gravity and is free from any assumptions about the dynamical state and complex baryon physics. Informed by the optical counterparts, the shear-selected clusters span a redshift range of z≲0.7 with a median of z≈0.3. The lensing sources are securely selected at z≳0.7 with a median of z≈1.3, leading to nearly zero cluster member contamination. We carefully account for (1) the bias in the photometric redshift of sources, (2) the bias and scatter in the weak-lensing mass using a simulation-based calibration, and (3) the measurement uncertainty that is directly estimated on the aperture-mass maps using an injection-based method developed in a companion paper (Chen et al. submitted). In a blind analysis, the fully marginalized posteriors of the cosmological parameters are obtained as Ωm=0.50−0.24+0.28, σ8=0.685−0.088+0.161, Ŝ8≡σ8(Ωm/0.3)0.25=0.835−0.044+0.041, and σ8Ωm/0.3=0.993−0.126+0.084 in a flat ΛCDM model. We compare our cosmological constraints with other studies, including those based on cluster abundances, galaxy-galaxy lensing and clustering, and Cosmic Microwave Background observed by Planck, and find good agreement at levels of ≲2σ. [abridged]

IllustrisTNG in the HSC-SSP: No Shortage of Thin Disk Galaxies in TNG50

We perform a thorough analysis of the projected shapes of nearby galaxies in both observations and cosmological simulations. We implement a forward-modeling approach to overcome the limitations in previous studies, which hinder accurate comparisons between observations and simulations. We measure axis ratios of z = 0 (snapshot 99) TNG50 galaxies from their synthetic Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) images and compare them with those obtained from real HSC-SSP images of a matched galaxy sample. Remarkably, the comparison shows excellent agreement between the observations and the TNG50 simulation, challenging previous claims that ΛCDM models underproduced the abundance of thin galaxies. Specifically, for galaxies with stellar masses 10≤log(M⋆/M☉)≤11.5 , we find ≲0.1σ tensions between the observations and the simulation, a stark contrast to the previously reported ≳10σ tensions. We reveal that low-mass galaxies (M⋆≲109.5M☉) in TNG50 are thicker than their observed counterparts in HSC-SSP and attribute this to the spurious dynamical heating effects that artificially puff up galaxies. We also find that, despite the overall broad agreement, TNG50 galaxies are more concentrated than the HSC-SSP ones at the low- and high-mass end of the stellar mass range of 9.0≤log(M⋆/M☉)≤11.2 and are less concentrated at intermediate stellar masses. But we argue that the higher concentrations of the low-mass TNG50 galaxies are not likely the cause of their thicker/rounder appearances. Our study underscores the critical importance of conducting mock observations of simulations and applying consistent measurement methodologies to facilitate proper comparison with observations.

DiffLense: a conditional diffusion model for super-resolution of gravitational lensing data

Abstract Gravitational lensing data is frequently collected at low resolution due to instrumental limitations and observing conditions. Machine learning-based super-resolution techniques offer a method to enhance the resolution of these images, enabling more precise measurements of lensing effects and a better understanding of the matter distribution in the lensing system. This enhancement can significantly improve our knowledge of the distribution of mass within the lensing galaxy and its environment, as well as the properties of the background source being lensed. Traditional super-resolution techniques typically learn a mapping function from lower-resolution to higher-resolution samples. However, these methods are often constrained by their dependence on optimizing a fixed distance function, which can result in the loss of intricate details crucial for astrophysical analysis. In this work, we introduce DiffLense, a novel super-resolution pipeline based on a conditional diffusion model specifically designed to enhance the resolution of gravitational lensing images obtained from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). Our approach adopts a generative model, leveraging the detailed structural information present in Hubble space telescope (HST) counterparts. The diffusion model, trained to generate HST data, is conditioned on HSC data pre-processed with denoising techniques and thresholding to significantly reduce noise and background interference. This process leads to a more distinct and less overlapping conditional distribution during the model’s training phase. We demonstrate that DiffLense outperforms existing state-of-the-art single-image super-resolution techniques, particularly in retaining the fine details necessary for astrophysical analyses.

Environments around Quasars at z ∼ 3 Revealed by Wide-field Imaging with Subaru HSC and CFHT

We examine the local density environments around 67 quasars at z ∼ 3 by combining the imaging data of Hyper Suprime-Cam Subaru Strategic Program and Canada–France–Hawaii Telescope Large Area U-band Deep Survey over about 20 deg2. Our measurements exploit U-dropout galaxies in the vicinities of quasars taken from the Sloan Digital Sky Survey. We find that the quasars have an indistinguishable surrounding density distribution from the U-dropout galaxies and that three quasars are associated with protocluster candidates within a projected separation of 3′. According to a halo evolutionary model, our results suggest that quasars at this epoch occupy haloes with a typical mass of 1.3−0.9+1.4×1013h−1M⊙ . We also investigate the dependence of the local galaxy overdensity on ultraviolet (UV) luminosities, black hole masses, and proximity zone sizes of the quasars, but no statistically significant correlation was found. Finally, we find that the local density of faint U-dropout galaxies are lower than that of bright U-dropout galaxies within a projected distance of 0.51 ± 0.05 physical Mpc, where the quasar UV radiation is 30 times more intense than background UV radiation. We argue that photoevaporation may suppress galaxy formation at short distances where the quasar UV intensity is strong, even in massive haloes.

DAVOS: Dwarf Active Galactic Nuclei from Variability for the Origins of Seeds: Properties of Variability-selected Active Galactic Nuclei in the COSMOS Field and Expectations for the Rubin Observatory

We study the black hole mass–host galaxy stellar mass relation, M BH–M *, of a sample of z < 4 optically variable active galactic nuclei (AGNs) in the COSMOS field. The parent sample of 491 COSMOS AGNs were identified by optical variability from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) program. Using publicly available catalogs and spectra, we consolidate their spectroscopic redshifts and estimate virial black hole masses using broad-line widths and luminosities. We show that variability searches with deep, high-precision photometry like the HSC-SSP can identity AGNs in low-mass galaxies up to z ∼ 1. However, their black holes are more massive given their host galaxy stellar masses than predicted by the local relation for active galaxies. We report that z ∼ 0.5–4 variability-selected AGNs are meanwhile more consistent with the M BH–M * relation for local inactive early-type galaxies. This result is in agreement with most previous studies of the M BH–M * relation at similar redshifts and indicates that AGNs selected from variability are not intrinsically different from the broad-line Type 1 AGN population at similar luminosities. Our results demonstrate the need for robust black hole and stellar mass estimates for intermediate-mass black hole candidates in low-mass galaxies at similar redshifts to anchor this scaling relation. Assuming that these results do not reflect a selection bias, they appear to be consistent with self-regulated feedback models wherein the central black hole and stars in galaxies grow in tandem.

Automating galaxy morphology classification using k-nearest neighbours and non-parametric statistics

ABSTRACT Morphology is a fundamental property of any galaxy population. It is a major indicator of the physical processes that drive galaxy evolution and in turn the evolution of the entire Universe. Historically, galaxy images were visually classified by trained experts. However, in the era of big data, more efficient techniques are required. In this work, we present a k-nearest neighbours based approach that utilizes non-parametric morphological quantities to classify galaxy morphology in Sloan Digital Sky Survey images. Most previous studies used only a handful of morphological parameters to identify galaxy types. In contrast, we explore 1023 morphological spaces (defined by up to 10 non-parametric statistics) to find the best combination of morphological parameters. Additionally, while most previous studies broadly classified galaxies into early types and late types or ellipticals, spirals, and irregular galaxies, we classify galaxies into 11 morphological types with an average accuracy of ${\sim} 80\!-\!90 \, {{\rm per\, cent}}$ per T-type. Our method is simple, easy to implement, and is robust to varying sizes and compositions of the training and test samples. Preliminary results on the performance of our technique on deeper images from the Hyper Suprime-Cam Subaru Strategic Survey reveal that an extension of our method to modern surveys with better imaging capabilities might be possible.

Luminosity Functions of the Host Galaxies of Supernova

We present the luminosity functions and stellar mass functions of supernova (SN) host galaxies and test if they differ from the functions of normal field galaxies. We utilize homogeneous samples consisting of 273 SNe Ia (z ≤ 0.3) and 44 core-collapse (CC) SNe (z ≤ 0.1) from the Sloan Digital Sky Survey II Supernova Survey and the high-signal-to-noise-ratio photometry of galaxies from the Hyper Suprime-Cam Subaru Strategic Program. SN hosts are classified into star-forming and passive galaxy groups based on the spectral energy distribution fitting. We find that the SN host luminosity functions and stellar mass functions deviate from those of normal field galaxies. Star-forming galaxies dominate the low-mass end of the SN Ia host mass function, while passive galaxies dominate the high-mass end. CC SNe are predominantly hosted by star-forming galaxies. In addition, intermediate-mass hosts produce CC SNe with the highest efficiency, while the efficiency of producing SNe Ia monotonically increases as the hosts become more massive. Furthermore, we derive the pseudo mass normalized SN rates (pSNuM) based on the mass functions. We find that the star-forming component of pSNuM Ia is less sensitive to the changes in stellar mass, in comparison with the total rate. The behavior of pSNuM CC suggests that the CC rate is proportional to the star-forming rate.

PAC. V. The Roles of Mass and Environment in the Quenching of Galaxies

The roles that mass and environment play in galaxy quenching are still under debate. Leveraging the Photometric objects Around Cosmic webs method, we analyze the excess surface distribution n¯2wp(rp) of photometric galaxies in different colors (rest-frame u − r) within the stellar mass range of 109.0 ∼ 1011.0 M ⊙ around spectroscopic massive central galaxies (1010.9 ∼ 1011.7 M ⊙) at the redshift interval of 0 < z s < 0.7, utilizing data from the Hyper Suprime-Cam Subaru Strategic Program and the spectroscopic samples of Slogan Digital Sky Survey (i.e., Main, LOWZ, and CMASS samples). We find that both mass and environmental quenching contribute to the evolution of companion galaxies. To isolate the environmental effect, we quantify the quenched fraction excess (QFE) of companion galaxies encircling massive central galaxies within 0.01h −1 Mpc < r p < 20h −1 Mpc, representing the surplus quenched fraction relative to the average. We find that the high-density halo environment affects star formation quenching up to about three times the virial radius, and this effect becomes stronger at lower redshift. We also find that even after being scaled by the virial radius, the environmental quenching efficiency is higher for more massive halos or for companion galaxies of higher stellar mass, though the trends are quite weak. We present a fitting formula that comprehensively captures the QFE across central and companion stellar mass bins, halo-centric distance bins, and redshift bins, offering a valuable tool for constraining galaxy formation models. Furthermore, we have made a quantitative comparison with IllustrisTNG that underscores some important differences, particularly in the excessive quenching of low-mass companion galaxies (<109.5 M ⊙) in the simulation.

Galaxy merger challenge: A comparison study between machine learning-based detection methods

Aims. Various galaxy merger detection methods have been applied to diverse datasets. However, it is difficult to understand how they compare. Our aim is to benchmark the relative performance of merger detection methods based on machine learning (ML). Methods. We explore six leading ML methods using three main datasets. The first dataset consists of mock observations from the IllustrisTNG simulations, which acts as the training data and allows us to quantify the performance metrics of the detection methods. The second dataset consists of mock observations from the Horizon-AGN simulations, introduced to evaluate the performance of classifiers trained on different, but comparable data to those employed for training. The third dataset is composed of real observations from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) survey. We also compare mergers and non-mergers detected by the different methods with a subset of HSC-SSP visually identified galaxies. Results. For the simplest binary classification task (i.e. mergers vs. non-mergers), all six methods perform reasonably well in the domain of the training data. At the lowest redshift explored 0.1 &lt; ɀ &lt; 0.3, precision and recall generally range between ~70% and 80%, both of which decrease with increasing ɀ as expected (by ~5% for precision and ~10% for recall at the highest ɀ explored 0.76 &lt; ɀ &lt; 1.0). When transferred to a different domain, the precision of all classifiers is only slightly reduced, but the recall is significantly worse (by ~20–40% depending on the method). Zoobot offers the best overall performance in terms of precision and F1 score. When applied to real HSC observations, different methods agree well with visual labels of clear mergers, but can differ by more than an order of magnitude in predicting the overall fraction of major mergers. For the more challenging multi-class classification task to distinguish between pre-mergers, ongoing-mergers, and post-mergers, none of the methods in their current set-ups offer good performance, which could be partly due to the limitations in resolution and the depth of the data. In particular, ongoing-mergers and post-mergers are much more difficult to classify than pre-mergers. With the advent of better quality data (e.g. from JWST and Euclid), it is of great importance to improve our ability to detect mergers and distinguish between merger stages.

The Relationship of Supermassive Black Holes and Host Galaxies at z < 4 in the Deep Optical Variability-selected Active Galactic Nuclei Sample in the COSMOS Field

We present the study on the relationship between supermassive black holes (SMBHs) and their host galaxies using our variability-selected active galactic nuclei (AGNs) sample (i AB ≤ 25.9 and z ≤ 4.5) constructed from the Hyper Suprime-Cam Subaru Strategic Program Ultradeep survey in the COSMOS field. We estimated the black hole (BH) mass (M BH = 105.5−10 M ⊙) based on the single-epoch virial method and the total stellar mass (M star = 1010−12 M ⊙) by separating the AGN component with spectral energy distribution fitting. We found that the redshift evolution of the BH–stellar mass ratio (M BH/M star) depends on the M BH, which is caused by no significant correlation between M BH and M star. Variable AGNs with massive SMBHs (M BH > 109 M ⊙) at 1.5 < z < 3 show considerably higher BH–stellar mass ratios (> ∼1%) than the BH–bulge ratios (M BH/M bulge) observed in the local Universe for the same BH range. This implies that there is a typical growth path of massive SMBHs, which is faster than the formation of the bulge component as final products seen in the present day. For the low-mass SMBHs (M BH < 108 M ⊙) at 0.5 < z < 3, on the other hand, variable AGNs show similar BH–stellar mass ratios with the local objects (∼0.1%), but smaller than those observed at z > 4. We interpret that host galaxies harboring less massive SMBHs at intermediate redshift have already acquired sufficient stellar mass, although high-z galaxies are still in the early stage of galaxy formation relative to those at the intermediate/local Universe.

Final results of the search for new Milky Way satellites in the Hyper Suprime-Cam Subaru Strategic Program survey: Discovery of two more candidates

Abstract We present the final results of our search for new Milky Way (MW) satellites using the data from the Hyper Suprime-Cam (HSC) Subaru Strategic Program (SSP) survey over ∼1140 deg2. In addition to three candidates that we have already reported, we have identified two new MW satellite candidates in the constellations of Sextans, at a heliocentric distance of D⊙ ≃ 126 kpc, and Virgo, at D⊙ ≃ 151 kpc, named Sextans II and Virgo III, respectively. Their luminosities (Sext II: MV ≃ −3.9 mag; Vir III: MV ≃ −2.7 mag) and half-light radii (Sext II: rh ≃ 154 pc; Vir III: rh ≃ 44 pc) place them in the region of size–luminosity space of ultra-faint dwarf galaxies (UFDs). Including four previously known satellites, there are a total of nine satellites in the HSC-SSP footprint. This discovery rate of UFDs is much higher than that predicted from the recent models for the expected population of MW satellites in the framework of cold dark matter models, thereby suggesting that we encounter a too many satellites problem. Possible solutions to settle this tension are also discussed.

Detecting galaxy tidal features using self-supervised representation learning

ABSTRACT Low surface brightness substructures around galaxies, known as tidal features, are a valuable tool in the detection of past or ongoing galaxy mergers, and their properties can answer questions about the progenitor galaxies involved in the interactions. The assembly of current tidal feature samples is primarily achieved using visual classification, making it difficult to construct large samples and draw accurate and statistically robust conclusions about the galaxy evolution process. With upcoming large optical imaging surveys such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time, predicted to observe billions of galaxies, it is imperative that we refine our methods of detecting and classifying samples of merging galaxies. This paper presents promising results from a self-supervised machine learning model, trained on data from the Ultradeep layer of the Hyper Suprime-Cam Subaru Strategic Program optical imaging survey, designed to automate the detection of tidal features. We find that self-supervised models are capable of detecting tidal features, and that our model outperforms previous automated tidal feature detection methods, including a fully supervised model. An earlier method applied to real galaxy images achieved 76 per cent completeness for 22 per cent contamination, while our model achieves considerably higher (96 per cent) completeness for the same level of contamination. We emphasize a number of advantages of self-supervised models over fully supervised models including maintaining excellent performance when using only 50 labelled examples for training, and the ability to perform similarity searches using a single example of a galaxy with tidal features.

HaloFlow. I. Neural Inference of Halo Mass from Galaxy Photometry and Morphology

We present HaloFlow, a new machine-learning approach for inferring the mass of host dark matter halos, M h , from the photometry and morphology of galaxies (https://github.com/changhoonhahn/haloflow/). HaloFlow uses simulation-based inference with normalizing flows to conduct rigorous Bayesian inference. It is trained on state-of-the-art synthetic galaxy images from Bottrell et al. that are constructed from the IllustrisTNG hydrodynamic simulation and include realistic effects of the Hyper Suprime-Cam Subaru Strategy Program observations. We design HaloFlow to infer M h and stellar mass, M *, using grizy band magnitudes, morphological properties quantifying characteristic size, concentration and asymmetry, total measured satellite luminosity, and number of satellites. We demonstrate that HaloFlow infers accurate and unbiased posteriors of M h . Furthermore, we quantify the full information content in the photometric observations of galaxies in constraining M h . With magnitudes alone, we infer M h with σlogMh∼0.115 and 0.182 dex for field and group galaxies. Including morphological properties significantly improves the precision of M h constraints, as does total satellite luminosity: σlogMh∼0.095 and 0.132 dex. Compared to the standard approach using the stellar-to-halo mass relation, we improve M h constraints by ∼40%. In subsequent papers, we will validate and calibrate HaloFlow with galaxy–galaxy lensing measurements on real observational data.

Size–mass relation of the brightest cluster galaxies at z ∼ 1

ABSTRACT We present the size–mass relation of the brightest cluster galaxies (BCGs) at 0.1 &amp;lt; z &amp;lt; 1.4 using the imaging data obtained by the Hyper Suprime-Cam Subaru Strategic Program. Our sample consists of 471 photometrically selected BCGs with stellar mass logM*/M⊙ = 11–12. We measure the size of the BCGs using i-band imaging and model fits based on a single Sérsic light profile. Stellar masses are determined through spectral energy distribution fitting using Sérsic-modelled photometry data across five optical bands (grizy). The size–mass scaling relation of BCGs is $r_\mathrm{ e}\propto M_*^{0.73-1.00}$ at z &amp;lt; 1, with a slope that does not evolve significantly. The slope of the size–mass relation for BCGs is steeper than other non-BCG galaxies, which implies that BCGs are a special galaxy population. The size of BCGs at a given stellar mass evolves rapidly as ∝ (1 + z)−1.58 ± 0.13 and increases with redshift by a factor of 2.5 from z ∼ 1.2 to z ∼ 0.2. The rapid size growth is in agreement with semi-analytical model results, indicating that the BCG growth is dominated by frequent minor mergers. Furthermore, we explore the size–mass relationship of BCGs with respect to the halo mass of the cluster and find there is no significant correlation, which might imply that a dependence on the environment predominantly affects the outer envelope of the BCGs.

The Massive and Distant Clusters of WISE Survey 2: Equatorial First Data Release

The Massive and Distant Clusters of WISE Survey 2 (MaDCoWS2) is a new survey designed as the successor of the original MaDCoWS survey. MaDCoWS2 improves upon its predecessor by using deeper optical and infrared data and a more powerful detection algorithm (PZWav). As input to the search, we use grz photometry from the DECam Legacy Survey (DECaLS) in combination with W1 and W2 photometry from the CatWISE2020 catalog to derive the photometric redshifts with full redshift probability distribution functions for Wide-field Infrared Survey Explorer (WISE)-selected galaxies. Cluster candidates are then detected using the PZWav algorithm to find three-dimensional galaxy overdensities from the sky positions and photometric redshifts. This paper provides the first MaDCoWS2 data release, covering 1461 (1838 without masking) deg2 centered on the Hyper-SuprimeCam Subaru Strategic Program equatorial fields. Within this region, we derive a catalog of 22,970 galaxy cluster candidates detected at a signal-to-noise ratio (S/N) > 5. These clusters span the redshift range 0.1 < z < 2, including 1312 candidates at z > 1.5. We compare MaDCoWS2 to six existing catalogs in the area. We rediscover 60%–92% of the clusters in these surveys at S/N > 5. The medians of the absolute redshift offset are <0.02 relative to these surveys, while the standard deviations are less than 0.06. The median offsets between the detection position from MaDCoWS2 and other surveys are less than 0.25 Mpc. We quantify the relation between S/N and gas mass, total mass, luminosity, and richness from other surveys using a redshift-dependent power law relation. We find that the S/N-richness relation exhibits the lowest scatter.

The Hyper Suprime-Cam extended Point Spread Functions and applications

Abstract We present extended point spread function (PSF) models for the Hyper Suprime-Cam Subaru Strategic Program Public Data Release 3 (HSC-SSP PDR3) in all g,r,i,Z and Y-bands. Due to its 8.2m primary mirror and long exposure periods, HSC combines deep images with wide-field coverage. Both properties make HSC one of the most suitable observing facilities for low surface brightness (LSB) studies, which are particularly sensitive to the PSF. By applying a median stacking technique of point-like sources with different brightness, we show how to construct the HSC-SSP PDR3 PSF models to an extent of R ∼ 5.6 arcmin. These models are appropriate for the HSC-PDR3 intermediate-state data which do not have applied the final aggressive background subtraction. The intermediate-state data is especially stored for users interested in large extended objects, where our new PSFs provide them with a crucial tool to characterise LSB properties at large angles. We demonstrate that our HSC PSFs behave reasonably in two scenarios. In the first one, we generate 2-D models of a bright star, showing no evidence of residual structures across the five bands. In the second scenario, we recreate the PSF-scattered light on mock images with special consideration of the effect of this additional flux on LSB measurements. We find that, despite the well-behaved nature of the HSC-PDR3 PSFs, there is a non-negligible impact on the faint light present in the mock images. This impact could lead to incorrect LSB measurements if a proper star subtraction is not applied.