Wide-field Survey Research Articles

First Results from the Dragonfly Ultrawide Survey: The Largest 11 Quenched Diffuse Dwarf Galaxies in 3100 deg2 with Spectroscopic Confirmation

The Dragonfly Telephoto Array employs a unique design to detect very large and diffuse galaxies that might be missed with conventional telescopes. The Dragonfly Ultrawide Survey (DFUWS) is a new wide-field survey that will cover 10,000 deg2 of the northern sky, and it provides an ideal data set to find these large diffuse galaxies. From 3100 deg2 of DFUWS data, we identified 11 large, low surface brightness galaxies as a pilot sample for spectroscopic follow-up. These are the largest galaxies in the examined area that appear smooth and isolated, with effective radii of 12″–27″. Eight are below 24 magarcsec−2 in central g-band surface brightness. Keck Cosmic Web Imager spectra of the diffuse light show that all 11 galaxies in this sample are quiescent, and seven qualify as ultradiffuse galaxies (UDGs). Eight galaxies have distances between 15 and 30 Mpc, while the other three are in the Pegasus cluster at 50 Mpc. Their spectra show evidence of a ∼1 Gyr old stellar population in addition to an even older stellar population. The intermediate-age component is present in group and satellite galaxies but not in the Pegasus cluster UDGs. All galaxies in this sample are detected in both Dragonfly and Legacy imaging, and the sample partially overlaps with existing UDG catalogs. This pilot sample provides an excellent training set for our analysis of the upcoming full 10,000 deg2 DFUWS data, from which we may expect to discover even larger, previously unknown galaxies.

Luminosity Evolution of the Hot Gas in Normal Galaxies from the Near Universe to z = 0.5

We explore the evolution of the ∼107 K hot gas in normal galaxies out to redshift = 0.5 (lookback time = 5 Gyr), using X-ray luminosity functions (XLFs) built from a sample of 575 normal galaxies with z < 0.6 detected in five high-galactic-latitude Chandra wide-field surveys. After estimating the emission due to the hot gas component (reducing the sample to ∼400 galaxies), we compared the XLF in three redshift bins (z = 0.1, 0.3, and 0.5), finding increases in the number of galaxies per unit comoving volume from z = 0.1 to 0.3 and then from z = 0.3 to 0.5. These XLF changes suggest a significant (∼5σ) X-ray luminosity evolution of the hot gas, with L X,GAS decreasing by a factor of 6–10 in the last 5 Gyr (from z = 0.5 to 0.1). The relative abundance of L X,GAS ∼ 1041 erg s−1 galaxies at higher z suggests that high-z, moderate-L X,GAS galaxies may be the optimal target to solve the missing baryon problem. In early-type galaxies, this observational trend is qualitatively consistent with (but larger than) the expected time-dependent mass-loss rate in cooling flow models without active galactic nucleus feedback. In late-type galaxies, the observational trend is also qualitatively consistent with (but larger than) the effect of the z-dependent star formation rate.

A two-minute burst of highly polarized radio emission originating from low Galactic latitude

ABSTRACT Several sources of repeating coherent bursts of radio emission with periods of many minutes have now been reported in the literature. These ‘ultralong period’ (ULP) sources have no clear multiwavelength counterparts and challenge canonical pulsar emission models, leading to debate regarding their nature. In this work, we report the discovery of a bright, highly polarized burst of radio emission at low Galactic latitude as part of a wide-field survey for transient and variable radio sources. ASKAP J175534.9$-$252749.1 does not appear to repeat, with only a single intense two-minute $\sim$200-mJy burst detected from 60 h of observations. The burst morphology and polarization properties are comparable to those of classical pulsars but the duration is more than one hundred times longer, analogous to ULPs. Combined with the existing ULP population, this suggests that these sources have a strong Galactic latitude dependence and hints at an unexplored population of transient and variable radio sources in the thin disc of the Milky Way. The resemblance of this burst with both ULPs and pulsars calls for a unified coherent emission model for objects with spin periods from milliseconds to tens of minutes. However, whether or not these are all neutron stars or have the same underlying power source remains open for debate.

Superphot+: Real-time Fitting and Classification of Supernova Light Curves

Photometric classifications of supernova (SN) light curves have become necessary to utilize the full potential of large samples of observations obtained from wide-field photometric surveys, such as the Zwicky Transient Facility (ZTF) and the Vera C. Rubin Observatory. Here, we present a photometric classifier for SN light curves that does not rely on redshift information and still maintains comparable accuracy to redshift-dependent classifiers. Our new package, Superphot+, uses a parametric model to extract meaningful features from multiband SN light curves. We train a gradient-boosted machine with fit parameters from 6061 ZTF SNe that pass data quality cuts and are spectroscopically classified as one of five classes: SN Ia, SN II, SN Ib/c, SN IIn, and SLSN-I. Without redshift information, our classifier yields a class-averaged F 1-score of 0.61 ± 0.02 and a total accuracy of 0.83 ± 0.01. Including redshift information improves these metrics to 0.71 ± 0.02 and 0.88 ± 0.01, respectively. We assign new class probabilities to 3558 ZTF transients that show SN-like characteristics (based on the ALeRCE Broker light-curve and stamp classifiers) but lack spectroscopic classifications. Finally, we compare our predicted SN labels with those generated by the ALeRCE light-curve classifier, finding that the two classifiers agree on photometric labels for 82% ± 2% of light curves with spectroscopic labels and 72% ± 0% of light curves without spectroscopic labels. Superphot+ is currently classifying ZTF SNe in real time via the ANTARES Broker, and is designed for simple adaptation to six-band Rubin light curves in the future.

Self-supervised learning on MeerKAT wide-field continuum images

Context. Self-supervised learning (SSL) applied to natural images has demonstrated a remarkable ability to learn meaningful, low-dimension representations without labels, resulting in models that are adaptable to many different tasks. Until now, applications of SSL to astronomical images have been limited to Galaxy Zoo datasets, which require a significant amount of preprocessing to prepare sparse images centered on a single galaxy. With wide-field survey instruments at the forefront of the Square Kilometer Array (SKA) era, this approach to gathering training data is impractical. Aims. We demonstrate that continuum images from surveys such as the MeerKAT Galactic Cluster Legacy Survey (MGCLS) can be successfully used with SSL, without extracting single-galaxy cutouts. Methods. Using the SSL framework DINO, we experimented with various preprocessing steps, augmentations, and architectures to determine the optimal approach for this data. We trained both ResNet50 and Vision Transformer (ViT) backbones. Results. Our models match state-of-the-art results (trained on Radio Galaxy Zoo) for FRI/FRII morphology classification. Furthermore, they predict the number of compact sources via linear regression with much higher accuracy. Open-source foundation models trained on natural images such as DINOv2 also excel at simple FRI/FRII classification; the advantage of domain-specific backbones is much smaller models trained on far less data. Smaller models are more efficient to fine-tune, and doing so results in a similar performance between our models, the state-of-the-art, and open-source models on multi-class morphology classification. Conclusions. Using source-rich crops from wide-field images to train multi-purpose models is an easily scalable approach that significantly reduces data preparation time. For the tasks evaluated in this work, twenty thousand crops is sufficient training data for models that produce results similar to state-of-the-art. In the future, complex tasks like source detection and characterization, together with domain-specific tasks, ought to demonstrate the true advantages of training models with radio astronomy data over natural-image foundation models.

Photometric redshifts probability density estimation from recurrent neural networks in the DECam local volume exploration survey data release 2

Photometric wide-field surveys are imaging the sky in unprecedented detail. These surveys face a significant challenge in efficiently estimating galactic photometric redshifts while accurately quantifying associated uncertainties. In this work, we address this challenge by exploring the estimation of Probability Density Functions (PDFs) for the photometric redshifts of galaxies across a vast area of 17,000 square degrees, encompassing objects with a median 5σ point-source depth of g= 24.3, r=23.9, i= 23.5, and z= 22.8 mag. Our approach uses deep learning, specifically integrating a Recurrent Neural Network architecture with a Mixture Density Network, to leverage magnitudes and colors as input features for constructing photometric redshift PDFs across the whole DECam Local Volume Exploration (DELVE) survey sky footprint. Subsequently, we rigorously evaluate the reliability and robustness of our estimation methodology, gauging its performance against other well-established machine learning methods to ensure the quality of our redshift estimations. Our best results constrain photometric redshifts with the bias of −0.0013, a scatter of 0.0293, and an outlier fraction of 5.1%. These point estimates are accompanied by well-calibrated PDFs evaluated using diagnostic tools such as Probability Integral Transform and Odds distribution. We also address the problem of the accessibility of PDFs in terms of disk space storage and the time demand required to generate their corresponding parameters.We present a novel Autoencoder model that reduces the size of PDF parameter arrays to one-sixth of their original length, significantly decreasing the time required for PDF generation to one-eighth of the time needed when generating PDFs directly from the magnitudes.

Uncovering tidal treasures: automated classification of faint tidal features in DECaLS data

ABSTRACT Tidal features are a key observable prediction of the hierarchical model of galaxy formation and contain a wealth of information about the properties and history of a galaxy. Modern wide-field surveys such as LSST and Euclid will revolutionize the study of tidal features. However, the volume of data will prohibit visual inspection to identify features, thereby motivating a need to develop automated detection methods. This paper presents a visual classification of ∼2000 galaxies from the DECaLS survey into different tidal feature categories: arms, streams, shells, and diffuse. We trained a convolutional neural network (CNN) to reproduce the assigned visual classifications using these labels. Evaluated on a testing set where galaxies with tidal features were outnumbered $\sim 1:10$, our network performed very well and retrieved a median $98.7\pm 0.3$, $99.1\pm 0.5$, $97.0\pm 0.8$, and $99.4^{+0.2}_{-0.6}$ per cent of the actual instances of arm, stream, shell, and diffuse features respectively for just 20 per cent contamination. A modified version that identified galaxies with any feature against those without achieved scores of $0.981^{+0.001}_{-0.003}$, $0.834^{+0.014}_{-0.026}$, $0.974^{+0.008}_{-0.004}$, and $0.900^{+0.073}_{-0.015}$ for the accuracy, precision, recall, and F1 metrics, respectively. We used a gradient-weighted class activation mapping analysis to highlight important regions on images for a given classification to verify the network was classifying the galaxies correctly. This is the first demonstration of using CNNs to classify tidal features into sub-categories, and it will pave the way for the identification of different categories of tidal features in the vast samples of galaxies that forthcoming wide-field surveys will deliver.

Selection functions of strong lens finding neural networks

ABSTRACT We show that convolution neural networks (CNNs) trained to find strong gravitational lens systems are biased towards systems with larger Einstein radii and large concentrated sources. This selection function is key to fully realizing the potential of the large samples of strong gravitational lens systems that will be found in upcoming wide-field surveys. In this paper, we use a CNN and three training data sets to quantify the network selection function and its implication for the many scientific applications of strong gravitational lensing. We use CNNs with similar architecture as is commonly found in the literature. The networks preferentially select systems with larger Einstein radii and larger sources with more concentrated source-light distributions. Increasing the detection significance threshold to 12$\sigma$ from 8$\sigma$ results in 50 per cent of the selected strong lens systems having Einstein radii $\theta _\mathrm{E}$$\ge$ 1.04 arcsec from $\theta _\mathrm{E}$$\ge$ 0.879 arcsec, source radii $R_S$$\ge$ 0.194 arcsec from $R_S$$\ge$ 0.178 arcsec, and source Sérsic indices $n_{\mathrm{Sc}}^{\mathrm{S}}$$\ge$ 2.62 from $n_{\mathrm{Sc}}^{\mathrm{S}}$$\ge$ 2.55. The model trained to find lensed quasars shows a stronger preference for higher lens ellipticities than those trained to find lensed galaxies. The selection function is independent of the slope of the power law of the mass profiles, hence measurements of this quantity will be unaffected. The lens finder selection function reinforces that of the lensing cross-section, and thus we expect our findings to be a general result for all galaxy–galaxy and galaxy–quasar lens finding neural networks.

XLSSC 122 caught in the act of growing up

Context. How protoclusters evolved from sparse galaxy overdensities to mature galaxy clusters is still not well understood. In this context, detecting and characterizing the hot intracluster medium (ICM) at high redshifts (z ∼ 2) is key to understanding how the continuous accretion from the filamentary large-scale structure and the mergers along it impact the first phases of cluster formation. Aims. We study the dynamical state and morphology of the z = 1.98 galaxy cluster XLSSC 122 with high-resolution observations (≈5″) of the ICM through the Sunyaev–Zeldovich (SZ) effect. XLSSC 122 is the highest redshift optically confirmed galaxy cluster found in an unbiased, widefield survey. Methods. Via Bayesian forward modeling, we mapped the ICM on scales from the virial radius down to the core of the cluster. To constrain such a broad range of spatial scales, we employed a new technique that jointly forward-models parametric descriptions of the pressure distribution to interferometric ACA and ALMA observations and multiband imaging data from ACT. Results. We detect the SZ effect with 11σ significance in the ALMA+ACA observations and find a flattened inner pressure profile that is consistent with a noncool core classification with a significance of ≥3σ. In contrast to the previous works, we find better agreement between the SZ effect signal and the X-ray emission as well as the cluster member distribution. Further, XLSSC 122 exhibits an excess of SZ flux in the south of the cluster where no X-ray emission is detected. By reconstructing the interferometric observations and modeling in the uv-plane, we obtain a tentative detection of an infalling group or filamentary-like structure in the southeast that is believed to boost and heat up the ICM while the density of the gas is still low. In addition to characterizing the dynamical state of the cluster, we provide an improved SZ mass estimate M500,c = 1.66−0.20+0.23 × 1014 M⊙. Conclusions. Altogether, the observations indicate that we see XLSSC 122 in a dynamic phase of cluster formation while a large reservoir of gas is already thermalized.

Comparing extragalactic megahertz-peaked spectrum and gigahertz-peaked spectrum sources

Recent sensitive wide-field radio surveys, such as the LOFAR Two-metre Sky Survey (LoTSS), the LOFAR LBA Sky Survey (LoLSS), and the Very Large Array Sky Survey (VLASS), enable the selection of statistically large samples of peaked spectrum (PS) sources. PS sources are radio sources that have a peak in their radio continuum spectrum and are observed to be compact. They are often considered to be the precursors to large radio galaxies. We present a sample of 8032 gigahertz-peaked spectrum (GPS) sources with spectral turnovers near 1400 MHz, and a sample of 506 megahertz-peaked spectrum (MPS) sources with turnovers near 144 MHz. Our GPS sample is over five times larger than any previously known sample of PS sources. These large sample sizes allow us to make a robust comparison between GPS sources and MPS sources, such that we can investigate the differences between these types of sources, and study their lifetimes. The shape of the source counts of both samples match that of the general radio-loud active galactic nuclei (AGN) samples, scaled down by a factor 44 ± 2 for the MPS sample, and a factor 28 ± 1 for the GPS sample. Assuming no cosmological evolution, these offsets imply that both MPS and GPS sources have a shorter duration than general radio-loud AGN, with MPS sources having an ≈1.6 times shorter lifespan than GPS sources. The shorter duration of MPS sources relative to GPS sources can be explained by the transition between GPS and MPS sources coinciding with the jet breakout phase of PS sources, such that GPS sources traverse through the surrounding medium at a lower speed than MPS sources. Such evolution has been observed in simulations of PS source evolution.

PineTree : A generative, fast, and differentiable halo model for wide-field galaxy surveys

PineTree : A generative, fast, and differentiable halo model for wide-field galaxy surveys

Site selection for the second Flyeye telescope: A simulation study for optimizing near-earth object discovery

The European Space Agency (ESA) is developing a network of wide-field survey telescopes, named Flyeye, to improve the discovery of Near-Earth Objects (NEOs). The first telescope in the network will be located in the Northern Hemisphere on Mount Mufara (Italy), and a second Flyeye telescope, featuring increased detection capabilities, has just started the critical design phase.The potential location for the second Flyeye telescope is investigated by performing simulations of NEOs on impacting trajectories. Approximately 3000 impacting asteroids of two absolute magnitudes (H = 25 and H = 28) were propagated and tested for detectability by major existing surveys (Catalina, Pan-STARRS, ATLAS), the upcoming Vera Rubin Observatory (LSST), and possible Flyeye locations. Chile, South Africa, and a second facility in the Northern Hemisphere were considered. For each observatory, their past or planned pointing strategies were taken into account in the simulation.Before LSST deployment, a single Flyeye in the Southern Hemisphere performs similarly to a telescope in the Northern Hemisphere. When combined, having one telescope in the north and one in the south maximizes detections and number of unique objects detected. After LSST, southern and northern Flyeye telescopes remain complementary. Overall, simulations show that a second Flyeye in the south complements a Flyeye telescope in the north both before and after LSST. A Flyeye located at La Silla would take advantage of the excellent atmospheric conditions, while allowing a balance of assets across hemispheres.

Probing Thermal Electrons in Gamma-Ray Burst Afterglows

Particle-in-cell simulations have unveiled that shock-accelerated electrons do not follow a pure power-law distribution, but have an additional low-energy “thermal” part, which owns a considerable portion of the total energy of the electrons. Investigating the effects of these thermal electrons on gamma-ray burst (GRB) afterglows may provide valuable insights into the particle acceleration mechanisms. We solve the continuity equation of electrons in energy space, from which multiwavelength afterglows are derived by incorporating processes including synchrotron radiation, synchrotron self-absorption, synchrotron self-Compton scattering, and γ–γ annihilation. First, there is an underlying positive correlation between the temporal and spectral indices due to the cooling of electrons. Moreover, thermal electrons result in simultaneous nonmonotonic variations of both the spectral and temporal indices at multiple wavelengths, which could be individually recorded by the 2.5 m Wide Field Survey Telescope and Vera Rubin Observatory Legacy Survey of Space and Time (LSST). The thermal electrons could also be diagnosed using afterglow spectra from synergistic observations in the optical (with LSST) and X-ray (with the Microchannel X-ray Telescope on board the Space Variable Objects Monitor) bands. Finally, we use Monte Carlo simulations to obtain the distribution of the peak flux ratio (R X) between the soft and hard X-rays, and of the time delay (Δt) between the peak times of the soft X-ray and optical light curves. The thermal electrons significantly raise the upper limits of both R X and Δt. Thus, the distribution of GRB afterglows with thermal electrons is more scattered in the R X−Δt plane.

Breaking the mass-sheet degeneracy in strong lensing mass modelling with weak lensing observations

ABSTRACT The Hubble constant ($H_0$), a crucial parameter in cosmology, quantifies the expansion rate of the universe so its precise measurement is important to understand the fundamental dynamics of our evolving universe. One of the major limitations of measuring $H_0$ using time-delay cosmography is the presence of the mass-sheet degeneracy (MSD) in the lens mass modelling. We propose and quantitatively assess the use of galaxy–galaxy shear measurements to break the MSD in the strong lensing mass modelling. We use stacked galaxy–galaxy lensing profiles and corresponding covariance matrices from Huang et al. to constrain the MSD in lens mass modelling with a highly flexible mass profile. Our analyses show that if ideally all galaxy–galaxy lensing measurements from the Hyper Suprime-Cam survey can be used to constrain the MSD, we can achieve $\sim 10~{{\ \rm per\ cent}}$ precision on the MSD constraint. We forecast that galaxy–galaxy lensing measurements from Legacy Survey of Space and Time (LSST)-like surveys can in general constrain the MSD with $\sim 1\,\mathrm{ per\,cent}-3~{{\ \rm per\ cent}}$ precision. Furthermore, if we push weak lensing measurements to a lower angular scale of $\sim 0.04\,\rm Mpc$, a survey like LSST can provide $\sim 1~{{\ \rm per\ cent}}$ precision on the MSD constraint, enabling a measurement of $H_0$ at the 1 per cent level. We demonstrate that galaxy–galaxy weak lensing can robustly constrain the MSD independent of stellar kinematics of the deflector, with wide-field survey data alone.

Forecast of strongly lensed supernovae rates in the China Space Station Telescope surveys

Strong gravitationally lensed supernovae (SNe) are a powerful probe for cosmology and stellar physics. The relative time delays between lensed SN images provide an independent way of measuring a fundamental cosmological parameter --- the Hubble constant ---, the value of which is currently under debate. The time delays also serve as a ``time machine'', offering a unique opportunity to capture the extremely early phase of the SN explosion, which can be used to constrain the SN progenitor and explosion mechanism. Although there are only a handful of strongly lensed SN discoveries so far, which greatly hinders scientific applications, the sample size is expected to grow substantially with next-generation surveys. In this work, we investigate the capability of detecting strongly lensed SNe with the China Space Station Telescope (CSST), a two-meter space telescope to be launched around 2026. Through Monte Carlo simulations, we predict that CSST can detect 1008.53 and 51.78 strongly lensed SNe from its Wide Field Survey (WFS, covering 17,500 deg$^2$) and Deep Field Survey (DFS, covering 400 deg$^2$) over the course of ten years. In both surveys, about 35&lt;!PCT!&gt; of the events involve Type Ia SNe as the background sources. Our results suggest that the WFS and DFS of CSST, although not designed or optimized for discovering transients, can still make a great contribution to the strongly lensed SNe studies.

Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows

Evaluating the accuracy and calibration of the redshift posteriors produced by photometric redshift (photo-z) estimators is vital for enabling precision cosmology and extragalactic astrophysics with modern wide-field photometric surveys. Evaluating photo-z posteriors on a per-galaxy basis is difficult, however, as real galaxies have a true redshift but not a true redshift posterior. We introduce PZFlow, a Python package for the probabilistic forward modeling of galaxy catalogs with normalizing flows. For catalogs simulated with PZFlow, there is a natural notion of “true” redshift posteriors that can be used for photo-z validation. We use PZFlow to simulate a photometric galaxy catalog where each galaxy has a redshift, noisy photometry, shape information, and a true redshift posterior. We also demonstrate the use of an ensemble of normalizing flows for photo-z estimation. We discuss how PZFlow will be used to validate the photo-z estimation pipeline of the Dark Energy Science Collaboration, and the wider applicability of PZFlow for statistical modeling of any tabular data.

AMICO-COSMOS galaxy cluster and group catalogue up to z = 2: Sample properties and X-ray counterparts

Aims. We present the results of a new galaxy cluster search in the COSMOS field obtained using the Adaptive Matched Identifier of Clustered Objects (AMICO). Our aim was to produce a new cluster and group catalogue up to z = 2 by performing an innovative application of AMICO with respect to previous successful applications to wide-field surveys in terms of depth (down to r &lt; 26.7), small area covered (∼1.69 deg2 of unmasked effective area), and redshift extent. This sample and the comparative analysis we performed with the X-rays, allowed for the calibration of mass-proxy scaling relations up to z = 2 and down to less than 1013 M⊙, and constitutes the basis for the refinement of the cluster model for future applications of AMICO, such as the analysis of upcoming Euclid data. Methods. The AMICO algorithm is based on an optimal linear matched filter and detects clusters in photometric galaxy catalogues using galaxy location, photometric redshift and, in the simplest case, one galaxy property. We chose to use a single magnitude as the galaxy property, avoiding the explicit use of galaxy colour for the selection of clusters. We used three different magnitudes by performing three independent runs in the r-, Y-, and H-bands using both COSMOS2020 and COSMOS2015 galaxy catalogues. We created a composite visibility mask and cluster models for the signal to detect, and we estimated the noise directly from the data. Results. We performed a matching of the catalogues resulting from the three runs and merged them to produce a final catalogue that contains 1269 and 666 candidate clusters and groups with S/N &gt; 3.0 and &gt; 3.5, respectively. A total of 490 candidates are detected in all three runs. Most of the detections unmatched between runs have S/N &lt; 3.5, which can be set as a threshold for selecting a more robust sample. We assigned X-ray properties to our detections by matching the catalogue with a public X-ray selected group sample and by estimating, for unmatched detections, the X-ray properties at the location of AMICO candidates using Chandra+XMM-Newton data. There are in total 622 candidate clusters and groups with an X-ray flux estimate. This large sample of candidates with X-ray properties allowed the calibration of the scaling relations between two AMICO mass-proxies (richness and cluster amplitude) and X-ray mass and the study of their redshift dependence for the selection of the most stable photometric bands.

Elucidating galaxy population properties using a model-free analysis of quadruply imaged quasar lenses from large surveys

ABSTRACT The population of strong lensing galaxies is a subset of intermediate-redshift massive galaxies, whose population-level properties are not yet well understood. In the near future, thousands of multiply imaged systems are expected to be discovered by wide-field surveys like Rubin Observatory’s Legacy Survey of Space and Time and Euclid. With the soon-to-be robust population of quadruply lensed quasars, or quads, in mind, we introduce a novel technique to elucidate the empirical distribution of the galaxy population properties. Our re-imagining of the prevailing strong lensing analysis does not fit mass models to individual lenses, but instead starts with parametric models of many galaxy populations, which include generally ignored mass distribution complexities and exclude external shear for now. We construct many mock galaxy populations with different properties and obtain populations of quads from each of them. The mock ‘observed’ population of quads is then compared to those from the mocks using a model-free analysis based on a three-dimensional subspace of directly observable quad image properties. The distance between two quad populations in the space of image properties is measured by a metric $\eta$, and the distance between their parent galaxy populations in the space of galaxy properties is measured by $\zeta$. We find a well-defined relation between $\eta$ and $\zeta$. The discovered relation between the space of image properties and the space of galaxy properties allows for the observed galaxy population properties to be estimated from the properties of their quads, which will be conducted in a future paper.

The fifth data release of the Kilo Degree Survey: Multi-epoch optical/NIR imaging covering wide and legacy-calibration fields

We present the final data release of the Kilo-Degree Survey (KiDS-DR5), a public European Southern Observatory (ESO) wide-field imaging survey optimised for weak gravitational lensing studies. We combined matched-depth multi-wavelength observations from the VLT Survey Telescope and the VISTA Kilo-degree INfrared Galaxy (VIKING) survey to create a nine-band optical-to-near-infrared survey spanning 1347 deg2. The median r-band 5σ limiting magnitude is 24.8 with median seeing 0.7″. The main survey footprint includes 4 deg2 of overlap with existing deep spectroscopic surveys. We complemented these data in DR5 with a targeted campaign to secure an additional 23 deg2 of KiDS- and VIKING-like imaging over a range of additional deep spectroscopic survey fields. From these fields, we extracted a catalogue of 126 085 sources with both spectroscopic and photometric redshift information, which enables the robust calibration of photometric redshifts across the full survey footprint. In comparison to previous releases, DR5 represents a 34% areal extension and includes an i-band re-observation of the full footprint, thereby increasing the effective i-band depth by 0.4 magnitudes and enabling multi-epoch science. Our processed nine-band imaging, single- and multi-band catalogues with masks, and homogenised photometry and photometric redshifts can be accessed through the ESO Archive Science Portal.

Detecting strongly lensed type Ia supernovae with LSST

ABSTRACT Strongly lensed supernovae are rare and valuable probes of cosmology and astrophysics. Upcoming wide-field time-domain surveys, such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), are expected to discover an order-of-magnitude more lensed supernovae than have previously been observed. In this work, we investigate the cosmological prospects of lensed type Ia supernovae (SNIa) in LSST by quantifying the expected annual number of detections, the impact of stellar microlensing, follow-up feasibility, and how to best separate lensed and unlensed SNIa. We simulate SNIa lensed by galaxies, using the current LSST baseline v3.0 cadence, and find an expected number of 44 lensed SNIa detections per year. Microlensing effects by stars in the lensing galaxy are predicted to lower the lensed SNIa detections by ∼8 per cent. The lensed events can be separated from the unlensed ones by jointly considering their colours and peak magnitudes. We define a ‘gold sample’ of ∼10 lensed SNIa per year with time delay &amp;gt;10 d, &amp;gt;5 detections before light curve peak, and sufficiently bright (mi &amp;lt; 22.5 mag) for follow-up observations. In 3 yr of LSST operations, such a sample is expected to yield a 1.5 per cent measurement of the Hubble constant.