arXiv subjects

Simon Dye

Publications and source records attributed to Simon Dye.

At least 19 recordsLinked to original sources

arXiv2026

Galaxy-LRD Strong Lenses: A Missing Population?

The physical nature of Little Red Dots (LRDs) remains uncertain, although these abundant, compact, and red sources may offer important insights into early black-hole growth and galaxy formation. Strong gravitational lensing can magnify LRDs and spatially resolve their internal structure, thereby helping to discriminate among competing physical scenarios. However, no galaxy-scale strongly lensed LRD has yet been securely confirmed. To predict the abundance of such systems in current and future surveys and to guide dedicated searches, we present the first benchmark estimate of the detectable population of galaxy-scale lensed LRDs by combining literature-based LRD source models with a population of foreground early-type galaxy deflectors. Our Monte Carlo simulation spans $50~{\rm deg}^{2}$ and contains 270,713 LRDs and 5,460,841 deflectors. We predict idealized surface densities of $10.70\pm3.76~{\rm deg}^{-2}$ for doubles and $0.64\pm0.69~{\rm deg}^{-2}$ for quads. After accounting for the JWST point-spread function and survey limiting magnitudes, the detectable surface densities decrease to $3.70\pm1.89~{\rm deg}^{-2}$ and $0.52\pm0.58~{\rm deg}^{-2}$, respectively. For the de-duplicated $0.66~{\rm deg}^{2}$ footprint covered by COSMOS-Web, PRIMER-UDS, PRIMER-COSMOS, CEERS, JADES GOODS-S, and JADES GOODS-N, for which the reported limiting depths are combined through area-weighted averaging in flux space, the predicted probabilities of detecting no systems are $8.6\%$ for doubles and $70.8\%$ for quads.

astro-ph.GA↗

arXiv2026

Polar coordinate transformations for machine learning based dark matter subhalo detection in strong gravitational lenses

Strong gravitational lensing provides a powerful probe of dark matter, particularly on small scales where the gravitational effects of dark matter subhalos within galaxies can manifest as perturbations within the extended arcs of gravitationally lensed sources. We investigate whether transforming lensed images into polar coordinates improves the ability of convolutional neural networks to infer subhalo mass. We introduce a machine learning architecture that outputs a prediction uncertainty alongside a mass prediction to enable assessment of network reliability. Using simulated Hubble Space Telescope observations, we compare our models trained on Cartesian and polar representations under different initialisation schemes, noise levels, and subhalo concentrations ($c=60$, $c=30$). We find that polar-transformed inputs consistently yield higher subhalo detection fractions than standard Cartesian images across all tested masses. For subhalos with mass $10^9M_\odot \leq M \leq 10^{9.5}M_\odot$, the fraction of subhalos the network is able to detect increases by $\sim 15$ per cent. Pretrained networks outperform randomly initialized networks, and the polar transform consistently improves network performance in both low signal-to-noise data and for lower-concentration subhalos. The relative improvement is highest in regimes where subhalo perturbations are most difficult to detect, such as low signal-to-noise data or systems containing low concentration subhalos. These results demonstrate that presenting strong lensing images in a polar representaion provides a computationally inexpensive way of improving CNN-based subhalo detection.

astro-ph.GA↗

arXiv2025

The Second and Third Data Releases from the UKIRT Hemisphere Survey

This paper describes the second and third data releases (DR2 and DR3, respectively) from the ongoing United Kingdom Infrared Telescope (UKIRT) Hemisphere Survey (UHS). DR2 is primarily comprised of the $K$-band portion of the UHS survey, and was released to the public on June 1, 2023. DR3 mainly includes the $H$-band portion of the survey, with a public release scheduled for September 2025. The $H$- and $K$-band data releases complement the previous $J$-band data release (DR1) from 2018. The survey covers approximately 12,700 square degrees between declinations of 0 degrees and $+$60 degrees and achieves median 5$\sigma$ point source sensitivities of 19.0 mag and 18.0 mag (Vega) for $H$ and $K$, respectively. The data releases include images and source catalogs which include $\sim$581 million $H$-band detections and $\sim$461 million $K$-band detections. DR2 and DR3 also include merged catalogs, created by combining $J$- and $K$-band detections (DR2) and $J$-, $H$-, and $K$-band detections (DR3). The DR2 merged catalog has a total of $\sim$513 million sources, while the DR3 merged catalog contains $\sim$560 million sources.

astro-ph.GA↗

arXiv2024

A kiloparsec-scale ordered magnetic field in a galaxy at z=5.6

Magnetic fields are widely observed in various astronomical contexts, yet much remains unknown about their significance across different systems and cosmic epochs. Our current knowledge of the evolution of magnetic fields is limited by scarce observations in the distant Universe, where galaxies have recently been found to be more evolved than most model predictions. To address this gap, we conducted rest-frame 131 um full-polarisation observations of dust emission in a strongly lensed dusty star-forming galaxy, SPT0346-52, at z=5.6, when the Universe was only 1 Gyr old. Dust grains can become aligned with local magnetic fields, resulting in the emission of linearly polarised thermal infrared radiation. Our observations have revealed a median polarisation level of $0.9\pm0.2$ percent with a variation of $\pm0.4$ percent across the 3 kpc extention, indicating the presence of large-scale ordered magnetic fields. The polarised dust emission is patchy, offset from the total dust emission and mostly overlaps with the [C II] emission at a velocity of about -150 km/s. The bimodal distribution of field orientations, their spatial distribution, and the connection with the cold gas kinematics further emphasise the complexity of the magnetic environment in this galaxy and the potential role of mergers in shaping its magnetic fields. Such early formation of ordered galactic magnetic fields also suggests that both small-scale and large-scale dynamos could be efficient in early galaxies. Continued observations of magnetic fields in early galaxies, as well as expanding surveys to a wider galaxy population, are essential for a comprehensive understanding of the prevalence and impact of magnetic fields in the evolving Universe.

astro-ph.GA↗

arXiv2024

The Impact of $\texttt{CLEAN}$ing on Strong Gravitational Lens Modelling

We present a comparison of image and uv-plane galaxy-galaxy strong lensing modelling results for simulated ALMA observations with different antenna configurations and on-source integration times. Image-plane modelling is carried out via use of the $\texttt{CLEAN}$ algorithm, and we explore the effects of different visibility weighting schemes on the inferred lens model parameters. We find that direct modelling of the visibility data consistently outperforms image-plane modelling for both the naturally and Briggs-weighted images. We also find that the modelling of images created with Briggs weighting generally produces more accurate results than those obtained by modelling images constructed with natural weighting. We explain this by quantifying the suppression of information due to $\texttt{CLEAN}$ing on scales at which the modelling is sensitive, and how this differs between Briggs and natural weighting. At higher resolutions, the differences between the lens modelling techniques are much less pronounced and overall, modelling errors are significantly reduced. We also find that time-binning the visibilities by up to a factor of three makes no significant difference to the inferred lens parameters when directly modelling in the uv-plane. This work provides some guidance on navigating the many choices faced when modelling strong lens interferometric imaging data.

astro-ph.GA↗

arXiv2023

Characterisation of Herschel-selected strong lens candidates through HST and sub-mm/mm observations

We have carried out HST snapshot observations at 1.1 $\mu$m of 281 candidate strongly lensed galaxies identified in the wide-area extragalactic surveys conducted with the Herschel space observatory. Our candidates comprise systems with flux densities at $500\,\mu$m$ S_{500}\geq 80$ mJy. We model and subtract the surface brightness distribution for 130 systems, where we identify a candidate for the foreground lens candidate. After combining visual inspection, archival high-resolution observations, and lens subtraction, we divide the systems into different classes according to their lensing likelihood. We confirm 65 systems to be lensed. Of these, 30 are new discoveries. We successfully perform lens modelling and source reconstruction on 23 systems, where the foreground lenses are isolated galaxies and the background sources are detected in the HST images. All the systems are successfully modelled as a singular isothermal ellipsoid. The Einstein radii of the lenses and the magnifications of the background sources are consistent with previous studies. However, the background source circularised radii (between 0.34 kpc and 1.30 kpc) are $\sim$3 times smaller than the ones measured in the sub-mm/mm for a similarly selected and partially overlapping sample. We compare our lenses with those in the SLACS survey, confirming that our lens-independent selection is more effective at picking up fainter and diffuse galaxies and group lenses. This sample represents the first step towards characterising the near-IR properties and stellar masses of the gravitationally lensed dusty star-forming galaxies.

astro-ph.GA↗

arXiv2023

Metal Factories in the Early Universe

We have measured the mass of metals in the molecular gas in 13 submillimetre galaxies at z~4 in which the gas, based on previous observations, lies in a cold rotating disk. We measured the metal masses using either the submillimetre line or continuum emission from three tracers of the overall metal content - carbon atoms, carbon monoxide molecules and dust grains - using the first simultaneous calibration of all three tracers (Dunne et al. 2022). We obtain very similar mass estimates from the different tracers, which are similar to the entire metal content of a present-day massive early-type galaxy. We used the dynamical masses of these galaxies to set an upper limit on the mass of the molecular gas in each galaxy, allowing us to set a lower limit on the metal abundance in the gas, finding values for many of the galaxies well above the solar value. We use chemical evolution models to show that such high metal masses and abundances are what is expected shortly after the formation of a galaxy for a top-heavy IMF. We suggest a scenario for galaxy evolution in which massive galaxies reach a high metal abundance during their formation phase, which is then gradually reduced by dry mergers with lower mass galaxies. We use the chemical-evolution models to show that the metals in the outflows from massive early-type galaxies in their formation phase can quantitatively explain the long-standing puzzle that approximately 75% of the metals in clusters of galaxies is in the intracluster gas rather than in the galaxies.

astro-ph.GA↗

arXiv2023

Discovering strongly lensed quasar candidates with catalogue-based methods from DESI Legacy Surveys

The Hubble tension, revealed by a $\sim 5\sigma$ discrepancy between measurements of the Hubble-Lemaitre constant from early- and local-Universe observations, is one of the most significant problems in modern cosmology. In order to better understand the origin of this mismatch, independent techniques to measure $H_0$, such as strong lensing time delays, are required. Notably, the sample size of such systems is key to minimising statistical uncertainties and cosmic variance, which can be improved by exploring the datasets of large-scale sky surveys like DESI (Dark Energy Spectroscopic Instrument). We identify possible strong lensing time-delay systems within DESI by selecting candidate multiply imaged lensed quasars from a catalogue of 24,440,816 candidate QSOs contained in the 9th data release of the DESI Legacy Imaging Surveys (DESI-LS). Using a friend-of-friends-like algorithm on spatial co-ordinates, our method generates an initial list of compact quasar groups. This list is subsequently filtered using a measure of the similarity of colours of a group's members and the likelihood that they are quasars. A visual inspection finally selects candidate strong lensing systems based on the spatial configuration of the group members. We identify 620 new candidate multiply imaged lensed quasars (101 Grade-A, 214 Grade-B, 305 Grade-C). This number excludes 53 known spectroscopically confirmed systems and existing candidate systems identified in other similar catalogues. When available, these new candidates will be further checked by combining the spectroscopic and photometric data from DESI. The catalogues and images of the candidates in this work are available online (https://github.com/EigenHermit/lensed_qso_cand_catalogue_He-22/).

astro-ph.CO↗

arXiv2023

The impact of human expert visual inspection on the discovery of strong gravitational lenses

We investigate the ability of human 'expert' classifiers to identify strong gravitational lens candidates in Dark Energy Survey like imaging. We recruited a total of 55 people that completed more than 25$\%$ of the project. During the classification task, we present to the participants 1489 images. The sample contains a variety of data including lens simulations, real lenses, non-lens examples, and unlabeled data. We find that experts are extremely good at finding bright, well-resolved Einstein rings, whilst arcs with $g$-band signal-to-noise less than $\sim$25 or Einstein radii less than $\sim$1.2 times the seeing are rarely recovered. Very few non-lenses are scored highly. There is substantial variation in the performance of individual classifiers, but they do not appear to depend on the classifier's experience, confidence or academic position. These variations can be mitigated with a team of 6 or more independent classifiers. Our results give confidence that humans are a reliable pruning step for lens candidates, providing pure and quantifiably complete samples for follow-up studies.

astro-ph.GA↗

arXiv2021

Modelling high-resolution ALMA observations of strongly lensed dustystar forming galaxies detected by Herschel

We present modelling of ~0.1arcsec resolution Atacama Large Millimetre/sub-millimeter Array imaging of seven strong gravitationally lensed galaxies detected by the Herschel Space Observatory. Four of these systems are galaxy-galaxy scale strong lenses, with the remaining three being group-scale lenses. Through careful modelling of visibilities, we infer the mass profiles of the lensing galaxies and by determining the magnification factors, we investigate the intrinsic properties and morphologies of the lensed sub-millimetre sources. We find that these sub-millimetre sources all have ratios of star formation rate to dust mass that is consistent with or in excess of the mean ratio for high-redshift sub-millimetre galaxies and low redshift ultra-luminous infrared galaxies. The contribution to the infrared luminosity from possible AGN is not quantified and so could be biasing our star formation rates to higher values. The majority of our lens models have mass density slopes close to isothermal, but some systems show significant differences.

astro-ph.GA↗

arXiv2021

Strong lens modelling: comparing and combining Bayesian neural networks and parametric profile fitting

The vast quantity of strong galaxy-galaxy gravitational lenses expected by future large-scale surveys necessitates the development of automated methods to efficiently model their mass profiles. For this purpose, we train an approximate Bayesian convolutional neural network (CNN) to predict mass profile parameters and associated uncertainties, and compare its accuracy to that of conventional parametric modelling for a range of increasingly complex lensing systems. These include standard smooth parametric density profiles, hydrodynamical EAGLE galaxies and the inclusion of foreground mass structures, combined with parametric sources and sources extracted from the Hubble Ultra Deep Field. In addition, we also present a method for combining the CNN with traditional parametric density profile fitting in an automated fashion, where the CNN provides initial priors on the latter's parameters. On average, the CNN achieved errors 19 $\pm$ 22 per cent lower than the traditional method's blind modelling. The combination method instead achieved 27 $\pm$ 11 per cent lower errors over the blind modelling, reduced further to 37 $\pm$ 11 per cent when the priors also incorporated the CNN-predicted uncertainties, with errors also 17 $\pm$ 21 per cent lower than the CNN by itself. While the CNN is undoubtedly the fastest modelling method, the combination of the two increases the speed of conventional fitting alone by factors of 1.73 and 1.19 with and without CNN-predicted uncertainties, respectively. This, combined with greatly improved accuracy, highlights the benefits one can obtain through combining neural networks with conventional techniques in order to achieve an efficient automated modelling approach.

astro-ph.IM↗

arXiv2020

Auto-identification of unphysical source reconstructions in strong gravitational lens modelling

With the advent of next-generation surveys and the expectation of discovering huge numbers of strong gravitational lens systems, much effort is being invested into developing automated procedures for handling the data. The several orders of magnitude increase in the number of strong galaxy-galaxy lens systems is an insurmountable challenge for traditional modelling techniques. Whilst machine learning techniques have dramatically improved the efficiency of lens modelling, parametric modelling of the lens mass profile remains an important tool for dealing with complex lensing systems. In particular, source reconstruction methods are necessary to cope with the irregular structure of high-redshift sources. In this paper, we consider a Convolutional Neural Network (CNN) that analyses the outputs of semi-analytic methods which parametrically model the lens mass and linearly reconstruct the source surface brightness distribution. We show the unphysical source reconstructions that arise as a result of incorrectly initialised lens models can be effectively caught by our CNN. Furthermore, the CNN predictions can be used to automatically re-initialise the parametric lens model, avoiding unphysical source reconstructions. The CNN, trained on reconstructions of lensed S\'ersic sources, accurately classifies source reconstructions of the same type with a precision $P > 0.99$ and recall $R > 0.99$. The same CNN, without re-training, achieves $P=0.89$ and $R=0.89$ when classifying source reconstructions of more complex lensed HUDF sources. Using the CNN predictions to re-initialise the lens modelling procedure, we achieve a 69 per cent decrease in the occurrence of unphysical source reconstructions. This combined CNN and parametric modelling approach can greatly improve the automation of lens modelling.

astro-ph.GA↗

arXiv2020

The impact of line-of-sight structures on measuring $H_0$ with strong lensing time-delays

Measurements of The Hubble-Lemaitre constant from early- and local-universe observations show a significant discrepancy. In an attempt to understand the origin of this mismatch, independent techniques to measure H0 are required. One such technique, strong lensing time delays, is set to become a leading contender amongst the myriad methods due to forthcoming large strong lens samples. It is therefore critical to understand the systematic effects inherent in this method. In this paper, we quantify the influence of additional structures along the line-of-sight by adopting realistic light cones derived from the CosmoDC2 semi-analytical extra-galactic catalogue. Using multiple lens plane ray-tracing to create a set of simulated strong lensing systems, we have investigated the impact of line-of-sight structures on time-delay measurements and in turn, on the inferred value of H0. We have also tested the reliability of existing procedures for correcting for line-of-sight effects. We find that if the integrated contribution of the line-of-sight structures is close to a uniform mass sheet, the bias in H0 can be adequately corrected by including a constant external convergence $\kappa_{ext}$ in the lens model. However, for realistic line-of-sight structures comprising many galaxies at different redshifts, this simple correction overestimates the bias by an amount that depends linearly on the median external convergence. We, therefore, conclude that lens modelling must incorporate multiple lens planes to account for line-of-sight structures for accurate and precise inference of H0.

astro-ph.CO↗

arXiv2019

Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder

In this paper we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder (CAE), and a clustering algorithm consisting of a Bayesian Gaussian mixture model (BGM). We apply this technique to visual band space-based simulated imaging data from the Euclid Space Telescope using data from the Strong Gravitational Lenses Finding Challenge. Our technique promisingly captures a variety of lensing features such as Einstein rings with different radii, distorted arc structures, etc, without using predefined labels. After the clustering process, we obtain several classification clusters separated by different visual features which are seen in the images. Our method successfully picks up $\sim$63\ percent of lensing images from all lenses in the training set. With the assumed probability proposed in this study, this technique reaches an accuracy of $77.25\pm 0.48$\% in binary classification using the training set. Additionally, our unsupervised clustering process can be used as the preliminary classification for future surveys of lenses to efficiently select targets and to speed up the labelling process. As the starting point of the astronomical application using this technique, we not only explore the application to gravitationally lensed systems, but also discuss the limitations and potential future uses of this technique.

astro-ph.IM↗

arXiv2019

Spitzer catalog of Herschel-selected ultrared dusty, star-forming galaxies

The largest Herschel extragalactic surveys, H-ATLAS and HerMES, have selected a sample of "ultrared" dusty, star-forming galaxies (DSFGs) with rising SPIRE flux densities ($S_{500} > S_{350} > S_{250}$; so-called "500 $\mu$m-risers") as an efficient way for identifying DSFGs at higher redshift ($z > 4$). In this paper, we present a large Spitzer follow-up program of 300 Herschel ultrared DSFGs. We have obtained high-resolution ALMA, NOEMA, and SMA data for 63 of them, which allow us to securely identify the Spitzer/IRAC counterparts and classify them as gravitationally lensed or unlensed. Within the 63 ultrared sources with high-resolution data, $\sim$65% appear to be unlensed, and $\sim$27% are resolved into multiple components. We focus on analyzing the unlensed sample by directly performing multi-wavelength spectral energy distribution (SED) modeling to derive their physical properties and compare with the more numerous $z \sim 2$ DSFG population. The ultrared sample has a median redshift of 3.3, stellar mass of 3.7 $\times$ 10$^{11}$ $M_{\odot}$, star formation rate (SFR) of 730 $M_{\odot}$yr$^{-1}$, total dust luminosity of 9.0 $\times$ 10$^{12}$ $L_{\odot}$, dust mass of 2.8 $\times$ 10$^9$ $M_{\odot}$, and V-band extinction of 4.0, which are all higher than those of the ALESS DSFGs. Based on the space density, SFR density, and stellar mass density estimates, we conclude that our ultrared sample cannot account for the majority of the star-forming progenitors of the massive, quiescent galaxies found in infrared surveys. Our sample contains the rarer, intrinsically most dusty, luminous and massive galaxies in the early universe that will help us understand the physical drivers of extreme star formation.

astro-ph.GA↗

arXiv2019

The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples

We explore the effectiveness of deep learning convolutional neural networks (CNNs) for estimating strong gravitational lens mass model parameters. We have investigated a number of practicalities faced when modelling real image data, such as how network performance depends on the inclusion of lens galaxy light, the addition of colour information and varying signal-to-noise. Our CNN was trained and tested with strong galaxy-galaxy lens images simulated to match the imaging characteristics of the Large Synoptic Survey Telescope (LSST) and Euclid. For images including lens galaxy light, the CNN can recover the lens model parameters with an acceptable accuracy, although a 34 per cent average improvement in accuracy is obtained when lens light is removed. However, the inclusion of colour information can largely compensate for the drop in accuracy resulting from the presence of lens light. While our findings show similar accuracies for single epoch Euclid VIS and LSST r-band datasets, we find a 24 per cent increase in accuracy by adding g- and i-band images to the LSST r-band without lens light and a 20 per cent increase with lens light. The best network performance is obtained when it is trained and tested on images where lens light exactly follows the mass, but when orientation and ellipticity of the light is allowed to differ from those of the mass, the network performs most consistently when trained with a moderate amount of scatter in the difference between the mass and light profiles.

astro-ph.IM↗

arXiv2019

The molecular-gas properties in the gravitationally lensed merger HATLAS J142935.3-002836

Follow-up observations of (sub-)mm-selected gravitationally-lensed systems have allowed a more detailed study of the dust-enshrouded phase of star-formation up to very early cosmic times. Here, the case of the gravitationally lensed merger in HATLAS J142935.3-002836 (also known as H1429-0028; z_lens=0.218, z_bkg=1.027) is revisited following recent developments in the literature and new APEX observations targeting two carbon monoxide (CO) rotational transitions J_up=3 and 6. We show that the line-profiles comprise three distinct velocity components, where the fainter high-velocity one is less magnified and more compact. The modelling of the observed spectral line energy distribution of CO J_up=2 to 6 and [CI]3P_1-3P_0 assumes a large velocity gradient scenario, where the analysis is based on four statistical approaches. Since the detected gas and dust emission comes exclusively from only one of the two merging components (the one oriented North-South, NS), we are only able to determine upper-limits for the companion. The molecular gas in the NS component in H1429-0028 is found to have a temperature of ~70K, a volume density of log(n[/cm3])~3.7, to be expanding at ~10km/s/pc, and amounts to M_H2=4(-2,+3)*1e9 Msun. The CO to H2 conversion factor is estimated to be alpha_CO=0.4(-0.2,+0.3) Msun/(K.km/s.pc2). The NS galaxy is expected to have a factor of >10x more gas than its companion (M_H2<3e8 Msun). Nevertheless, the total amount of molecular gas in the system comprises only up to 15 per cent (1sigma upper-limit) of the total (dynamical) mass.

astro-ph.GA↗

arXiv2018

Filling in the Quasar Redshift Gap at $z \sim 5.5$ II: A Complete Survey of Luminous Quasars in the Post-Reionization Universe

We present the final results from our survey of luminous $z \sim $ 5.5 quasars. This is the first systematic quasar survey focusing on quasars at $z \sim$ 5.5, during the post-reionization epoch. It has been challenging to select quasars at $5.3 < z < 5.7$ using conventional color selections, due to their similar optical colors to those of late-type stars, especially M dwarfs. We developed a new selection technique for $z \sim$ 5.5 quasars based on optical, near-IR, and mid-IR photometry, using data from the Sloan Digital Sky Survey (SDSS), PanSTARR1 (PS1), the UKIRT Infrared Deep Sky Surveys - Large Area Survey, the UKIRT Hemisphere Survey, the VISTA Hemisphere Survey, and the Wide Field Infrared Survey Explorer ($WISE$), covering $\sim$ 11000 deg$^2$ of high galactic latitude sky. In this paper, we present the discovery of 15 new quasars at $z\sim 5.5$. Together with results from Yang et al. (2017), our survey provides a complete, flux-limited sample of 31 quasars at $5.3 \le z \le 5.7$. We measure the quasar spatial density at $z \sim 5.5$ and $M_{1450} < -26.2$. Our result is consistent with the rapid decline of quasar spatial density from $z = 5$ to 6, with $k=-0.66 \pm 0.05$ ($\rho(z) \propto 10^{kz}$). In addition, we present a new survey using optical colors only from the full PS1 area for luminous quasars at $z = 5.0 - 5.5$, beyond the SDSS footprint, and report the preliminary results from this survey, including 51 new quasars discovered at $4.61\le z \le5.71$.

astro-ph.GA↗