arXiv subjects

C. Conselice

Publications and source records attributed to C. Conselice.

At least 73 records · Page 4Linked to original sources

arXiv2021

C/2014 UN271 (Bernardinelli-Bernstein): the nearly spherical cow of comets

C/2014 UN271 (Bernardinelli-Bernstein) is a comet incoming from the Oort cloud which is remarkable in having the brightest (and presumably largest) nucleus of any well-measured comet, and having been discovered at heliocentric distance $r_h\approx29$ au farther than any Oort-cloud member. We describe the properties that can be inferred from images recorded until the first reports of activity in June 2021. The orbit has $i=95^\circ,$ with perihelion of 10.97 au to be reached in 2031, and previous aphelion at $40,400\pm260$ au. Backwards integration of the orbit under a standard Galactic tidal model and known stellar encounters suggests this is a pristine new comet, with a perihelion of $q\approx18$ au on its previous perihelion passage 3.5 Myr ago. The photometric data show an unresolved nucleus with absolute magnitude $H_r=8.0,$ colors that are typical of comet nuclei or Damocloids, and no secular trend as it traversed the range 34--23 au. For $r$-band geometric albedo $p_r,$ this implies a diameter of $150 (p_r/0.04)^{-0.5}$ km. There is strong evidence of brightness fluctuations at $\pm0.2$ mag level, but no rotation period can be discerned. A coma consistent with a ``stationary' $1/ρ$ surface-brightness distribution grew in scattering cross-section at an exponential rate from $A f ρ\approx1$ m to $\approx150$ m as the comet approached from 28 to 20 au. The activity is consistent with a simple model of sublimation of a surface species in radiative equilibrium with the Sun. The inferred enthalpy of sublimation matches those of $CO_2$ and $NH_3$. More-volatile species -- $N_2,$ $CH_4,$ and $CO$ -- must be far less abundant on the sublimating surfaces.

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arXiv2021

Finding quadruply imaged quasars with machine learning. I. Methods

Strongly lensed quadruply imaged quasars (quads) are extraordinary objects. They are very rare in the sky -- only a few tens are known to date -- and yet they provide unique information about a wide range of topics, including the expansion history and the composition of the Universe, the distribution of stars and dark matter in galaxies, the host galaxies of quasars, and the stellar initial mass function. Finding them in astronomical images is a classic "needle in a haystack" problem, as they are outnumbered by other (contaminant) sources by many orders of magnitude. To solve this problem, we develop state-of-the-art deep learning methods and train them on realistic simulated quads based on real images of galaxies taken from the Dark Energy Survey, with realistic source and deflector models, including the chromatic effects of microlensing. The performance of the best methods on a mixture of simulated and real objects is excellent, yielding area under the receiver operating curve in the range 0.86 to 0.89. Recall is close to 100% down to total magnitude i~21 indicating high completeness, while precision declines from 85% to 70% in the range i~17-21. The methods are extremely fast: training on 2 million samples takes 20 hours on a GPU machine, and 10^8 multi-band cutouts can be evaluated per GPU-hour. The speed and performance of the method pave the way to apply it to large samples of astronomical sources, bypassing the need for photometric pre-selection that is likely to be a major cause of incompleteness in current samples of known quads.

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arXiv2021

A search of the full six years of the Dark Energy Survey for outer Solar System objects

We present the results of a search for outer Solar System objects in the full six years of data (Y6) from the Dark Energy Survey (DES). The DES covered a contiguous $5000$ deg$^2$ of the southern sky with $\approx 80,000$ $3$ deg$^2$ exposures in the $grizY$ optical/IR filters between 2013 and 2019. This search yielded 815 trans-Neptunian objects (TNOs), one Centaur and one Oort cloud comet, with 461 objects reported for the first time in this paper. We present methodology that builds upon our previous search carried out on the first four years of data. Here, all DES images were reprocessed with an improved detection pipeline that leads to an average completeness gain of 0.47 mag per exposure, as well as an improved transient catalog production and optimized algorithms for linkage of detections into orbits. All objects were verified by visual inspection and by computing the sub-threshold significance, the total signal-to-noise ratio in the stack of images in which the object's presence is indicated by the orbit fit, but no detection was reported. This yields a highly pure catalog of TNOs complete to $r \approx 23.8$ mag and distances $29 150$ au, $q>30$ au) are consistent with the null hypothesis of azimuthal isotropy; and non-resonant TNOs with $q>38$ au, $a>50$ au show a highly significant tendency to be sunward of the major mean motion resonances, whereas this tendency is not present for $q<38$ au.

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arXiv2021

The Dark Energy Survey Data Release 2

We present the second public data release of the Dark Energy Survey, DES DR2, based on optical/near-infrared imaging by the Dark Energy Camera mounted on the 4-m Blanco telescope at Cerro Tololo Inter-American Observatory in Chile. DES DR2 consists of reduced single-epoch and coadded images, a source catalog derived from coadded images, and associated data products assembled from 6 years of DES science operations. This release includes data from the DES wide-area survey covering ~5000 deg2 of the southern Galactic cap in five broad photometric bands, grizY. DES DR2 has a median delivered point-spread function full-width at half maximum of g= 1.11, r= 0.95, i= 0.88, z= 0.83, and Y= 0.90 arcsec photometric uniformity with a standard deviation of < 3 mmag with respect to Gaia DR2 G-band, a photometric accuracy of ~10 mmag, and a median internal astrometric precision of ~27 mas. The median coadded catalog depth for a 1.95 arcsec diameter aperture at S/N= 10 is g= 24.7, r= 24.4, i= 23.8, z= 23.1 and Y= 21.7 mag. DES DR2 includes ~691 million distinct astronomical objects detected in 10,169 coadded image tiles of size 0.534 deg2 produced from 76,217 single-epoch images. After a basic quality selection, benchmark galaxy and stellar samples contain 543 million and 145 million objects, respectively. These data are accessible through several interfaces, including interactive image visualization tools, web-based query clients, image cutout servers and Jupyter notebooks. DES DR2 constitutes the largest photometric data set to date at the achieved depth and photometric precision.

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arXiv2021

Dark Energy Survey Year 3 Results: Redshift Calibration of the Weak Lensing Source Galaxies

Determining the distribution of redshifts of galaxies observed by wide-field photometric experiments like the Dark Energy Survey is an essential component to mapping the matter density field with gravitational lensing. In this work we describe the methods used to assign individual weak lensing source galaxies from the Dark Energy Survey Year 3 Weak Lensing Source Catalogue to four tomographic bins and to estimate the redshift distributions in these bins. As the first application of these methods to data, we validate that the assumptions made apply to the DES Y3 weak lensing source galaxies and develop a full treatment of systematic uncertainties. Our method consists of combining information from three independent likelihood functions: Self-Organizing Map $p(z)$ (SOMPZ), a method for constraining redshifts from galaxy photometry; clustering redshifts (WZ), constraints on redshifts from cross-correlations of galaxy density functions; and shear ratios (SR), which provide constraints on redshifts from the ratios of the galaxy-shear correlation functions at small scales. Finally, we describe how these independent probes are combined to yield an ensemble of redshift distributions encapsulating our full uncertainty. We calibrate redshifts with combined effective uncertainties of $σ_{\langle z \rangle}\sim 0.01$ on the mean redshift in each tomographic bin.

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arXiv2021

Assessing tension metrics with Dark Energy Survey and Planck data

Quantifying tensions -- inconsistencies amongst measurements of cosmological parameters by different experiments -- has emerged as a crucial part of modern cosmological data analysis. Statistically-significant tensions between two experiments or cosmological probes may indicate new physics extending beyond the standard cosmological model and need to be promptly identified. We apply several tension estimators proposed in the literature to the Dark Energy Survey (DES) large-scale structure measurement and Planck cosmic microwave background data. We first evaluate the responsiveness of these metrics to an input tension artificially introduced between the two, using synthetic DES data. We then apply the metrics to the comparison of Planck and actual DES Year 1 data. We find that the parameter differences, Eigentension, and Suspiciousness metrics all yield similar results on both simulated and real data, while the Bayes ratio is inconsistent with the rest due to its dependence on the prior volume. Using these metrics, we calculate the tension between DES Year 1 $3\times 2$pt and Planck, finding the surveys to be in $\sim 2.3σ$ tension under the $Λ$CDM paradigm. This suite of metrics provides a toolset for robustly testing tensions in the DES Year 3 data and beyond.

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arXiv2021

Dark Energy Survey Year 3 Results: Galaxy clustering and systematics treatment for lens galaxy samples

In this work we present the galaxy clustering measurements of the two DES lens galaxy samples: a magnitude-limited sample optimized for the measurement of cosmological parameters, MagLim, and a sample of luminous red galaxies selected with the redMaGiC algorithm. MagLim / redMaGiC sample contains over 10 million / 2.5 million galaxies and is divided into six / five photometric redshift bins spanning the range $z\in[0.20,1.05]$ / $z\in[0.15,0.90]$. Both samples cover 4143 deg$^2$ over which we perform our analysis blind, measuring the angular correlation function with a S/N $\sim 63$ for both samples. In a companion paper (DES Collaboration et al. 2021)), these measurements of galaxy clustering are combined with the correlation functions of cosmic shear and galaxy-galaxy lensing of each sample to place cosmological constraints with a 3$\times$2pt analysis. We conduct a thorough study of the mitigation of systematic effects caused by the spatially varying survey properties and we correct the measurements to remove artificial clustering signals. We employ several decontamination methods with different configurations to ensure the robustness of our corrections and to determine the systematic uncertainty that needs to be considered for the final cosmology analyses. We validate our fiducial methodology using log-normal mocks, showing that our decontamination procedure induces biases no greater than $0.5σ$ in the $(Ω_m, b)$ plane, where $b$ is galaxy bias. We demonstrate that failure to remove the artificial clustering would introduce strong biases up to $\sim 7 σ$ in $Ω_m$ and of more than $4 σ$ in galaxy bias.

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arXiv2021

Dark Energy Survey Year 3 Results: Photometric Data Set for Cosmology

We describe the Dark Energy Survey (DES) photometric data set assembled from the first three years of science operations to support DES Year 3 cosmology analyses, and provide usage notes aimed at the broad astrophysics community. Y3 Gold improves on previous releases from DES, Y1 Gold and Data Release 1 (DES DR1), presenting an expanded and curated data set that incorporates algorithmic developments in image detrending and processing, photometric calibration, and object classification. Y3 Gold comprises nearly 5000 square degrees of grizY imaging in the south Galactic cap, including nearly 390 million objects, with depth reaching S/N ~ 10 for extended objects up to $i_{AB}\sim 23.0$, and top-of-the-atmosphere photometric uniformity $< 3$ mmag. Compared to DR1, photometric residuals with respect to Gaia are reduced by $50\%$, and per-object chromatic corrections are introduced. Y3 Gold augments DES DR1 with simultaneous fits to multi-epoch photometry for more robust galaxy color measurements and corresponding photometric redshift estimates. Y3 Gold features improved morphological star-galaxy classification with efficiency $>98\%$ and purity $>99\%$ for galaxies with $19 < i_{AB} < 22.5$. Additionally, it includes per-object quality information, and accompanying maps of the footprint coverage, masked regions, imaging depth, survey conditions, and astrophysical foregrounds that are used to select the cosmology analysis samples. This paper will be complemented by online resources.

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arXiv2021

Pushing automated morphological classifications to their limits with the Dark Energy Survey

We present morphological classifications of $\sim$27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classification scheme separates: (a) early-type galaxies (ETGs) from late-types (LTGs); and (b) face-on galaxies from edge-on. Our Convolutional Neural Networks (CNNs) are trained on a small subset of DES objects with previously known classifications. These typically have $\mathrm{m}_r \lesssim 17.7~\mathrm{mag}$; we model fainter objects to $\mathrm{m}_r < 21.5$ mag by simulating what the brighter objects with well determined classifications would look like if they were at higher redshifts. The CNNs reach 97\% accuracy to $\mathrm{m}_r<21.5$ on their training sets, suggesting that they are able to recover features more accurately than the human eye. We then used the trained CNNs to classify the vast majority of the other DES images. The final catalog comprises five independent CNN predictions for each classification scheme, helping to determine if the CNN predictions are robust or not. We obtain secure classifications for $\sim$ 87\% and 73\% of the catalog for the ETG vs. LTG and edge-on vs. face-on models, respectively. Combining the two classifications (a) and (b) helps to increase the purity of the ETG sample and to identify edge-on lenticular galaxies (as ETGs with high ellipticity). Where a comparison is possible, our classifications correlate very well with Sérsic index (\textit{n}), ellipticity ($ε$) and spectral type, even for the fainter galaxies. This is the largest multi-band catalog of automated galaxy morphologies to date.

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arXiv2021

A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest

We demonstrate that highly accurate joint redshift-stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. We build two ML models: one containing deep photometry in the $griz$ bands, and the second reflecting the photometric scatter present in the main DES survey, with carefully constructed representative training data in each case. We validate our joint PDFs for $10,699$ test galaxies by utilizing the copula probability integral transform and the Kendall distribution function, and their univariate counterparts to validate the marginals. Benchmarked against a basic set-up of the template-fitting code BAGPIPES, our ML-based method outperforms template fitting on all of our predefined performance metrics. In addition to accuracy, the RF is extremely fast, able to compute joint PDFs for a million galaxies in just under $6$ min with consumer computer hardware. Such speed enables PDFs to be derived in real time within analysis codes, solving potential storage issues. As part of this work we have developed GALPRO, a highly intuitive and efficient Python package to rapidly generate multivariate PDFs on-the-fly. GALPRO is documented and available for researchers to use in their cosmology and galaxy evolution studies.

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arXiv2020

A statistical standard siren measurement of the Hubble constant from the LIGO/Virgo gravitational wave compact object merger GW190814 and Dark Energy Survey galaxies

We present a measurement of the Hubble constant $H_0$ using the gravitational wave (GW) event GW190814, which resulted from the coalescence of a 23 $M_\odot$ black hole with a 2.6 $M_\odot$ compact object, as a standard siren. No compelling electromagnetic counterpart has been identified for this event, thus our analysis accounts for thousands of potential host galaxies within a statistical framework. The redshift information is obtained from the photometric redshift (photo-$z$) catalog from the Dark Energy Survey. The luminosity distance is provided by the LIGO/Virgo gravitational wave sky map. Since this GW event has the second-smallest localization volume after GW170817, GW190814 is likely to provide the best constraint on cosmology from a single standard siren without identifying an electromagnetic counterpart. Our analysis uses photo-$z$ probability distribution functions and corrects for photo-$z$ biases. We also reanalyze the binary-black hole GW170814 within this updated framework. We explore how our findings impact the $H_0$ constraints from GW170817, the only GW merger associated with a unique host galaxy. From a combination of GW190814, GW170814 and GW170817, our analysis yields $H_0 = 72.0^{+ 12}_{- 8.2 }~{\rm km~s^{-1}~Mpc^{-1}}$ (68\% Highest Density Interval, HDI) for a prior in $H_0$ uniform between $[20,140]~{\rm km~s^{-1}~Mpc^{-1}}$. The addition of GW190814 and GW170814 to GW170817 improves the 68\% HDI from GW170817 alone by $\sim 18\%$, showing how well-localized mergers without counterparts can provide a significant contribution to standard siren measurements, provided that a complete galaxy catalog is available at the location of the event.

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arXiv2020

Shadows in the Dark: Low-Surface-Brightness Galaxies Discovered in the Dark Energy Survey

We present a catalog of 23,790 extended low-surface-brightness galaxies (LSBGs) identified in $\sim 5000 °^2$ from the first three years of imaging data from the Dark Energy Survey (DES). Based on a single-component Sérsic model fit, we define extended LSBGs as galaxies with $g$-band effective radii $R_{eff}(g) > 2.5''$ and mean surface brightness $\barμ_{eff}(g) > 24.2 \,mag ȧrcsec^{-2}$. We find that the distribution of LSBGs is strongly bimodal in $(g-r)$ vs.\ $(g-i$) color space. We divide our sample into red ($g-i \geq 0.60$) and blue ($g-i<0.60$) galaxies and study the properties of the two populations. Redder LSBGs are more clustered than their blue counterparts and are correlated with the distribution of nearby ($z < 0.10$) bright galaxies. Red LSBGs constitute $\sim 33\%$ of our LSBG sample, and $\sim 30\%$ of these are located within 1 deg of low-redshift galaxy groups and clusters (compared to $\sim 8\%$ of the blue LSBGs). For nine of the most prominent galaxy groups and clusters, we calculate the physical properties of associated LSBGs assuming a redshift derived from the host system. In these systems, we identify 41 objects that can be classified as ultra-diffuse galaxies, defined as LSBGs with projected physical effective radii $R_{eff} > 1.5 \,kpc$ and central surface brighthness $μ_0(g) > 24.0\, mag \,arcsec^{-2}$. The wide-area sample of LSBGs in DES can be used to test the role of environment on models of LSBG formation and evolution.

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arXiv2020

Dark Energy Survey Year 3 Results: Clustering Redshifts -- Calibration of the Weak Lensing Source Redshift Distributions with redMaGiC and BOSS/eBOSS

We present the calibration of the Dark Energy Survey Year 3 (DES Y3) weak lensing source galaxy redshift distributions $n(z)$ from clustering measurements. In particular, we cross-correlate the weak lensing (WL) source galaxies sample with redMaGiC galaxies (luminous red galaxies with secure photometric redshifts) and a spectroscopic sample from BOSS/eBOSS to estimate the redshift distribution of the DES sources sample. Two distinct methods for using the clustering statistics are described. The first uses the clustering information independently to estimate the mean redshift of the source galaxies within a redshift window, as done in the DES Y1 analysis. The second method establishes a likelihood of the clustering data as a function of $n(z)$, which can be incorporated into schemes for generating samples of $n(z)$ subject to combined clustering and photometric constraints. Both methods incorporate marginalisation over various astrophysical systematics, including magnification and redshift-dependent galaxy-matter bias. We characterise the uncertainties of the methods in simulations; the first method recovers the mean $z$ of tomographic bins to RMS (precision) of $\sim 0.014$. Use of the second method is shown to vastly improve the accuracy of the shape of $n(z)$ derived from photometric data. The two methods are then applied to the DES Y3 data.

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arXiv2020

Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects

In this paper we investigate how implementing machine learning could improve the efficiency of the search for Trans-Neptunian Objects (TNOs) within Dark Energy Survey (DES) data when used alongside orbit fitting. The discovery of multiple TNOs that appear to show a similarity in their orbital parameters has led to the suggestion that one or more undetected planets, an as yet undiscovered "Planet 9", may be present in the outer Solar System. DES is well placed to detect such a planet and has already been used to discover many other TNOs. Here, we perform tests on eight different supervised machine learning algorithms, using a dataset consisting of simulated TNOs buried within real DES noise data. We found that the best performing classifier was the Random Forest which, when optimised, performed well at detecting the rare objects. We achieve an area under the receiver operating characteristic (ROC) curve, (AUC) $= 0.996 \pm 0.001$. After optimizing the decision threshold of the Random Forest, we achieve a recall of 0.96 while maintaining a precision of 0.80. Finally, by using the optimized classifier to pre-select objects, we are able to run the orbit-fitting stage of our detection pipeline five times faster.

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arXiv2020

The Atacama Cosmology Telescope: A Catalog of > 4000 Sunyaev-Zel'dovich Galaxy Clusters

We present a catalog of 4195 optically confirmed Sunyaev-Zel'dovich (SZ) selected galaxy clusters detected with signal-to-noise > 4 in 13,211 deg$^2$ of sky surveyed by the Atacama Cosmology Telescope (ACT). Cluster candidates were selected by applying a multi-frequency matched filter to 98 and 150 GHz maps constructed from ACT observations obtained from 2008-2018, and confirmed using deep, wide-area optical surveys. The clusters span the redshift range 0.04 < z < 1.91 (median z = 0.52). The catalog contains 222 z > 1 clusters, and a total of 868 systems are new discoveries. Assuming an SZ-signal vs. mass scaling relation calibrated from X-ray observations, the sample has a 90% completeness mass limit of M500c > 3.8 x 10$^{14}$ MSun, evaluated at z = 0.5, for clusters detected at signal-to-noise ratio > 5 in maps filtered at an angular scale of 2.4'. The survey has a large overlap with deep optical weak-lensing surveys that are being used to calibrate the SZ-signal mass-scaling relation, such as the Dark Energy Survey (4566 deg$^2$), the Hyper Suprime-Cam Subaru Strategic Program (469 deg$^2$), and the Kilo Degree Survey (825 deg$^2$). We highlight some noteworthy objects in the sample, including potentially projected systems; clusters with strong lensing features; clusters with active central galaxies or star formation; and systems of multiple clusters that may be physically associated. The cluster catalog will be a useful resource for future cosmological analyses, and studying the evolution of the intracluster medium and galaxies in massive clusters over the past 10 Gyr.

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arXiv2020

Euclid: impact of nonlinear prescriptions on cosmological parameter estimation from weak lensing cosmic shear

Upcoming surveys will map the growth of large-scale structure with unprecented precision, improving our understanding of the dark sector of the Universe. Unfortunately, much of the cosmological information is encoded by the small scales, where the clustering of dark matter and the effects of astrophysical feedback processes are not fully understood. This can bias the estimates of cosmological parameters, which we study here for a joint analysis of mock Euclid cosmic shear and Planck cosmic microwave background data. We use different implementations for the modelling of the signal on small scales and find that they result in significantly different predictions. Moreover, the different nonlinear corrections lead to biased parameter estimates, especially when the analysis is extended into the highly nonlinear regime, with both the Hubble constant, $H_0$, and the clustering amplitude, $σ_8$, affected the most. Improvements in the modelling of nonlinear scales will therefore be needed if we are to resolve the current tension with more and better data. For a given prescription for the nonlinear power spectrum, using different corrections for baryon physics does not significantly impact the precision of Euclid, but neglecting these correction does lead to large biases in the cosmological parameters. In order to extract precise and unbiased constraints on cosmological parameters from Euclid cosmic shear data, it is therefore essential to improve the accuracy of the recipes that account for nonlinear structure formation, as well as the modelling of the impact of astrophysical processes that redistribute the baryons.

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arXiv2020

Dark Energy Survey Year 1 Results: Cosmological Constraints from Cluster Abundances, Weak Lensing, and Galaxy Correlations

Combining multiple observational probes is a powerful technique to provide robust and precise constraints on cosmological parameters. In this letter, we present the first joint analysis of cluster abundances and auto/cross correlations of three cosmic tracer fields measured from the first year data of the Dark Energy Survey: galaxy density, weak gravitational lensing shear, and cluster density split by optical richness. From a joint analysis of cluster abundances, three cluster cross-correlations, and auto correlations of galaxy density, we obtain $Ω_{\rm{m}}=0.305^{+0.055}_{-0.038}$ and $σ_8=0.783^{+0.064}_{-0.054}$. This result is consistent with constraints from the DES-Y1 galaxy clustering and weak lensing two-point correlation functions for the flat $νΛ$CDM model. We thus combine cluster abundances and all two-point correlations from three cosmic tracer fields and find improved constraints on cosmological parameters as well as on the cluster observable--mass scaling relation. This analysis is an important advance in both optical cluster cosmology and multi-probe analyses of upcoming wide imaging surveys.

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arXiv2020

Free-form lens model and mass estimation of the high redshift galaxy cluster ACT-CL J0102-4915, "El Gordo"

We examine the massive colliding cluster El Gordo, one of the most massive clusters at high redshift. We use a free-form lensing reconstruction method that avoids making assumptions about the mass distribution. We use data from the RELICS program and identify new multiply lensed system candidates. The new set of constraints and free-form method provides a new independent mass estimate of this intriguing colliding cluster. Our results are found to be consistent with earlier parametric models, indirectly confirming the assumptions made in earlier work. By fitting a double gNFW profile to the lens model, and extrapolating to the virial radius, we infer a total mass for the cluster of $M_{200c}=(1.08^{+0.65}_{-0.12})\times10^{15}$M$_{\odot}$. We estimate the uncertainty in the mass due to errors in the photometric redshifts, and discuss the uncertainty in the inferred virial mass due to the extrapolation from the lens model. We also find in our lens map a mass overdensity corresponding to the large cometary tail of hot gas, reinforcing its interpretation as a large tidal feature predicted by hydrodynamical simulations that mimic El Gordo. Finally, we discuss the observed relation between the plasma and the mass map, finding that the peak in the projected mass map may be associated with a large concentration of colder gas, exhibiting possible star formation. El Gordo is one of the first clusters that will be observed with JWST, which is expected to unveil new high redshift lensed galaxies around this interesting cluster, and provide a more accurate estimation of its mass.

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