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R. Benton Metcalf

Publications and source records attributed to R. Benton Metcalf.

At least 19 recordsLinked to original sources

A Consistent Implementation of Cluster Strong Lensing in Cosmological Simulation Light Cones

Galaxy cluster strong gravitational lensing plays a central role in precision cosmology, yet robust theoretical predictions have lagged behind an abundance of high-quality strong lensing observations. This shortfall reflects both a mismatch between the geometry of the strong-lensing problem and standard cubic simulation boxes, and the fundamental tension between simulation volume and resolution. Consequently, many current forecasts adopt hybrid approaches that extract individual lenses from simulations and combine them with analytic or observed source populations positioned near caustics. These methods often omit correlated and/or uncorrelated line-of-sight (LoS) structure, or include it in ways that do not preserve correlations across redshift. Here we present a fully simulation-based procedure that generates strong-lensing images directly from particle data, drawing the lens, source, and all intervening resolved objects self-consistently from the simulated large-scale structure. Our approach combines a structure-preserving remapping of the simulation volume into a lensing-appropriate geometry with multi-plane ray tracing, enabling the use of uniform simulation boxes that resolve both cluster-scale primary lenses and high-redshift source galaxies. We demonstrate the method by generating example light cones and images using IllustrisTNG data, then use these results to conservatively quantify the impact of LoS structure on image configurations and critical-curve morphology. We find that uncorrelated LoS structure can shift the relative positions of lensed images by several arcseconds, introduces a $\sim 6\%$ scatter in the area of a cluster's primary critical curve, and changes the total critical area within 100$^{\prime\prime}$ of the cluster potential minimum by $16^{+20\%}_{-14\%}$ at a source plane redshift of $z_s=4$.

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Till the core collapses: the evolution and properties of self-interacting dark matter subhalos

One of the hottest questions in the cosmology of self-interacting dark matter (SIDM) is whether scatterings can induce detectable core-collapse in halos by the present day. Because gravitational tides can accelerate core-collapse, the most promising targets to observe core-collapse are satellite galaxies and subhalo systems. However, simulating small subhalos is computationally intensive, especially when subhalos start to core-collapse. In this work, we present a hierarchical framework for simulating a population of SIDM subhalos, which reduces the computation time to linear order in the total number of subhalos. With this method, we simulate substructure lensing systems with multiple velocity-dependent SIDM models, and show how subhalo evolution depends on the SIDM model, subhalo mass and orbits. We find that an SIDM cross section of $\gtrsim 200$ cm$^2$/g at velocity scales relevant for subhalos' internal heat transfer is needed for a significant fraction of subhalos to core-collapse in a typical lens system at redshift $z=0.5$, and that core-collapse has unique observable features in lensing. We show quantitatively that core-collapse in subhalos is typically accelerated compared to field halos, except when the SIDM cross section is non-negligible ($\gtrsim \mathcal{O}(1)$ cm$^2$/g) at subhalos' orbital velocities, in which case evaporation by the host can delay core-collapse. This suggests that substructure lensing can be used to probe velocity-dependent SIDM models, especially if line-of-sight structures (field halos) can be distinguished from lens-plane subhalos. Intriguingly, we find that core-collapse in subhalos can explain the recently reported ultra-steep density profiles of substructures found by lensing with the \emph{Hubble Space Telescope}

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Weak Lensing the non-Linear Ly-alpha Forest

We evaluate the performance of the Lyman-$α$ forest weak gravitational lensing estimator of Metcalf et al. on forest data from hydrodynamic simulations and ray-traced simulated lensing potentials. We compare the results to those obtained from the Gaussian random field simulated Ly$α$ forest data and lensing potentials used in previous work. We find that the estimator is able to reconstruct the lensing potentials from the more realistic data, and investigate dependence on spectrum signal to noise. The non-linearity and non-Gaussianity in this forest data arising from gravitational instability and hydrodynamics causes a reduction in signal to noise by a factor of $\sim2.7$ for noise free data and a factor of $\sim 1.5$ for spectra with signal to noise of order unity (comparable to current observational data). Compared to Gaussian field lensing potentials, using ray-traced potentials from N-body simulations incurs a further signal to noise reduction of a factor of $\sim1.3$ at all noise levels. The non-linearity in the forest data is also observed to increase bias in the reconstructed potentials by $5-25\%$, and the ray-traced lensing potential further increases the bias by $20-30\%$. We demonstrate methods for mitigating these issues including Gaussianization and bias correction which could be used in real observations.

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Forecasting the Detection of Lyman-Alpha Forest Weak Lensing from the Dark Energy Spectroscopic Instrument and Other Future Surveys

The apparent angular positions of quasars are deflected on the sky by the gravitational field sourced by foreground matter. This weak lensing effect is measurable through the distortions it introduces in the lensed quasar spectra. Discrepancies in the statistics of the Lyman-$α$ forest spectral absorption features can be used to reconstruct the foreground lensing potential. We extend the study of this method of Lyman-$α$ forest weak gravitational lensing to lower angular forest spectrum source densities than previous work. We evaluate the performance of the Lyman-$α$ lensing estimator of Metcalf et al. (2020) on mock data based on the angular forest source density ($50$ per square degree) and volume ($\sim$700,000 spectra total) of the DESI survey. We simulate the foreground galaxy distribution and lensing potentials with redshift evolution approximated by N-body simulation and simulate Gaussian-random Lyman-$α$ forests to produce mock data for the entire DESI footprint. By correlating the foreground galaxy distribution with the potential reconstructed by the estimator, we find that a weak lensing detection with signal to noise of $\sim4$ will be possible with the full DESI data. We show that spectral surveys with low density and high volume are promising candidates for forest weak lensing in addition to the high resolution data that have been considered in previous work. We present forecasts for future spectral surveys and show that with larger datasets a detection with signal to noise $>10$ will be possible.

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LCDM halo substructure properties revealed with high resolution and large volume cosmological simulations

We investigate the structural properties, distribution and abundance of LCDM dark matter subhaloes using the Phi-4096 and Uchuu suite of N-body cosmological simulations. Thanks to the combination of their large volume, high mass resolution and superb statistics, we are able to quantify -- for the first time consistently over more than seven decades in ratio of subhalo-to-host-halo mass -- dependencies of subhalo properties with mass, maximum circular velocity, Vmax, host halo mass and distance to host halo centre. We also dissect the evolution of these dependencies over cosmic time. We provide accurate fits for the subhalo mass and velocity functions, both exhibiting decreasing power-law slopes in the expected range of values and with no significant dependence on redshift. We also find subhalo abundance to depend weakly on host halo mass. We explore the distribution of subhaloes within their hosts and its evolution over cosmic time for subhaloes located as deep as ~0.1 per cent of the host virial radius. Subhalo structural properties are codified via a concentration parameter, cV, that does not depend on any specific, pre-defined density profile and relies only on Vmax. We derive the cV-Vmax relation in the range 7-1500 km/s and find an important dependence on distance of the subhalo to the host halo centre, as already described in Moliné et al. (2017). Interestingly, we also find subhaloes of the same mass to be significantly more concentrated into more massive hosts. Finally, we investigate the redshift evolution of cV, and provide accurate fits that take into account all mentioned dependencies. Our results offer an unprecedented detailed characterization of the subhalo population, consistent over a wide range of subhalo and host halo masses, as well as cosmic times. Our work enables precision work in any future research involving dark matter halo substructure.

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Rubin-Euclid Derived Data Products: Initial Recommendations

This report is the result of a joint discussion between the Rubin and Euclid scientific communities. The work presented in this report was focused on designing and recommending an initial set of Derived Data products (DDPs) that could realize the science goals enabled by joint processing. All interested Rubin and Euclid data rights holders were invited to contribute via an online discussion forum and a series of virtual meetings. Strong interest in enhancing science with joint DDPs emerged from across a wide range of astrophysical domains: Solar System, the Galaxy, the Local Volume, from the nearby to the primaeval Universe, and cosmology.

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Testing strong lensing subhalo detection with a cosmological simulation

Strong gravitational lensing offers a compelling test of the cold dark matter paradigm, as it allows for subhaloes with masses of $\sim10^{9}$ M$_\odot$ and below to be detected. We test commonly-used techniques for detecting subhaloes superposed in images of strongly lensed galaxies. For the lens we take a simulated galaxy in a $\sim10^{13}$ M$_\odot$ halo grown in a high-resolution cosmological hydrodynamical simulation, which we view from two different directions. Though the resolution is high, we note the simulated galaxy still has an artificial core which adds additional complexity to the baryon dominated region. To remove particle noise, we represent the projected galaxy mass distribution by a series of Gaussian profiles which precisely capture the features of the projected galaxy. We first model the lens mass as a (broken) power-law density profile and then search for small haloes. Of the two projections, one has a regular elliptical shape, while the other has distinct deviations from an elliptical shape. For the former, the broken power-law model gives no false positives and correctly recovers the mass of the superposed small halo, but for the latter we find false positives and the inferred halo mass is overestimated by $\sim4-5$ times. We then use a more complex model in which the lens mass is decomposed into stellar and dark matter components. In this case, we show that we can capture the simulated galaxy's complex projected structures and correctly infer the input small halo.

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Developing a Victorious Strategy to the Second Strong Gravitational Lensing Data Challenge

Strong Lensing is a powerful probe of the matter distribution in galaxies and clusters and a relevant tool for cosmography. Analyses of strong gravitational lenses with Deep Learning have become a popular approach due to these astronomical objects' rarity and image complexity. Next-generation surveys will provide more opportunities to derive science from these objects and an increasing data volume to be analyzed. However, finding strong lenses is challenging, as their number densities are orders of magnitude below those of galaxies. Therefore, specific Strong Lensing search algorithms are required to discover the highest number of systems possible with high purity and low false alarm rate. The need for better algorithms has prompted the development of an open community data science competition named Strong Gravitational Lensing Challenge (SGLC). This work presents the Deep Learning strategies and methodology used to design the highest-scoring algorithm in the II SGLC. We discuss the approach used for this dataset, the choice for a suitable architecture, particularly the use of a network with two branches to work with images in different resolutions, and its optimization. We also discuss the detectability limit, the lessons learned, and prospects for defining a tailor-made architecture in a survey in contrast to a general one. Finally, we release the models and discuss the best choice to easily adapt the model to a dataset representing a survey with a different instrument. This work helps to take a step towards efficient, adaptable and accurate analyses of strong lenses with deep learning frameworks.

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Detecting gravitational lenses using machine learning: exploring interpretability and sensitivity to rare lensing configurations

Forthcoming large imaging surveys such as Euclid and the Vera Rubin Observatory Legacy Survey of Space and Time are expected to find more than $10^5$ strong gravitational lens systems, including many rare and exotic populations such as compound lenses, but these $10^5$ systems will be interspersed among much larger catalogues of $\sim10^9$ galaxies. This volume of data is too much for visual inspection by volunteers alone to be feasible and gravitational lenses will only appear in a small fraction of these data which could cause a large amount of false positives. Machine learning is the obvious alternative but the algorithms' internal workings are not obviously interpretable, so their selection functions are opaque and it is not clear whether they would select against important rare populations. We design, build, and train several Convolutional Neural Networks (CNNs) to identify strong gravitational lenses using VIS, Y, J, and H bands of simulated data, with F1 scores between 0.83 and 0.91 on 100,000 test set images. We demonstrate for the first time that such CNNs do not select against compound lenses, obtaining recall scores as high as 76\% for compound arcs and 52\% for double rings. We verify this performance using Hubble Space Telescope (HST) and Hyper Suprime-Cam (HSC) data of all known compound lens systems. Finally, we explore for the first time the interpretability of these CNNs using Deep Dream, Guided Grad-CAM, and by exploring the kernels of the convolutional layers, to illuminate why CNNs succeed in compound lens selection.

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SEAGLE--III: Towards resolving the mismatch in the dark-matter fraction in early-type galaxies between simulations and observations

The central dark-matter fraction of galaxies is sensitive to feedback processes during galaxy formation. Strong gravitational lensing has been effective in the precise measurement of the dark-matter fraction inside massive early-type galaxies. Here, we compare the projected dark-matter fraction of early-type galaxies inferred from the SLACS strong-lens survey, with those obtained from the EAGLE, Illustris, and IllustrisTNG hydro-dynamical simulations. Previous comparisons with some simulations revealed a large discrepancy, with considerably higher inferred dark-matter fractions -- by factors 2-3 -- inside half of the effective radius in observed strong-lens galaxies as compared to simulated galaxies. Here, we report good agreement between EAGLE and SLACS for the dark-matter fractions inside both half of the effective radius and the effective radius as a function of the galaxy's stellar mass, effective radius, and total mass-density slope. However, for IllustrisTNG and Illustris, the dark-matter fractions are lower than observed. This work consistently assumes a Chabrier IMF, which suggests that a different IMF (although not excluded) is not necessary to resolve this mismatch. The differences in the stellar feedback model between EAGLE and Illustris and IllustrisTNG, are likely the dominant cause of the difference in their dark-matter fraction, and density slope.

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The Uchuu Simulations: Data Release 1 and Dark Matter Halo Concentrations

We introduce the Uchuu suite of large high-resolution cosmological $N$-body simulations. The largest simulation, named Uchuu, consists of 2.1 trillion ($12800^3$) dark matter particles in a box of side-length 2.0 Gpc/h, with particle mass $3.27 \times 10^{8}$ Msun/h. The highest resolution simulation, Shin-Uchuu, consists of 262 billion ($6400^3$) particles in a box of side-length 140 Mpc/h, with particle mass $8.97 \times 10^{5}$ Msun/h. Combining these simulations we can follow the evolution of dark matter halos and subhalos spanning those hosting dwarf galaxies to massive galaxy clusters across an unprecedented volume. In this first paper, we present basic statistics, dark matter power spectra, and the halo and subhalo mass functions, which demonstrate the wide dynamic range and superb statistics of the Uchuu suite. From an analysis of the evolution of the power spectra we conclude that our simulations remain accurate from the Baryon Acoustic Oscillation scale down to the very small. We also provide parameters of a mass-concentration model, which describes the evolution of halo concentration and reproduces our simulation data to within 5 per cent for halos with masses spanning nearly eight orders of magnitude at redshift 0 0.5, whereas no upturn is detected at z<0.5. We make publicly available various $N$-body products as part of Uchuu Data Release 1 on the Skies & Universes site. Future releases will include gravitational lensing maps and mock galaxy, X-ray cluster, and active galactic nuclei catalogues.

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Euclid preparation: XII. Optimizing the photometric sample of the Euclid survey for galaxy clustering and galaxy-galaxy lensing analyses

The accuracy of photometric redshifts (photo-zs) particularly affects the results of the analyses of galaxy clustering with photometrically-selected galaxies (GCph) and weak lensing. In the next decade, space missions like Euclid will collect photometric measurements for millions of galaxies. These data should be complemented with upcoming ground-based observations to derive precise and accurate photo-zs. In this paper, we explore how the tomographic redshift binning and depth of ground-based observations will affect the cosmological constraints expected from Euclid. We focus on GCph and extend the study to include galaxy-galaxy lensing (GGL). We add a layer of complexity to the analysis by simulating several realistic photo-z distributions based on the Euclid Consortium Flagship simulation and using a machine learning photo-z algorithm. We use the Fisher matrix formalism and these galaxy samples to study the cosmological constraining power as a function of redshift binning, survey depth, and photo-z accuracy. We find that bins with equal width in redshift provide a higher Figure of Merit (FoM) than equipopulated bins and that increasing the number of redshift bins from 10 to 13 improves the FoM by 35% and 15% for GCph and its combination with GGL, respectively. For GCph, an increase of the survey depth provides a higher FoM. But the addition of faint galaxies beyond the limit of the spectroscopic training data decreases the FoM due to the spurious photo-zs. When combining both probes, the number density of the sample, which is set by the survey depth, is the main factor driving the variations in the FoM. We conclude that there is more information that can be extracted beyond the nominal 10 tomographic redshift bins of Euclid and that we should be cautious when adding faint galaxies into our sample, since they can degrade the cosmological constraints.

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SEAGLE--II: Constraints on feedback models in galaxy formation from massive early type strong lens galaxies

We use nine different galaxy formation scenarios in ten cosmological simulation boxes from the EAGLE suite of ΛCDM hydrodynamical simulations to assess the impact of feedback mechanisms in galaxy formation and compare these to observed strong gravitational lenses. To compare observations with simulations, we create strong lenses with $M_\star$ > $10^{11}$ $M_\odot$ with the appropriate resolution and noise level, and model them with an elliptical power-law mass model to constrain their total mass density slope. We also obtain the mass-size relation of the simulated lens-galaxy sample. We find significant variation in the total mass density slope at the Einstein radius and in the projected stellar mass-size relation, mainly due to different implementations of stellar and AGN feedback. We find that for lens selected galaxies, models with either too weak or too strong stellar and/or AGN feedback fail to explain the distribution of observed mass-density slopes, with the counter-intuitive trend that increasing the feedback steepens the mass density slope around the Einstein radius ($\approx$ 3-10 kpc). Models in which stellar feedback becomes inefficient at high gas densities, or weaker AGN feedback with a higher duty cycle, produce strong lenses with total mass density slopes close to isothermal (i.e. -d log(ρ)/d log(r) $\approx$ 2.0) and slope distributions statistically agreeing with observed strong lens galaxies in SLACS and BELLS. Agreement is only slightly worse with the more heterogeneous SL2S lens galaxy sample. Observations of strong-lens selected galaxies thus appear to favor models with relatively weak feedback in massive galaxies.

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An excess of small-scale gravitational lenses observed in galaxy clusters

Cold dark matter (CDM) constitutes most of the matter in the Universe. The interplay between dark and luminous matter in dense cosmic environments like galaxy clusters is studied theoretically using cosmological simulations. Observed gravitational lensing is used to test and characterize the properties of substructures - the small-scale distribution of dark matter - in clusters. An apt metric, the probability of strong lensing events produced by dark matter substructure, is devised and computed for 11 galaxy clusters. We report that observed cluster substructures are more efficient lenses than predicted by CDM simulations, by more than an order of magnitude. We suggest that hitherto undiagnosed systematic issues with simulations or incorrect assumptions about the properties of dark matter could explain our results.

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Euclid preparation: VIII. The Complete Calibration of the Colour-Redshift Relation survey: VLT/KMOS observations and data release

The Complete Calibration of the Colour-Redshift Relation survey (C3R2) is a spectroscopic effort involving ESO and Keck facilities designed to empirically calibrate the galaxy colour-redshift relation - P(z|C) to the Euclid depth (i_AB=24.5) and is intimately linked to upcoming Stage IV dark energy missions based on weak lensing cosmology. The aim is to build a spectroscopic calibration sample that is as representative as possible of the galaxies of the Euclid weak lensing sample. In order to minimise the number of spectroscopic observations to fill the gaps in current knowledge of the P(z|C), self-organising map (SOM) representations of the galaxy colour space have been constructed. Here we present the first results of an ESO@ VLT Large Programme approved in the context of C3R2, which makes use of the two VLT optical and near-infrared multi-object spectrographs, FORS2 and KMOS. This paper focuses on high-quality spectroscopic redshifts of high-z galaxies observed with the KMOS spectrograph in the H- and K-bands. A total of 424 highly-reliable z are measured in the 1.3<=z<=2.5 range, with total success rates of 60.7% in the H-band and 32.8% in the K-band. The newly determined z fill 55% of high and 35% of lower priority empty SOM grid cells. We measured Halpha fluxes in a 1."2 radius aperture from the spectra of the spectroscopically confirmed galaxies and converted them into star formation rates. In addition, we performed an SED fitting analysis on the same sample in order to derive stellar masses, E(B-V), total magnitudes, and SFRs. We combine the results obtained from the spectra with those derived via SED fitting, and we show that the spectroscopic failures come from either weakly star-forming galaxies (at z<1.7, i.e. in the H-band) or low S/N spectra (in the K-band) of z>2 galaxies.

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Testing the Reliability of Fast Methods for Weak Lensing Simulations: WL-MOKA on PINOCCHIO

The generation of simulated convergence maps is of key importance in fully exploiting weak lensing by Large Scale Structure (LSS) from which cosmological parameters can be derived. In this paper we present an extension of the PINOCCHIO code which produces catalogues of dark matter haloes so that it is capable of simulating weak lensing by LSS. Like WL-MOKA, the method starts with a random realisation of cosmological initial conditions, creates a halo catalogue and projects it onto the past-light-cone, and paints in haloes assuming parametric models for the mass density distribution within them. Large scale modes that are not accounted for by the haloes are constructed using linear theory. We discuss the systematic errors affecting the convergence power spectra when Lagrangian Perturbation Theory at increasing order is used to displace the haloes within PINOCCHIO, and how they depend on the grid resolution. Our approximate method is shown to be very fast when compared to full ray-tracing simulations from an N-Body run and able to recover the weak lensing signal, at different redshifts, with a few percent accuracy. It also allows for quickly constructing weak lensing covariance matrices, complementing PINOCCHIO's ability of generating the cluster mass function and galaxy clustering covariances and thus paving the way for calculating cross covariances between the different probes. This work advances these approximate methods as tools for simulating and analysing surveys data for cosmological purposes.

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Reconstructing the Gravitational Lensing Potential from the Lyman-$α$ Forest

We demonstrate a method for reconstructing the weak lensing potential from the Lyman-$α$ forest data. We derive an optimal estimator for the lensing potential on the sky based on the correlation between pixels in real space. This method effectively deals with irregularly spaced data, holes in the survey, missing data and inhomogeneous noise. We demonstrate an implementation of the method with simulated spectra and weak lensing. It is shown that with a source density of $>\sim 0.5$ per square arcminutes and $\sim 200$ pixels in each spectrum ($λ/ Δλ= 1300$) the lensing potential can be reconstructed with high fidelity if the relative absorption in the spectral pixels is signal dominated. When noise dominates the measurement of the absorption in each pixel the noise in the lensing potential is higher, but for reasonable numbers of sources and noise levels and a high fidelity map the lensing potential is obtainable. The lensing estimator could also be applied to lensing of the Cosmic Microwave Background (CMB), 21 cm intensity mapping (IM) or any case in which the correlation function of the source can be accurately estimated.

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UNIT project: Universe $N$-body simulations for the Investigation of Theoretical models from galaxy surveys

We present the UNIT $N$-body cosmological simulations project, designed to provide precise predictions for nonlinear statistics of the galaxy distribution. We focus on characterizing statistics relevant to emission line and luminous red galaxies in the current and upcoming generation of galaxy surveys. We use a suite of precise particle mesh simulations (FastPM) as well as with full $N$-body calculations with a mass resolution of $\sim 1.2\times10^9\,h^{-1}$M$_{\odot}$ to investigate the recently suggested technique of Angulo & Pontzen 2016 to suppress the variance of cosmological simulations We study redshift space distortions, cosmic voids, higher order statistics from $z=2$ down to $z=0$. We find that both two- and three-point statistics are unbiased. Over the scales of interest for baryon acoustic oscillations and redshift-space distortions, we find that the variance is greatly reduced in the two-point statistics and in the cross correlation between halos and cosmic voids, but is not reduced significantly for the three-point statistics. We demonstrate that the accuracy of the two-point correlation function for a galaxy survey with effective volume of 20 ($h^{-1}$Gpc)$^3$ is improved by about a factor of 40, indicating that two pairs of simulations with a volume of 1 ($h^{-1}$Gpc)$^3$ lead to the equivalent variance of $\sim$150 such simulations. The $N$-body simulations presented here thus provide an effective survey volume of about seven times the effective survey volume of DESI or Euclid. The data from this project, including dark matter fields, halo catalogues, and their clustering statistics, are publicly available at http://www.unitsims.org.

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