arXiv subjects

Andrés N. Salcedo

Publications and source records attributed to Andrés N. Salcedo.

At least 19 recordsLinked to original sources

arXiv2026

Multiprobe cosmology forecasts from halo-occupation-distribution-based forward modeling of galaxy and void statistics

The large under-dense regions in the cosmological matter density field, known as cosmic voids, are powerful probes of cosmology but their potential is currently under-exploited. Observationally, voids are identified within the large scale distribution of galaxies and are therefore sensitive to certain features of the galaxy-halo connection. This sensitivity makes the combination of void and galaxy summary statistics particularly powerful probes of both cosmology and the galaxy-halo connection through self-calibration of the void-galaxy relation. In particular the combination of void and galaxy summary statistics breaks degeneracies in the galaxy-halo connection and cosmology relative to the case of galaxy clustering alone. To demonstrate this we forecast cosmological constraints attainable from the combination of the void size function $n_v$, projected void-galaxy cross-correlation function $w_{p,vg}$ and projected galaxy auto-correlation function $w_{p,gg}$ measured in Dark Energy Spectroscopic Instrument (DESI) Year 5 data. We use a grid of cosmological N-body simulations to model this datavector as a function of $σ_8$, $Ω_m$ and a flexible halo occupation distribution (HOD) model that includes central and satellite galaxy assembly bias. For our fiducial scenario combining $n_v$, $w_{p,vg}$ and $w_{p,gg}$ we forecast $1.5\%$ and $0.8\%$ constraints on $Ω_m$ and $σ_8$ from DESI-Y5 data. We also forecast constraints from the combination of the void size function and stacked weak lensing signal of voids.

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arXiv2026

A Parameter-Masked Mock Data Challenge for Beyond-Two-Point Galaxy Clustering Statistics

The last few years have seen the emergence of a wide array of novel techniques for analyzing high-precision data from upcoming galaxy surveys, which aim to extend the statistical analysis of galaxy clustering data beyond the linear regime and the canonical two-point (2pt) statistics. We test and benchmark some of these new techniques in a community data challenge "Beyond-2pt", initiated during the Aspen 2022 Summer Program "Large-Scale Structure Cosmology beyond 2-Point Statistics," whose first round of results we present here. The challenge dataset consists of high-precision mock galaxy catalogs for clustering in real space, redshift space, and on a light cone. Participants in the challenge have developed end-to-end pipelines to analyze mock catalogs and extract unknown ("masked") cosmological parameters of the underlying $Λ$CDM models with their methods. The methods represented are density-split clustering, nearest neighbor statistics, BACCO power spectrum emulator, void statistics, LEFTfield field-level inference using effective field theory (EFT), and joint power spectrum and bispectrum analyses using both EFT and simulation-based inference. In this work, we review the results of the challenge, focusing on problems solved, lessons learned, and future research needed to perfect the emerging beyond-2pt approaches. The unbiased parameter recovery demonstrated in this challenge by multiple statistics and the associated modeling and inference frameworks supports the credibility of cosmology constraints from these methods. The challenge data set is publicly available and we welcome future submissions from methods that are not yet represented.

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arXiv2026

Setting SAIL: Leveraging Scientist-AI-Loops for Rigorous Visualization Tools

Scientists across all disciplines share a common challenge: the divide between their theoretical knowledge and the specialized skills and time needed to build interactive tools to communicate this expertise. While large language models (LLMs) offer unparalleled acceleration in code generation, they frequently prioritize functional syntax over scientific accuracy, risking visually convincing but scientifically invalid results. This work advocates the Scientist-AI-Loop (SAIL), a framework designed to harness this speed without compromising rigor. By separating domain logic from code syntax, SAIL enables researchers to maintain strict oversight of scientific concepts and constraints while delegating code implementation to AI. We illustrate this approach through two open-source, browser-based astrophysics tools: an interactive gravitational lensing visualization and a large-scale structure formation sandbox, both publicly available. Our methodology condensed development to mere days while maintaining scientific integrity. We specifically address failure modes where AI-generated code neglects phenomenological boundaries or scientific validity. While cautioning that research-grade code requires stringent protocols, we demonstrate through two examples that SAIL provides an effective code generation workflow for outreach, teaching, professional presentations, and early-stage research prototyping. This framework contributes to a foundation for the further development of AI-assisted scientific software.

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arXiv2026

Unifying cluster and galaxy cosmology analyses using the galaxy-halo connection

Galaxies and galaxy clusters trace the same cosmic density field, but their statistics have been modeled separately in cosmological analyses. We present a unified, simulation-based framework to model them using the galaxy-halo connection. Our analysis includes cluster lensing, galaxy clustering, and galaxy-cluster cross-correlation. We validate our method on the FLAMINGO hydrodynamic simulation. Relative to the cluster-only approach, combining these probes improves the $σ_8-Ω_m$ figure of merit by a factor of 15. Our framework enables stringent tests of cosmological models and exploits small-scale information.

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arXiv2025

What Galaxy Clusters Have to Say About Dynamical Dark Energy and $H_0$

We show that, in flat $Λ$CDM, low-redshift structure probes -- cluster abundances, 3$\times$2-point analyses, and full-shape clustering -- are mutually consistent, jointly delivering precise constraints on $σ_8$ and $Ω_{\rm m}$ that agree with geometrical datasets (CMB+BAO+SN). In $w_0w_a$CDM, adding clusters to the geometry dataset reduces the evidence for evolving dark energy while relaxing the $H_0$ tension, suggesting a $Λ$CDM evolution of the late-time Universe and a sound horizon that differs from its standard value.

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arXiv2025

One Galaxy Sample to Rule Them All: Halo Occupation Distribution Modeling of DES Year 3 Source Galaxies

For the joint analysis of second-order weak lensing and galaxy clustering statistics, so-called $3{\times}2$ analyses, the selection and characterization of optimal galaxy samples is a major area of research. One promising choice is to use the same galaxy sample as lenses and sources, which reduces the systematics parameter space that describes uncertainties related to galaxy samples. Such a ``lens-equal-source'' analysis significantly improves self-calibration of photo-z systematics leading to improved cosmological constraints. With the aim to enable a lens-equal-source analysis on small scales we investigate the halo-galaxy connection of DES-Y3 source galaxies. We develop a technique to construct mock source galaxy populations by matching COSMOS/UltraVISTA photometry onto {\sc{UniverseMachine}} galaxies. These mocks predict a source halo occupation distribution (HOD) that exhibits significant redshift evolution, non-trivial central incompleteness and galaxy assembly bias. We produce multiple realizations of mock source galaxies drawn from the {\sc{UniverseMachine}} posterior with added uncertainties in measured DES photometry and galaxy shapes. We fit a modified HOD formalism to these realizations to produce priors on the galaxy-halo connection for cosmological analyses. We additionally train an emulator that predicts this HOD to $\sim2\%$ accuracy from redshift $z = 0.1 - 1.3$ that models the dependence of this HOD on 1) observational uncertainties in galaxy size and photometry, and 2) uncertainties in the {\sc{UniverseMachine}} predictions.

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arXiv2025

Cosmological Constraints from Dark Energy Survey Year 1 Cluster Lensing and Abundances with Simulation-based Forward-Modeling

We present a simulation-based forward-modeling framework for cosmological inference from optical galaxy-cluster samples, and apply it to the abundance and weak-lensing signals of DES-Y1 redMaPPer clusters. The model embeds cosmology-dependent optical selection using a counts-in-cylinders approach, while also accounting for cluster miscentering and baryonic feedback in lensing. Applied to DES-Y1, and assuming a flat $Λ$CDM cosmology, we obtain $Ω_m=0.254^{+0.026}_{-0.020}$ and $σ_8=0.826^{+0.030}_{-0.034}$, consistent with a broad suite of low-redshift structure measurements, including recent full-shape analyses, the DES/KiDS/HSC 3$\times$2 results, and most cluster-abundance studies. Our results are also consistent with \textit{Planck}, with the difference being significant at $2.58σ$. These results establish simulation-based forward-modeling of cluster abundances as a promising new tool for precision cosmology with Stage~IV survey data.

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arXiv2025

Direct Measurement of Galaxy Assembly Bias using DESI DR1 Data

We report the first direct measurement of galaxy assembly bias, a critical systematic in cosmology, from the Dark Energy Spectroscopic Instrument (DESI) Bright Galaxy Survey. We introduce a novel, cosmology-independent method to measure the halo occupation distribution (HOD) by combining a state-of-the-art group catalog with weak gravitational lensing. For groups binned by total luminosity, we determine the galaxy occupation number $N_{\rm gal}$ from group-galaxy cross-correlations, while weak lensing constrains the average halo mass $M_h$. Applying this to a volume-limited sample at $z{\in}[0.05,0.2]$, we measure the dependence of HOD, $N_{\rm gal}(M_h)$, on large-scale overdensity $δ_{g}$. Focusing on the satellite galaxies, we find an assembly bias parameter of $Q_{\rm sat}{=}0.05{\pm}0.14$, a result consistent with zero and in tension with many empirical galaxy formation models. Our method provides a robust approach for characterizing galaxy assembly bias to achieve precision cosmology with DESI and future Stage-V surveys.

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arXiv2025

Association between optically identified galaxy clusters and the underlying dark matter halos

Clusters of galaxies trace massive dark matter halos in the Universe, but they can include multiple halos projected along lines of sight. As a case study, we quantify the properties of halos contributing to clusters identified by the redMaPPer algorithm using the Cardinal simulation, which mimics the Dark Energy Survey data. For each cluster, we identify the halos hosting its member galaxies, and we define the main halo as the one contributing the most to the cluster's richness ($λ$, the estimated number of member galaxies). At $z=0.3$, for clusters with $λ> 60$, the main halo typically contributes to $92\%$ of the richness, and this fraction drops to $67\%$ for $λ\approx 20$. Defining "clean" clusters as those with $\geq50\%$ of the richness contributed by the main halo, we find that $100\%$ of the $λ> 60$ clusters are clean, while $73\%$ of the $λ\approx 20$ clusters are clean. Three halos can usually account for more than $80\%$ of the richness of a cluster. The main halos associated with redMaPPer clusters have a completeness ranging from $98\%$ at virial mass $10^{14.6}~h^{-1}M_{\odot}$ to $64\%$ at $10^{14}~h^{-1}M_{\odot}$. In addition, we compare the inferred cluster centers with true halo centers, finding that $30\%$ of the clusters are miscentered with a mean offset $40\%$ of the cluster radii, in agreement with recent X-ray studies. These systematics worsen as redshift increases, but we expect that upcoming surveys extending to longer wavelengths will improve the cluster finding at high redshifts. Our results affirm the robustness of the redMaPPer algorithm and provide a framework for benchmarking other cluster-finding strategies.

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arXiv2025

Optical galaxy cluster mock catalogs with realistic projection effects: Validations with the SDSS clusters

Galaxy clusters identified in optical imaging surveys suffer from projection effects: Physically unassociated galaxies along a cluster's line of sight can be counted as its members and boost the observed richness (the number of cluster members). To model the impact of projection on cluster cosmology analyses, we apply a halo occupation distribution model to N-body simulations to simulate the red galaxies contributing to cluster members, and we use the number of galaxies in a cylinder along the line of sight (counts in cylinders) to model the impact of projection on cluster richness. We compare three projection models: uniform, quadratic, and Gaussian, and we convert between them by matching their effective cylinder volumes. We validate our mock catalogs using SDSS redMaPPer clusters' data vectors, including counts vs. richness, stacked lensing signal, spectroscopic redshift distribution of member galaxies, and richness remeasured on a redshift grid. We find the former two are insensitive to the projection model, while the latter two favor a quadratic projection model with a width of approximately 180 Mpc/h (equivalent to the volume of a uniform model with a width of 100 Mpc/h and a Gaussian model with a width of 110 Mpc/h, or a Gaussian redshift error of 0.04). Our framework provides an efficient and flexible way to model optical cluster data vectors, paving the way for a simulation-based joint analysis for clusters, galaxies, and shear.

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arXiv2023

Forecasting the constraints on optical selection bias and projection effects of galaxy cluster lensing with multiwavelength data

Galaxy clusters identified with optical imaging tend to suffer from projection effects, which impact richness (the number of member galaxies in a cluster) and lensing coherently. Physically unassociated galaxies can be mistaken as cluster members due to the significant uncertainties in their line-of-sight distances, thereby changing the observed cluster richness; at the same time, projection effects alter the weak gravitational lensing signals of clusters, leading to a correlated scatter between richness and lensing at a given halo mass. As a result, the lensing signals for optically selected clusters tend to be biased high. This optical selection bias problem of cluster lensing is one of the key challenges in cluster cosmology. Fortunately, recently available multiwavelength observations of clusters provide a solution. We analyze a simulated data set mimicking the observed lensing of clusters identified by both optical photometry and gas properties, aiming to constrain this selection bias. Assuming a redMaPPer sample from the Dark Energy Survey with South Pole Telescope Sunyaev-Zeldovich effect observations, we find that an overlapping survey of 1300 square deg, 0.2 < z < 0.65, can constrain the average lensing bias to an accuracy of 5 percent. This provides an exciting opportunity for directly constraining optical selection bias from observations. We further show that our approach can remove the optical selection bias from the lensing signal, paving the way for future optical cluster cosmology analyses.

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arXiv2023

Dark Energy Survey Year 1 Clusters are Consistent with Planck

The recent Dark Energy Survey Year 1 (DES-Y1) analysis of galaxy cluster abundances and weak lensing produced $Ω_{\rm m}$ and $σ_8$ constraints in 5.6$σ$ tension with Planck. It is suggested in that work that this tension is driven by unmodelled systematics in optical cluster selection. We present a novel simulation-based forward modeling framework that explicitly incorporates cluster selection into its model predictions. Applying this framework to the DES-Y1 data we find consistency with Planck, resolving the tension found in the DES-Y1 analysis. An extension of this approach to the final DES data set will produce robust constraints on $Λ$CDM parameters and correspondingly strong tests of cosmological models.

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arXiv2023

Self-calibrating optical galaxy cluster selection bias using cluster, galaxy, and shear cross-correlations

The clustering signals of galaxy clusters are known to be powerful tools for self-calibrating the mass-observable relation and are complementary to cluster abundance and lensing. In this work, we explore the possibility of combining three correlation functions -- cluster lensing, the cluster-galaxy cross-correlation function, and the galaxy auto-correlation function -- to self-calibrate optical cluster selection bias, the boosted clustering and lensing signals in a richness-selected sample mainly caused by projection effects. We develop mock catalogues of redMaGiC-like galaxies and redMaPPer-like clusters by applying Halo Occupation Distribution (HOD) models to N-body simulations and using counts-in-cylinders around massive haloes as a richness proxy. In addition to the previously known small-scale boost in projected correlation functions, we find that the projection effects also significantly boost 3D correlation functions out to scales 100 $h^{-1} \mathrm{Mpc}$. We perform a likelihood analysis assuming survey conditions similar to that of the Dark Energy Survey (DES) and show that the selection bias can be self-consistently constrained at the 10% level. We discuss strategies for applying this approach to real data. We expect that expanding the analysis to smaller scales and using deeper lensing data would further improve the constraints on cluster selection bias.

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arXiv2022

Elucidating Galaxy Assembly Bias in SDSS

We investigate the level of galaxy assembly bias in the Sloan Digital Sky Survey (SDSS) main galaxy sample using ELUCID, a state-of-the-art constrained simulation that accurately reconstructed the initial density perturbations within the SDSS volume. On top of the ELUCID haloes, we develop an extended HOD model that includes the assembly bias of central and satellite galaxies, parameterized as $\mathcal{Q}_\mathrm{cen}$ and $\mathcal{Q}_\mathrm{sat}$, respectively, to predict a suite of one- and two-point observables. In particular, our fiducial constraint employs the probability distribution of the galaxy number counts measured on $8\,\mathrm{Mpc}\,h^{-1}$ scales $N_8^g$ and the projected cross-correlation functions of quintiles of galaxies selected by $N_8^g$ with our entire galaxy sample. We perform extensive tests of the efficacy of our method by fitting the same observables to mock data using both constrained and non-constrained simulations. We discover that in many cases the level of cosmic variance between the two simulations can produce biased constraints that lead to an erroneous detection of galaxy assembly bias if the non-constrained simulation is used. When applying our method to the SDSS data, the ELUCID reconstruction effectively removes an otherwise strong degeneracy between cosmic variance and galaxy assembly bias in SDSS, enabling us to derive an accurate and stringent constraint on the latter. Our fiducial ELUCID constraint, for galaxies above a stellar mass threshold $M_*{=}10^{10.2}\,h^{-2}\,M_\odot$, is $\mathcal{Q}_\mathrm{cen}{=}{-}0.09\pm{0.05}$ and $\mathcal{Q}_\mathrm{sat}{=}0.09\pm{0.10}$, indicating no evidence for a significant~($>2σ$) galaxy assembly bias in the local Universe probed by SDSS. Finally, our method provides a promising path to the robust modelling of the galaxy-halo connection within future surveys like DESI and PFS.

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arXiv2022

Optical selection bias and projection effects in stacked galaxy cluster weak lensing

Cosmological constraints from current and upcoming galaxy cluster surveys are limited by the accuracy of cluster mass calibration. In particular, optically identified galaxy clusters are prone to selection effects that can bias the weak lensing mass calibration. We investigate the selection bias of the stacked cluster lensing signal associated with optically selected clusters, using clusters identified by the redMaPPer algorithm in the Buzzard simulations as a case study. We find that at a given cluster halo mass, the residuals of redMaPPer richness and weak lensing signal are positively correlated. As a result, for a given richness selection, the stacked lensing signal is biased high compared with what we would expect from the underlying halo mass probability distribution. The cluster lensing selection bias can thus lead to overestimated mean cluster mass and biased cosmology results. We show that the lensing selection bias exhibits a strong scale-dependence and is approximately 20 to 60 percent for $ΔΣ$ at large scales. This selection bias largely originates from spurious member galaxies within +/- 20 to 60 Mpc/h along the line of sight, highlighting the importance of quantifying projection effects associated with the broad redshift distribution of member galaxies in photometric cluster surveys. While our results qualitatively agree with those in the literature, accurate quantitative modelling of the selection bias is needed to achieve the goals of cluster lensing cosmology and will require synthetic catalogues covering a wide range of galaxy-halo connection models.

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arXiv2022

LINNA: Likelihood Inference Neural Network Accelerator

Bayesian posterior inference of modern multi-probe cosmological analyses incurs massive computational costs. For instance, depending on the combinations of probes, a single posterior inference for the Dark Energy Survey (DES) data had a wall-clock time that ranged from 1 to 21 days using a state-of-the-art computing cluster with 100 cores. These computational costs have severe environmental impacts and the long wall-clock time slows scientific productivity. To address these difficulties, we introduce LINNA: the Likelihood Inference Neural Network Accelerator. Relative to the baseline DES analyses, LINNA reduces the computational cost associated with posterior inference by a factor of 8--50. If applied to the first-year cosmological analysis of Rubin Observatory's Legacy Survey of Space and Time (LSST Y1), we conservatively estimate that LINNA will save more than US $\$300,000$ on energy costs, while simultaneously reducing $\rm{CO}_2$ emission by $2,400$ tons. To accomplish these reductions, LINNA automatically builds training data sets, creates neural network surrogate models, and produces a Markov chain that samples the posterior. We explicitly verify that LINNA accurately reproduces the first-year DES (DES Y1) cosmological constraints derived from a variety of different data vectors with our default code settings, without needing to retune the algorithm every time. Further, we find that LINNA is sufficient for enabling accurate and efficient sampling for LSST Y10 multi-probe analyses. We make LINNA publicly available at https://github.com/chto/linna, to enable others to perform fast and accurate posterior inference in contemporary cosmological analyses.

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arXiv2021

Exploiting Non-linear Scales in Galaxy-Galaxy Lensing and Galaxy Clustering: A Forecast for the Dark Energy Survey

The combination of galaxy-galaxy lensing (GGL) and galaxy clustering is a powerful probe of low redshift matter clustering, especially if it is extended to the non-linear regime. To this end, we extend the N-body and halo occupation distribution (HOD) emulator method of arxiv:1907.06293 to model the redMaGiC sample of colour-selected passive galaxies in the Dark Energy Survey (DES), adding parameters that describe central galaxy incompleteness, galaxy assembly bias, and a scale-independent multiplicative lensing bias $A_{lens}$. We use this emulator to forecast cosmological constraints attainable from the GGL surface density profile $ΔΣ(r_p)$ and the projected galaxy correlation function $w_{p,gg}(r_p)$ in the final (Year 6) DES data set over scales $r_p=0.3-30h^{-1}$ Mpc. For a $3\%$ prior on $A_{lens}$ we forecast precisions of $1.9\%$, $2.0\%$, and $1.9\%$ on $Ω_m$, $σ_8$, and $S_8 \equiv σ_8Ω_m^{0.5}$, marginalized over all halo occupation distribution (HOD) parameters as well as $A_{lens}$ and a point-mass contribution to $ΔΣ$. Adding scales $r_p=0.3-3h^{-1}$ Mpc improves the $S_8$ precision by a factor of $\sim1.6$ relative to a large scale ($3.0-30.0h^{-1}$ Mpc) analysis, equivalent to increasing the survey area by a factor of ${\sim}2.6$. Sharpening the $A_{lens}$ prior to $1\%$ further improves the $S_8$ precision by a factor of $1.7$ (to $1.1\%$), and it amplifies the gain from including non-linear scales. Our emulator achieves percent-level accuracy similar to the projected DES statistical uncertainties, demonstrating the feasibility of a fully non-linear analysis. Obtaining precise parameter constraints from multiple galaxy types and from measurements that span linear and non-linear clustering offers many opportunities for internal cross-checks, which can diagnose systematics and demonstrate the robustness of cosmological results.

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arXiv2021

Cosmology with galaxy cluster weak lensing: statistical limits and experimental design

We forecast constraints on the amplitude of matter clustering sigma_8(z) achievable with the combination of cluster weak lensing and number counts, in current and next-generation weak lensing surveys. We advocate an approach, analogous to galaxy-galaxy lensing, in which the observables in each redshift bin are the mean number counts and the mean weak lensing profile of clusters above a mass proxy threshold. The primary astrophysical nuisance parameter is the logarithmic scatter between the mass proxy and true mass near the threshold. For surveys similar to the Dark Energy Survey (DES), the Roman Space Telescope High Latitude Survey (HLS), and the Rubin Observatory Legacy Survey of Space and Time (LSST), we forecast aggregate precision on sigma_8 of 0.26%, 0.24%, and 0.10%, respectively, if the mass-observable scatter has an external prior better than 0.01. These constraints would be degraded by about 20% for a 0.05 prior on scatter in the case of DES or HLS and for a 0.016 prior for LSST. A one-month observing program with Roman Space Telescope targeting approximately 2500 massive clusters could achieve a 0.5% constraint on sigma_8(z=0.7) on its own, or a ~0.33% constraint in combination with the HLS. Realizing the constraining power of clusters requires accurate knowledge of the mass-observable relation and stringent control of systematics. We provide analytic approximations to our numerical results that allow easy scaling to other survey assumptions or other methods of cluster mass estimation.

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