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.Comment: 11 pages, 10 figures, submitted to MNRA

Self-Calibrating Optical Galaxy Cluster Selection Bias Using Cluster, Galaxy, and Shear Cross-Correlations

The clustering signals of galaxy clusters are 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 autocorrelation 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 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 three-dimensional correlation functions to scales of 100 h-1 Mpc. We perform a likelihood analysis assuming survey conditions similar to the Dark Energy Survey and show that the selection bias can be self-consistently constrained at the 10 per cent 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

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 $\Delta\Sigma(r_p)$ 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 $\Omega_m$, $\sigma_8$, and $S_8 \equiv \sigma_8\Omega_m^{0.5}$, marginalized over all halo occupation distribution (HOD) parameters as well as $A_{lens}$ and a point-mass contribution to $\Delta\Sigma$. 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.Comment: 17 pages, 7 figures, to be submitted to MNRA

Spatial Clustering of Dark Matter Halos: Secondary Bias, Neighbor Bias, and the Influence of Massive Neighbors on Halo Properties

We explore the phenomenon commonly known as halo assembly bias, whereby dark matter halos of the same mass are found to be more or less clustered when a second halo property is considered, for halos in the mass range $3.7 \times 10^{11} \; h^{-1} \mathrm{M_{\odot}} - 5.0 \times 10^{13} \; h^{-1} \mathrm{M_{\odot}}$. Using the Large Suite of Dark Matter Simulations (LasDamas) we consider nine commonly used halo properties and find that a clustering bias exists if halos are binned by mass or by any other halo property. This secondary bias implies that no single halo property encompasses all the spatial clustering information of the halo population. The mean values of some halo properties depend on their halo's distance to a more massive neighbor. Halo samples selected by having high values of one of these properties therefore inherit a neighbor bias such that they are much more likely to be close to a much more massive neighbor. This neighbor bias largely accounts for the secondary bias seen in halos binned by mass and split by concentration or age. However, halos binned by other mass-like properties still show a secondary bias even when the neighbor bias is removed. The secondary bias of halos selected by their spin behaves differently than that for other halo properties, suggesting that the origin of the spin bias is different than of other secondary biases.Comment: 14 pages, LaTeX; minor revisions, and added references; results unchange

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 $\Omega_{\rm m}$ and $\sigma_8$ constraints in 5.6$\sigma$ 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 $\Lambda$CDM parameters and correspondingly strong tests of cosmological models.Comment: 6 pages, 2 figures, 1 table, Supplemental material with 2 figures. Submitted to Physical Review Letter

Emulating galaxy clustering and galaxy–galaxy lensing into the deeply non-linear regime: methodology, information, and forecasts

The combination of galaxy-galaxy lensing (GGL) with galaxy clustering is one of the most promising routes to determining the amplitude of matter clustering at low redshifts. We show that extending clustering+GGL analyses from the linear regime down to similar to 0.5 h(-1) Mpc scales increases their constraining power considerably, even after marginalizing over a flexible model of non-linear galaxy bias. Using a grid of cosmological N-body simulations, we construct a Taylor-expansion emulator that predicts the galaxy autocorrelation xi(gg)(r) and galaxy-matter cross-correlation xi(gm) (r) as a function of sigma(8), Omega(m), and halo occupation distribution (HOD) parameters, which are allowed to vary with large-scale environment to represent possible effects of galaxy assembly bias. We present forecasts for a fiducial case that corresponds to BOSS LOWZ galaxy clustering and SDSS-depth weak lensing (effective source density similar to 0.3 arcmin(-2)). Using tangential shear and projected correlation function measurements over 0.5 2 h(-1) Mpc, 4 h(-1) Mpc for gamma(t) , omega(p)). Much of this improvement comes from the non-linear clustering information, which breaks degeneracies among HOD parameters. Increasing the effective source density to 3 arcmin(-2) sharpens the constraint on sigma(8)Omega(0.6 )(m)by a further factor of two. With robust modelling into the non-linear regime, low-redshift measurements of matter clustering at the 1-per cent level with clustering+GGL alone are well within reach of current data sets such as those provided by the Dark Energy Survey.National Science Foundation Graduate Research Fellowship Program [DGE-1343012]; Department of Energy Computational Science Graduate Fellowship Program of the Office of Science; National Nuclear Security Administration in the Department of Energy [DE-FG02-97ER25308]; National Science Foundation [AST-1516997, AST-1313285, 1228509]; Department of Energy Office of Science grant [DOE-SC0013718]; Simons Foundation Investigator; Center for Cosmology and AstroParticle Physics at the Ohio State University; Faculty of Arts and Sciences Division of Science, Research Computing Group at Harvard UniversityThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Servicios ecosistémicos de la cuenca alta del río Fucha

112 páginasEl presente estudio, desarrollado por un grupo de estudiantes del Colegio Técnico José Félix Restrepo IED, busca reinterpretar los servicios ecosistémicos de las coberturas vegetales de la cuenca alta del río Fucha, entre las carreras sexta y sexta Este. Este fue el tema principal de la investigación. Contiene información de los recursos naturales que nos ofrecen y que nos benefician no solo a los seres humanos, sino a toda a la biodiversidad que se encuentra sobre la cuenca del río. Entre los servicios se encuentran, por ejemplo, el agua potable limpia y los procesos de descomposición de desechos. Estos se pueden dividir en cuatro categorías amplias como aprovisionamientos (es decir la producción de agua y de alimentos), regulación (el control del clima y de las enfermedades), polinización de cultivos de aves e insectos y, finalmente, la cultural, de la que nos beneficiamos los estudiantes, vecinos y demás personas que hacemos uso de los servicios ecosistémicos que nos ofrece la cuenca. Con esta investigación se busca, por otra parte, conocer un poco más de los grandes beneficios que podemos recibir de la naturaleza y que hacen que la vida humana sea posible

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications