arXiv subjects

Daniel Forero-Sánchez

Publications and source records attributed to Daniel Forero-Sánchez.

8 recordsLinked to original sources

arXiv2025

The Impact of Spectroscopic Redshift Errors on Cosmological Measurements

Spectroscopic redshift errors, including redshift uncertainty and catastrophic failures, can bias cosmological measurements from galaxy redshift surveys at sub-percent level. In this work, we investigate their impact on the full-shape analysis using contaminated mock catalogs. We find that redshift uncertainty introduces a scale-dependent damping effect on the power spectrum, which is absorbed by counterterms in clustering model, keeping parameter biases below $5\%$. Catastrophic failures suppress the power spectrum amplitude by an approximately constant factor that scales with the catastrophic rate $f_c$. While this effect is negligible for DESI galaxy populations ($f_c=1\%$), the slitless-like errors, combining redshift uncertainty with $f_c=5\%$ catastrophics, introduce significant biases in cosmological constraints. In this case, we observe $6\%$ to $16\%$ shifts ($\sim2.2σ$ level) in estimating the fractional growth rate $df\equiv f/f^{\rm{fid}}$ and the log primordial amplitude $\ln(10^{10} A_{s})$. Applying the correction factor $(1-f_c)^2$ on the galaxy power spectrum mitigates the bias but weakens the parameter constraints due to new degeneracies. Alternatively, fixing $f_c$ to its expected value restores the constraining power with a modest bias of $1.0σ$. Our results indicate that for space-based slitless surveys such as \textit{Euclid}, at minimum accurate estimation of $f_c$ and its incorporation into the clustering model are essential to get unbiased cosmological inference. Extending to evolving dark energy and massive neutrino cosmologies, redshift errors do not bias the dark energy properties parametrized by $w_0$ and $w_a$, but can degrade constraints on the summed neutrino mass $\sum m_ν$ by up to 80% in the worst case.

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arXiv2025

Analytical and EZmock covariance validation for the DESI 2024 results

The estimation of uncertainties in cosmological parameters is an important challenge in Large-Scale-Structure (LSS) analyses. For standard analyses such as Baryon Acoustic Oscillations (BAO) and Full Shape, two approaches are usually considered. First: analytical estimates of the covariance matrix use Gaussian approximations and (nonlinear) clustering measurements to estimate the matrix, which allows a relatively fast and computationally cheap way to generate matrices that adapt to an arbitrary clustering measurement. On the other hand, sample covariances are an empirical estimate of the matrix based on en ensemble of clustering measurements from fast and approximate simulations. While more computationally expensive due to the large amount of simulations and volume required, these allow us to take into account systematics that are impossible to model analytically. In this work we compare these two approaches in order to enable DESI's key analyses. We find that the configuration space analytical estimate performs satisfactorily in BAO analyses and its flexibility in terms of input clustering makes it the fiducial choice for DESI's 2024 BAO analysis. On the contrary, the analytical computation of the covariance matrix in Fourier space does not reproduce the expected measurements in terms of Full Shape analyses, which motivates the use of a corrected mock covariance for DESI's Full Shape analysis.

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arXiv2024

ELG Spectroscopic Systematics Analysis of the DESI Data Release 1

Dark Energy Spectroscopic Instrument (DESI) uses more than 2.4 million Emission Line Galaxies (ELGs) for 3D large-scale structure (LSS) analyses in its Data Release 1 (DR1). Such large statistics enable thorough research on systematic uncertainties. In this study, we focus on spectroscopic systematics of ELGs. The redshift success rate ($f_{\rm goodz}$) is the relative fraction of secure redshifts among all measurements. It depends on observing conditions, thus introduces non-cosmological variations to the LSS. We, therefore, develop the redshift failure weight ($w_{\rm zfail}$) and a per-fibre correction ($η_{\rm zfail}$) to mitigate these dependences. They have minor influences on the galaxy clustering. For ELGs with a secure redshift, there are two subtypes of systematics: 1) catastrophics (large) that only occur in a few samples; 2) redshift uncertainty (small) that exists for all samples. The catastrophics represent 0.26\% of the total DR1 ELGs, composed of the confusion between O\,\textsc{ii} and sky residuals, double objects, total catastrophics and others. We simulate the realistic 0.26\% catastrophics of DR1 ELGs, the hypothetical 1\% catastrophics, and the truncation of the contaminated $1.31<z<1.33$ in the \textsc{AbacusSummit} ELG mocks. Their $P_\ell$ show non-negligible bias from the uncontaminated mocks. But their influences on the redshift space distortions (RSD) parameters are smaller than $0.2σ$. The redshift uncertainty of \Yone ELGs is 8.5 km/s with a Lorentzian profile. The code for implementing the catastrophics and redshift uncertainty on mocks can be found in https://github.com/Jiaxi-Yu/modelling_spectro_sys.

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arXiv2024

CosmoMIA: Cosmic Web-based redshift space halo distribution

Modern galaxy surveys demand extensive survey volumes and resolutions surpassing current dark matter-only simulations' capabilities. To address this, many methods employ effective bias models on the dark matter field to approximate object counts on a grid. However, realistic catalogs necessitate specific coordinates and velocities for a comprehensive understanding of the Universe. In this research, we explore sub-grid modeling to create accurate catalogs, beginning with coarse grid number counts at resolutions of approximately $5.5\,h^{-1}\rm Mpc$ per side. These resolutions strike a balance between modeling nonlinear damping of baryon acoustic oscillations and facilitating large-volume simulations. Augmented Lagrangian Perturbation Theory (ALPT) is utilized to model the dark matter field and motions, replicating the clustering of a halo catalog derived from a massive simulation at $z=1.1$. Our approach involves four key stages: Tracer Assignment: Allocating dark matter particles to tracers based on grid cell counts, generating additional particles to address discrepancies. Attractor Identification: Defining attractors based on particle cosmic web environments, acting as gravitational focal points. Tracer Collapse: Guiding tracers towards attractors, simulating structure collapse. Redshift Space Distortions: Introducing redshift space distortions to simulated catalogs using ALPT and a random dispersion term. Results demonstrate accurate reproduction of monopoles and quadrupoles up to wave numbers of approximately $k=0.6\,h$ Mpc$^{-1}$. This method holds significant promise for galaxy surveys like DESI, EUCLID, and LSST, enhancing our understanding of the cosmos across scales.

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arXiv2023

DESI Mock Challenge: Constructing DESI galaxy catalogues based on FastPM simulations

Together with larger spectroscopic surveys such as the Dark Energy Spectroscopic Instrument (DESI), the precision of large scale structure studies and thus the constraints on the cosmological parameters are rapidly improving. Therefore, one must build realistic simulations and robust covariance matrices. We build galaxy catalogues by applying a Halo Occupation Distribution (HOD) model upon the \textsc{FastPM} simulations, such that the resulting galaxy clustering reproduces high resolution $N$-body simulations. While the resolution and halo finder are different from the reference simulations, we reproduce the reference galaxy two-point clustering measurements -- monopole and quadrupole -- to a precision required by the DESI Year 1 Emission Line Galaxy sample down to non-linear scales, i.e. $k<0.5\,h\mathrm{Mpc}$ or $s>10\,\mathrm{Mpc}/h$. Furthermore, we compute covariance matrices based on the resulting \textsc{FastPM} galaxy clustering -- monopole and quadrupole. We study for the first time the effect of fitting on Fourier conjugate [e.g. power spectrum] on the covariance matrix of the Fourier counterpart [e.g. correlation function]. We estimate the uncertainties of the two parameters of a simple clustering model and observe a maximum variation of 20 per cent for the different covariance matrices. Nevertheless, for most studied scales the scatter is between two to ten per cent Consequently, using the current pipeline we can precisely reproduce the clustering of $N$-body simulations and the resulting covariance matrices provide robust uncertainty estimations against HOD fitting scenarios. We expect our methodology will be useful for the coming DESI data analyses and their extension for other studies.

astro-ph.CO↗

arXiv2023

Cosmic void exclusion models and their impact on the distance scale measurements from large scale structure

Baryonic Acoustic Oscillations (BAOs) studies based on the clustering of voids and matter tracers provide important constraints on cosmological parameters related to the expansion of the Universe. However, modelling the void exclusion effect is an important challenge for fully exploiting the potential of this kind of analyses. We thus develop two numerical methods to describe the clustering of cosmic voids. Neither model requires additional cosmological information beyond that assumed within the galaxy de-wiggled model. The models consist in power spectra whose performance we assess in comparison to a parabolic model on Patchy cubic and light-cone mocks. Moreover, we test their robustness against systematic effects and the reconstruction technique. The void model power spectra and the parabolic model with a fixed parameter provide strongly correlated values for the Alcock-Paczynski ($α$) parameter, for boxes and light-cones likewise. The resulting $α$ values -- for all three models -- are unbiased and their uncertainties are correctly estimated. However, the numerical models show less variation with the fitting range compared to the parabolic one. The Bayesian evidence suggests that the numerical techniques are often favoured compared to the parabolic model. Moreover, the void model power spectra computed on boxes can describe the void clustering from light-cones as well as from boxes. The same void model power spectra can be used for the study of pre- and post-reconstructed data-sets. Lastly, the two numerical techniques are resilient against the studied systematic effects. Consequently, using either of the two new void models, one can more robustly measure cosmological parameters.

astro-ph.CO↗

arXiv2022

Cosmic Void Baryon Acoustic Oscillation Measurement: Evaluation of Sensitivity to Selection Effects

Cosmic voids defined as a subset of Delaunay Triangulation (DT) circumspheres have been used to measure the Baryon Acoustic Oscillations (BAO) scale; providing tighter constraints on cosmological parameters when combined with matter tracers. These voids are defined as spheres larger than a given radius threshold, which is constant over the survey volume. However, the response of these void tracers to observational systematics has not yet been studied. In this work we analyse the response of void clustering to selection effects. We find for the case of moderate (<20 per cent) incompleteness, void selection based on a constant radius cut yields robust measurements. This is particularly true for BAO-reconstructed galaxy samples, where large-scale void exclusion effects are mitigated. Moreover, we observe for the case of severe (up to 90 per cent) incompleteness -- such as can be found at the edges of the radial selection function -- that an accurate estimation of the void distribution is necessary for unbiased clustering measurements. In addition, we find that without reconstruction, using a constant threshold under these conditions produces a stronger void exclusion effect that can affect the clustering on large scales. A new void selection criteria dependent on the (local) observed tracer density that maximises the BAO peak significance prevents the aforementioned exclusion features from contaminating the BAO signal. Finally, we verify, with large simulations including light cone evolution, that both void sample definitions (local and constant) yield unbiased and consistent BAO scale measurements.

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arXiv2022

A Machine Learning Approach to Correct for Mass Resolution Effects in Simulated Halo Clustering Statistics

The increase in the observed volume in cosmological surveys imposes various challenges on simulation preparations. Firstly, the volume of the simulations required increases proportionally to the observations. However, large-volume simulations are quickly becoming computationally intractable. Secondly, on-going and future large-volume survey are targeting smaller objects, e.g. emission line galaxies, compared to the earlier focus, i.e. luminous red galaxies. They require the simulations to have higher mass resolutions. In this work we present a machine learning (ML) approach to calibrate the halo catalogue of a low-resolution (LR) simulation by training with a paired high-resolution (HR) simulation with the same background white noise, thus we can build the training data by matching HR haloes to LR haloes in a one-to-one fashion. After training, the calibrated LR halo catalogue reproduces the mass-clustering relation for mass down to $2.5\times 10^{11}~h^{-1}M_\odot$ within $5~{\rm per~cent}$ at scales $k<1~h\,\rm Mpc^{-1}$. We validate the performance of different statistics including halo mass function, power spectrum, two-point correlation function, and bispectrum in both real and redshift space. Our approach generates high-resolution-like halo catalogues ($>200$ particles per halo) from low-resolution catalogues ($>25$ particles per halo) containing corrected halo masses for each object. This allows to bypass the computational burden of a large-volume real high-resolution simulation without much compromise in the mass resolution of the result. The cost of our ML approach ($\sim 1$ CPU-hour) is negligible compared to the cost of a $N$-body simulation (e.g. millions of CPU-hours), The required computing time is cut a factor of 8.

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