arXiv subjects

Marcos Pellejero-Ibañez

Publications and source records attributed to Marcos Pellejero-Ibañez.

6 recordsLinked to original sources

arXiv2026

Cosmological inference from a joint DESI DR1 full-shape power spectrum and bispectrum analysis

The galaxy bispectrum directly probes the non-linear gravitational evolution of large-scale structure (LSS) and can break parameter degeneracies that remain in power-spectrum analyses. We present a joint full-shape cosmological analysis of three luminous red galaxy (LRG) redshift bins and the quasar (QSO) sample from the first Data Release (DR1) of the Dark Energy Spectroscopic Instrument (DESI). We model the redshift-space power spectrum at one loop and the tree-level bispectrum within the Effective Field Theory of LSS. The bispectrum is decomposed in the Tripolar Spherical Harmonics basis, for which the convolution with the survey window function can be formulated as a direct linear transformation of the theoretical multipoles. Our power-spectrum constraints are in good agreement with the official DESI DR1 full-modelling results. We investigate the impact of including the bispectrum in the inference, finding that the monopole substantially improves the constraints on the cold dark matter density and amplitude of matter fluctuations by 9-18% and 8-20%, respectively, in the individual-tracer analyses. The corresponding reductions are 15% and 10% for the combined LRG sample, and 6% and 4% when all tracers are combined. In a restricted test using the first LRG bin, the bispectrum quadrupole changes the marginalised uncertainties by only a few percent. Extending the analysis to $w_0w_a$CDM substantially broadens the cosmological posteriors, while the bispectrum produces only a mild change in the allowed dark-energy parameter region, which remains sensitive to the adopted prior ranges. Our results demonstrate the potential of higher-order clustering statistics to improve cosmological constraints, while providing a framework for incorporating the bispectrum into full-shape analyses of current and future spectroscopic galaxy surveys.

astro-ph.CO↗

arXiv2026

Web-Halo Model (WHM): Accurate non-linear matter power spectrum predictions without free parameters

We present a parameter-free variant of the halo model that significantly improves the precision of matter clustering predictions, particularly in the challenging 1-halo to 2-halo transition regime, where standard halo models often fail. Unlike HMcode-2020, which relies on 12 phenomenological parameters, our approach achieves comparable or superior accuracy without any free fitting parameters. This new web-halo model (WHM) extends the traditional halo model by incorporating structures that have collapsed along two dimensions (filaments) and one dimension (sheets), in addition to haloes, and combines these with 1-loop Lagrangian Perturbation Theory (1$\ell$-LPT) in a consistent framework. We show that WHM matches N-body simulation power spectra within the precision of state-of-the-art emulators at the 2-halo to 1-halo transition regime at all redshifts. Specifically, the WHM achieves better than 2\% accuracy up to scales of $k = 0.4\, h\,\mathrm{Mpc}^{-1}$, $0.7\, h\,\mathrm{Mpc}^{-1}$, and $1.3\, h\,\mathrm{Mpc}^{-1}$ at redshifts $z = 0.0$, $0.8$, and $1.5$, respectively, for both the baccoemu and EuclidEmu2 emulators, across their full $w_0w_a\mathrm{CDM} + \sum m_ν$ cosmological parameter space. This marks a substantial improvement over 1$\ell$-LPT and HMcode-2020, the latter of which performs similarly at low redshift but deteriorates at higher redshifts despite its 12 tuned parameters. We publicly release WHM as WHMcode, integrated into existing HMcode implementations for CAMB and CLASS.

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arXiv2021

The BACCO Simulation Project: Exploiting the full power of large-scale structure for cosmology

We present the BACCO project, a simulation framework specially designed to provide highly-accurate predictions for the distribution of mass, galaxies, and gas as a function of cosmological parameters. In this paper, we describe our main suite of simulations (L $\sim2$ Gpc and $4320^3$ particles) and present various validation tests. Using a cosmology-rescaling technique, we predict the nonlinear mass power spectrum over the redshift range $0<z<1.5$ and over scales $10^{-2} < k/(h Mpc^{-1} ) < 5$ for 800 points in an 8-dimensional cosmological parameter space. For an efficient interpolation of the results, we build an emulator and compare its predictions against several widely-used methods. Over the whole range of scales considered, we expect our predictions to be accurate at the 2\% level for parameters in the minimal $Λ$ CDM model and to 3\% when extended to dynamical dark energy and massive neutrinos. We make our emulator publicly available under http://www.dipc.org/bacco

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arXiv2020

The BACCO Simulation Project: A baryonification emulator with Neural Networks

We present a neural-network emulator for baryonic effects in the non-linear matter power spectrum. We calibrate this emulator using more than 50,000 measurements in a 15-dimensional parameters space, varying cosmology and baryonic physics. Baryonic physics is described through a baryonification algorithm, that has been shown to accurately capture the relevant effects on the power spectrum and bispectrum in state-of-the-art hydrodynamical simulations. Cosmological parameters are sampled using a cosmology-rescaling approach including massive neutrinos and dynamical dark energy. The specific quantity we emulate is the ratio between matter power spectrum with baryons and gravity-only, and we estimate the overall precision of the emulator to be 1-2%, at all scales 0.01 < k < 5 h/Mpc, and redshifts 0 < z < 1.5. We also obtain an accuracy of 1-2%, when testing the emulator against a collection of 74 different cosmological hydrodynamical simulations and their respective gravity-only counterparts. We show also that only one baryonic parameter, namely Mc, which set the gas fraction retained per halo mass, is enough to have accurate and realistic predictions of the baryonic feedback at a given epoch. Our emulator will become publicly available in http://www.dipc.org/bacco.

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arXiv2020

Cosmological parameter estimation via iterative emulation of likelihoods

The interpretation of cosmological observables requires the use of increasingly sophisticated theoretical models. Since these models are becoming computationally very expensive and display non-trivial uncertainties, the use of standard Bayesian algorithms for cosmological inferences, such as MCMC, might become inadequate. Here, we propose a new approach to parameter estimation based on an iterative Gaussian emulation of the target likelihood function. This requires a minimal number of likelihood evaluations and naturally accommodates for stochasticity in theoretical models. We apply the algorithm to estimate 9 parameters from the monopole and quadrupole of a mock power spectrum in redshift space. We obtain accurate posterior distribution functions with approximately 100 times fewer likelihood evaluations than an affine invariant MCMC, roughly independently from the dimensionality of the problem. We anticipate that our parameter estimation algorithm will accelerate the adoption of more accurate theoretical models in data analysis, enabling more comprehensive exploitation of cosmological observables.

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arXiv2020

Modelling the large scale structure of the Universe as a function of cosmology and baryonic physics

We present and test a framework that models the three-dimensional distribution of mass in the Universe as a function of cosmological and astrophysical parameters. Our approach combines two different techniques: a rescaling algorithm that modifies the cosmology of gravity-only N-body simulations, and a baryonification algorithm which mimics the effects of astrophysical processes induced by baryons, such as star formation and AGN feedback. We show how this approach can accurately reproduce the effects of baryons on the matter power spectrum of various state-of-the-art hydro-dynamical simulations (EAGLE, Illustris, Illustris-TNG, Horizon-AGN, and OWLS,Cosmo-OWLS and BAHAMAS), to percent level from very large down to small, highly nonlinear scales, k= 5 h/Mpc, and from z=0 up to z=2. We highlight that, thanks to the heavy optimisation of the algorithms, we can obtain these predictions for arbitrary baryonic models and cosmology (including massive neutrinos and dynamical dark energy models) with an almost negligible CPU cost. Therefore, this approach is efficient enough for cosmological data analyses. With these tools in hand we explore the degeneracies between cosmological and astrophysical parameters in the nonlinear mass power spectrum. Our findings suggest that after marginalising over baryonic physics, cosmological constraints inferred from weak gravitational lensing should be moderately degraded.

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