Related papers: The BACCO simulation project: biased tracers in re…
In this paper we test the perturbative halo bias model at the field level. The advantage of this approach is that any analysis can be done without sample variance if the same initial conditions are used in simulations and perturbation…
We advocate for a new paradigm of cosmological likelihood-based inference, leveraging recent developments in machine learning and its underlying technology, to accelerate Bayesian inference in high-dimensional settings. Specifically, we…
Recent cosmological bounds on the sum of neutrino masses, M_nu = sum m_nu, are in tension with laboratory oscillation experiments, making cosmological tests of neutrino free-streaming imperative. In order to study the scale-dependent…
The next generation of cosmological surveys is expected to generate unprecedented high-quality data, consequently increasing the already substantial computational costs of Bayesian statistical methods. This will pose a significant challenge…
We present a new class of machine-learning emulators that accurately model the cosmic shear, galaxy-galaxy lensing, and galaxy clustering real space correlation functions in the context of Rubin Observatory year one simulated data. To…
The Baryon Acoustic Oscillations (BAO) refer to the ripples of material density in the Universe. As the most direct density tracers in the universe, galaxies have been commonly used in studies of BAO peak detection. The spatial number…
Neural network emulators are widely used in astrophysics and cosmology to approximate complex simulations inside Bayesian inference loops. Ad hoc rules of thumb are often used to justify the emulator accuracy required for reliable posterior…
We introduce an emulator approach to predict the non-linear matter power spectrum for broad classes of beyond-$\Lambda$CDM cosmologies, using only a suite of $\Lambda$CDM $N$-body simulations. By including a range of suitably modified…
We perform multi-plane ray-tracing using the GLAMER gravitational lensing code within high-resolution light-cones extracted from the CoDECS simulations: a suite of cosmological runs featuring a coupling between Dark Energy and Cold Dark…
Measuring the sum of the three active neutrino masses, $M_\nu$, is one of the most important challenges in modern cosmology. Massive neutrinos imprint characteristic signatures on several cosmological observables in particular on the…
The standard cosmological model is inherently relativistic, and yet a wide range of cosmological observations can be predicted accurately from essentially Newtonian theory. This is not the case on `ultra-large' distance scales, around the…
The recent DESI BAO measurements have revealed a potential deviation from a cosmological constant, suggesting a dynamic nature of dark energy. To rigorously test this result, complementary probes such as weak gravitational lensing are…
We test the cosmological implications of studying galaxy clustering using a tomographic approach, by computing the galaxy two-point angular correlation function $\omega(\theta)$ in thin redshift shells using a spectroscopic-redshift galaxy…
Statistical emulators of computer simulators have proven to be useful in a variety of applications. The widely adopted model for emulator building, using a Gaussian process model with strictly positive correlation function, is…
Small- and intermediate-scale galaxy clustering can be used to establish the galaxy-halo connection to study galaxy formation and evolution and to tighten constraints on cosmological parameters. With the increasing precision of galaxy…
The Lagrangian peaks of a 1D cosmological random field representing dark matter are used as a proxy for a catalogue of biased tracers in order to investigate the small-scale exclusion in the two-halo term. The two-point correlation function…
The Baryon Acoustic Oscillation (BAO) scale in the clustering of galaxies is a powerful standard ruler to measure cosmological distances and determine the geometry of the Universe. Past surveys have detected the BAO feature in the…
We develop an accurate and computationally efficient emulator to model the gravitational lensing magnification probability distribution function (PDF), enabling robust cosmological inference of point sources such as supernovae and…
Accurate modelling of redshift-space distortions (RSD) is essential for maximizing the cosmological information extracted from large galaxy redshift surveys. In preparation for the forthcoming analysis of the Euclid spectroscopic data, we…
We present a simulation-based inference framework using a convolutional neural network to infer dynamical masses of galaxy clusters from their observed 3D projected phase-space distribution, which consists of the projected galaxy positions…