Publication

The two-point correlation function covariance with fewer mocks

Related publications (30)

XXXIV. The effect of linear redshift-space distortions in photometric galaxy clustering and its cross-correlation with cosmic shear

Frédéric Courbin, Georges Meylan, Gianluca Castignani, Maurizio Martinelli, Malte Tewes, Slobodan Ilic, Alessandro Pezzotta, Yi Wang, Richard Massey, Fabio Finelli

Context. The cosmological surveys that are planned for the current decade will provide us with unparalleled observations of the distribution of galaxies on cosmic scales, by means of which we can probe the underlying large-scale structure (LSS) of the Univ ...

Edp Sciences S A2024

Euclid: The search for primordial features

Frédéric Courbin, Yiming Li, Georges Meylan, Yi Wang, Richard Massey, Fabio Finelli, Marcello Farina

Primordial features, in particular oscillatory signals, imprinted in the primordial power spectrum of density perturbations represent a clear window of opportunity for detecting new physics at high-energy scales. Future spectroscopic and photometric measur ...

Edp Sciences S A2024

Time-Delay Cosmography: Measuring the Hubble Constant and Other Cosmological Parameters with Strong Gravitational Lensing

Frédéric Courbin, Martin Raoul Robert Millon

Multiply lensed images of a same source experience a relative time delay in the arrival of photons due to the path length difference and the different gravitational potentials the photons travel through. This effect can be used to measure absolute distance ...

Springer2024

TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression

Mathieu Salzmann, Alexandre Massoud Alahi, Megh Hiren Shukla

Deep heteroscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood. However, recent works show that this may result in sub-optimal convergence due to the challenges associated ...

2024

SUNBIRD : a simulation-based model for full-shape density-split clustering

Daniel Felipe Forero Sanchez

Combining galaxy clustering information from regions of different environmental densities can help break cosmological parameter degeneracies and access non-Gaussian information from the density field that is not readily captured by the standard two-point c ...

2024

Euclid: Validation of the MontePython forecasting tools

Frédéric Courbin, Georges Meylan, Jean-Luc Starck, Maurizio Martinelli, Julien Lesgourgues, Slobodan Ilic, Yi Wang, Richard Massey

Context. The Euclid mission of the European Space Agency will perform a survey of weak lensing cosmic shear and galaxy clustering in order to constrain cosmological models and fundamental physics. Aims. We expand and adjust the mock Euclid likelihoods of t ...

Edp Sciences S A2024

CURLING - I. The influence of point-like image approximation on the outcomes of cluster strong lens modelling

Jean-Paul Richard Kneib, Huanyuan Shan, Nan Li

Cluster-scale strong lensing is a powerful tool for exploring the properties of dark matter and constraining cosmological models. However, due to the complex parameter space, pixelized strong lens modelling in galaxy clusters is computationally expensive, ...

Oxford Univ Press2024

Strong gravitational lensing's 'external shear' is not shear

David Richard Harvey, Mathilde Jauzac, Richard Massey

The distribution of mass in galaxy-scale strong gravitational lenses is often modelled as an elliptical power-law plus 'external shear', which notionally accounts for neighbouring galaxies and cosmic shear along our line of sight. A small amount of externa ...

Oxford Univ Press2024

The DESI one-per cent survey: exploring the halo occupation distribution of luminous red galaxies and quasi-stellar objects with ABACUSSUMMIT

Jiaxi Yu, Zheng Zheng, Hanyu Zhang

We present the first comprehensive halo occupation distribution (HOD) analysis of the Dark Energy Spectroscopic Instrument (DESI) One-Percent Survey luminous red galaxy (LRG) and Quasi Stellar Object (QSO) samples. We constrain the HOD of each sample and t ...

2024

Random matrix methods for high-dimensional machine learning models

Antoine Philippe Michel Bodin

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

EPFL2024