中文
相关论文

相关论文: CosmoForge I: A unified framework for QML power sp…

200 篇论文

Conventional algorithms for galaxy power spectrum estimation measure the true spectrum convolved with a survey window function, which, for parameter inference, must be compared with a similarly convolved theory model. In this work, we…

宇宙学与河外天体物理 · 物理学 2021-05-12 Oliver H. E. Philcox

We use a Quadratic Maximum Likelihood (QML) method to estimate the angular power spectrum of the cross-correlation between cosmic microwave background and large scale structure maps as well as their individual auto-spectra. We describe our…

宇宙学与河外天体物理 · 物理学 2015-06-04 F. Schiavon , F. Finelli , A. Gruppuso , A. Marcos-Caballero , P. Vielva , R. G. Crittenden , R. B. Barreiro , E. Martinez-Gonzalez

Developing accurate analysis techniques to combine various probes of cosmology is essential to tighten constraints on cosmological parameters and to check for inconsistencies in our model of the Universe. In this paper we develop a joint…

宇宙学与河外天体物理 · 物理学 2015-06-15 Tim Eifler , Elisabeth Krause , Peter Schneider , Klaus Honscheid

Understanding how cosmological parameters influence the cosmic microwave background (CMB) power spectra is a central component of modern cosmology education, but interactive exploration is often limited by computational cost or technical…

天体物理仪器与方法 · 物理学 2026-01-26 Andreas Nygaard , Steen Hannestad , Thomas Tram

We present a further development of a method for accelerating the calculation of CMB power spectra, matter power spectra and likelihood functions for use in cosmological Bayesian inference. The algorithm, called {\sc CosmoNet}, is based on…

天体物理学 · 物理学 2009-11-13 T. Auld , M. Bridges , M. P. Hobson

$ $Future surveys could obtain tighter constraints on the cosmological parameters with the galaxy power spectrum than with the Cosmic Microwave Background. However, the inclusion of multiple overlapping tracers, redshift bins, and more…

宇宙学与河外天体物理 · 物理学 2024-09-24 Yan Lai , Cullan Howlett , Tamara M. Davis

In this work, we propose a framework in the form of a Python package, specifically designed for the analysis of Quantum Machine Learning models. This framework is based on the PennyLane simulator and facilitates the evaluation and training…

量子物理 · 物理学 2025-09-17 Melvin Strobl , Maja Franz , Eileen Kuehn , Wolfgang Mauerer , Achim Streit

Standard cosmic microwave background (CMB) analyses constrain cosmological and astrophysical parameters by fitting parametric models to multifrequency power spectra (MFPS). However, such methods do not optimally weight maps in power…

宇宙学与河外天体物理 · 物理学 2024-06-25 Kristen M. Surrao , J. Colin Hill

A maximum-likelihood method is presented for estimating the power spectrum of anisotropies in the cosmic microwave background (CMB) from interferometer observations. The method calculates flat band-power estimates in separate bins in…

天体物理学 · 物理学 2009-11-07 M. P. Hobson , Klaus Maisinger

The package CosmoLib is a combination of a cosmological Boltzmann code and a simulation toolkit to forecast the constraints on cosmological parameters from future observations. In this paper we describe the released linear-order part of the…

宇宙学与河外天体物理 · 物理学 2012-06-12 Zhiqi Huang

We develop the XFaster Cosmic Microwave Background (CMB) temperature and polarization anisotropy power spectrum and likelihood technique for the Planck CMB satellite mission. We give an overview of this estimator and its current…

宇宙学与河外天体物理 · 物理学 2015-03-13 G. Rocha , C. R. Contaldi , J. R. Bond , K. M. Gorski

In the context of cosmic microwave background (CMB) data analysis, we compare the efficiency at large scale of two angular power spectrum algorithms, implementing, respectively, the quadratic maximum likelihood (QML) estimator and the…

宇宙学与河外天体物理 · 物理学 2015-06-19 Diego Molinari , Alessandro Gruppuso , Gianluca Polenta , Carlo Burigana , Adriano De Rosa , Paolo Natoli , Fabio Finelli , Francesco Paci

Fast robust methods for calculating likelihoods from CMB observations on small scales generally rely on approximations based on a set of power spectrum estimators and their covariances. We investigate the optimality of these approximation,…

宇宙学与河外天体物理 · 物理学 2009-11-06 Samira Hamimeche , Antony Lewis

We introduce $\sf{CosmoBit}$, a module within the open-source $\sf{GAMBIT}$ software framework for exploring connections between cosmology and particle physics with joint global fits. $\sf{CosmoBit}$ provides a flexible framework for…

A new and promising avenue was recently developed for analyzing large-scale structure data with a model-independent approach, in which the linear power spectrum shape is parametrized with a large number of freely varying wavebands rather…

宇宙学与河外天体物理 · 物理学 2024-01-24 Luca Amendola , Marco Marinucci , Massimo Pietroni , Miguel Quartin

We undertake the first comprehensive and quantitative real-space analysis of the cosmological information content in the environments of the cosmic web (voids, filaments, walls, and nodes) up to non-linear scales, $k = 0.5$ $h$/Mpc. Relying…

宇宙学与河外天体物理 · 物理学 2022-05-25 Tony Bonnaire , Nabila Aghanim , Joseph Kuruvilla , Aurélien Decelle

A number of important cosmological questions can be addressed only by probing perturbation modes on the largest accessible scales. One promising probe of these modes is the Kamionkowski-Loeb effect, i.e., the polarization induced in the…

宇宙学与河外天体物理 · 物理学 2025-09-17 Arsalan Adil , Reid Koutras , Emory F. Bunn

We propose a new internal linear combination (ILC) method in the pixel space, applicable on large angular scales of the sky, to estimate a foreground minimized Cosmic Microwave Background (CMB) temperature anisotropy map by incorporating…

宇宙学与河外天体物理 · 物理学 2018-11-07 Vipin Sudevan , Rajib Saha

Large galaxy surveys demand fast and scalable estimators for anisotropic clustering statistics beyond the monopole. We present a suite of efficient FFT-based estimators for power-spectrum and bispectrum multipoles, built upon exact…

宇宙学与河外天体物理 · 物理学 2026-05-07 Yunchen Xie , Ruiyang Zhao , Gan Gu , Xiaoma Wang , Xiaoyong Mu , Yuting Wang , Gong-Bo Zhao , Florian Beutler , John A. Peacock