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We propose a novel kernel-based nonparametric two-sample test, employing the combined use of kernel mean and kernel covariance embedding. Our test builds on recent results showing how such combined embeddings map distinct probability…

Machine Learning · Statistics 2025-09-16 Leonardo V. Santoro , Victor M. Panaretos

In this work, we investigate Gaussian process regression used to recover a function based on noisy observations. We derive upper and lower error bounds for Gaussian process regression with possibly misspecified correlation functions. The…

Statistics Theory · Mathematics 2022-07-20 Wenjia Wang , Bing-Yi Jing

We study the ability of future CMB anisotropy experiments and redshift surveys to constrain a thirteen-dimensional parameterization of the adiabatic cold dark matter model. Each alone is unable to determine all parameters to high accuracy.…

Astrophysics · Physics 2009-10-07 Daniel J. Eisenstein , Wayne Hu , Max Tegmark

Systematic effects that limit the achievable sensitivity of current low-frequency radio telescopes to the 21 cm signal are among the foremost challenges in observational 21 cm cosmology. The standard approach to retrieving the 21 cm signal…

Cosmology and Nongalactic Astrophysics · Physics 2025-12-23 S. Munshi , L. V. E. Koopmans , F. G. Mertens , A. R. Offringa , S. A. Brackenhoff , E. Ceccotti , J. K. Chege , L. Y. Gao , S. Ghosh , M. Mevius , S. Zaroubi

Probabilistic world models increase data efficiency of model-based reinforcement learning (MBRL) by guiding the policy with their epistemic uncertainty to improve exploration and acquire new samples. Moreover, the uncertainty-aware learning…

Machine Learning · Computer Science 2024-03-26 Can Bogoclu , Robert Vosshall , Kevin Cremanns , Dirk Roos

Testing the equality of two conditional distributions is crucial in various modern applications, including transfer learning and causal inference. Despite its importance, this fundamental problem has received surprisingly little attention…

Methodology · Statistics 2025-09-04 Jian Yan , Zhuoxi Li , Xianyang Zhang

We perform model-independent distance measurements on four Type Ia supernovae (SNe Ia) compilations (Pantheon, Pantheon+, DES-Dovekie, Union3) and compress each dataset into the values of $\log r_p(z)$ at eleven redshift knots, where…

Cosmology and Nongalactic Astrophysics · Physics 2026-05-20 Zhenyuan Wang , Yun Wang

Recently, using Bayesian Machine Learning, a deviation from the cold dark matter model on cosmological scales has been put forward. Such model might replace a proposed non-gravitational interaction between dark energy and dark matter, and…

General Relativity and Quantum Cosmology · Physics 2024-02-14 Martiros Khurshudyan , Emilio Elizalde

Proposed third generation gravitational wave (GW) interferometers such as Cosmic Explorer will have the sensitivity to observe double neutron star (DNS) mergers up to a redshift of $\sim 5$ with good signal to noise ratios. We argue that…

General Relativity and Quantum Cosmology · Physics 2020-06-16 Shilpa Kastha , M. Saleem , K G Arun

Theoretical predictions from a modified theory of gravity with a nonminimal coupling between matter and curvature are compared to data from recent cosmological surveys. We use type Ia supernovae data from the Pantheon+ sample and the recent…

Cosmology and Nongalactic Astrophysics · Physics 2025-02-27 Miguel Barroso Varela , Orfeu Bertolami

Although Gaussian processes (GPs) with deep kernels have been successfully used for meta-learning in regression tasks, its uncertainty estimation performance can be poor. We propose a meta-learning method for calibrating deep kernel GPs for…

Machine Learning · Statistics 2023-12-14 Tomoharu Iwata , Atsutoshi Kumagai

The cosmographic approach is adopted to determine the spatial curvature (i.e., $\Omega_K$) combining the latest released cosmic chronometers data (CC), the Pantheon sample of type Ia supernovae observations, and the baryon acoustic…

Cosmology and Nongalactic Astrophysics · Physics 2020-01-08 En-Kun Li , Minghui Du , Lixin Xu

If the Universe is endowed with cosmic anisotropy, it will have a preferred direction of expansion. Reconstructing the expansion history by Gaussian Process (GP) can be used to probe the cosmic anisotropy model-independently. In this paper,…

Cosmology and Nongalactic Astrophysics · Physics 2025-02-25 Zong-Fan Yang , Da-Wei Yao , Ke Wang

Tests of cosmological models via measurements of galaxy correlations will require increasing modeling accuracy, given the high precision of measurements promised by forthcoming galaxy surveys. In this work we investigate the biases…

Cosmology and Nongalactic Astrophysics · Physics 2024-08-06 Francesco Spezzati , Alvise Raccanelli

We propose a data fusion method based on multi-fidelity Gaussian process regression (GPR) framework. This method combines available data of the quantity of interest (QoI) and its gradients with different fidelity levels, namely, it is a…

Computational Engineering, Finance, and Science · Computer Science 2020-12-30 Yixiang Deng , Guang Lin , Xiu Yang

Gaussian processes provide a method for extracting cosmological information from observations without assuming a cosmological model. We carry out cosmography -- mapping the time evolution of the cosmic expansion -- in a model-independent…

Cosmology and Nongalactic Astrophysics · Physics 2012-07-11 Arman Shafieloo , Alex G. Kim , Eric V. Linder

Gravitational waves (GWs) directly measure the luminosity distance to the merger, which, when combined with an independent measurement of the source's redshift, provides a novel probe of cosmology. The proposed next generation of…

Cosmology and Nongalactic Astrophysics · Physics 2021-08-11 Christine Ye , Maya Fishbach

We propose a new and highly model-independent test of cosmic acceleration by comparing observations of the baryon acoustic oscillation (BAO) scale at low and intermediate redshifts: we derive a new inequality relating BAO observables at two…

Cosmology and Nongalactic Astrophysics · Physics 2012-03-19 Will Sutherland

We propose a novel kernel-based two-sample test that leverages the spectral decomposition of the maximum mean discrepancy (MMD) statistic to identify and utilize well-estimated directional components in reproducing kernel Hilbert space…

Methodology · Statistics 2025-08-21 Rui Cui , Yuhao Li , Xiaojun Song

A photometric redshift sample of Luminous Red Galaxies (hereafter LRGs) obtained from The DECam Legacy Survey (DECaLS) is analysed to probe cosmic distances by exploiting the wedge approach of the two-point correlation function. Although…

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