Related papers: Statistical methods in cosmology
In this essay a critical review of present conceptual problems in current cosmology is provided from a more philosophical point of view. In essence, a digression on how could philosophy help cosmologists in what is strictly their…
In the early seventies, Alan Sandage defined cosmology as the search for two numbers: Hubble parameter ${{H}_{0}}$ and deceleration parameter ${{q}_{0}}$. The first of the two basic cosmological parameters (the Hubble parameter) describes…
Cosmological solutions of the equations with scalar interaction are being studied. It is shown, that the scalar field can effectively change the equation of state of statistical system, that leads to series of cosmological consequences.
Astronomy has entered the big data era and Machine Learning based methods have found widespread use in a large variety of astronomical applications. This is demonstrated by the recent huge increase in the number of publications making use…
Dynamical systems techniques are a powerful tool to analyse systems of ordinary differential equations, written in an appropriate form. For a given theory of gravity, the cosmological field equations typically lead to a system of ordinary…
Machine learning has been widely applied to clearly defined problems of astronomy and astrophysics. However, deep learning and its conceptual differences to classical machine learning have been largely overlooked in these fields. The broad…
Since cosmology is no longer "the data-starved science", the problem of how to best analyze large data sets has recently received considerable attention, and Karhunen-Loeve eigenvalue methods have been applied to both galaxy redshift…
A method is presented for performing joint analyses of cosmological datasets, in which the weight assigned to each dataset is determined directly by it own statistical properties. The weights are considered in a Bayesian context as a set of…
In the early seventies, Alan Sandage defined cosmology as the search for two numbers: Hubble parameter ${{H}_{0}}$ and deceleration parameter ${{q}_{0}}$. The first of the two basic cosmological parameters (the Hubble parameter) describes…
We describe the application of data mining algorithms to research problems in astronomy. We posit that data mining has always been fundamental to astronomical research, since data mining is the basis of evidence-based discovery, including…
Over the past century, major advances in astronomy and astrophysics have been largely driven by improvements in instrumentation and data collection. With the amassing of high quality data from new telescopes, and especially with the advent…
Problems with uniform probabilities on an infinite support show up in contemporary cosmology. This paper focuses on the context of inflation theory, where it complicates the assignment of a probability measure over pocket universes. The…
In this thesis, I describe a number of recent important developments in neutrino cosmology on three fronts. Firstly, focusing on Large-Scale Structure (LSS) data, I will show that current cosmological probes contain a wealth of information…
Big data are data on a massive scale in terms of volume, intensity, and complexity that exceed the capacity of standard software tools. They present opportunities as well as challenges to statisticians. The role of computational…
It is known that modeling uncertainties and astrophysical foregrounds can potentially introduce appreciable bias in the deduced values of cosmological parameters. While it is commonly assumed that these uncertainties will be accounted for…
Thanks to new technology of observations and fresh inputs from particle physics, cosmology has advanced on both observational and theoretical fronts. It is therefore opportune that we take stock of the cosmological situation today and…
This is a review article for The Review of Particle Physics 2006 (aka the Particle Data Book). It forms a compact review of knowledge of the cosmological parameters as at May 2006. Topics included are Parametrizing the Universe; Extensions…
The understanding and modeling of the structure and evolution of stars is based on statistical physics as well as on hydrodynamics. Today, a precise identification and proper description of the physical processes at work in stellar…
I will briefly present my work on cosmological parameters estimation. Classical methods for parameters estimation involve the exploration of the parameter space on a precalculated grid of cosmological models. Here we try to estimate the…
This textbook provides a systematic treatment of statistical machine learning for astronomical research through the lens of Bayesian inference, developing a unified framework that reveals connections between modern data analysis techniques…