Related papers: Probabilistic cosmological inference on HI tomogra…

Once we have a measurement, how do we extract this information from the signal? This chapter focusses on the inference of the interesting astrophysics and cosmology once we obtain a detection of the 21-cm signal. Essentially, inference of…

Diffusion models indirectly estimate the probability density over a data space, which can be used to study its structure. In this work, we show that geodesics can be computed in diffusion latent space, where the norm induced by the…

We quantify the physical memory of the cosmic density field using mutual information between $N$-body snapshots at different redshifts, removing a random baseline to isolate gravitational correlations. The shared mutual information rises…

The highly redshifted 21 cm line of neutral hydrogen has become recognized as a unique probe of cosmology from relatively low redshifts (z ~ 1) up through the Epoch of Reionization (z ~ 8) and even beyond. To date, most work has focused on…

In recent years, cosmic shear has emerged as a powerful tool to study the statistical distribution of matter in our Universe. Apart from the standard two-point correlation functions, several alternative methods like peak count statistics…

Low-frequency radio observations of neutral hydrogen during and before the epoch of cosmic reionization will provide ~ 1000 quasi-independent source planes, each of precisely known redshift, if a resolution of ~1 arcminutes or better can be…

Deep Neural Networks trained as image auto-encoders have recently emerged as a promising direction for advancing the state-of-the-art in image compression. The key challenge in learning such networks is twofold: To deal with quantization,…

Precise cosmological inference from next-generation weak lensing surveys requires extracting non-Gaussian information beyond standard two-point statistics. We present a hybrid machine-learning (ML) framework that integrates field-level…

We analyse various low-redshift cosmological data from Type-Ia Supernova, Baryon Acoustic Oscillations, Time-Delay measurements using Strong-Lensing, $H(z)$ measurements using Cosmic Chronometers and growth measurements from large scale…

We present a method for reducing cosmological data to constraints on the amplitudes of modes of the dark energy density as a function of redshift. The modes are chosen so that (1) one of them has constant density and (2) the others are…

We measure the scale dependence and redshift dependence of 21 cm line emitted from the neutral hydrogen gas at redshift 1<z<5 using full cosmological hydrodynamic simulations by taking the ratios between the power spectra of HI-dark matter…

We present cosmological constraints from Dark Energy Survey Year 3 (DES Y3) weak lensing data using persistent homology, a topological data analysis technique that tracks how features like clusters and voids evolve across density…

We investigate the possibility of performing cosmological studies in the redshift range $2.5<z<5$ through suitable extensions of existing and upcoming radio-telescopes like CHIME, HIRAX and FAST. We use the Fisher matrix technique to…

During cosmic reionization, the 21-cm brightness fluctuations were highly non-Gaussian, and complementary statistics can be extracted from the distribution of pixel brightness temperatures that are not derivable from the 21-cm power…

In previous works, we proposed to estimate cosmological parameters with the artificial neural network (ANN) and the mixture density network (MDN). In this work, we propose an improved method called the mixture neural network (MNN) to…

We present a Bayesian hierarchical framework for a principled data analysis pipeline of peculiar velocity surveys, which makes explicit the inference problem of constraining cosmological parameters from redshift-independent distance…

This work presents a deep-learning approach to estimate atmospheric density profiles for use in planetary entry guidance problems. A long short-term memory (LSTM) neural network is trained to learn the mapping between measurements available…

We apply two Bayesian hierarchical inference schemes to infer shear power spectra, shear maps and cosmological parameters from the CFHTLenS weak lensing survey - the first application of this method to data. In the first approach, we sample…

In this paper, we develop a novel high-dimensional coefficient estimation procedure based on high-frequency data. Unlike usual high-dimensional regression procedures such as LASSO, we additionally handle the heavy-tailedness of…

We present a new Bayesian methodology to learn the unknown material density of a given sample by inverting its two-dimensional images that are taken with a Scanning Electron Microscope. An image results from a sequence of projections of the…