Abhirup Datta

TL;DR: A class of highly scalable nearest-neighbor Gaussian process (NNGP) models to provide fully model-based inference for large geostatistical datasets are developed and it is established that the NNGP is a well-defined spatial process providing legitimate finite-dimensional Gaussian densities with sparse precision matrices.

TL;DR: In this paper, the authors investigated the extragalactic point source contamination and how accurately bright sources (≳ Jy) must be removed in order to detect 21 cm emission with upcoming radio telescopes such as the Murchison Widefield Array.

TL;DR: This study provides an introductory overview of several methods for analyzing large spatial data and describes the results of a predictive competition among the described methods as implemented by different groups with strong expertise in the methodology.

TL;DR: In this article, the results of a predictive competition among the described methods as implemented by different groups with strong expertise in the methodology have been presented, and each group then wrote their own implementation of their method to produce predictions at the given location and each which was subsequently run on a common computing environment.

TL;DR: In this paper, a newly constructed Deep mosaic with a resolution of 2.5 was used to search for sources down to 4σ with 1σ ≈ 12 μJy beam in the central 50' × 50'.