Statistical inversion of the LOFAR Epoch of Reionization experiment data model

TL;DR

This paper presents a statistically optimal map-making method for LOFAR EoR data, addressing non-linearity and noise challenges to improve the detection of the 21-cm cosmological signal.

Contribution

It introduces a new statistical inversion technique tailored for LOFAR EoR data, enhancing data accuracy and noise handling in the map-making process.

Findings

Method effectively handles non-linear data response.

Assumptions of Gaussian noise are validated.

Improves dynamic range during offline processing.

Abstract

LOFAR is a new and innovative effort to build a radio-telescope operating at the multi-meter wavelength spectral window. One of the most exciting applications of LOFAR will be the search for redshifted 21-cm line emission from the Epoch of Reionization (EoR). It is currently believed that the Dark Ages, the period after recombination when the Universe turned neutral, lasted until around the Universe was 400,000 years old. During the EoR, objects started to form in the early universe and they were energetic enough to ionize neutral hydrogen. The precision and accuracy required to achieve this scientific goal, can be essentially translated into accumulating large amounts of data. The data model describing the response of the LOFAR telescope to the intensity distribution of the sky is characterized by the non-linearity of the parameters and the large level of noise compared to the desired cosmological signal. In this poster, we present the implementation of a statistically optimal map-making process and its properties. The basic assumptions of this method are that the noise is Gaussian and independent between the stations and frequency channels and that the dynamic range of the data can been enhanced significantly during the off-line LOFAR processing. These assumptions match our expectations for the LOFAR Epoch of Reionization Experiment.