Related papers: Application of Machine Learning to 21 cm Cosmology
Machine learning techniques offer a precious tool box for use within astronomy to solve problems involving so-called big data. They provide a means to make accurate predictions about a particular system without prior knowledge of the…
With the rise of electronic data, particularly Earth observation data, data-based geospatial modelling using machine learning (ML) has gained popularity in environmental research. Accurate geospatial predictions are vital for domain…
During the epoch of reionization (EoR), the 21-cm signal allows direct observation of the neutral hydrogen (HI) in the intergalactic medium (IGM). In the post-reionization era, this signal instead probes HI in galaxies, which traces the…
The 21cm line probes the evolution of matter perturbations over a wide range of redshifts, from the dark ages down to the completion of reionization. Observing the 21cm cosmological signal will extend our understanding of the evolution of…
Next-generation 21cm observations will enable imaging of reionization on very large scales. These images will contain more astrophysical and cosmological information than the power spectrum, and hence providing an alternative way to…
Upcoming 21cm surveys with the SKA1-LOW telescope will enable imaging of the neutral hydrogen distribution on cosmological scales in the early Universe. These surveys are expected to generate huge imaging datasets that will encode more…
I review the potential for observing cosmic reionization using the HI 21cm line of neutral hydrogren. Studies include observations of the evolution of large scale structure of the IGM (density, excitation temperature, and neutral fraction),…
Machine learning (ML) is a revolutionary technology with demonstrable applications across multiple disciplines. Within the Earth science community, ML has been most visible for weather forecasting, producing forecasts that rival modern…
We discuss the prospects for ``tomography'' of the intergalactic medium (IGM) at high redshifts using the 21 cm transition of neutral hydrogen. Existing observational constraints on the epoch of reionization imply a complex ionization…
Observations of the hydrogen hyperfine transition through the 21 cm line near the end of the cosmic dark ages provide unique opportunities to probe new physics. In this work, we investigate the potential of the sky-averaged 21 cm signal to…
Measurement of the spatial distribution of neutral hydrogen via the redshifted 21 cm line promises to revolutionize our knowledge of the epoch of reionization and the first galaxies, and may provide a powerful new tool for observational…
Machine learning (ML) is often viewed as a black-box regression technique that is unable to provide considerable scientific insight. ML models are universal function approximators and - if used correctly - can provide scientific information…
Understanding the nature of dark matter (DM) particles remains a pivotal challenge in modern cosmology. Current cosmological research on these phenomena primarily utilizes cosmic microwave background (CMB) observations and other late-time…
An array of large observational programs using ground-based and space-borne telescopes is planned in the next decade. The forthcoming wide-field sky surveys are expected to deliver a sheer volume of data exceeding an exabyte. Processing the…
Separating the cosmological redshifted 21-cm signal from foregrounds is a major challenge. We present the cross-correlation of the redshifted 21-cm emission from neutral hydrogen (HI) in the post-reionization era with the Ly-alpha forest as…
Machine learning techniques are utilised in several areas of astrophysical research today. This dissertation addresses the application of ML techniques to two classes of problems in astrophysics, namely, the analysis of individual…
Future Square Kilometre Array (SKA) surveys are expected to generate huge datasets of 21cm maps on cosmological scales from the Epoch of Reionization (EoR). We assess the viability of exploiting machine learning techniques, namely,…
Upcoming experiments such as Hydrogen Epoch of Reionization Array (HERA) and Square Kilometre Array (SKA) are intended to measure the 21cm signal over a wide range of redshifts, representing an incredible opportunity in advancing our…
Machine learning (ML) can be used to construct surrogate models for the fast prediction of a property of interest. ML can thus be applied to chemical projects, where the usual experimentation or calculation techniques can take hours or days…
A novel application of machine-learning (ML) based image processing algorithms is proposed to analyze an all-sky map (ASM) obtained using the Fermi Gamma-ray Space Telescope. An attempt was made to simulate a one-year ASM from a…