All Outputs (4)

Possible evidence for a large-scale enhancement in the Lyman-α forest power spectrum at redshift z ≥ 4 (2023)
Journal Article

Inhomogeneous reionization enhances the 1D Ly α forest power spectrum on large scales at redshifts z ≥ 4. This is due to coherent fluctuations in the ionized hydrogen fraction that arise from large-scale variations in the post-reionization gas temper... Read More about Possible evidence for a large-scale enhancement in the Lyman-α forest power spectrum at redshift z ≥ 4.

Deep learning-based super-resolution and de-noising for XMM-newton images (2022)
Journal Article

The field of artificial intelligence based image enhancement has been rapidly evolving over the last few years and is able to produce impressive results on non-astronomical images. In this work, we present the first application of Machine Learning ba... Read More about Deep learning-based super-resolution and de-noising for XMM-newton images.

A new emulated Monte Carlo radiative transfer disc-wind model: X-Ray Accretion Disc-wind Emulator – XRADE (2022)
Journal Article

Abstract We present a new X-Ray Accretion Disk-wind Emulator (xrade) based on the 2.5D Monte Carlo radiative transfer code which provides a physically-motivated, self-consistent treatment of both absorption and emission from a disk-wind by computing... Read More about A new emulated Monte Carlo radiative transfer disc-wind model: X-Ray Accretion Disc-wind Emulator – XRADE.

AGN X-ray spectroscopy with neural networks (2022)
Journal Article

We explore the possibility of using machine learning to estimate physical parameters directly from active galactic nucleus (AGN) X-ray spectra without needing computationally expensive spectral fitting. Specifically, we consider survey quality data,... Read More about AGN X-ray spectroscopy with neural networks.