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MAGGIE LIEU's Outputs (3)

Deep learning-based super-resolution and de-noising for XMM-newton images (2022)
Journal Article
Sweere, S. F., Valtchanov, I., Lieu, M., Vojtekova, A., Verdugo, E., Santos-Lleo, M., Pacaud, F., Briassouli, A., & Cámpora Pérez, D. (2022). Deep learning-based super-resolution and de-noising for XMM-newton images. Monthly Notices of the Royal Astronomical Society, 517(3), 4054-4069. https://doi.org/10.1093/mnras/stac2437

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
Matzeu, G. A., Lieu, M., Costa, M. T., Reeves, J. N., Braito, V., Dadina, M., Nardini, E., Boorman, P. G., Parker, M. L., Sim, S. A., Barret, D., Kammoun, E., Middei, R., Giustini, M., Brusa, M., Cabrera, J. P., & Marchesi, S. (2022). A new emulated Monte Carlo radiative transfer disc-wind model: X-Ray Accretion Disc-wind Emulator – XRADE. Monthly Notices of the Royal Astronomical Society, 515(4), 6172-6190. https://doi.org/10.1093/mnras/stac2155

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
Parker, M. L., Lieu, M., & Matzeu, G. A. (2022). AGN X-ray spectroscopy with neural networks. Monthly Notices of the Royal Astronomical Society, 514(3), 4061-4068. https://doi.org/10.1093/mnras/stac1639

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.