All Outputs (6)

Possible evidence for a large-scale enhancement in the Lyman-α forest power spectrum at redshift z ≥ 4 (2023)
Journal Article
Molaro, M., Iršič, V., Bolton, J. S., Lieu, M., Keating, L. C., Puchwein, E., Haehnelt, M. G., & Viel, M. (2023). Possible evidence for a large-scale enhancement in the Lyman-α forest power spectrum at redshift z ≥ 4. Monthly Notices of the Royal Astronomical Society, 521(1), 1489–1501. https://doi.org/10.1093/mnras/stad598

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
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.

The X-CLASS survey: A catalogue of 1646 X-ray-selected galaxy clusters up to z ∼1.5 (2021)
Journal Article
Koulouridis, E., Clerc, N., Sadibekova, T., Chira, M., Drigga, E., Faccioli, L., Le Fèvre, J. P., Garrel, C., Gaynullina, E., Gkini, A., Kosiba, M., Pacaud, F., Pierre, M., Ridl, J., Tazhenova, K., Adami, C., Altieri, B., Baguley, J. C., Cabanac, R., Cucchetti, E., …Valtchanov, I. (2021). The X-CLASS survey: A catalogue of 1646 X-ray-selected galaxy clusters up to z ∼1.5. Astronomy and Astrophysics, 652, Article A12. https://doi.org/10.1051/0004-6361/202140566

Context. Cosmological probes based on galaxy clusters rely on cluster number counts and large-scale structure information. X-ray cluster surveys are well suited for this purpose because they are far less affected by projection effects than optical su... Read More about The X-CLASS survey: A catalogue of 1646 X-ray-selected galaxy clusters up to z ∼1.5.

Learning to denoise astronomical images with U-nets (2020)
Journal Article
Vojtekova, A., Lieu, M., Valtchanov, I., Altieri, B., Old, L., Chen, Q., & Hroch, F. (2021). Learning to denoise astronomical images with U-nets. Monthly Notices of the Royal Astronomical Society, 503(3), 3204-3215. https://doi.org/10.1093/mnras/staa3567

Astronomical images are essential for exploring and understanding the Universe. Optical telescopes capable of deep observations, such as the Hubble Space Telescope (HST), are heavily oversubscribed in the Astronomical Community. Images also often con... Read More about Learning to denoise astronomical images with U-nets.