128 citations · 1k across the 37 of their papers we have counts for
7 papers · 1 filter
Percent-level timing of reionization: self-consistent, implicit-likelihood inference from XQR-30+ Ly forest data
Yuxiang Qin, Andrei Mesinger, David Prelogović +13
The Lyman alpha (Lya) forest in the spectra of z>5 quasars provides a powerful probe of the late stages of the Epoch of Reionization (EoR). With the recent advent of exquisite data…
Signatures of warm dark matter in the cosmological density fields extracted using Machine Learning
Ander Artola, Sarah E. I. Bosman, Prakash Gaikwad +6
We aim to construct a machine-learning approach that allows for a pixel-by-pixel reconstruction of the intergalactic medium (IGM) density field for various warm dark matter (WDM) m…
The impact of faint AGN discovered by JWST on reionization
Shikhar Asthana, Martin G. Haehnelt, Girish Kulkarni +4
The relative contribution of emission from stellar sources and accretion onto supermassive black holes to reionization has been brought into focus again by the apparent high abunda…
Damping Wing-Like Features in the Stacked Ly Forest: Potential Neutral Hydrogen Islands at
Yongda Zhu, George D. Becker, Sarah E. I. Bosman +28
Recent quasar absorption line observations suggest that reionization may end as late as . As a means to search for large neutral hydrogen islands at , we revisi…
Damping wings in the Lyman-α forest: a model-independent measurement of the neutral fraction at 5.4<z<6.1
Benedetta Spina, Sarah E. I. Bosman, Frederick B. Davies +2
Recent observations have positioned the endpoint of the Epoch of Reionisation (EoR) at redshift . However, observations of the Lyman- forest have not yet been able t…
Deep Learning the Intergalactic Medium using Lyman-alpha Forest at
Fahad Nasir, Prakash Gaikwad, Frederick B. Davies +3
Unveiling the thermal history of the intergalactic medium (IGM) at holds the potential to reveal early onset HeII reionization or lingering thermal fluctuations f…
FLAME: Fitting Ly Absorption lines using Machine learning
Priyanka Jalan, Vikram Khaire, M. Vivek +1
We introduce FLAME, a machine-learning algorithm designed to fit Voigt profiles to HI Lyman-alpha (Ly) absorption lines using deep convolutional neural networks. FLAME integrate…