Related papers: Ly$\alpha$NNA: A Deep Learning Field-level Inferen…
In this paper we critically examine predictions of the Lya forest within the standard cold dark matter (SCDM) model, paying particular attention to the low end of the column-density distribution. We show in particular that the width of…
We use the LUQAS sample of 27 high resolution, high signal-to-noise QSO absorption spectra (Kim et al. 2004) and the results from Croft et al. (2002) together with a suite of high-resolution hydro-dynamical simulations run with the…
When performing cosmological inference, standard analyses of the Lyman-$\alpha$ (Ly$\alpha$) three-dimensional correlation functions only consider the information carried by the distinct peak produced by baryon acoustic oscillations (BAO).…
Unpredictability of renewable energy sources coupled with the complexity of those methods used for various purposes in this area calls for the development of robust methods such as DL models within the renewable energy domain. Given the…
Deep learning (DL) models are gaining popularity in forest variable prediction using Earth Observation images. However, in practical forest inventories, reference datasets are often represented by plot- or stand-level measurements, while…
We present a general framework for obtaining robust bounds on the nature of dark matter using cosmological $N$-body simulations and Lyman-alpha forest data. We construct an emulator of hydrodynamical simulations, which is a flexible,…
This study investigates the relationships which deep learning methods can identify between the input and output data. As a case study, rainfall-runoff modeling in a snow-dominated watershed by means of a long- and short-term memory (LSTM)…
We present new cosmological parameter constraints from the eBOSS Lyman-$\alpha$ forest survey. We use a new theoretical model and likelihood based on the PRIYA simulation suite. PRIYA is the first suite to resolve the Lyman-$\alpha$ forest…
We present the PRIYA suite of cosmological simulations, based on the code and hydrodynamic model of the ASTRID simulation, and designed for cosmological analyses of the Lyman-$\alpha$ forest. Our simulation suite spans a $9$-dimensional…
We propose a novel method for approximate inference in Bayesian networks (BNs). The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and when online, make inference with the LTM instead of the…
We quantify the impact of massive neutrinos on the statistics of low density regions in the intergalactic medium (IGM) as probed by the Lyman-alpha forest at redshifts z=2.2--4. Based on mock but realistic quasar (QSO) spectra extracted…
The Lyman alpha forest power spectrum has been measured on large scales by the BOSS survey in SDSS-III at $z\sim 2.3$, has been shown to agree well with linear theory predictions, and has provided the first measurement of Baryon Acoustic…
We use the Sherwood-Relics suite of hybrid hydrodynamical and radiative transfer simulations to model the effect of inhomogeneous reionisation on the 1D power spectrum of the \Lya forest transmitted flux at redshifts $4.2\leq z \leq 5$.…
We use an automated Voigt-profile fitting procedure to extract statistical properties of the Ly$\alpha$ forest in a numerical simulation of an $\Omega=1$, cold dark matter (CDM) universe. Our analysis method is similar to that used in most…
We investigate Lyman-alpha (Ly$\alpha$) transmission spikes at $5.2 < z < 6.8$ using synthetic quasar spectra from the ``Cosmic Reionization On Computers" simulations. We focus on understanding the relationship between these spikes and the…
The z > 2 Lyman-alpha (Lya) forest traces the underlying dark-matter distribution on large scales and, given sufficient sightlines, can be used to create 3D maps of large-scale structure. We examine the observational requirements to…
(abridged) We present an effective implementation of analytical calculations of the Lyalpha opacity distribution of the Intergalactic Medium (IGM) along multiple lines of sight (LOS) to distant quasars in a cosmological setting. This method…
We model the $\lya$ forest in a spatially flat, CDM model with $\Omega=0.4$, with an Eulerian hydrodynamic simulation, and find that the intergalactic, photoionized gas collapses into sheet-like and filamentary structures which produce…
We report the application of implicit likelihood inference to the prediction of the macro-parameters of strong lensing systems with neural networks. This allows us to perform deep learning analysis of lensing systems within a well-defined…
Deep forest is a non-differentiable deep model which has achieved impressive empirical success across a wide variety of applications, especially on categorical/symbolic or mixed modeling tasks. Many of the application fields prefer…