中文
相关论文

相关论文: Optimal 1D Ly-$\alpha$ Forest Power Spectrum Estim…

200 篇论文

On large scales, the Lyman-$\alpha$ forest provides insights into the expansion history of the Universe, while on small scales, it imposes strict constraints on the growth history, the nature of dark matter, and the sum of neutrino masses.…

We perform for the first time full simulation-based inference on the Lyman-$\alpha$ forest 1D power spectrum. In particular, we consider the prediction of the Lyman-$\alpha$ forest $P_{\rm 1D}(k)$ at $2.0<z<3.5$ from the CAMELS cosmological…

宇宙学与河外天体物理 · 物理学 2026-05-26 Francesco Sinigaglia , Patricia Iglesias-Navarro , Matteo Viel

We present a novel, fast method to recover the density field through the statistics of the transmitted flux in high redshift quasar absorption spectra. The proposed technique requires the computation of the probability distribution function…

宇宙学与河外天体物理 · 物理学 2015-05-20 Simona Gallerani , Francisco-Shu Kitaura , Andrea Ferrara

We present a method to make predictions with sets of correlated data values, in this case QSO flux spectra. We predict the continuum in the Lyman-Alpha forest of a QSO, from 1020 -- 1216 A, using the spectrum of that QSO from 1216 -- 1600 A…

天体物理学 · 物理学 2009-11-10 Nao Suzuki , David Tytler , David Kirkman , John M. O'Meara , Dan Lubin

The Lyman-alpha forest is a portion of the observed light spectrum of distant galactic nuclei which allows us to probe remote regions of the Universe that are otherwise inaccessible. The observed Lyman-alpha forest of a quasar light…

We present a model for one-dimensional (1D) matter power spectra in redshift space as estimated from data provided along individual lines of sight. We derive analytic expressions for these power spectra in the linear and nonlinear regimes,…

天体物理学 · 物理学 2009-11-10 Vincent Desjacques , Adi Nusser

(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…

天体物理学 · 物理学 2009-11-06 M. Viel , S. Matarrese , H. J. Mo , M. G. Haehnelt , Tom Theuns

We describe a new algorithm for the "perfect" extraction of one-dimensional spectra from two-dimensional (2D) digital images of optical fiber spectrographs, based on accurate 2D forward modeling of the raw pixel data. The algorithm is…

天体物理仪器与方法 · 物理学 2015-05-14 Adam S. Bolton , David J. Schlegel

Deep learning (DL) has been shown to outperform traditional, human-defined summary statistics of the Ly{\alpha} forest in constraining key astrophysical and cosmological parameters owing to its ability to tap into the realm of non-Gaussian…

天体物理仪器与方法 · 物理学 2025-10-24 Parth Nayak , Michael Walther , Daniel Gruen

We evaluate the performance of the Lyman-$\alpha$ forest weak gravitational lensing estimator of Metcalf et al. on forest data from hydrodynamic simulations and ray-traced simulated lensing potentials. We compare the results to those…

宇宙学与河外天体物理 · 物理学 2025-01-30 Patrick Shaw , Rupert A. C. Croft , R. Benton Metcalf

The full-shape correlations of the Lyman alpha (Ly$\alpha$) forest contain a wealth of cosmological information through the Alcock-Paczy\'{n}ski effect. However, these measurements are challenging to model without robustly testing and…

Spectroscopy of the Ly$\alpha$ forest in quasar spectra proved to be a useful tool for probing the intergalactic gas. We developed the automatic program for Voigt profile fitting of Ly$\alpha$ forest lines. We run this code on 9 high…

宇宙学与河外天体物理 · 物理学 2018-08-01 K. N. Telikova , S. A. Balashev , P. S. Shternin

The inference of astrophysical and cosmological properties from the Lyman-$\alpha$ forest conventionally relies on summary statistics of the transmission field that carry useful but limited information. We present a deep learning framework…

宇宙学与河外天体物理 · 物理学 2024-09-11 Parth Nayak , Michael Walther , Daniel Gruen , Sreyas Adiraju

The standard cosmological analysis with the Ly$\alpha$ forest relies on a continuum fitting procedure that suppresses information on large scales and distorts the three-dimensional correlation function on all scales. In this work, we…

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…

宇宙学与河外天体物理 · 物理学 2024-07-19 M. A. Fernandez , Simeon Bird , Ming-Feng Ho

The angular positions of quasars are deflected by the gravitational lensing effect of foreground matter. The Lyman-alpha forest seen in the spectra of these quasars is therefore also lensed. We propose that the signature of weak…

宇宙学与河外天体物理 · 物理学 2018-06-21 Rupert A. C. Croft , Alessandro Romeo , R. Benton Metcalf

Our goal in this paper is to test some popular dark matter models by Ly-alpha forest in QSO spectra. Recent observations of the size and velocity of Ly-alpha forest clouds have indicated that the Ly-alpha absorption is probably not given by…

天体物理学 · 物理学 2009-10-28 Hongguang BI , Jian GE , Li-Zhi FANG

We present constraints on the amplitude and shape of the matter power spectrum and the density of dark matter within the framework of a standard LambdaCDM model. We use a Markov Chain Monte Carlo approach to combine independent measurements…

天体物理学 · 物理学 2008-11-26 J. Lesgourgues , M. Viel , M. G. Haehnelt , R. Massey

We present a suite of cosmological N-body simulations with cold dark matter and baryons aiming at modeling the low-density regions of the IGM as probed by the Lyman-$\alpha$ forests at high redshift. The simulations are designed to match…

宇宙学与河外天体物理 · 物理学 2015-06-18 A. Borde , N. Palanque-Delabrouille , G. Rossi , M. Viel , J. Bolton , Ch. Yèche , J. -M. LeGoff , J. Rich

We present an efficient implementation of Wiener filtering of real-space linear field and optimal quadratic estimator of its power spectrum Band-powers. We first recast the field reconstruction into an optimization problem, which we solve…

宇宙学与河外天体物理 · 物理学 2019-10-16 Benjamin Horowitz , Uros Seljak , Grigor Aslanyan