相关论文: FLAME: Fitting Ly$\alpha$ Absorption lines using M…

We use new observations of very weak CIV absorption lines associated with high-redshift Lyman-alpha absorption systems to measure the high-redshift Lyman-alpha line two-point correlation function (TPCF). These very weak CIV absorption lines…

The distribution of the absorption line broadening observed in the Ly$\alpha$ forest carries information about the temperature and widths of the filaments in the intergalactic medium (IGM). In this work, we present and test a new method for…

We present flame, a pipeline for reducing spectroscopic observations obtained with multi-slit near-infrared and optical instruments. Because of its flexible design, flame can be easily applied to data obtained with a wide variety of…

Deep Learning has revolutionized machine learning, reaching unprecedented levels of accuracy, but at the cost of reduced interpretability. Especially in image processing systems, deep networks transform local pixel information into more…

Flow Matching (FM) is a simulation-free method for learning a continuous and invertible flow to interpolate between two distributions, and in particular to generate data from noise. Inspired by the variational nature of the diffusion…

Simulations of the formation of large-scale structure predict that dark matter, low density highly ionized gas, and galaxies form 10 40 Mpc scale filaments. These structure are easily recognized in the distribution of galaxies, but have not…

Galaxy formation depends critically on the physical state of gas in the circumgalactic medium (CGM) and its interface with the intergalactic medium (IGM), determined by the complex interplay between inflows from the IGM and outflows from…

We investigate the physical state of HI absorbing gas at low redshift (z = 0.25) using a subset of cosmological, hydrodynamic simulations from the OWLS project, focusing in particular on broad (b_HI > 40 km/s) HI Lyman-Alpha absorbers…

The rapid adoption of Large Language Models (LLMs) has raised significant environmental concerns. Unlike the one-time cost of training, LLM inference occurs continuously and dominates the AI energy footprint. Yet most sustainability studies…

Current state-of-the-art segmentation models encode entire images before focusing on specific objects. As a result, they waste computational resources - particularly when small objects are to be segmented in high-resolution scenes. We…

In a previous paper we presented the results of applying machine learning to classify whether an HI 21-cm absorption spectrum arises in a source intervening the sight-line to a more distant radio source or within the host of the radio…

Accurate flux density calibration is essential for precise analysis and interpretation of observations across different observation modes and instruments. In this research, we firstly introduce the flux calibration model incorporated in…

[Abridged] We present FLO (From Lines to Over-densities), a new technique to reconstruct the hydrogen density field for the Lya forest lines observed in high resolution QSO spectra. The method is based on the hypothesis that the Lya lines…

Optical flow estimation is a fundamental and long-standing visual task. In this work, we present a novel method, dubbed HMAFlow, to improve optical flow estimation in challenging scenes, particularly those involving small objects. The…

We present a novel maximum a posteriori estimator to jointly estimate band-powers and the covariance of the three-dimensional power spectrum (P3D) of Lyman-alpha forest flux fluctuations, called MAPLE. Our Wiener-filter based algorithm…

Opacities of molecules in exoplanet atmospheres rely on increasingly detailed line-lists for these molecules. The line lists available today contain for many species up to several billions of lines. Computation of the spectral line profile…

In this paper, we explore a novel image matting task aimed at achieving efficient inference under various computational cost constraints, specifically FLOP limitations, using a single matting network. Existing matting methods which have not…

Numerical hydrodynamical simulations are used to predict the expected absorption properties of the Lyman-Alpha forest for a variety of Cold Dark Matter dominated cosmological scenarios: CHDM, OCDM, LCDM, SCDM, and tCDM. Synthetic spectra…

We propose a machine learning framework based on Flow Matching (FM) to identify critical properties in many-body systems efficiently. Using the 2D XY model as a benchmark, we demonstrate that a single network, trained only on configurations…

We present early science results from the First Large Absorption Survey in HI (FLASH), a spectroscopically blind survey for 21-cm absorption lines in cold hydrogen HI gas at cosmological distances using the Australian Square Kilometre Array…