相关论文: Parameter estimation from Ly$\alpha$ forest in Fou…
We present a sparse representation of model uncertainty for Deep Neural Networks (DNNs) where the parameter posterior is approximated with an inverse formulation of the Multivariate Normal Distribution (MND), also known as the information…
We introduce the Fourier Learning Machine (FLM), a neural network (NN) architecture designed to represent a multidimensional nonharmonic Fourier series. The FLM uses a simple feedforward structure with cosine activation functions to learn…
Parameter estimation remains a challenging task across many areas of engineering. Because data acquisition can often be costly, limited, or prone to inaccuracies (noise, uncertainty) it is crucial to identify sensor configurations that…
We propose a lightweight deep convolutional neural network (lCNN) to estimate cosmological parameters from simulated three-dimensional dark matter (DM) halo distributions and associated statistics. The training dataset comprises 2000…
Modern machine learning will allow for simulation-based inference from reionization-era 21cm observations at the Square Kilometre Array. Our framework combines a convolutional summary network and a conditional invertible network through a…
Confronting measurements of the Lyman-$\alpha$ forest with cosmological hydrodynamical simulations has produced stringent constraints on models of particle dark matter and the thermal and ionization state of the intergalactic medium. We…
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…
Using high redshift radio sources as background, the 21cm forest observations probe the neutral hydrogen absorption signatures of early structures along the lines of sight. Directly sensitive to the spin temperature, it complements the 21cm…
Missing data imputation is a critical challenge in various domains, such as healthcare and finance, where data completeness is vital for accurate analysis. Large language models (LLMs), trained on vast corpora, have shown strong potential…
We present a new Bayesian algorithm making use of Markov Chain Monte Carlo sampling that allows us to simultaneously estimate the unknown continuum level of each quasar in an ensemble of high-resolution spectra, as well as their common…
The Lyman-$\alpha$ forest refers to the series of absorption features observed in the spectra of distant quasars that are produced by neutral hydrogen in the intergalactic medium. Observed over a wide range of redshifts with both ground-…
This study investigates the potential accuracy boundaries of physics-informed neural networks, contrasting their approach with previous similar works and traditional numerical methods. We find that selecting improved optimization algorithms…
Deep neural networks (DNNs) depend on the storage of a large number of parameters, which consumes an important portion of the energy used during inference. This paper considers the case where the energy usage of memory elements can be…
The WMAP team has recently highlighted the usefulness of combining the Ly-alpha forest constraints with those from the cosmic microwave background (CMB). This combination is particularly powerful as a probe of the primordial shape of the…
Magnetic resonance fingerprinting (MRF) enables fast and multiparametric MR imaging. Despite fast acquisition, the state-of-the-art reconstruction of MRF based on dictionary matching is slow and lacks scalability. To overcome these…
We investigate the relationship between the Lyman-alpha (Lya) forest transmission in the intergalactic medium (IGM) and the environmental density of galaxies, focusing on its implications for the measurement of ionizing radiation escape…
Recently, NIRSpec PRISM/CLEAR observations by JWST have begun providing rest-frame UV continuum measurements of galaxies at $z\gtrsim7$, revealing signatures of Ly$\alpha$ damping-wing (DW) absorption by the intergalactic medium (IGM). We…
The Lyman-alpha forest is a unique probe of large-scale matter density fluctuations at high redshift z > 2. We measure the one-dimensional Lyman-alpha forest power spectrum using the first data provided by the Dark Energy Spectroscopic…
[abridged] We describe LyMAS (Ly-alpha Mass Association Scheme), a method of predicting clustering statistics in the Ly-alpha forest on large scales from moderate resolution simulations of the dark matter distribution, with calibration from…
As large language models (LLMs) continue to evolve, efficient evaluation metrics are vital for assessing their ability to compress information and reduce redundancy. While traditional metrics like Matrix Entropy offer valuable insights,…