Related papers: Emulating Radiative Transfer with Artificial Neura…
The cosmic 21-cm line of hydrogen is expected to be measured in detail by the next generation of radio telescopes. The enormous dataset from future 21-cm surveys will revolutionize our understanding of early cosmic times. We present a…
Recent studies have demonstrated that the representations of artificial neural networks (ANNs) can exhibit notable similarities to cortical representations when subjected to identical auditory sensory inputs. In these studies, the ability…
Bridging theory and observations is a key task to understand galaxy formation and evolution. With the advent of state-of-the-art observational facilities, an accurate modelling of galaxy observables through radiative transfer simulations…
In modern astronomy, the quantity of data collected has vastly exceeded the capacity for manual analysis, necessitating the use of advanced artificial intelligence (AI) techniques to assist scientists with the most labor-intensive tasks. AI…
Over the last few years deep artificial neural networks (DNNs) have very successfully been used in numerical simulations for a wide variety of computational problems including computer vision, image classification, speech recognition,…
Estimating physical quantities such as the star formation rate, stellar mass and active galactic nucleus (AGN) fraction of galaxies is a key step in understanding galaxy formation and evolution. In order to estimate the uncertainties in the…
We calculate photometric redshifts from the Sloan Digital Sky Survey Data Release 2 Galaxy Sample using artificial neural networks (ANNs). Different input patterns based on various parameters (e.g. magnitude, color index, flux information)…
We show that in addition to providing effective and competitive closures, when analysed in terms of dynamics and physically-relevant diagnostics, artificial neural networks (ANNs) can be both interpretable and provide useful insights in the…
Context. Computing spectra from 3D simulations of stellar atmospheres when allowing for departures from local thermodynamic equilibrium (non-LTE) is computationally very intensive. Aims. We develop a machine learning based method to speed…
A method is proposed and evaluated to model large and inconvenient phase space files used in Monte Carlo simulations by a compact Generative Adversarial Network (GAN). The GAN is trained based on a phase space dataset to create a neural…
Determining the physical parameters of pulsating variable stars such as RR Lyrae is essential for understanding their internal structure, pulsation mechanisms, and evolutionary state. In this study, we present a machine learning framework…
Understanding the impact of dust on the spectral energy distributions (SEDs) of galaxies is crucial for inferring their physical properties and for studying the nature of interstellar dust. We analyze dust attenuation curves for $\sim 6400$…
We explore how information in images of nearby galaxies can be used to estimate their distance. We train a convolutional Neural Network (NN) to do this, using galaxy images from the Illustris simulation. We show that if the NN is trained on…
Precise and reliable estimation of reference evapotranspiration (ET o ) is an essential for the irrigation and water resources management. ET o is difficult to predict due to its complex processes. This complexity can be solved using…
The treatment of dust attenuation is crucial in order to compare the predictions of galaxy formation models with multiwavelength observations. Most past studies have either used simple analytic prescriptions or else full radiative transfer…
This work explores the construction of a fast emulator for the calculation of the final pattern of nucleosynthesis in the rapid neutron capture process (the $r$-process). An emulator is built using a feed-forward artificial neural network…
Supervised artificial neural networks are used to predict useful properties of galaxies in the Sloan Digital Sky Survey, in this instance morphological classifications, spectral types and redshifts. By giving the trained networks unseen…
Throughout this paper we present a new method to detect and measure emission lines in J-PAS up to $z = 0.35$. J-PAS will observe $8000$~deg$^2$ of the northern sky in the upcoming years with 56 photometric bands. The release of such amount…
Machine learning and artificial neural networks (ANNs) have increasingly become integral to data analysis research in astrophysics due to the growing demand for fast calculations resulting from the abundance of observational data.…
The use of Artificial Neural Networks (ANNs) as a classifier of digital spectra is investigated. Using both simulated and real data, it is shown that neural networks can be trained to discriminate between the spectra of different classes of…