Related papers: How to measure metallicity from five-band photomet…
Geostatistical methods are powerful tools for understanding the spatial structure of the metallicity distribution of galaxies, and enable construction of accurate predictive models of the 2D metallicity distribution. However, so far these…
A growing number of indicators are now being used with some confidence to measure the metallicity(Z) of photoionisation regions in planetary nebulae, galactic HII regions(GHIIRs), extra-galactic HII regions(EGHIIRs) and HII galaxies(HIIGs).…
We present a new approach to obtaining photometric redshifts using a kernel learning technique called Support Vector Machines (SVMs). Unlike traditional spectral energy distribution fitting, this technique requires a large and…
Data-driven approaches play a crucial role in space computing, and our paper focuses on analyzing data to learn more about celestial objects. Photometric redshift, a measure of the shift of light towards the red part of the spectrum, helps…
Aims. The derivation of spectroscopic parameters for M dwarf stars is very important in the fields of stellar and exoplanet characterization. The goal of this work is the creation of an automatic computational tool, able to derive quickly…
Although stellar SED models are constantly improving and seem to show similar differential tendencies as measured spectra, their absolute properties (eg. absolute flux in different wavelength regions) can still differ significantly from the…
We present a new machine learning model for estimating photometric redshifts with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on the estimation range of redshifts, this model based on neural networks can handle…
Purpose: Many useful image quality metrics for evaluating linear image reconstruction techniques do not apply to or are difficult to interpret for non-linear image reconstruction. The vast majority of metrics employed for evaluating…
Automating the quality control of shot-blasted steel surfaces is crucial for improving manufacturing efficiency and consistency. This study presents a dataset of 1654 labeled RGB images (512x512) of steel surfaces, classified as either…
We present a star/galaxy classification for the Southern Photometric Local Universe Survey (S-PLUS), based on a Machine Learning approach: the Random Forest algorithm. We train the algorithm using the S-PLUS optical photometry up to $r$=21,…
A methodology is proposed, which addresses the caveat that line-of-sight emission spectroscopy presents in that it cannot provide spatially resolved temperature measurements in nonhomogeneous temperature fields. The aim of this research is…
We outline how redshift-space distortions (RSD) can be measured from the angular correlation function w({\theta}), of galaxies selected from photometric surveys. The natural degeneracy between RSD and galaxy bias can be minimized by…
We determine photometric metal abundance estimates for individual main-sequence stars in the Virgo Overdensity (VOD), which covers almost 1000 deg^2 on the sky, based on a calibration of the metallicity sensitivity of stellar isochrones in…
We present a catalogue of galaxy photometric redshifts for the Sloan Digital Sky Survey (SDSS) Data Release 12. We use various supervised learning algorithms to calculate redshifts using photometric attributes on a spectroscopic training…
Deriving atmospheric parameters of a large sample of stars is of vital importance to understand the formation and evolution of the Milky Way. Photometric surveys, especially those with near-ultraviolet filters, can offer accurate…
In this article, we argue that models based on machine learning (ML) can be very effective in estimating the non-linear matter power spectrum ($P(k)$). We employ the prediction ability of the supervised ML algorithms to build an estimator…
We used the updated [Fe/H] abundances of 168 F-G type dwarfs and calibrated them to a third order polynomial in terms of reduced ultraviolet excess, $\delta_{0.41}$ defined with $ugr$ data in the SDSS. We estimated the $M_g$ absolute…
In this study, we establish a basis for selecting similarity measures when applying machine learning techniques to solve materials science problems. This selection is considered with an emphasis on the distinctiveness between materials that…
We describe a new method for measuring the true redshift distribution of any set of objects studied only photometrically. The angular cross-correlation between objects in a photometric sample with objects in some spectroscopic sample as a…
The upcoming generation of optical spectrographs on four meter-class telescopes, with their huge multiplexing capabilities, excellent spectral resolution, and unprecedented wavelength coverage, will provide high-quality spectra for…