Related papers: How to measure metallicity from five-band photomet…

Aims. We explore machine learning techniques to forecast star formation rate, stellar mass, and metallicity across galaxies with redshifts ranging from 0.01 to 0.3. Methods. Leveraging CatBoost and deep learning architectures, we utilize…

Obtaining accurate photometric redshift estimations is an important aspect of cosmology, remaining a prerequisite of many analyses. In creating novel methods to produce redshift estimations, there has been a shift towards using machine…

The research focuses on determining the metallicity ([Fe/H]) predicted in the solar twin stars by using various regression modeling techniques which are, Random Forest, Linear Regression, Decision Tree, Support Vector, and Gradient…

Getting spectra at good signal-to-noise ratios takes orders of magnitudes more time than photometric observations. Building on the technique developed for photometric redshift estimation of galaxies, we develop and demonstrate a…

We have identified several tens of extremely metal-poor star candidates from SDSS and LAMOST, which we follow-up with the 4.2m WHT telescope to confirm their metallicity.We follow a robust two-step methodology. We first analyze the SDSS and…

Forthcoming astronomical surveys are expected to detect new sources in such large numbers that measuring their spectroscopic redshift measurements will be not be practical. Thus, there is much interest in using machine learning to yield the…

Aims. In this work we develop a technique to obtain high precision determinations of both metallicity and effective temperature of M dwarfs in the optical. Methods. A new method is presented that makes use of the information of 4104 lines…

Based on high resolution and high signal-to-noise ratio (S/N) spectra analysis of 90 solar type stars, we have established several new metallicity calibrations in Teff range [5600, 6500] K based on red spectra with the wavelength range of…

Precise Soil Moisture (SM) assessment is essential in agriculture. By understanding the level of SM, we can improve yield irrigation scheduling which significantly impacts food production and other needs of the global population. The…

We develop a method to estimate photometric metallicities by simultaneously fitting the dereddened colors u-g, g-r, r-i and i-z from the SDSS with those predicted by the metallicity-dependent stellar loci. The method is tested with a…

Accurate determinations of metallicity for large, complete stellar samples are essential for advancing various studies of the Milky Way. In this paper, we present a data-driven algorithm that leverages photometric data from the KiDS and the…

In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised…

We describe the methodology required for estimation of photometric estimates of metallicity based on the SDSS gri passbands, which can be used to probe the properties of main-sequence stars beyond ~ 10 kpc, complementing studies of nearby…

In this paper we study the accuracy of photometric redshifts computed through a standard SED fitting procedure, where SEDs are obtained from broad-band photometry. We present our public code hyperz, which is presently available on the web.…

We present a machine learning method to assign stellar parameters (temperature, surface gravity, metallicity) to the photometric data of large photometric surveys such as SDSS and SKYMAPPER. The method makes use of our previous effort in…

Metallicity is a fundamental parameter that contributes to the physical characteristics of a star. However, the low temperatures and complex molecules present in M dwarf atmospheres make it difficult to measure their metallicities using…

Stellar parameters are not easily derived from M dwarf spectra, which are dominated by complex bands of diatomic and triatomic molecules and not well described at the line by line level by atmospheric models. M dwarf metallicities are…

Deep metrics have been shown effective as similarity measures in multi-modal image registration; however, the metrics are currently constructed from aligned image pairs in the training data. In this paper, we propose a strategy for learning…

Context. Reliable metallicity values for M dwarfs are important for studies of the chemical evolution and advancement of planet formation theory in low-mass environments. Historically the determination of stellar parameters of low-mass…

In table grape cultivation, harvesting depends on accurately assessing fruit quality. While some characteristics, like color, are visible, others, such as Soluble Solid Content (SSC), or sugar content measured in degrees Brix ({\deg}Brix),…