Related papers: Automated Methods for Abundance Determination
Raman spectroscopy is a powerful tool for material characterization. However, careful preprocessing is required for the identification and handling of noise, baseline drift, and random spikes. This paper presents a comprehensive approach to…
Recent applications of machine learning and statistical inference provide case studies demonstrating how such approaches can accelerate the discovery process in physical chemistry and related fields. Examples discussed in this review…
Multispectral and hyperspectral images are increasingly popular in different research fields, such as remote sensing, astronomical imaging, or precision agriculture. However, the amount of free data available to perform machine learning…
We recently presented a new "artificial intelligence" method for the analysis of high-resolution absorption spectra (Bainbridge and Webb, Mon. Not. R. Astron. Soc. 2017, 468,1639-1670). This new method unifies three established numerical…
A precise quantitative spectral analysis - encompassing atmospheric parameter and chemical elemental abundance determination - is time consuming due to its iterative nature and the multi-parameter space to be explored, especially when done…
This paper presents an automated method to determine detailed abundances for A and F-type stars. This method is applied on spectra taken with the ELODIE spectrograph. Since the standard reduction procedure of ELODIE is optimized to obtain…
The goal of the paper is to automatize the selection of mechanisms which are able to describe a set of measurements. In order to do so first we construct a set of possible mechanism fulfilling chemically reasonable requirements with a given…
Approaches to automated grouping in singular spectrum analysis are considered. A new method for the identification of periodic components is proposed. The possibilities of extensions to multivariate time series and images are discussed.
We propose a multi-level method to increase the accuracy of machine learning algorithms for approximating observables in scientific computing, particularly those that arise in systems modeled by differential equations. The algorithm relies…
For many tasks of data analysis, we may only have the information of the explanatory variable and the evaluation of the response values are quite expensive. While it is impractical or too costly to obtain the responses of all units, a…
Static analysis by abstract interpretation aims at automatically proving properties of computer programs. To do this, an over-approximation of program semantics, defined as the least fixpoint of a system of semantic equations, must be…
Several Scientific and engineering applications require merging of sampled images for complex perception development. In most cases, for such requirements, images are merged at intensity level. Even though it gives fairly good perception of…
Precise continuum normalisation of merged \'{e}chelle spectra is a demanding task necessary for various detailed spectroscopic analyses. Automatic methods have limited effectiveness due to the variety of features present in the spectra of…
Spectral clustering is one of the most important algorithms in data mining and machine intelligence; however, its computational complexity limits its application to truly large scale data analysis. The computational bottleneck in spectral…
The Numerical Assembly Technique is extended to investigate arbitrary planar frame structures with the focus on the computation of natural frequencies. This allows us to obtain highly accurate results without resorting to spatial…
Orchestrating parametric fitting of multicomponent spectra at scale is an essential yet underappreciated task in high-throughput quantification of materials and chemical composition. To automate the annotation process for spectroscopic and…
A novel photonics-assisted method based on presampling and MDA technique is proposed for significantly improving the frequency estimation precision without introducing other complex algorithms. This method is also compatible with existing…
Cancer is one of the leading causes of death worldwide. Fast and safe early-stage, pre- and intra-operative diagnostics can significantly contribute to successful cancer identification and treatment. Artificial intelligence has played an…
Analyses of accelerated (momentum-based) gradient descent usually assume bounded condition number to obtain exponential convergence rates. However, in many real problems, e.g., kernel methods or deep neural networks, the condition number,…
The rapidly increasing volume of asteroseismic observations on solar-type stars has revealed a need for automated analysis tools. The reason for this is not only that individual analyses of single stars are rather time consuming, but more…