scholarly journals Fuzzy Cluster Analysis: Application to Determining Metallicities for Very Metal-poor Stars

2021 ◽  
Vol 923 (2) ◽  
pp. 183
Author(s):  
Haining Li
Keyword(s):  
Cluster Analysis ◽  
High Resolution ◽  
Large Data ◽  
Threshold Value ◽  
Fuzzy Cluster ◽  
High Resolution Spectroscopy ◽  
Fuzzy Cluster Analysis ◽  
Low Resolution ◽  
Stellar Spectra ◽  
Artificial Neural Network Ann

Abstract This work presents a first attempt to apply fuzzy cluster analysis (FCA) to analyzing stellar spectra. FCA is adopted to categorize line indices measured from LAMOST low-resolution spectra, and automatically remove the least metallicity-sensitive indices. The FCA-processed indices are then transferred to the artificial neural network (ANN) to derive metallicities for 147 very metal-poor (VMP) stars that have been analyzed by high-resolution spectroscopy. The FCA-ANN method could derive robust metallicities for VMP stars, with a precision of ∼0.2 dex compared with high-resolution analysis. The recommended FCA threshold value λ for this test is between 0.9965 and 0.9975. After reducing the dimension of the line indices through FCA, the derived metallicities are still robust, with no loss of accuracy, and the FCA-ANN method performs stably for different spectral quality from [Fe/H] ∼ −1.8 down to −3.5. Compared with traditional classification methods, FCA considers ambiguity in groupings and noncontinuity of data, and is thus more suitable for observational data analysis. Though this early test uses FCA to analyze low-resolution spectra, and feeds the input to the ANN method to derive metallicities, FCA should be able to, in the large data era, also analyze slitless spectroscopy and multiband photometry, and prepare the input for methods not limited to ANN, in the field of stellar physics for other studies, e.g., stellar classification, identification of peculiar objects. The literature-collected high-resolution sample can help improve pipelines to derive stellar metallicities, and systematic offsets in metallicities for VMP stars for three published LAMOST catalogs have been discussed.

Fuzzy Cluster Analysis of Environmental Pollution in 31 of China's Provinces

2013 ◽  
Vol 295-298 ◽  
pp. 882-887
Author(s):  
Jian Jin ◽  
Jing Chao Hu
Keyword(s):  
Economic Growth ◽  
Cluster Analysis ◽  
Environmental Pollution ◽  
Development Policy ◽  
Threshold Value ◽  
Intermediate Level ◽  
Fuzzy Cluster ◽  
Similarity Matrix ◽  
Fuzzy Cluster Analysis ◽  
Fuzzy Similarity

31 of China’s provinces was divided into seven categories by a process of collecting data, standardizing data, creating fuzzy similarity matrix and fuzzy equivalent matrix, and finding the threshold value. There is not strictly positive correlation between environmental pollution and economic growth in China’s provinces. Because of different industry development policy and industry feature, environmental pollution in some more developed provinces and cities is less serious, while that in some provinces of the intermediate level is more serious.

scholarly journals Joint Discussion 4 UV astronomy: stars from birth to death

2006 ◽  
Vol 2 (14) ◽  
pp. 169-194
Author(s):  
Ana I. Gómez de Castro ◽  
Martin A. Barstow
Keyword(s):  
High Resolution ◽  
Uv Spectroscopy ◽  
Broad Band ◽  
Mission Planning ◽  
High Resolution Spectroscopy ◽  
Low Resolution ◽  
Short Term ◽  
Uv Astronomy ◽  
Optical Telescope ◽  
The Moment

AbstractThe scientific program is presented as well a the abstracts of the contributions. An extended account is published in “The Ultraviolet Universe: stars from birth to death” (Ed. Gómez de Castro) published by the Editorial Complutense de Madrid (UCM), that can be accessed by electronic format through the website of the Network for UV Astronomy (www.ucm.es/info/nuva).There are five telescopes currently in orbit that have a UV capability of some description. At the moment, only FUSE provides any medium- to high-resolution spectroscopic capability. GALEX, the XMM UV-Optical Telescope (UVOT) and the Swift. UVOT mainly delivers broad-band imaging, but with some low-resolution spectroscopy using grisms. The primary UV spectroscopic capability of HST was lost when the Space Telescope Imaging Spectrograph failed in 2004, but UV imaging is still available with the HST-WFPC2 and HST-ACS instruments.With the expected limited lifetime of sl FUSE, UV spectroscopy will be effectively unavailable in the short-term future. Even if a servicing mission of HST does go ahead, to install COS and repair STIS, the availability of high-resolution spectroscopy well into the next decade will not have been addressed. Therefore, it is important to develop new missions to complement and follow on from the legacy of FUSE and HST, as well as the smaller imaging/low resolution spectroscopy facilities. This contribution presents an outline of the UV projects, some of which are already approved for flight, while others are still at the proposal/study stage of their development.This contribution outlines the main results from Joint Discussion 04 held during the IAU General Assembly in Prague, August 2006, concerning the rationale behind the needs of the astronomical community, in particular the stellar astrophysics community, for new UV instrumentation. Recent results from UV observations were presented and future science goals were laid out. These goals will lay the framework for future mission planning.

scholarly journals EVALUATING THE DEGREE OF COMPLEXITY OF TIGHT OIL RECOVERY BASED ON THE CLASSIFICATION OF OILS

2020 ◽  
pp. 76-81
Author(s):  
G. Efendiyev ◽  
M. Karazhanova ◽  
D. Akhmetov ◽  
I. Piriverdiyev
Keyword(s):  
Cluster Analysis ◽  
Oil Recovery ◽  
Fuzzy Cluster ◽  
Fuzzy Cluster Analysis ◽  
Recovery Coefficient ◽  
Reservoir Permeability ◽  
Oil Density ◽  
The Relationship ◽  
Degree Of Recovery

The article discusses the results of the use of cluster analysis in assessing the degree of oil recovery complexity and its impact on the performance indicator. For this purpose, clustering was performed using a fuzzy cluster analysis algorithm. It should be noted that along with the deposits of heavy and highly viscous oils, a large share of hard-to-recover reserves is also confined to conditions with very low reservoir permeability values. Data on viscosity, oil density and oil permeability of in-situ conditions from various fields of Kazakhstan are collected. Using the results of this classification, a statistical analysis of indicators of various types of hard-torecover oils was performed. In the process of analysis, a generalized characteristic was determined for each class of oil, including viscosity, oil density and reservoir permeability. The generic characteristic is a linear transformation of the three characteristics. The results were subjected to statistical processing. At the same time, an attempt was made to establish and analyze the relationship between the degree of recovery complexity of hard-to-recover oils and oil recovery coefficient. In the course of the analysis, the average values of the oil recovery coefficient and the index of the degree of recovery complexity of hard-to-recover oil within each cluster were calculated and the relationship between them was plotted. The observed dependence, built on averaged points, is close to a power law, and, as one would expect, with an increase in the degree of oil recovery complexity, the oil recovery coefficient falls. The obtained estimates of the degree of oil recovery complexity allow us to rank different types of oils by their viscosity, density and reservoir permeability, which can be used to compare types of hard-to-recover oils by the value of the quality indicator. Methods to solve the problem of hard-to-remove high-viscosity and heavy oils should be aimed at reducing the viscosity of oil in the reservoir: injection of hot water / steam into the reservoir, the use of electric heaters, etc. Purpose. Assessment of the degree of oil recovery complexity and its impact on the efficiency of field development. The technique. The solution of the tasks set in the work was carried out on the method of mathematical statistics and the theory of fuzzy sets. In this case, the methods of processing the results, the correlation analysis, and the algorithm of fuzzy cluster analysis were used. Results. As a result of studies, 4 classes were obtained, each of which characterizes the degree of oil recovery complexity, a parameter was proposed for quantifying the degree of complexity, including oil density and viscosity, reservoir permeability, a relationship between this parameter and oil recovery coefficient was obtained. Scientific novelty. A classification of hard-to-recover reserves based on a fuzzy cluster analysis has been performed, and a parameter has been proposed for quantifying the degree of oil recovery complexity, a relationship has been obtained that allows judging the oil recovery by the degree of oil recovery complexity. Practical significance. The results obtained make it possible to classify hard-to-recover reserves and make decisions on the choice of methods for influencing the reservoir in various geological conditions.

scholarly journals On the synergy between Ariel and ground-based high-resolution spectroscopy

2022 ◽  
Author(s):  
Gloria Guilluy ◽  
Alessandro Sozzetti ◽  
Paolo Giacobbe ◽  
Aldo S. Bonomo ◽  
Giuseppina Micela
Keyword(s):  
High Resolution ◽  
Near Infrared ◽  
Major Axis ◽  
High Resolution Spectroscopy ◽  
Correlation Technique ◽  
Temperature Structure ◽  
Low Resolution ◽  
Semi Major Axis ◽  
Species Discovery ◽  
Orbital Parameters

AbstractSince the first discovery of an extra-solar planet around a main-sequence star, in 1995, the number of detected exoplanets has increased enormously. Over the past two decades, observational instruments (both onboard and on ground-based facilities) have revealed an astonishing diversity in planetary physical features (i. e. mass and radius), and orbital parameters (e.g. period, semi-major axis, inclination). Exoplanetary atmospheres provide direct clues to understand the origin of these differences through their observable spectral imprints. In the near future, upcoming ground and space-based telescopes will shift the focus of exoplanetary science from an era of “species discovery” to one of “atmospheric characterization”. In this context, the Atmospheric Remote-sensing Infrared Exoplanet Large (Ariel) survey, will play a key role. As it is designed to observe and characterize a large and diverse sample of exoplanets, Ariel will provide constraints on a wide gamut of atmospheric properties allowing us to extract much more information than has been possible so far (e.g. insights into the planetary formation and evolution processes). The low resolution spectra obtained with Ariel will probe layers different from those observed by ground-based high resolution spectroscopy, therefore the synergy between these two techniques offers a unique opportunity to understanding the physics of planetary atmospheres. In this paper, we set the basis for building up a framework to effectively utilise, at near-infrared wavelengths, high-resolution datasets (analyzed via the cross-correlation technique) with spectral retrieval analyses based on Ariel low-resolution spectroscopy. We show preliminary results, using a benchmark object, namely HD 209458 b, addressing the possibility of providing improved constraints on the temperature structure and molecular/atomic abundances.

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