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تسجيل مستخدم جديدTLUSTY Users Guide II: Reference Manual
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This is the second part of a three-volume guide to TLUSTY and SYNSPEC. It presents a detailed reference manual for TLUSTY, which contains a detailed description of basic physical assumptions and equations used to model an atmosphere, together with an overview of the numerical methods to solve these equations.
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This paper presents a detailed operational manual for TLUSTY. It provides a guide for understanding the essential features and the basic modes of operation of the program. To help the user, it is divided into two parts. The first part describes the m
ost important input parameters and available numerical options. The second part covers additional details and a comprehensive description of all physical and numerical options, and a description of all input parameters, many of which needed only in special cases.
This is the first of three papers that present a detailed guide for working with the codes {sc tlusty} and {sc synspec} to generate model stellar atmospheres or accretion disks, and to produce detailed synthetic spectra. In this paper, we present a v
ery brief manual intended for casual users who intend to use these codes for simple, well defined tasks. This paper does not present any background theory, or a description of the adopted numerical approaches, but instead uses simple examples to explain how to employ these codes. In particular, it shows how to produce a simple model atmosphere from the scratch, or how to improve an existing model by considering more extended model atoms. This paper also presents a brief guide to the spectrum synthesis program {sc synspec}.
{sc SigSpec} computes the spectral significance levels for the DFT amplitude spectrum of a time series at arbitrarily given sampling. It is based on the analytical solution for the Probability Density Function (PDF) of an amplitude level, including d
ependencies on frequency and phase and referring to white noise. Using a time series dataset as input, an iterative procedure including step-by-step prewhitening of the most significant signal components and MultiSine least-squares fitting is provided to determine a whole set of signal components, which makes the program a powerful tool for multi-frequency analysis. Instead of the step-by-step prewhitening of the most significant peaks, the program is also able to take into account several steps of the prewhitening sequence simultaneously and check for the combination associated to a minimum residual scatter. This option is designed to overcome the aliasing problem caused by periodic time gaps in the dataset. {sc SigSpec} can detect non-sinusoidal periodicities in a dataset by simultaneously taking into account a fundamental frequency plus a set of harmonics. Time-resolved spectral significance analysis using a set of intervals of the time series is supported to investigate the development of eigenfrequencies over the observation time. Furthermore, an extension is available to perform the {sc SigSpec} analysis for multiple time series input files at once. In this MultiFile mode, time series may be tagged as target and comparison data. Based on this selection, {sc SigSpec} is capable of determining differential significance spectra for the target datasets with respect to coincidences in the comparison spectra. A built-in simulator to generate and superpose a variety of sinusoids and trends as well as different types of noise completes the software package at the present stage of development.
{sc Cinderella} is a software solution for the quantitative comparison of time series in the frequency domain. It assigns probabilities to coincident peaks in the DFT amplidude spectra of the datasets under consideration. Two different modes are avai
lable. In conditional mode, {sc Cinderella} examines target and comparison datasets on the assumption that the latter contain artifacts only, returning the conditional probability of a target signal, although there is a coincident signal in the comparison data within the frequency resolution. In composed mode, the probability of coincident signal components in both target and comparison data is evaluated. {sc Cinderella} permits to examine multiple target and comparison datasets at once.
{sc Combine} is an add-on to {sc SigSpec} and {sc Cinderella}. A {sc SigSpec} result file or a file generated by {sc Cinderella} contains the significant sinusoidal signal components in a time series. In this file, {sc Combine} checks one frequency a
fter the other for being a linear combination of previously examined frequencies. If this attempt fails, the corresponding frequency is considered ``genuine. Only genuine frequencies are used to form linear combinations subsequently. A purely heuristic model is employed to assign a reliability to each linear combination and to justify whether to consider a frequency genuine or a linear combination.
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