No Arabic abstract
{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 dependencies 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 available. 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 after 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.
This is the Users Manual for the Fisher Matrix software Fisher4Cast and covers installation, GUI help, command line basics, code flow and data structure, as well as cosmological applications and extensions. Finally we discuss the extensive tests performed on the software.
The spectroscopic development of classical novae is described as a narrative of the various stages of the outburst. The review highlights the multiwavelength aspects of the phenomenology and the recent developments related to structure, inhomogeneity, and dynamics of the ejecta. Special emphasis is placed on the distinct behavior of the symbiotic-like recurrent novae.
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.