Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAutomated period detection from variable stars time series database
111
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
The exact period determination of a multi-periodic variable star based on its luminosity time series data is believed a task requiring skill and experience. Thus the majority of available time series analysis techniques require human intervention to some extent. The present work is dedicated to establish an automated method of period (or frequency) determination from the time series database of variable stars. Relying on the SigSpec method (Reegen 2007), the technique established here employs a statistically unbiased treatment of frequency-domain noise and avoids spurious (i. e. noise induced) and alias peaks to the highest possible extent. Several add-ons were incorporated to tailor SigSpec to our requirements. We present tests on 386 stars taken from ASAS2 project database. From the output file produced by SigSpec, the frequency with maximum spectral significance is chosen as the genuine frequency. Out of 386 variable stars available in the ASAS2 database, our results contain 243 periods recovered exactly and also 88 half periods, 42 different periods etc. SigSpec has the potential to be effectively used for fully automated period detection from variable stars time series database. The exact detection of periods helps us to identify the type of variability and classify the variable stars, which provides a crucial information on the physical processes effective in stellar atmospheres.
rate research
Read More
The expert system for time series analysis of irregularly spaced signals is reviewed. It consists of a number of complementary algorithms and programs, which may be effective for different types of variability. Obviously, for a pure sine signal, all the methods should produce the same results. However, for irregularly spaced signals with a complicated structure, e.g. a sum of different components, different methods may produce significantly different results. The basic approach is based on classical method of the least squares (1994OAP.....7...49A). However, contrary to common step-by-step methods of removal important components (e.g. mean, trend (detrending), sine wave (prewhitening), where covariations between different components are ignored, i.e. erroneously assumed to be zero, we use complete mathematical models. Some of the methods are illustrated on the observations of the semi-regular pulsating variable RY UMa. The star shows a drastic cyclic change of semi-amplitude of pulsations between 0.01 to 0.37mag, which is interpreted as a bias between the waves with close periods and a beat period of 4000d (11yr). The dominating period has changed from 307.35(8)d before 1993 to 285.26(6)d after 1993. The initial epoch of the maximum brightness for the recent interval is 2454008.8(5). It is suggested that the apparent period switch is due to variability of amplitudes of these two waves and an occasional swap of the dominating wave.
We present the database of maser sources in H2O, OH and SiO lines that can be used to identify and study variable stars at evolved stages. Detecting the maser emission in H2O, OH and SiO molecules toward infrared-excess objects is one of the methods of identification long-period variables (LPVs, including Miras and Semi-Regular), because these stars exhibit maser activity in their circumstellar shells. Our sample contains 1803 known LPV objects. 46% of these stars (832 objects) manifest maser emission in the line of at least one molecule: H2O, OH or SiO. We use the database of circumstellar masers in order to search for long-periodic variables which are not included in the General Catalogue of Variable Stars (GCVS). Our database contains 4806 objects (3866 objects without associations in GCVS catalog) with maser detection in at least one molecule. Therefore it is possible to use the database in order to locate and study the large sample of long-period variable stars. Entry to the database at http://maserdb.net
The Mt. Suhora M,dwarf survey searching for pulsations in low mass main sequence stars has acquired CCD photometry of 46 M,dwarf stars during the first year of the project (Baran et al 2011). As a by-product of this search hundreds field stars have been checked for variability. This paper presents our initial result of a search for periodic variables in field stars observed in the course of the survey. On the basis of the periodicity and the shape of the light curves, eight new variables has been detected, among which five are $delta$ Scuti stars and three likely RR Lyrae stars. Although variation in one of the stars has been previously detected, it was classified incorrectly. To support our classification, in August 2010, we performed spectroscopic observations to derive spectral types and luminosity classes for all eight variable stars.
With recent developments in imaging and computer technology the amount of available astronomical data has increased dramatically. Although most of these data sets are not dedicated to the study of variable stars much of it can, with the application of proper software tools, be recycled for the discovery of new variable stars. Fits Viewer and Data Retrieval System is a new software package that takes advantage of modern computer advances to search astronomical data for new variable stars. More than 200 new variable stars have been found in a data set taken with the Calvin College Rehoboth Robotic telescope using FVDRS. One particularly interesting example is a very fast subdwarf B with a 95 minute orbital period, the fastest currently known of the HW Vir type.
We present a comparison of the Gaia DR1 samples of pulsating variable stars - Cepheids and RR Lyrae type - with the OGLE Collection of Variable Stars aiming at the characterization of the Gaia mission performance in the stellar variability domain. Out of 575 Cepheids and 2322 RR Lyrae candidates from the Gaia DR1 samples located in the OGLE footprint in the sky, 559 Cepheids and 2302 RR Lyrae stars are genuine pulsators of these types. The number of misclassified stars is low indicating reliable performance of the Gaia data pipeline. The completeness of the Gaia DR1 samples of Cepheids and RR Lyrae stars is at the level of 60-75% as compared to the OGLE Collection dataset. This level of completeness is moderate and may limit the applicability of the Gaia data in many projects.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
