اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدAstrokit -- an Efficient Program for High-Precision Differential CCD Photometry and Search for Variable Stars
108
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Having a need to perform differential photometry for tens of thousands stars in a several square degrees field, we developed Astrokit program. The software corrects the star brightness variations caused by variations of atmospheric transparency: to this end, the program selects for each star an individual ensemble of reference stars having similar magnitudes and positions in the frame. With ten or more reference stars in the ensemble, the differences between their spectral types and the spectral type of the object studied become unimportant. Astrokit searches for variable stars using Robust Median Statistics criterion, which allows candidate variables to be selected more efficiently than by analyzing the standard deviation of star magnitudes. The software allows very precise automatic analysis of long inhomogeneous sets of photometric observations of a large number of objects to be performed, making it possible to find hot Jupiter type exoplanet transits and low-amplitude variables. We describe the algorithm of the program and the results of its application to reduce the data of the photometric sky survey in Cygnus as well as observations of the open cluster NGC188 and the transit of the exoplanet WASP-11 b / HAT-P-10 b, performed with the MASTER-II-URAL telescope of the Kourovka Astronomical Observatory of the Ural Federal University.
قيم البحث
اقرأ أيضاً
Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null-hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. Uncorrected systemati
c errors limit the practical applicability of this approach to high-amplitude variability and well-behaving data sets. Searching for a new variability detection technique that would be applicable to a wide range of variability types while being robust to outliers and underestimated measurement uncertainties, we propose to consider variability detection as a classification problem that can be approached with machine learning. We compare several classification algorithms: Logistic Regression (LR), Support Vector Machines (SVM), k-Nearest Neighbors (kNN) Neural Nets (NN), Random Forests (RF) and Stochastic Gradient Boosting classifier (SGB) applied to 18 features (variability indices) quantifying scatter and/or correlation between points in a light curve. We use a subset of OGLE-II Large Magellanic Cloud (LMC) photometry (30265 light curves) that was searched for variability using traditional methods (168 known variable objects identified) as the training set and then apply the NN to a new test set of 31798 OGLE-II LMC light curves. Among 205 candidates selected in the test set, 178 are real variables, 13 low-amplitude variables are new discoveries. We find that the considered machine learning classifiers are more efficient (they find more variables and less false candidates) compared to traditional techniques that consider individual variability indices or their linear combination. The NN, SGB, SVM and RF show a higher efficiency compared to LR and kNN.
BRITE-Constellation (where BRITE stands for BRIght Target Explorer) is an international nanosatellite mission to monitor photometrically, in two colours, the brightness and temperature variations of stars generally brighter than mag(V) ~ 4, with prec
ision and time coverage not possible from the ground. The current mission design consists of six nanosats (hence Constellation): two from Austria, two from Canada, and two from Poland. Each 7 kg nanosat carries an optical telescope of aperture 3 cm feeding an uncooled CCD. One instrument in each pair is equipped with a blue filter, the other with a red filter. Each BRITE instrument has a wide field of view (~24 degrees), so up to about 15 bright stars can be observed simultaneously, sampled in 32 pixel x 32 pixel sub-rasters. Photometry of additional fainter targets, with reduced precision but thorough time sampling, will be possible through onboard data processing. The BRITE sample is dominated by the most intrinsically luminous stars: massive stars seen at all evolutionary stages, and evolved medium-class stars at the very end of their nuclear burning phases. The goals of BRITE-Constellation are to (1) measure p- and g-mode pulsations to probe the interiors and ages of stars through asteroseismology; (2) look for varying spots on the stars surfaces carried across the stellar disks by rotation, which are the sources of co-rotating interaction regions in the winds of the most luminous stars, probably arising from magnetic subsurface convection; and (3) search for planetary transits.
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 o
f 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 report CCD $V$ and $I$ time-series photometry of the globular cluster NGC 6333 (M9). The technique of difference image analysis has been used, which enables photometric precision better than 0.05 mag for stars brighter than $V sim 19.0$ mag, even
in the crowded central regions of the cluster. The high photometric precision has resulted in the discovery of two new RRc stars, three eclipsing binaries, seven long-term variables and one field RRab star behind the cluster. A detailed identification chart and equatorial coordinates are given for all the variable stars in the field of our images of the cluster. Our data together with literature $V$-data obtained in 1994 and 1995 allowed us to refine considerably the periods for all RR Lyrae stars. The nature of the new variables is discussed. We argue that variable V12 is a cluster member and an Anomalous Cepheid. Secular period variations, double mode pulsations and/or the Blazhko-like modulations in some RRc variables are addressed. Through the light curve Fourier decomposition of 12 RR Lyrae stars we have calculated a mean metallicity of [Fe/H]$_{rm ZW}$=$-1.70 pm 0.01{rm(statistical)} pm 0.14{rm(systematic)}$ or [Fe/H]$_{UVES}=-1.67 pm 0.01{rm(statistical)} pm 0.19{rm(systematic)}$.Absolute magnitudes, radii and masses are also estimated for the RR Lyrae stars. A detailed search for SX Phe stars in the Blue Straggler region was conducted but none were discovered. If SX Phe exist in the cluster then their amplitudes must be smaller than the detection limit of our photometry. The CMD has been corrected for heavy differential reddening using the detailed extinction map of the cluster of Alonso-Garcia et al. (2012). This has allowed us to set the mean cluster distance from two independent estimates; from the RRab and RRc absolute magnitudes, we find $8.04pm 0.19$ kpc and $7.88pm0.30$ kpc respectively.
We present the results of a precise near-infrared (NIR) radial velocity (RV) survey of 32 low-mass stars with spectral types K2-M4 using CSHELL at the NASA IRTF in the $K$-band with an isotopologue methane gas cell to achieve wavelength calibration a
nd a novel iterative RV extraction method. We surveyed 14 members of young ($approx$ 25-150 Myr) moving groups, the young field star $varepsilon$ Eridani as well as 18 nearby ($<$ 25 pc) low-mass stars and achieved typical single-measurement precisions of 8-15 m s$^{-1}$ with a long-term stability of 15-50 m s$^{-1}$. We obtain the best NIR RV constraints to date on 27 targets in our sample, 19 of which were never followed by high-precision RV surveys. Our results indicate that very active stars can display long-term RV variations as low as $sim$ 25-50 m s$^{-1}$ at $approx$ 2.3125 $mu$m, thus constraining the effect of jitter at these wavelengths. We provide the first multi-wavelength confirmation of GJ 876 bc and independently retrieve orbital parameters consistent with previous studies. We recovered RV variability for HD 160934 AB and GJ 725 AB that are consistent with their known binary orbits, and nine other targets are candidate RV variables with a statistical significance of 3-5$sigma$. Our method combined with the new iSHELL spectrograph will yield long-term RV precisions of $lesssim$ 5 m s$^{-1}$ in the NIR, which will allow the detection of Super-Earths near the habitable zone of mid-M dwarfs.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
