اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدDetecting Variability in Massive Astronomical Time-Series Data II: Variable Candidates in the Northern Sky Variability Survey
91
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
We present variability analysis of data from the Northern Sky Variability Survey (NSVS). Using the clustering method which defines variable candidates as outliers from large clusters, we cluster 16,189,040 light curves, having data points at more than 15 epochs, as variable and non-variable candidates in 638 NSVS fields. Variable candidates are selected depending on how strongly they are separated from the largest cluster and how rarely they are grouped together in eight dimensional space spanned by variability indices. All NSVS light curves are also cross-correlated to the Infrared Astronomical Satellite, AKARI, Two Micron All Sky Survey, Sloan Digital Sky Survey (SDSS), and Galaxy Evolution Explorer objects as well as known objects in the SIMBAD database. The variability analysis and cross-correlation results are provided in a public online database which can be used to select interesting objects for further investigation. Adopting conservative selection criteria for variable candidates, we find about 1.8 million light curves as possible variable candidates in the NSVS data, corresponding to about 10% of our entire NSVS samples. Multi-wavelength colors help us find specific types of variability among the variable candidates. Moreover, we also use morphological classification from other surveys such as SDSS to suppress spurious cases caused by blending objects or extended sources due to the low angular resolution of the NSVS.
قيم البحث
اقرأ أيضاً
We present a new framework to detect various types of variable objects within massive astronomical time-series data. Assuming that the dominant population of objects is non-variable, we find outliers from this population by using a non-parametric Bay
esian clustering algorithm based on an infinite GaussianMixtureModel (GMM) and the Dirichlet Process. The algorithm extracts information from a given dataset, which is described by six variability indices. The GMM uses those variability indices to recover clusters that are described by six-dimensional multivariate Gaussian distributions, allowing our approach to consider the sampling pattern of time-series data, systematic biases, the number of data points for each light curve, and photometric quality. Using the Northern Sky Variability Survey data, we test our approach and prove that the infinite GMM is useful at detecting variable objects, while providing statistical inference estimation that suppresses false detection. The proposed approach will be effective in the exploration of future surveys such as GAIA, Pan-Starrs, and LSST, which will produce massive time-series data.
The Northern Sky Variability Survey (NSVS) is a temporal record of the sky over the optical magnitude range from 8 to 15.5. It was conducted in the course of the first generation Robotic Optical Transient Search Experiment (ROTSE-I) using a robotic s
ystem of four co-mounted unfiltered telephoto lenses equipped with CCD cameras. The survey was conducted from Los Alamos, NM, and primarily covers the entire northern sky. Some data in southern fields between declinations 0 and -38 deg is also available, although with fewer epochs and noticeably lesser quality. The NSVS contains light curves for approximately 14 million objects. With a one year baseline and typically 100-500 measurements per object, the NSVS is the most extensive record of stellar variability across the bright sky available today. In a median field, bright unsaturated stars attain a point to point photometric scatter of ~0.02 mag and position errors within 2 arcsec. At Galactic latitudes |b| < 20 deg the data quality is limited by severe blending due to ~14 arcsec pixel size. We present basic characteristics of the data set and describe data collection, analysis, and distribution. All NSVS photometric measurements are available for on-line public access from the Sky Database for Objects in Time-Domain (SkyDOT; http://skydot.lanl.gov) at LANL. Copies of the full survey photometry may also be requested on tape.
We use data from the Northern Sky Variability Survey (NSVS), obtained from the first generation Robotic Optical Transient Search Experiment (ROTSE-I), to identify and study RR Lyrae variable stars in the solar neighborhood. We initially identified 11
97 RRab (RR0) candidate stars brighter than the ROTSE median magnitude V = 14. Periods, amplitudes, and mean V magnitudes are determined for a subset of 1188 RRab stars with well defined light curves. Metallicities are determined for 589 stars by the Fourier parameter method and by the relationship between period, amplitude, and [Fe/H]. We comment upon the difficulties of clearly classifying RRc (RR1) variables in the NSVS dataset. Distances to the RRab stars are calculated using an adopted luminosity-metallicity relation with corrections for interstellar extinction. The 589 RRab stars in our final sample are used to study the properties of the RRab population within 5 kpc of the Sun. The Bailey diagram of period versus amplitude shows that the largest component of this sample belongs to Oosterhoff type I. Metal-rich ([Fe/H] > -1) RRab stars appear to be associated with the Galactic disk. Our metal-rich RRab sample may include a thin disk as well as a thick disk population, although the uncertainties are too large to establish this. There is some evidence among the metal-rich RRab stars for a decline in scale height with increasing [Fe/H], as was found by Layden (1995). The distribution of RRab stars with -1 < [Fe/H] < -1.25 indicates that within this metallicity range the RRab stars are a mixture of stars belonging to halo and disk populations.
We used data from the QUEST-La Silla Active Galactic Nuclei (AGN) variability survey to construct light curves for 208,583 sources over $sim 70$ deg$^2$, with a a limiting magnitude $r sim 21$. Each light curve has at least 40 epochs and a length of
$geq 200$ days. We implemented a Random Forest algorithm to classify our objects as either AGN or non-AGN according to their variability features and optical colors, excluding morphology cuts. We tested three classifiers, one that only includes variability features (RF1), one that includes variability features and also $r-i$ and $i-z$ colors (RF2), and one that includes variability features and also $g-r$, $r-i$, and $i-z$ colors (RF3). We obtained a sample of high probability candidates (hp-AGN) for each classifier, with 5,941 candidates for RF1, 5,252 candidates for RF2, and 4,482 candidates for RF3. We divided each sample according to their $g-r$ colors, defining blue ($g-rleq 0.6$) and red sub-samples ($g-r>0.6$). We find that most of the candidates known from the literature belong to the blue sub-samples, which is not necessarily surprising given that, unlike for many literature studies, we do not cut our sample to point-like objects. This means that we can select AGN that have a significant contribution from redshifted starlight in their host galaxies. In order to test the efficiency of our technique we performed spectroscopic follow-up, confirming the AGN nature of 44 among 54 observed sources (81.5% of efficiency). From the campaign we concluded that RF2 provides the purest sample of AGN candidates.
We present a de-trending algorithm for the removal of trends in time series. Trends in time series could be caused by various systematic and random noise sources such as cloud passages, changes of airmass, telescope vibration or CCD noise. Those tren
ds undermine the intrinsic signals of stars and should be removed. We determine the trends from subsets of stars that are highly correlated among themselves. These subsets are selected based on a hierarchical tree clustering algorithm. A bottom-up merging algorithm based on the departure from normal distribution in the correlation is developed to identify subsets, which we call clusters. After identification of clusters, we determine a trend per cluster by weighted sum of normalized light-curves. We then use quadratic programming to de-trend all individual light-curves based on these determined trends. Experimental results with synthetic light-curves containing artificial trends and events are presented. Results from other de-trending methods are also compared. The developed algorithm can be applied to time series for trend removal in both narrow and wide field astronomy.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
