The Extended Catalog of Red AGB Variable Stars found in the NSVS Database

In this paper we present the catalog of new Mira-type and SR+L variable stars in the Northern Sky Variability Survey (NSVS) database, found after complementing the original NSVS object tables with a number of additional parameters. By extending the amount of criterias search queries can take advantage of and filtering known objects out, it became convenient to apply better constrained filters to search new variable star candidates. As an example of presented approach, the quest for red variables has resulted in 77 Mira-type and 717 SR+L stars found that have no identification in General Catalogue of Variable Stars, SIMBAD and VSX databases. Most likely the stars presented are new discoveries. The classification of these stars is based on the near-infrared colors from Two Micron All Sky Survey (2MASS) photometry, amplitude and period with the accuracy of the classification given ~90%, estimated basing on the current GCVS classification scheme. Methodology The Northern Sky Variability Survey (Wozniak et al, 2004), with approximately 19 million objects recorded, provides a wealth of data to process and even five years after publication, new discoveries are still made. The original NSVS database contains 19,995,106 objects with more than 3 billions of individual light curve measurements. One of the standard approaches to search objects within the database is to use an SQL query from the SkyDOT website. This provides the ability to constraint searches with basic criteria such as object’s coordinates, median object magnitude, standard magnitude deviation, median error and available number of observations. Obviously it gets rather time consuming to find a targeted object type manually using available criterias in such an extensive database, especially when coordinates are unknown. Additionally, since data contains so many semiregular or slow irregular variables of late spectral type, we decided to process the database automatically to extend each object record with a number of additional parameters such as period and it’s significance index, Stetson’s variability statistics, color indices and amplitude of light variations. We decided to narrow down the search field in a few steps, described below. Filtering the database: We required the resulting database objects to have at least 100 observational records and the following properties of light-curve parameters: σ > 0.1 mag and σ/E > 3.0, where σ is the RMS of magnitude scatter and E is the median of error estimate of all “good“ magnitude measurements without problem flags. Similar criterias applied have been described in (Wozniak et al, 2004), however we use softer requirements for the quality of observations. Once all objects not matching the required criteria have been removed, the original NSVS database has been reduced down to 81,358 objects. All objects from the reduced database have been further processed against the combined table of GCVS4 Vols. I-III and NL 67-78 (Kholopov et al, 1985-1988) with improved coordinates to filter out variable stars that are already discovered. The matching box in this proceure was chosen as 14”. 4 that corresponds to ROTSE-I pixel size. After this operation, we remove all known stars in the Catalog of Red Variables from NSVS according to NSVS object IDs, published on the SkyDot website; detected variables from the 3 All Sky Automated Survey (ASAS-3) and the list of MISAO project variable stars. The remaining database objects were filtered to exclude so called low quality data (TPLSTATS flag) and only measurements with flags ≤ 2 remained to exclude possible artifacts. This assures that analyzed light curve data does not contain errors such as pixel saturation, truncation by image boundaries, memory overflows, uncorrected photometry, mount flips and similar. After this, we select only those objects that show slow variability pattern, following similar procedure as described in (Wozniak et al, 2004). For this we estimate an AOV ratio of 15-day binned light curve as , where mi and si are i-th bin mean and standard deviation. Only bins with at least five valid points and objects with five or more bins were considered in this analysis. Objects with AOV ratio less than 1.6 are filtered out. More information on such an application of the AOV ratio can be obtained in (Wozniak et al, 2004). Multiple NSVS objects representing the same stars were joined into single records with the first NSVS ID mentioned and followed by it’s synonyms. Finally, the new variable candidates database has been reduced down to 7,338 objects after all the processing and filtering out already discovered variable stars and low quality data. > < = s m m RAOV / ) ( 2 / 1 2 2