THE FIRST MOTESS-GNAT VARIABLE-STAR SURVEY

The Astronomical Journal, 134:1488 1502, 2007 October # 2007. The American Astronomical Society. All rights reserved. Printed in U.S.A. THE FIRST MOTESS-GNAT VARIABLE-STAR SURVEY Adam L. Kraus Department of Astronomy, California Institute of Technology, Pasadena, CA 91125, USA; alk@astro.caltech.edu Eric R. Craine Global Network of Astronomical Telescopes; ercraine@wrc-inc.com Mark S. Giampapa National Solar Observatory; and National Optical Astronomy Observatory 1 ; giampapa@noao.edu Werner W. G. Scharlach Global Network of Astronomical Telescopes and Roy A. Tucker Goodricke- Pigott Observatory; gpobs@mindspring.com Received 2006 November 6; accepted 2007 June 24 ABSTRACT We present the results of the first MOTESS-GNAT variable-star survey, a deep, wide-field variability survey conducted over 2 yr with a total sky coverage of 300 deg 2. In this survey, we identified 26,042 variable-star candidates with magnitudes R ¼ 13 19, including 5271 that are periodic at the 99% confidence level. We recovered 59 out of 68 members of the General Catalogue of Variable Stars (GCVS) that are in this brightness range. We discuss the implications for completeness and accuracy for both this survey and the GCVS; the implied completeness for distinctly classifiable variable stars in our survey is 85% 90%. We also discuss some of the caveats of our survey results. We conclude that this instrument design is ideal for an inexpensive, longitudinally distributed telescope network that could be used to study faint or rare transient phenomena in a previously unexplored regime of parameter space. Key words: binaries: eclipsing catalogs stars: variables: other surveys techniques: photometric 1. INTRODUCTION The Moving Object and Transient Event Search System (MOTESS) is a sky survey instrument that uses unfiltered CCD imagers operating on a triplet of scan-mode, 35 cm aperture Newtonian telescopes (Tucker 2007). MOTESS produces repeated observations of its survey area each night, with the goal of identifying new solar system objects and providing updated astrometry of known objects. In 2004, this instrument contributed over 70,000 astrometric observations of asteroids and over 200 new object discoveries, including the Apollo asteroid 2004 MP7 and comet C/2004 Q1 (Tucker). The Global Network of Astronomical Telescopes (GNAT), Inc., is a nonprofit foundation with an interest in establishing a longitudinally distributed network of telescopes that can be dedicated to extended sky surveys conducting CCD photometry. Principals from GNAT and MOTESS have undertaken a collaboration to test the MOTESS telescopes as prototypes for the GNAT distributed network. The unfiltered MOTESS surveys, although lacking color information, offer an excellent opportunity to begin experimenting with the development of a GNAT data pipeline during the construction and installation of dedicated GNAT telescopes. The MOTESS program has completed 6 yr of observations in three successive declination strips. The first strip (centered at declination +03 18 0 20 00 ) was observed from 2001 April 21 to 2003 July 4, the second strip (+02 05 0 00 00 ) was observed from 2003 July to 2005 July, and the third strip (+12 18 0 20 00 ) was observed 1 The National Solar Observatory and National Optical Astronomical Observatory are each operated by the Association of Universities for Research in Astronomy, Inc., under cooperative agreement with the National Science Foundation. 1488 from 2005 July to 2007 July. A concerted effort was made to operate the telescopes on every clear night. An exception to this was that during bright time each month a variety of experiments were performed using different bandpass filters. The +03 18 0 20 00 declination strip has now been reduced to photometric observations of approximately 1.6 million stars with a detection limit of R 19. These data form the first GNATeffort at creation of a large photometric database using scan-mode telescopes, and are collectively referred to as the first MOTESS-GNAT survey, or MG1. In Figure 1, we show the surface area and magnitude depth for this and several other representative surveys. The number in parentheses following each survey name is the typical number of observations at each position. Small, low-cost dedicated telescopes can typically perform many observations of a small area (e.g., planet-search networks like the Trans-Atlantic Exoplanet Survey [TReS]; Alonso et al. 2004) or a smaller number of observations of the entire sky (e.g., the All-Sky Automated Survey [ASAS]; Pojmanski et al. 2005), but are limited in depth due to their size. Larger (1 2 m class) telescopes can reach fainter limiting magnitudes, but the optimal balance of integration time and readout time favors deeper surveys at the expense of either number of observations (e.g., the Sloan Digital Sky Survey; York et al. 2000) or surface area (e.g., Optical Gravitational Lensing Experiment II; Udalski et al. 1997). The MG1 survey fills a unique niche in parameter space, observing with intermediate depth, area, and return coverage with an observing strategy that is ideal for variability studies in the field. The MG1 photometry was analyzed in order to create a derivative catalog of variable objects, which is expected to give rise to a variety of further scientific studies. In this paper, we describe the reduction and analysis tools that are used to create the MG1

FIRST MOTESS-GNAT VARIABLE-STAR SURVEY 1489 Fig. 1. Plot of the coverage area and magnitude depth for a representative selection of surveys: Sloan Digital Sky Survey (York et al. 2000), ASAS (Pojmanski et al. 2005), Faint Sky Variability Survey (Groot et al. 2003), Northern Sky Variability Survey ( Wozniak et al. 2004), Optical Gravitational Lensing Experiment II ( Udalski et al. 1997), TReS (Alonso et al. 2004), and the MG1 Survey. The typical number of visits to each covered area is included in parentheses. variable-star catalog, as well as discussing the scientific results from our preliminary analysis of the catalog. We describe the MOTESS observations in x 2. In x 3, we describe our data reduction pipeline. The analysis tools used to identify variable-star candidates are described in x 4, and we present the resulting variable-star catalog in x 5. Finally, we discuss our results in the context of other variable-star studies in x 6. 2. OBSERVATIONS This data set was collected between 2001 April and 2003 July with MOTESS, a cluster of three 0.35 m telescopes operating at Goodricke-Pigott Observatory outside Tucson, Arizona. Each telescope is equipped with a locally designed camera based on a SITe TK1024 detector. The field of view of the cameras is 48:3 0 ; 48:3 0, and the scale is 2.83 00 pixel 1. Details of the telescope construction and operation can be found in Tucker (2007). The MOTESS telescopes operate in drift-scan mode, so each is fixed in declination. The CCDs are clocked to read out data as the sky tracks overhead, so that over the course of a night each telescope produces a continuous sequence of observations centered on this declination and with increasing right ascension (R.A.). The integration time, which is set by the field of view and the sidereal rate, is 193.2 s. For ease of storage and analysis, each sequence of observations is divided into square image segments equivalent to single stare-mode images. For this 2 yr data set, each telescope was pointed at the declination +03 18 0 20 00 with separations in hour angle of 16 m 06.0 s for the AB pair of telescopes and 19 m 19.2 s for the BC pair. This was done to create a time offset between each telescope s observations, which allowed for detection of moving objects. Most observations were made without filters in order to maximize the detection rate for faint moving objects. The approximate maximum depth of unfiltered observations was R 19, although the quantum efficiency curve of the detectors makes this dependent on object color. Time and funding constraints only allowed the reduction of data from the A and C telescopes. Including data from the B telescope would allow us to increase the significance level of our findings, but otherwise it would not add any fundamentally new capabilities or alter our conclusions. 3. DATA REDUCTION The data reduction process was performed with an automated script written in IRAF. 2 It was designed as a robust and scalable procedure that could reduce the data from a night during the following day with a minimum of human intervention. The script requires a user to specify the R.A. of the leading edge of the image sequence, its length, the telescope that produced it, and the sources of the dark and flat images. The time investment required from the user is approximately 5 minutes for each image strip, and the runtime is approximately 1 hr on a midrange PC running Linux. Reducing the data processing time was the primary design driver since a future goal is to identify potentially interesting transient events for follow-up as quickly as possible. 3.1. Image Reduction The dark frame for each night s observations was produced at the end of each night by closing the telescope enclosure while the camera was still integrating in drift-scan mode. The flat frame for each night was produced from a median of 10 science images chosen from an R.A. range with low stellar density. The resulting image files were then column-averaged to further reduce noise by simulating the drift-scan process. Finally, the IRAF task CCDPROCwasusedtoapplythedarkandflatframestothe nightly observations. Aperture photometry was performed with the IRAF task QDPhot ( Mighell 2000). QDPhot is designed to perform fast photometric analysis for data mining of image archives. It is optimized to minimize run time while still delivering acceptable accuracy and completeness. The primary optimizations are to round the stellar centroid to the nearest pixel and to use only a fixed pattern of whole pixels in the aperture. The disadvantage of its speed is a small increase in uncertainty (1% 2%) resulting from the use of integer pixels in the photometric aperture; the center of the stellar flux distribution can be offset from the center of the aperture by up to 0.7 pixels. The output from the processing of each segment of the set of nightly observations was then combined into a single file, and potential duplicate detections were removed. 3.2. Assembling the Light-Curve Database Since this was the first large data set from this instrument, we did not have a firm estimate of the number of transient detections (e.g., satellite trails and asteroids) we would find. As such, we decided to err on the side of caution in only analyzing objects that were independently confirmed by another survey. We used the US Naval Observatory catalog (Monet et al. 1998) as a basis for a master catalog of all objects in the declination band. For this initial declination band, the USNO-A2.0 catalog was chosen as the basis. The master catalog consisted of all objects in the USNO catalog with a starlike point-spread function ( PSF), without error warnings, and with colors that are not too extreme ( 3 < R B < 5). We also scanned the source list for close (<3 00 ) pairs of points and removed the fainter member of each pair. This angular separation limit is far less than the pixel scale (2.8 00 pixel 1 )or 2 IRAF is distributed by the National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy, Inc., under cooperative agreement with the National Science Foundation.

1490 KRAUS ET AL. Vol. 134 Fig. 2. Light-curve standard deviations for a region at maximum distance from the Galactic plane (top) and a region in the middle of the Galactic plane (bottom). the typical seeing (4 00 ), so none of these source pairs will be resolved in our data; taking this step avoids the possibility that an unresolved blend of two sources might be matched to different catalog entries at different epochs. However, as we discuss in x 5, this left some objects without a catalog entry; if erroneous photometry led to the removal of the wrong point, then the photocenter of the unresolved blend would not match with the position of the remaining catalog point. These selection criteria yielded a total of 2,070,486 objects. We subsequently found that the rate of transient detections is low enough to not saturate a dynamically updating database of all detections, so such a system will be implemented in the future. The entire data set spanned 2 yr, so precession of the objects in the field was significant. However, the scale of precessional rotation was small over the field of view, so it was corrected in the process of plate solving the nightly data. Matching between the list of objects observed and the master catalog was performed by a custom routine that finds all matches between the master catalog and the nightly observations and exports them into a single text file. All other detections were then exported to a separate text file for later analysis in the search for transient phenomena such as faint cataclysmic variables and supernovae. Since all observations were taken in drift-scan mode, the interval between subsequent observations is exactly 1 sidereal day. This causes significant aliasing effects that will factor into any subsequent period analysis; we therefore report all period information in terms of sidereal days rather than Julian days so that this aliasing occurs on integer and fractional-integer timescales. 3.3. Differential Photometry The size of the data set and the automated observation method dictated the use of the most robust differential photometry method possible. Since data reduction was meant to occur daily, we could not guarantee that a predetermined set of constant stars would always be visible for ensemble differential photometry. We instead used a modified version of inhomogeneous-ensemble differential photometry ( IEDF; Honeycutt 1992). In IEDF, all objects are assigned standard magnitudes based on the observations that are taken at dark time with the best seeing and atmospheric transparency. Since all objects have standard magnitudes, they can then all be treated as potential ensemble members. The magnitude offset between the nightly instrumental magnitude and the standard magnitude of each object is then determined by the mean of the difference for an arbitrarily sized ensemble of all objects surrounding it. In our implementation of IEDF, the ensemble for each object in the nightly data consisted of the 20 nearest objects with photonnoise uncertainties of less than 0.02 mag. After the mean and standard deviation of the ensemble offset were calculated, a set of sigma cuts was used to eliminate outliers that were likely to be variable or corrupted. Finally, the mean offset was recalculated and applied to the object in question. This process was applied separately to observations from each telescope in order to reduce the impact of systematics. We later combine the results into a single light curve by using the mean difference between pairs of observations to move the magnitude systems to a common zero point. 3.4. Photometric Uncertainties Since many sources contribute to the observational and statistical uncertainties of photometry, the scatter in photometric observations is almost always higher than that implied by the Poisson statistics of the observed flux. As such, we want to assign each light curve an effective photometric uncertainty based on the standard deviations of all objects of similar median magnitude and similar crowding conditions. We expect that the higher scatter in crowded regions is because of light contamination from nearby objects. Changes in seeing can increase or decrease the FWHM of the neighboring object s PSF, causing an increase or decrease in the total flux from that object inside a nearby aperture. We expect the scale of variability to be a function of the ratio of contaminating flux to total flux, F c /F. We use this ratio to define a parameter w to quantify the level of crowding, w / F c /F, under the assumption that each neighboring object is far enough away that its contribution comes from its Lorentzian PSF wings. Specifically, if we use an aperture to measure an object of brightness m with seeing of HWHM a, and this object has i neighbors with separation r i and brightness m i, then F c F / w ¼X i 10 0:4(m m i ) ri 2 ; þ a 2 where a and r i are measured in pixels for our implementation. We binned the light curves by hour of R.A. and by mean magnitude, and we then binned them by w. We determined the median standard deviation for each bin and adopted this as the photometric uncertainty for all objects in that bin. In Figure 2 we present plots of rms scatter versus unfiltered magnitude for observations with the A telescope for 5000 objects in a region high above the Galactic plane and 5000 objects in a region near the center of the Galactic plane. Objects in the Galactic plane appear to have higher scatter due to crowding, and many fainter objects are lost because a higher signal is required to pass our signal-to-noise ratio (S/ N) requirement. The increase in scatter at the high end represents objects above the nominal saturation limit of our system. Large amounts of atmospheric extinction can bring bright objects into the dynamic range of our system, but this extinction also introduces additional uncertainties. The decrease in scatter at the faint end represents objects whose normal distribution of brightnesses is cut off by the detection limit of our system.

No. 4, 2007 FIRST MOTESS-GNAT VARIABLE-STAR SURVEY 1491 Fig. 3. Light-curve standard deviations for objects with a low crowding parameter (top; w < 0:025) and a high crowding parameter (bottom;0:175 < w < 0:200) in our testbed region in the Galactic plane. Fig. 4. Light-curve standard deviations as a function of crowding parameter w for objects with 15:0 < R < 15:5 in our testbed region in the Galactic plane. The median standard deviation for each bin is plotted as a large circle. In Figure 3, we present similar plots to further illustrate the dependence of intrinsic scatter on crowding. The top panel contains objects in the Galactic plane characterized by crowding parameters of w < 0:025. The bottom panel contains objects in the Galactic plane with crowding parameters 0:175 < w < 0:200. Almost all objects that are defined as crowded are faint since less contaminating flux is required to significantly affect the measurement of a faint object. However, in the magnitude range that is well-represented in both w ranges, the crowded objects have a significantly higher median scatter than the uncrowded objects. We demonstrate this specific relation in Figure 4, which is a plot of the median standard deviation as a function of crowding parameter for objects with 15:0 < R < 15:5. This suggests that crowding is significant and that our method for estimating uncertainties provides a better assessment of the true measurement error. 3.5. Error Identification Since there are two observations per night, we can use the level of consistency between pairs of measurements as a diagnostic to identify errors. An inconsistent pair might occur if a single measurement is affected by a cosmic ray or a matching error. Variability on the timescale of the intranight interval (40 minutes) could also cause inconsistency between the measurements, so we must set our bar suitably high to avoid this. We quantify this by defining a nightly difference measurement m ¼ m a m c and then calculating its mean and standard deviation. Since there are 100 200 pairs of observations per object, we flag any pair that is inconsistent at the 10 5 confidence level as a statistically significant outlier. As we discuss in x 4.3, we have discovered some problematic light curves that have many extreme outliers due to matching errors. This can raise the standard deviation high enough to prevent the exclusion of any observations, so we also flag any pair that has a separation more than 1 mag from the mean. We neglect these observations in variability tests. Objects near the detection limit and near the beginning or end of a night s observations may not have two detections on a night. Also, observations from one telescope were rejected on some nights due to poor quality. We then have no way to determine the quality of any unpaired observations, but as we describe in x 4, our variability test only considers pairs of observations, so any erroneous unpaired measurements should not bias our results. 3.6. Calibration of the Magnitude System Since our object list is based on the USNO-A2.0 catalog, which includes blue (POSS-I 103aO) and red (POSS-I 103aE) photographic magnitudes, we choose this system for a preliminary calibration. The QDPhot photometric zero point is defined such that M ¼ 0 corresponds to a total flux of 1 photon s 1,or that M ¼ 7:5 corresponds to a photon flux of 1 photon s 1 cm 2 for our aperture size. This results in rather disconcerting object magnitudes that are 10. We therefore change this to a system roughly consistent with the USNO R phot magnitudes by adding 27.5 to each QDPhot magnitude. The quantum efficiency curve for our detectors is still substantially different from that of a red photographic plate. Hence, there will also be a color term. However, the uncertainties of the USNO photographic magnitudes are typically a few tenths of a magnitude, so the uncertainty of a color will typically be more than half a magnitude. We prefer to report results in our instrumental magnitude system rather than introduce such a large systematic uncertainty to each light curve. 3.7. Limitations of the Survey In order to expedite the development and troubleshooting of the pipeline, we made several design compromises that had significant impact on the final results. The most significant choice was to use an outside source as the basis for our catalog of all objects in the field. This approach has the advantage of emphasizing only objects that have been discovered in multiple previous epochs. However, it also introduces the disadvantages of the USNO-A2.0 catalog. Erroneous entries in the source catalog can lead to pseudovariability due to matching errors when more than one real source matches with a single catalog source. Also, since the seeing conditions are not uniform, visual binaries that

1492 KRAUS ET AL. Vol. 134 TABLE 1 Variability Test Results Variability Test P < 10 3 P < 10 4 P < 10 5 P < 10 6 Welch-Stetson... 44682 35246 29902 26249 Lomb-Scargle... 3972 3197 2667 2215 Problematic... 4054 3661 3550 3550 correlated variability with the Welch-Stetson variability test (Welch & Stetson 1993). Our implementation tested observations from the A and C telescopes. Thus, for each object there are sets of instrumental magnitudes a i and c i at each epoch i with means a and c and photometric uncertainties a and c. We define the normalized residuals for each set as and a;i ¼ a i a a ; Fig. 5. Histograms showing the number of objects as a function of Welch- Stetson index I for our testbed fields far from the Galactic plane and in the Galactic plane. are near the resolution limit will not always resolve. In both cases, the result is that the catalog object s light curve appears to switch randomly between two different constant values. We describe this effect, and our method for identifying the resulting erroneous variability detections, in x 4.3. Another compromise was our choice of QDPhot to perform aperture photometry. As we discuss in x 3.1, one of the speed optimizations for QDPhot is to use apertures consisting only of whole pixels, not partial pixels. This results in a limit of 2% in photometric precision, since the center of the aperture can be as many as 0.7 pixels away from the center of the object PSF. The dynamic range of our CCDs is sufficient to achieve photoncounting uncertainties of 3 5 mmag for the brightest objects, and so the ability to achieve <1% precision is potentially within our reach. The use of QDPhot limits our ability to pursue some scientific objectives. Finally, the spacing between telescopes was chosen to optimize the detection of near-earth asteroids, not variable stars. Our intranight observations are at intervals of 40 minutes, and our internight observations have spacing of exactly 1 sidereal day. This means that any variability on timescales longer than 6 hr will not appear within a single night s observations, while variability on timescales shorter than 2 days will be aliased to longer periods. This includes the period range for several interesting classes of objects, such as RR Lyrae variables and the shortestperiod eclipsing binaries. In fact, the periods of RR Lyrae variables will be aliased into the period range for Cepheids. This means that color information or observations with more suitable time resolution will have to be acquired from another source in order to distinguish between some classes of variable stars. 4. LIGHT-CURVE ANALYSIS 4.1. Testing for Temporal Correlation After 2 yr of observation of the first declination band, we have compiled a database of over 1.5 million light curves that have at least 20 detections. Each light curve was tested for temporally c;i ¼ c i c c : The corresponding Welch-Stetson variability index for an object with n observations is given by sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 X I ¼ ( a;i c;i ): n(n 1) If a i and c i are drawn from uncorrelated normal distributions, then the expected value of I will be zero. If the two distributions are correlated, then the product of each pair (and thus the sum of all products) will tend to be positive. The significance of the Welch-Stetson index was determined for groups of objects binned by magnitude (bin size = 1 mag) and by location on the declination band (bin size = 30 minutes R.A.). For each bin, we determined the mean and standard deviation of the distribution of I and then assigned a significance value to each object s variability detection. In Figure 5, we present histograms showing the distribution of Welch-Stetson indices for our test regions high above the Galactic plane and at the center of the Galactic plane. We interpret the small bias toward positive values of I as a result of systematic effects that cause some intratelescope correlation of all light curves. The small excess at high values of I is a result of the variable-star population. We determine the significance of each detection by binning all light curves by hour of R.A., then calculating the mean and standard deviation of the normal distribution of constant objects. In Table 1 we summarize the number of objects that show temporally correlated variability at the formal confidence levels of 10 3,10 4,10 5, and <10 6. 4.2. Testing for Periodicity Each light curve was also tested for periodic variability with the Lomb-Scargle periodogram method ( Lomb 1976; Scargle 1982) using the algorithm described in Press et al. (1992). The interval between nightly observations (40 minutes) is much less than the interval between nights (1 sidereal day), so the nightly observations are effectively simultaneous. As a result, the minimum period of variability that can be detected without aliasing is 2 days. Since there are 8 months between observing i

No. 4, 2007 FIRST MOTESS-GNAT VARIABLE-STAR SURVEY 1493 Fig. 6. Histogram showing log N as a function of log P FA for our testbed field in the Galactic plane. The value of log N should decline linearly for a set of Gaussian light curves; the line shown is an extrapolation of the first two bins, where nonvariable objects are expected to dominate. The clear excess at small P FA is due to the presence of real variable stars. seasons, we perform periodogram analysis separately on each season to search for variability on the order of the season length (4 months). Variability on longer timescales will be difficult to distinguish from aliasing due to the 1 yr interval between observing seasons, but long-period variables usually have a large amplitude (e.g., >2 mag for a typical Mira variable), so they should be easy to identify. The Lomb-Scargle periodogram algorithm returns a falsealarm probability P FA corresponding to the significance level of the period determination. In Figure 6 we present a histogram of the log of the number of detections as a function of log P FA for our test region at the center of the Galactic plane. In Table 1 we summarize the number of variable-star candidates that show a statistically significant periodicity at the formal confidence levels of 10 3,10 4,10 5, and <10 6. In Figure 7, we plot the spectral window function resulting from the observational cadence for a typical variable star, MG1-730057. This function is calculated by taking the Fourier transform of the observational cadence; peaks in the function denote the sidelobe frequencies at which our survey s regular observing rate will introduce aliasing into the power spectrum. The major aliasing in our data set occurs on two frequency scales: 1 day 1 (corresponding to the precise 1 day interval between observations) and 0.003 day 1 (corresponding to the interval between observing seasons). There are no other major peaks in the window function. 4.3. Testing for Problematic Light Curves A single inconsistent pair of observations (as determined in x 3.5) might occur if a single measurement is affected by a cosmic ray or a matching error. If more than a single pair is affected, this could indicate a persistent matching error or other problem in the light curve. For example, there is a small set of light curves that appear to consist of two bimodal normal distributions, with the nightly observations drawn randomly from either distribution. We interpret this as a case where an entry in our master object list Fig. 7. Spectral window function corresponding to our observations for a typical variable star, MG1-730057. We plot the function on two frequency scales, 1 day 1 and 0.003 day 1, corresponding to aliasing on daily and yearly sampling rates. There are no major peaks corresponding to other timescales. is roughly equidistant from two real objects on the sky. These tend to produce very large signatures in variability tests, so it is critical to identify and remove them. If we assume that one statistically significant discrepancy is acceptable as a random event, we can use the statistical significance of the second-largest discrepancy as a test to identify problematic light curves. In Table 1, we list the number of objects identified by the Welch-Stetson test at the confidence level of 10 5 that have a second inconsistent data pair at the formal confidence level of 10 3,10 4,10 5, and <10 6. Discrepancies of >1 mag are regarded as confirmed errors and included with these statistics, since there are few cases where we expect such a large change in brightness on timescales of less than an hour. 5. RESULTS 5.1. The MG1 Variable-Star Catalog We have created a catalog of variable sources based on the results of the variability tests described in xx 4.1 4.3, with membership requiring a result at the 10 5 confidence level in the Welch-Stetson test; the catalog can be accessed at the GNAT data access portal. 3 This produced a list of 29,902 candidates. For a sample of 1.5 million objects, a cut at the 10 5 confidence level should yield 15 false identifications, for a contamination percentage of 0.05%. However, the assumption of a normal distribution of nonvariable objects is not strictly true, as there are many effects (such as cosmic rays, matching errors, and variability of nearby objects) that produce erroneous variability. We anticipated a population of pseudovariable objects that are actually constant to the limits of our survey but have been affected 3 Available at http://www.egnat.org.

1494 KRAUS ET AL. Vol. 134 Fig. 9. Histogram showing the variable-star frequency as a function of R.A. for the MG1 survey. Galactic coordinates are shown for the points where our survey strip intersects the Galactic plane. Fig. 8. Histogram showing the number of newly-detected variable stars as a function of R for the MG1 survey. The equivalent plot for the GCVS is also provided, but with a vertical scale more than 2 orders of magnitude smaller. by these systematic errors. We addressed this by rejecting any object that has at least two intranight differences that are significant at the 10 5 confidence level. This removed 3550 candidates. We further rejected any object within 15 00 of a brighter variable candidate. This removed another 310 candidates, leaving a total of 26,042 variable-source candidates (1.6% of the total number of objects observed). We note that 5271 of these (0.33%) are found to be periodic at the 99% confidence level by the Lomb- Scargle periodogram test. Our estimate of the variable source frequency is only a lower limit, since fainter objects have higher observational uncertainties that can mask low-amplitude variability. A better estimate can be obtained from those objects with the lowest uncertainties. The variable source frequency among objects with 13 < R < 15 is 6295/156;590 ¼ 4:0% for variability with amplitudes k0.02 mag and timescales between a few hours and a few years. In Figure 8, we present a plot of the number of variable stars detected as a function of magnitude. We also include variablestar counts from the General Catalog of Variable Stars (GCVS; Samus et al. 2002), which is the largest compilation of known variable stars in these fields. In Figure 9 we present the corresponding frequency of variable sources in the brightest two magnitudes of our sample as a function of R.A. Several representative values of Galactic coordinates are also marked. In Table 2, we provide a representative table of the statistics associated with each light curve: the mean, amplitude, standard deviation, and skew of the brightness distribution, the assigned photometric error, and the total number of detections. We also list the best-fit period and its false alarm probability as determined via the Lomb-Scargle algorithm. As we describe above, the full table of statistics is available through the GNAT Web site. In Table 3, we provide a similar representative table of crossreferenced photometry from several sources: instrumental magnitudes from our survey, blue and red photographic magnitudes from the USNO-A2.0 catalog, and JHK near-infrared magnitudes from the Two Micron All Sky Survey (2MASS; Skrutskie et al. 2006). 5.2. Recovery of Known Variable Stars The GCVS (Samus et al. 2002) is the largest all-sky catalog of classified field variable stars, representing the accumulated observations of a century of astronomy. It currently consists of 13,480 objects of all variable classes. A total of 214 objects from the GCVS are found in the fields covered by the MG1 survey, and we recovered 59 of them. In Table 4 we summarize our recovery attempts as a function of variable class. In Table 5 we compare the location, brightness, and period determined by the GCVS and by our pipeline for each recovered object. We show light curves for each of the recovered variable stars in Figures 10 14. These indicate that for some objects, either the identification or the classification for the variable is incorrect (e.g., V0868 Oph is an RRc variable, not an eclipsing binary). Also, some objects have large position discrepancies that could indicate a problematic match between our data and the GCVS; we allowed for a maximum radius of 3 0 in determining matches for those cases where a counterpart was not found. Finally, while the vast majority of unrecovered objects were saturated, nine were unsaturated and unidentified by our variability tests. Six of these objects were missing from the USNO-A2.0 catalog, one appears to be nonvariable, and one was a cataclysmic variable with a quiescent brightness below our detection limit. One GCVS variable, SZ Sex, appears to have been rejected during catalog creation due to close proximity to another star. All but three of the 68 objects would have been identified with the USNO-B1.0 catalog, so future surveys will achieve completeness fractions much closer to unity. We discuss all of these cases in more detail in xx 5.2.1 5.2.3. Our survey fields have also been studied by ASAS (Pojmanski et al. 2005), an automated variability survey that studied the entire sky south of +28 to a depth of V 13:5. ASAS discovered a total of 384 variable-star candidates in the MG1 survey fields. The vast majority of these variable-star candidates are saturated in our data, but of the 101 ASAS candidates with 12:5 < V < 13:5, MG1 includes a significant number of unsaturated measurements (>100) for 29 objects and has classified 24 of them as variable. Four of the remaining objects were saturated in a significant fraction of epochs in our survey, which most likely truncated the brightness distribution and reduced their signatures of variability. The final object was not saturated, but shows no sign of variability, so it may have been erroneously included in the ASAS catalog. 5.2.1. Unidentified GCVS Variables V0688 Mon (Mira) does not appear in the USNO-A2.0 catalog, so it was not included in our initial catalog of point sources.

No. 4, 2007 FIRST MOTESS-GNAT VARIABLE-STAR SURVEY 1495 TABLE 2 The MG1 Variable-Star Catalog MG1- R.A. Decl. R MG1 Amplitude stdev photerr Skew N obs log P log P FA I WS WS 261... 00 00 45.5 +3 40 35 15.77 0.256 0.044 0.041 0.95 46 0.014 0.59 0.971 5.14 292... 00 00 49.2 +3 05 31 15.73 0.682 0.135 0.151 0.86 73 2.371 2.09 5.444 29.38 402... 00 01 05.0 +3 10 02 15.03 0.424 0.031 0.025 5.94 132 0.578 0.02 0.846 4.56 530... 00 01 27.7 +2 53 11 18.37 1.167 0.251 0.157 1.90 71 0.815 0.34 0.847 4.84 697... 00 01 57.4 +3 01 47 18.44 1.079 0.175 0.160 1.77 82 1.224 1.03 1.222 7.04 916... 00 02 26.5 +3 21 06 14.34 0.293 0.042 0.032 0.89 132 3.009 11.12 1.526 8.51 Notes. Units of right ascension are hours, minutes, and seconds, and units of declination are degrees, arcminutes, and arcseconds. The full data table is available through the GNAT data access portal (http://www.egnat.org). It appears to be heavily reddened based on available near-infrared photometry, and recent literature lists this object as a carbon star rather than a Mira variable (e.g., Bergeat & Chevallier 2005). There is no B-band detection in the original POSS plates, but it has R-band photometry from the USNO-B1.0 catalog and nearinfrared photometry from 2MASS: R ¼ 18:36, J ¼ 9:429, H ¼ 6:488, and K ¼ 4:277. SZ Sex (Mira) was eliminated from our master catalog during the creation process. It was originally represented by a close pair of points; as we describe in x 3, we eliminated one member of all such pairs in order to avoid matching errors between the data and the master catalog. In this case, the remaining point fell just outside the matching radius from the position of SZ Sex, so the observations were never matched to the catalog entry. Since this problem only affected a single GCVS object, this suggests that any systematic bias from this type of error should be small. Six GCVS objects were not present in the USNO-A2.0 catalog, so the inherent incompleteness of USNO-A2.0 is far more significant. V1592 Aql (Mira) also does not appear in the USNO-A2.0 catalog, although it is marginally visible in the B-band POSS plates and clearly visible in the R-band plates. There is USNO- B1.0 and 2MASS photometry available: B ¼ 19:95, R ¼ 15:37, J ¼ 13:48, H ¼ 12:539, and K ¼ 12:291. V1624 Aql (Mira) does not appear in the USNO-A2.0 catalog. It is not visible in the B-band POSS plate, but it is unambiguously detected in the R-band USNO-B1.0 and near-infrared 2MASS photometry: R ¼ 18:05, J ¼ 10:28, H ¼ 8:92, and K ¼ 8:01. V1626 Aql (Mira) also does not appear in the USNO-A2.0 catalog. It appears to be blended with another object in POSS and 2MASS images; the other object is far brighter in the optical, which is most likely the reason that the variable is not listed in USNO-A2.0. V1626 Aql is far brighter in the near infrared. These two objects are not resolved in our images, which have lower resolution. V1227 Aql ( RR Lyr) appears to correspond to MG1-1516554. However, our survey found no evidence of variability for that TABLE 3 MG1 Photometry MG1- R MG1 B USNO R USNO J 2MASS H 2MASS K 2MASS 261... 15.77 16.6 15.4 14.52 14.14 13.97 292... 15.73 18.2 16.3 14.08 13.33 13.16 402... 15.03 15.8 15.0 13.90 13.53 13.43 530... 18.37 18.7 18.1 16.78 16.04 0 697... 18.44 18.9 17.9 0 0 0 916... 14.34 10.4 9.5 13.87 13.02 12.34 Notes. Note that an entry of 0 denotes a null detection. The full data table is available through the GNAT data access portal (http://www.egnat.org). target, and there are no other objects in the immediate area with the amplitude and periodicity of a likely RR Lyrae variable. A search of the literature did not find any references to this object. V1254 Aql (RR Lyr) does not appear in the USNO-A2.0 catalog, although there does not appear to be anything unusual in the POSS plates. It appears in USNO-B1.0 and 2MASS photometry: B ¼ 15:10, R ¼ 13:86, J ¼ 12:26, H ¼ 11:60, and K ¼ 11:43. XY Psc (U Gem cataclysmic variable) is very faint in its quiescent state (V ¼ 21:1; Henden et al. 2001). We did not detect this object during the two observing seasons when it was accessible; since its quiescent brightness is just below our detection threshold, this suggests that no outbursts occurred during the seasons that we observed it. Henden et al. reported that it was not observed in outburst during follow up efforts between 1980 and 2001, implying that outbursts by this object are rare. U Equ (peculiar) was not identified by the USNO-A2.0 catalog since it is very faint in the B band. Optical and near-infrared spectroscopy (Barnbaum et al. 1996; Geballe et al. 2005) imply the presence of a large quantity of circumstellar CO and water vapor but no obscuring dust. Geballe et al. suggested that U Equ is entering a stage of rapid post-main-sequence evolution, but the state of this object is still unclear. 5.2.2. Misclassified Variable Stars V0868 Oph ( MG1-1001730) is classified as an eclipsing binary by the GCVS, but the light curve is unambiguously that of an RRc variable. This has been previously noted in the literature by Pejcha et al. (2003), who reported a classification of RRc and a period of 0.287381 days. This period is roughly consistent with the aliased period observed in our data. V1249 Aql (MG1-1573684) is classified as an RR Lyrae variable in the GCVS, but the light curve is unambiguously that of an eclipsing binary. This has not been reported in the literature, so it is possible that the object we report is not the actual counterpart. However, there is no apparent counterpart in our images at the TABLE 4 Recovery Statistics from the GCVS Category Total Recovered Not Recovered Saturated Pulsating, RR... 30 25 2 3 Pulsating, CEP/CW... 5 1 0 4 Pulsating, Mira/SR... 101 19 5 77 Pulsating, Other... 28 0 1 27 Eclipsing... 37 12 0 25 Eruptive... 3 0 0 3 Rotating... 6 0 0 6 Cataclysmic... 3 2 1 0 Other... 1 0 0 1 Total... 214 56 12 146

TABLE 5 Recovered Variables from the GCVS Name MG1- Class R.A. Decl. RA (arcsec) decl (arcsec) V GC (mag) R MG (mag) R MG (mag) P GC (days) P MG (days) log P FA CE Mon... 331254 EA/SD 06 46 57.4 +03 03 26 0.4 0.0 14.1 13.626 1.806 4.10943 2.066 6.0 V0462 Mon... 392638 EA 07 04 30.2 +03 23 39 0.5 0.4 15.3 14.421 1.383 3.4869059 2.332 8.7 V0463 Mon... 396190 EA 07 05 32.7 +02 56 08 0.7 0.4 14.3 13.166 0.675 1.1238384 8.858 13.4 RS CMi... 497388 EA/SD 07 38 50.8 +03 00 28 0.8 0.4 13.8 13.707 2.563 5.02775 1.669 3.8 CT Hya... 592904 UGSS 08 51 07.4 +03 08 34 0.2 0.1 14.1 17.642 5.099 151.7 93.091 8.7 CW Hya... 595897 RRAB 08 55 07.8 +03 39 24 0.3 1.4 14.1 15.167 1.422 0.4820734 1.082 24.0 ST Sex... 628775 RRAB 09 52 20.0 +02 54 49 3.6 0.4 15.2 16.010 1.213... 6.649 26.6 U Sex... 631202 RRAB 09 57 25.0 +03 40 05 5.9 0.9 13.2 14.193 1.100 0.534008 7.529 26.9 T Leo... 672320 UG 11 38 26.8 +03 22 07 4.9 3.6 10.0 15.501 2.093... 120.941 2.6 BN Vir... 692720 RR 12 34 12.0 +02 57 34 35.4 4.5 14.2 13.918 0.714 0.3912845 2.198 13.9 GI Vir... 699271 RRAB 12 51 21.0 +02 55 36 19.2 21.7 13.2 13.951 0.661 0.587521 1.436 30.2 DX Vir a... 762436 RRAB 15 06 03.0 +02 54 15 120.1 18.0 15.4 15.971 1.053 0.582594 1.4066 29.8 DX Vir a... 762450 RRAB 15 06 03.0 +02 54 15 145.9 42.8 15.4 13.344 1.069 0.582594 88 28.8 V0751 Oph... 934179 RR 17 21 35.2 +03 08 53 0.5 0.1 14.0 14.921 1.288... 10.368 29.1 V0758 Oph... 940108 RR 17 23 49.7 +03 38 10 0.6 0.5 14.2 15.230 1.128... 1.186 27.3 V0777 Oph... 968302 RR 17 33 24.9 +02 59 36 0.1 0.9 13.4 13.808 1.347 0.521392 11.439 27.6 V0792 Oph... 982786 M 17 37 31.3 +03 27 14 5.1 9.9 13.3 16.585 0.956 273 102 4.3 V0868 Oph... 1001730 EB:/KW: 17 42 31.1 +03 03 41 0.0 0.1 12.9 13.653 0.761 0.443226 1.893 33.3 V0977 Oph... 1008071 RRC 17 44 11.7 +03 01 31 0.3 0.4 16.2 15.930 0.537 0.36264 1.329 35.4 V0979 Oph... 1011933 RRAB 17 45 14.2 +03 10 10 0.3 0.0 15.4 15.380 1.065 0.46164 1.191 27.9 V0980 Oph... 1014404 RRC 17 45 55.4 +03 07 12 3.6 6.3 15.6 14.789 0.597 0.34697 1.144 28.0 V0563 Oph... 1028454 RRAB 17 49 29.3 +03 19 23 0.3 0.2 14.4 14.900 0.971 0.511313 20.13 28.3 V2329 Oph... 1041820 EA 17 52 19.5 +03 37 48 0.7 0.3 16.1 14.849 0.975... 1.729 18.7 V1079 Oph... 1050447 RR 17 54 03.5 +03 19 59 0.6 0.5 16.0 16.464 0.903... 221.429 23.6 V1083 Oph... 1085101 RR 18 00 19.9 +03 06 18 3.7 4.2 16.0 16.277 0.976... 1.242 25.7 V0416 Oph... 1089379 M 18 01 07.5 +03 33 02 0.3 0.8 14.0 13.169 2.112 152 150.948 20.5 V1086 Oph... 1098444 M: 18 02 32.0 +03 05 11 0.6 0.1 16.5 14.363 2.082... 258.333 41.7 V1087 Oph... 1105404 RR 18 03 36.3 +03 04 19 5.7 1.2 16.0 16.095 0.984... 1.435 27.8 V0492 Oph... 1117392 M 18 05 23.8 +02 56 36 0.1 0.1 14.7 13.654 1.998 190 96.625 28.1 V0496 Oph... 1152032 E /SD 18 10 14.6 +03 08 42 0.4 0.7 14.3 13.624 1.350 2.576225 4.422 9.3 V0968 Oph... 1218119 RRC 18 19 21.6 +03 22 11 2.3 1.6 14.5 13.999 0.485 0.267954 4.117 34.8 V0969 Oph... 1225282 EW/KE 18 20 17.9 +03 30 32 0.2 0.4 13.5 13.164 0.820 0.589412 1.465 9.7 V2571 Oph... 1287551 SR: 18 29 34.6 +03 28 12 6.7 3.5 12.0 13.525 1.488... 206.133 20.8 LT Ser... 1300086 M 18 31 55.4 +03 02 15 0.7 0.5 15.5 13.208 1.305 275 201.6 17.2 V1566 Aql... 1375418 M 19 04 51.8 +02 54 18 1.0 2.0 12.4 15.122 2.893... 442.857 29.5 V1110 Aql b... 1385133 EA/DM 19 06 17.1 +03 19 09 43.3 51.3 13.5 13.358 0.499 2.564714 190 1.7 V1588 Aql... 1386815 M 19 06 28.4 +03 10 19 2.7 8.2 12.0 13.517 1.170... 109.12 20.2 V1591 Aql... 1388633 M 19 06 43.1 +03 17 12 0.3 0.5 12.9 13.647 1.920... 193.25 18.7 V1595 Aql... 1391053 M 19 06 58.2 +02 59 09 4.8 4.5 13.1 14.112 1.525... 442.857 33.1 V1621 Aql... 1410977 M 19 09 53.0 +02 54 21 0.7 0.5 11.6 13.404 1.755... 344.444 29.9 V1629 Aql... 1414532 M 19 10 21.7 +02 58 08 6.8 3.1 12.8 13.388 1.373... 309.2 20.5 V1120 Aql... 1477416 M: 19 19 00.1 +03 30 47 0.3 0.3 16.0 13.369 1.964 142.3 134.435 17.6 FR Aql... 1500618 M 19 22 23.2 +03 18 47 0.1 0.2 15.5 13.131 0.963 291.3 2720.001 11.0 V1128 Aql... 1517845 E: 19 24 44.0 +03 17 49 0.2 0.1 16.0 15.688 1.126 1 8.986 23.7 V1131 Aql... 1540903 M 19 27 36.9 +03 23 12 0.4 0.3 15.7 13.685 2.448 271.5 281.818 21.5 V1249 Aql... 1573684 RR 19 31 41.7 +03 23 36 1.9 2.3 15.5 14.428 0.617... 4.27 11.1 V0979 Aql... 1576977 DCEP 19 32 07.2 +03 00 45 0.5 0.2 14.1 13.809 0.662 2.36562 2.381 22.3 V1139 Aql... 1589819 RR 19 33 48.1 +03 26 49 0.5 0.1 16.0 16.584 1.090... 5.458 14.5 V0824 Aql... 1601702 M 19 35 19.6 +03 39 13 0.2 0.5 15.1 13.626 1.529 139 143.4 21.7 V0390 Aql... 1618000 M 19 37 31.9 +03 28 15 0.6 0.2 14.7 14.060 2.618 301 256 6.3 V0399 Aql b... 1641850 M 19 40 42.8 +03 12 06 2.9 7.5 12.4 14.297 1.004 230.4 140 12.2 V1160 Aql b... 1703168 M 19 49 59.6 +03 09 08 1.8 17.1 14.5 15.469 0.984... 282 13.9 V0557 Aql... 1731376 EA/SD 19 54 41.9 +03 21 47 1.9 1.0 13.4 13.685 2.114 2.75045 1.381 5.8 V1069 Aql... 1746901 RR 19 57 18.5 +03 34 32 0.7 0.2 14.0 14.199 0.834... 1.231 16.8 V1085 Aql... 1780883 RRAB 20 03 45.8 +03 03 20 0.6 0.7 15.0 15.470 1.066 0.5085416 25.62 22.2 V1090 Aql... 1791956 EA/SD: 20 05 58.5 +03 23 57 0.3 0.8 15.5 13.730 1.767... 1.901 1.9 V0785 Aql... 1800142 RRAB 20 07 37.5 +02 53 31 0.6 0.0 13.3 14.510 1.130 0.428819 1.486 15.9 V1176 Aql... 1801508 RR 20 07 55.0 +03 00 57 0.4 0.2 15.0 15.882 1.197... 3.116 19.2 V0911 Aql... 1861763 RR 20 23 47.3 +03 36 50 0.1 0.2 15.6 15.479 1.096... 1.192 22.3 EL Del... 1936815 RR 20 55 23.1 +02 57 35 0.2 0.2 14.1 14.630 1.285 0.595432 1.484 18.8 Note. Units of right ascension are hours, minutes, and seconds, and units of declination are degrees, arcminutes, and arcseconds. a No variable stars were found at the reported position of DX Vir, but these apparent RR Lyrae variables are nearby. Based on its brightness and aliased period, it appears that MG1-762436 is more likely to be DX Vir. b No variable stars were found at the reported position; this is the closest likely match.

FIRST MOTESS-GNAT VARIABLE-STAR SURVEY 1497 Fig. 10. Phased light curves for the RR Lyrae variables recovered in our dataset from the GCVS. The light curves were phased using our derived period; as we discuss in the text, this period is almost certainly aliased for short-period variables. positions reported in the GCVS, so MG1-1573684 is the best candidate counterpart. 5.2.3. Other Problematic Variable Stars BN Vir (MG1-692720) lies 35 00 from the position reported in the GCVS. However, the brightness and classification are consistent, and the period is roughly consistent, so this appears to be the correct counterpart. GI Vir (MG1-699271) lies 29 00 from the position reported in the GCVS. However, the brightness, classification and period are all consistent, strongly suggesting that this is the correct counterpart. DX Vir does not have a counterpart at the position reported by the GCVS, but there are two similar RRab variables within 2 0 : MG1-762436 and MG1-762450. MG1-762450 is 2 mag brighter in R than DX Vir is listed in V, while MG1-762436 is of the appropriate brightness. Also, the aliased period of MG1-762436 is consistent with the GCVS period given for DX Vir, while the aliased period of MG1-762450 is consistent with a true period of 0.5 days. These results imply that MG1-762436 is more likely to be the correct counterpart. V0792 Oph (MG1-982762) lies 11 00 from the position reported in the GCVS, and the average brightness we report is substantially fainter. However, as can be seen from Figure 13, we detected MG1-982762 only during the second observing season, when it appeared to be at minimum light. This suggests an average brightness that is much closer to the real value, so this appears to be the correct counterpart. V1110 Aql (MG1-1385133) lies 68 00 from the position reported in the GCVS. The average brightness is approximately correct, but the light curve appears to be that of a pulsating variable (either a short-period variable with an aliased light curve or a long-period