UNSW variable star catalogue

The University of New South Wales Extrasolar Planet Search: a catalogue of variable stars from fields observed 2004–2007

Abstract

We present a new catalogue of variable stars compiled from data taken for the University of New South Wales Extrasolar Planet Search. From 2004 October to 2007 May, 25 target fields were each observed for 1–4 months, resulting in  high precision light curves with 1600–4400 data points. We have extracted a total of 850 variable light curves, 659 of which do not have a counterpart in either the General Catalog of Variable Stars, the New Suspected Variables catalogue or the All Sky Automated Survey southern variable star catalogue. The catalogue is detailed here, and includes 142 Algol-type eclipsing binaries, 23  Lyrae-type eclipsing binaries, 218 contact eclipsing binaries, 53 RR Lyrae stars, 26 Cepheid stars, 13 rotationally variable active stars, 153 uncategorised pulsating stars with periods 10 d, including Scuti stars, and 222 long period variables with variability on timescales of 10 d. As a general application of variable stars discovered by extrasolar planet transit search projects, we discuss several astrophysical problems which could benefit from carefully selected samples of bright variables. These include: (i) the quest for contact binaries with the smallest mass ratio, which could be used to test theories of binary mergers; (ii) detached eclipsing binaries with pre-main-sequence components, which are important test objects for calibrating stellar evolutionary models; and (iii) RR Lyrae-type pulsating stars exhibiting the Blazhko-effect, which is one of the last great mysteries of pulsating star research.

keywords:
stars: AGB and post-AGB – stars: oscillations – stars: pre-main-sequence – stars: variables: Scuti– binaries: eclipsing
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1 Introduction

The University of New South Wales (UNSW) is conducting a wide-field survey for transiting extrasolar planets, and is one of an increasing number teams around the world using this method. The nature of wide-field surveys has resulted in an enormous number of high-precision light curves being produced, numbering in the millions for some teams (e.g. Collier Cameron et al. (2007)).

In order to maximise the output efficiency from wide-field surveys, it is important to make the data available for use in other studies once planet candidates have been identified. The most extensive results produced by these projects to date have been long lists of newly discovered variable stars, inevitably with very limited information apart from the period and amplitude in a single band (Hartman et al., 2004; Pepper & Burke, 2006). Therefore, one can imagine the main use of these variable star catalogues is to define starting samples for astrophysically interesting follow-up studies that benefit from large samples of carefully selected stars. A recent example is the list of variable stars coincident with x-ray sources presented by Norton et al. (2007).

The UNSW Extrasolar Planet Search is performed with the largest clear aperture telescope of the wide-field transit surveys. With a diameter of 0.5-m, this project occupies the niche between the typical wide-field transit surveys observing brighter targets with 0.1–0.2-m diameter telescopes, and the deeper surveys with narrower fields of view using 1-m diameter telescopes. The larger collecting area in this project has been exploited to increase the acquisition rate for the observations, as compared to observing deeper targets, since brighter targets have a higher potential for interesting follow-up studies. The large data set of light curves we have obtained is therefore the ideal starting point from which to compile a bright variable star catalogue of particularly well-sampled light curves with high precision photometry and moderately long observing baselines (1–4 months).

The paper is organised as follows. Section 2 describes the observations and reduction pipeline. Section 3 describes the methods by which the variable light curves were selected. The final catalogue is presented in Section 4, while three possible applications of the sample are discussed in Section 5. The data are publicly available at the University of New South Wales Virtual Observatory (VO) facility, which is described in Section 6. We close the main body of the paper with a short summary of the project in Section 7. Cross-references to the General Catalogue of Variable Stars (GCVS) and the All Sky Automated Survey (ASAS) database are given in the Appendix.

2 Observations and Reduction

2.1 Photometry

The data were obtained using the dedicated 0.5-m Automated Patrol Telescope at Siding Spring Observatory, Australia. Observing is performed remotely on every clear night when the Moon is not full and is almost entirely automated; the observer initiates the observing script and monitors the weather conditions. The CCD camera used for these observations consists of an EEV CCD05-20 chip, with pixels. The pixel size of 22.5 m produces a relatively low spatial resolution of 9.4 arcsec pixel, and the field of view of each image is  deg. The observations were taken through a Johnson filter, a decision designed to maximise the contribution to the photometry of later spectral type dwarf stars, around which it is easier to detect transiting planets. A new CCD camera covering  deg with a higher spatial resolution of 4.19 arcsec pixel has been constructed for this project and will be installed on the telescope in 2008.

Observations were obtained for 32 months from 2004 October to 2007 May on 25 target fields, listed in Table 1, resulting in a total sky coverage of deg. The equatorial coordinates of the centre of each field is given, as are the Galactic coordinates. The strategy employed for field selection was again motivated by transit detection. The most southerly fields were chosen in order to reduce the airmass variations over the course of the night, with a maximum allowable declination of 70 due to building constraints. At the same time, the Galactic latitude was constrained to  to alleviate crowding effects in the field, which led to several more northern fields being selected. This latter constraint was relaxed for the final field in order to observe a more crowded stellar field.

Most of the fields were observed in pairs in order to increase the number of target stars, with observations alternating between the two fields over the course of the night. For the majority of the fields the rate of acquisition is 15 images per hour, however for the first pair of fields it is half as often as this. For the final three fields we implemented an automated script that adjusted the exposure times according to the sky brightness levels, and the rate of acquisition ranges from 10–40 images per hour. These fields were also observed singly instead of pairwise, which accounts for the higher rates achieved. Each field was observed for a minimum of 20 nights for at least hours per night, resulting in observed data points for each star. Each field contained stars with , depending on the Galactic coordinates. The numbers of stars observed down to 14 magnitude in each field are included in Table 1, with  light curves being generated.

Field RA Dec Obs Stars Image rate
(J2000.0) (J2000.0)
L1 04 56 24  00 00 1791 2195 7.5
L2 04 45 00  18 00 1692 1943 7.5
N1 09 05 00  30 00 1824 3271 15
N2 09 25 00  30 00 1619 2495 15
O3 12 00 00  00 00 2441 3066 15
O4 12 00 00  10 00 2420 2008 15
Q1 17 06 00  00 00 2083 8096 15
Q2 17 09 00  55 00 1854 7559 15
R1 00 00 00  00 00 3708 1401 15
R2 00 00 00  00 00 3446 1496 15
S5 04 03 00  55 00 1980 1327 15
S6 04 06 00  55 00 1944 1284 15
Jan06_1 09 20 00  30 00 1713 3261 15
Jan06_2 09 15 00  30 00 1773 3520 15
Feb06_1 12 55 00  15 00 2732 4734 15
Feb06_2 13 15 00  10 00 2631 4975 15
Apr06_1 14 48 00  00 00 1840 5176 15
Apr06_2 14 48 00  00 00 1840 3996 15
May06_1 18 27 00  00 00 2579 4421 15
May06_2 18 27 00  00 00 2731 4269 15
Jul06_1 21 09 00  30 00 2065 2246 15
Jul06_2 21 09 00  30 00 2034 2303 15
Sep06 23 42 00  24 00 3497 1630 10–40
Dec06 08 03 00  24 00 3007 3387 10–40
Mar07 14 15 00  00 00 4450 6714 10–40
Table 1: Details of the target fields observed with the Automated Patrol Telescope. The columns for each field are as follows: the field name, the coordinates and Galactic latitude of the centre of the field, the total observations obtained, the number of stars in each field brighter than magnitude, and the number of images obtained per hour.

2.2 Reduction pipeline

In order to achieve the extremely precise photometry required for transit detection, we have developed a simple, robust, automated aperture photometry reduction pipeline. A detailed description can be found in Hidas et al. (2005); a summary is included here for completeness, and there have been no modifications.

Using the tools developed by Irwin & Lewis (2001), each image is processed in the standard manner, including bias subtraction, flat-field correction and catalogue generation. For each field, a master image is generated by combining consecutive, low airmass images with small image-to-image shifts, and a master coordinate list is produced. Each image frame is then transformed into the master reference frame with a positional accuracy of better than 0.01 pixels. Aperture photometry is performed on the transformed images, with a fixed aperture radius of 3 pixels, which equals 30on the sky. At the typical Galactic latitude of our fields, this results in multiple stars falling within the same photometry aperture more than 80% of the time; these stars can only be resolved in higher spatial resolution images. As a result, the magnitudes listed in this catalogue should be taken as upper limits on the true magnitude, and the variability amplitudes as lower limits. Magnitude variations from image to image are calibrated by using a subset of the brightest stars. The magnitude residuals for each star are fit iteratively with a position-dependent function of the form

(1)

where and are the pixel coordinates of the star on the CCD, and are a set of constants for each image. With each iteration, the stars with the highest r.m.s. residuals are removed.

After the light curves are generated by the reduction pipeline, they are processed to remove the significant systematic signals using an implementation of the trend-filtering algorithm described by Kovács, Bakos & Noyes (2005). A random subset of several hundred stars are chosen as template light curves. For each of the remaining light curves, the closest matching synthetic light curve that can be reconstructed from a linear combination of the template light curves is subtracted. Signals that are common to the template and original light curve will be removed, and signals that are unique to the original light curve remain. Figure 1 shows an example of the precision achievable in a typical field of data, before and after being processed with the trend-filtering algorithm. For the stars brighter than the r.m.s. precision of the filtered light curves is less than 10 mmag.

Figure 1: A typical example of the precision of the photometry we have obtained with our reduction pipeline and additional trend-filtering to remove systematic noise. The target field is Jan06_1 and light curves are comprised of 1713 data points. The solid circles are the original light curves produced by the pipeline; the hollow triangles show the improvement in the precision with the trend-filtering. The solid line shows the theoretical Poisson noise limits from the sky flux (short dashed line) and stellar flux (long dashed line).

3 Selection of variable candidates

Three methods were used to extract the variable light curves from the full data set: (i) visual inspection of the filtered light curves down to 13 magnitude, (ii) implementation of a box-search algorithm on all filtered light curves (down to 14 magnitude), and (iii) implementation of the Stetson Variability Index on the entire set of filtered and unfiltered light curves. The first two methods formed part of the search for transiting extrasolar planets, the main science driver of this project (Hidas et al., 2005). The third was implemented to improve the completeness of the catalogue.

3.1 Visual inspection

As a first pass, all light curves down to 13 magnitude are visually inspected. This has the advantage of being reasonably immune to the effects of systematic variability in the light curves, since the brain can be quickly trained to filter out similar signals appearing in multiple light curves. However, it becomes less useful for light curves fainter than 13 magnitude due to the increased shot noise; it can also fail for brighter stars where the variable signal is of low significance, and will not be evident until the light curve is phased with the correct period. Importantly, this method is not successful for the detection of variable light curves with periods greater than 5 d. In these cases, the light curve for each night appears essentially flat, especially to someone specifically searching for transit events on the order of a few hours. Also, visual inspection has the potential to miss light curves that exhibit only single or partial events on a single pass through.

3.2 Transit detection algorithm

The light curves are subsequently processed with a transit detection algorithm (Aigrain & Irwin, 2004) which searches for box-shaped transit events within specified transit duration and period windows. For each light curve, the algorithm determines the combination of epoch, transit duration and period within a specified range that returns the highest signal-to-noise (S/N) ratio. Subsequently, light curves with a S/N greater than some cut-off (typically 8.0, although this value varied with the degree to which each field was affected by systematics) when folded at the these parameters are visually inspected in both raw and folded formats. We have chosen the transit duration and period windows for maximum efficiency in the extrasolar planet transit search, using windows of 0.04–0.25 d and 1.0–5.0 d respectively. The algorithm will detect the variable light curves with parameters that fall within these windows: both the shallow transit events that are flagged as potential transiting planet candidates and the deeper events produced by detached eclipsing binary systems. Additionally, it returns many variable light curves which can be approximated to some extent by a box-shaped model lying within the required period range, including: grazing eclipsing binaries exhibiting V-shaped transits; continuously varying light curves that give a significant result when folded to an optimum period; and variable light curves with periods outside the specified window but where an integer number of periods is located within the window. It also has the additional advantage of detecting those light curves with only single or partial events and providing a potential period. However, it is not useful for detecting variable light curves with periods much shorter (0.5 d) or much longer (10 d) than the specified period window.

3.3 Stetson Variability Index

Neither of the preceding methods will rigorously detect the longest period variables in our light curves. In order to increase the completeness of this variable star catalogue it was essential to correct this bias. An additional method of detecting variability in light curves is the Stetson Variability Index (Stetson, 1996), a measure of the correlated signal in a light curve.

Using the notation of Stetson (1996), the index is given by

(2)

where pairs of observations have been defined. For the th pair, with a weighting of , the magnitude residuals are and , where and are the observations forming the pair. We can therefore define the product of the magnitude residuals as , or for single observations (where ). The term is the sign (positive or negative) of . We have calculated the magnitude residuals in the same manner as Stetson (1996), scaling by the individual observational errors and correcting for the statistical bias to the mean, giving

(3)

where is the measured magnitude of the observation, is the mean magnitude over all observations, and is the individual error on the observed magnitude. To form the pairs of observations we chose a timescale of 10 minutes; all observations that lie within 10 minutes of each other are paired. All pairs with are assigned a weight of 1.0, and those with a weight of 0.1. We found the best results in terms of detecting longer period variables ( 1 d) when the data were binned on a similar timescale of 10 minutes, although this was at the cost of lowering the detection of the very shortest period variables. The solid squares in Figure 2 show a typical distribution of this variability index for a single field, in this case the Dec06 field as shown in Figure 1, prior to the trend-filtering stage.

One problem we encountered was the tendency for our implementation of the trend-filtering algorithm to suppress or entirely remove the night-to-night magnitude jumps present in the long-period variable light curves, resulting in a smaller than expected variability index. This was solved by running the variability index on both the filtered light curves to detect the shorter period variables and the unfiltered light curves to detect the longer period variables. The caveat to this is that long period trends in the systematic signals in the data, for instance signals correlated with moon phase, are not removed from the long period light curves. In an effort to overcome this, we have removed those long period light curves where multiple light curves in the same field demonstrate the same morphology and are described well by the same period and epoch. In the future we plan to resolve this problem by replacing the trend-filtering algorithm with an implementation of SYSREM (Tamuz, Mazeh & Zucker, 2005). Preliminary tests indicate this will not affect the longer period variable light curves in the same manner. The hollow triangles in Figure 2 show the distribution of the variability index after trend-filtering. For the unfiltered light curves, we set a cut-off of , and for the filtered light curves we set . We found these limits recovered 90–97% of the variables previously identified by visual inspection and the box-search algorithm, as well as over 150 long-period variables that had previously been undetected. The variables that were not recovered were generally the shallower longer period eclipsing binary light curves where the occasional small excursion from the mean magnitude was not sufficient to increase the variability index above the cut-off. Also missing were the shortest period variables with periods less than 1 hr, where the timescales for pairing and binning of 10 minutes were long enough to reduce the effectiveness of the variability index as a true measure of variability.

Figure 2: The Stetson Variability Index as a function of magnitude for the Dec06 field. The solid circles are the original light curves, and the hollow triangles are the filtered light curves. During the variable selection process unfiltered light curves with (the dotted line) and filtered light curves with (the dashed line) were flagged.

4 The light curve catalogue of variable stars

Using these methods, we find a total of 850 variable light curves in our data set. These have been analysed in a similar iterative fashion to Derekas et al. (2007) as follows. Initial periods have been determined with either the transit detection algorithm (for eclipsing light curves) or fitting of sine waves using discrete Fourier transforms (for continuously varying light curves). Each of the resulting phased curves was then visually inspected to assign a type of variability, and also to confirm that the automatically determined period was not half or an integer multiple of the real period, a common occurrence for light curves of eclipsing binaries. A visual inspection of every phase diagram was usually sufficient to show whether the determined period was an alias or was slightly inaccurate. In the case of an alias, we multiplied the initial period by different constants (in most cases by 2) until the shape of the curve was consistent with that of an eclipsing binary. For the long period variables, the observing baseline was typically insufficient to determine if the variability was periodic; in these cases the period and epoch are not supplied in the catalogue.

We next used the string-length method (Lafler & Kinman 1965; Clarke 2002) to improve the period determination (see also Derekas et al. 2007 for further details). We applied the method for 500 periods within % of the best initial period guess. The typical period improvement resulted in a change in the 3rd-4th decimal place, consistent with the limited frequency resolution of the data (which scales with 1/T, where T is the time-span of the observations).

During the individual inspection of the phase diagrams, we made a visual classification of all 850 variables. Based on the light curve shapes alone, phased with the final adopted periods, we placed each star into one of the following categories: Algol-type (EA), Lyrae-type (EB), W Ursae Majoris-type (EW), RR Lyrae stars (RRL), Cepheids (DCEP), long period variables with periods d (LPV), and pulsating variables with periods d (including Scuti and other multiply periodic variables, referred to as PUL). We follow the convention of using a colon to indicate a loose classification (for example, EB:). In several cases we used the “spotted variable” type, which refers to singly periodic variables with periods of several days, light curve amplitudes of a few hundredths of a magnitude and light curve shapes characteristic of known rotationally variable active stars. These can be binaries or single stars, and have multi-periodic light variations on time scales of years and decades (see for example Oláh et al. 2000). We note that for sinusoidal light curve shapes, it is difficult to differentiate between the EW, PUL and spotted variable classifications by eye. Where there is an ambiguity between several classifications they are listed as, for example, EW/PUL. If there are two types of variability present they are listed as, for example, EA+PUL. If there is additional information it is given as, for example, RRL-Blazhko.

Figure 3: The variability amplitude detection limits for this catalogue. The mean -band magnitude and the variability amplitude are plotted for the entire data set of variable light curves.

The detection limits of the catalogue are shown in Figure 3, with the mean -band magnitude and variability amplitude plotted for all 850 variable light curves. For the detached eclipsing binaries the amplitude was the best-fitting transit depth as recorded by the box-search algorithm; for the continuously varying light curves we have used the amplitude from the sinusoid-fitting. For the multi-periodic light curves this will represent an approximate amplitude of the dominant frequency. As discussed in Section 2.2, due to dilution of the signal in crowded photometry apertures, the variability amplitudes presented here are lower limits on the true amplitudes. From Figure 3 it is apparent that around mag we lose sensitivity to the lowest amplitude variables (such as the multi-periodic Scuti stars or pulsating red giants), while for mag only the highest amplitude pulsators (RR Lyrae stars) and eclipsing binaries remain.

The last step in the variable star analysis was cross-correlation with existing databases to supplement the catalogue with as much additional information as possible. Namely, we queried the most recent update of the General Catalogue of Variable Stars (GCVS, Samus et al. 2007), including the New Suspected Variables catalogue, to identify already known variable stars. In addition, we checked the ASAS-3 database of southern variables (Pojmanski 2002). This revealed that 191 out of 850 variables are positionally coincident with previously published variable stars, leaving the total number of our new discoveries at 659. This corresponds to 78%, which is a lower fraction than, for instance, the 90% new discoveries found by Hartman et al. (2004) in the HATNet observations of the Kepler field. However, it is still surprisingly large, given the fact that the ASAS-3 project had previously observed each of our fields, whereas the Kepler field had not been targeted with variability surveys prior to the Hartman et al. study. We also performed a cross-correlation with the 2MASS Point Source Catalog (Skrutskie et al. 2006) to provide magnitudes. Where multiple 2MASS sources are present within the APT photometry aperture, the source closest to the centre of the photometry aperture that is brighter than  mag is selected. Finally the catalogue was cross-correlated with the ROSAT X-Ray Source Catalog (Voges et al., 1999, 2000) to determine which sources, if any, might be active stars with hot coronae.

Figure 4: A comparison of the colour histograms for LPV and non-LPV variables.

A histogram of the colour indices (Figure 4) for the LPV and non-LPV variables (the latter including all eclipsing binaries and classical pulsators) demonstrates the expected dichotomy, with LPVs mostly having mag, i.e. being red giant stars. A few LPVs have bluer colours, which might indicate early-type stars with longer periods unrelated to red giant pulsations (e.g. ellipsoidal variability in binaries, rotational modulation due to starspots), or mismatches with the 2MASS catalogue. Conversely, most of the non-LPVs have mag, corresponding to spectral types A–K. The few redder non-LPVs are all located at lower galactic latitudes, suggesting strong interstellar reddening in their cases.

Figure 5: The vs. colour-colour diagram with the three broad categories and the stellar loci taken from Bessell & Brett (1988).

We also tested the consistency between the assigned variability types and their expected stellar types via the vs. colour-colour diagram. Using the intrinsic stellar loci determined for dwarfs and giants by Bessell & Brett (1988) and transformed into the 2MASS system (Carpenter 2001), we plot the locations of stars in three broad categories (eclipsing, pulsating, LPV) in Figure 5. Here we find a good agreement: almost all LPVs follow the intrinsic location of red giant stars, even showing hints of the separate carbon-rich LPV sequence for mag and mag. There are several outliers towards both bluer and redder colours, almost exclusively eclipsing binaries, where we may suspect high reddening, mismatches with the 2MASS catalogue, composite colours of stars blended in the 2MASS catalogue, which has a spatial resolution of 1 arcsec pixel, or large photometric errors in the 2MASS magnitudes.

Figure 6: Sample light curves for contact eclipsing binaries (top three rows), detached eclipsing binaries (next three rows), RR Lyrae stars (next two rows) and LPV (bottom two rows). These light curves have not been processed with the trend-filtering algorithm.

As an indication of the quality of the light curves in this catalogue, we plot a representative sample of eclipsing binaries, pulsating variables and LPVs (Figure 6). All data are publicly available at the University of New South Wales Virtual Observatory facility (see Section 6). Table 2 contains an extract of the complete summary table available in the electronic version of this paper. For each star the ID, J2000 coordinates, galactic coordinates, 2MASS magnitudes, mean -band magnitude, -band amplitude, period, epoch of minimum light, previous identifier where appropriate and classification in this catalogue are shown.

ID RA Dec Period Epoch Alternate ID Type
(J2000.0) (J2000.0) (mag) (mag) (mag) (mag) (mag) (d) HJD-2450000.0
UNSW-V-001 04:52:56.7 29:48:14.3 231.0476 37.4715 8.258 7.805 7.650 8.66 0.012 - - LPV
UNSW-V-002 04:53:38.1 29:06:38.0 230.2417 37.1672 11.228 10.933 10.821 11.23 0.200 0.38555 3289.1020 ASAS 045338-2906.6 EW
UNSW-V-003 04:53:48.2 29:53:49.2 231.2140 37.3108 12.475 12.078 11.969 12.71 0.026 0.69570 3289.0900 EA
UNSW-V-004 04:54:43.8 29:34:07.0 230.8696 37.0407 12.388 12.111 12.083 12.46 0.018 - - LPV
UNSW-V-005 04:57:28.8 29:09:48.3 230.5523 36.3636 8.540 8.235 8.160 8.75 0.005 3.2684 3289.1200 EB
UNSW-V-006 04:57:45.4 30:14:05.8 231.8658 36.5529 9.342 9.162 9.109 9.38 0.003 - - LPV
UNSW-V-007 04:58:03.5 29:55:59.1 231.5185 36.4208 10.068 9.823 9.745 10.21 0.128 3.0683 3324.2200 ASAS 045804-2956.0 EA
UNSW-V-008 04:58:18.2 29:04:54.7 230.5073 36.1695 13.720 13.279 13.238 13.73 0.050 0.31036 3288.9700 EW
UNSW-V-009 04:50:06.9 30:39:50.7 231.9448 38.2536 13.548 13.424 13.386 13.69 0.139 1.0641 3351.9800 EA
UNSW-V-010 04:50:18.7 30:21:29.1 231.5754 38.1480 11.697 11.139 10.976 12.31 0.032 1.9175 3290.4200 PUL
Table 2: Extract from the complete catalogue included in the electronic version of this paper. See text for an explanation of the columns.

5 Discussion

An extensive collection of variable stars always leads to some unexpected results: in the course of analysing transit candidates, the University of New South Wales Extrasolar Planet Search has identified a low-mass K7 Ve detached eclipsing binary (M M, Young et al. 2006) and the first high-amplitude Scuti star in an eclipsing binary system (Christiansen et al. 2007). While these alone are interesting, the full breadth of the data is much more extensive. Below we discuss several possible applications, making no attempt at completeness.

5.1 Close eclipsing binaries with extreme properties

Contact binaries (or W UMa-type eclipsing variables) are among the most common types of variable stars, occurring at a rate of roughly 1 in every 500 FGK dwarfs (Rucinski 2006), which explains their large occurrence rate in variable star catalogues (e.g. 218 out of 850 in this catalogue). One intriguing problem related to these stars is that of binary mergers. When the total angular momentum of a binary system is at a certain critical (minimum) value, a secular tidal instability occurs which eventually forces the stars to merge into a single, rapidly rotating object (Arbutina 2007 and references therein). In the case of contact binaries, the instability occurs at a minimum mass-ratio of (Rasio 1995, Li & Zhang 2006), which has been the explanation for the very few contact systems with (see Arbutina 2007 for the updated lists of ten contact systems in the range of 0.065-0.13). The exact limit depends on assumptions on the stellar structure and dynamical stability (Li & Zhang 2006). Since it is likely that at least a fraction of blue straggler stars in star clusters formed via binary mergers (Mapelli et al. 2004), there is an exciting opportunity to constrain binary merger theories by increasing the number of known contact binaries with extremely low mass-ratios, and probing the limits of the observed .

Figure 7: Possible candidates for low mass-ratio contact binaries.

Examining the published light curves of the lowest mass-ratio systems (e.g. AW UMa: Pribulla et al. 1999; V870 Ara: Szalai et al. 2007), a single flat-bottomed minimum is always present, which corresponds to the full eclipse of the much smaller component that occurs within a certain range of inclinations. In our sample we find about five binaries with very similar periods (0.3–0.4 d) and light curve shapes (three are shown Figure 7), which might therefore be low mass-ratio systems deserving further attention. This could include obtaining and modelling multi-colour light curves in several bands (see, e.g., Qian et al. 2005).

Similarly to the mass-ratio, contact binary periods also have a very well-defined cut-off, which occurs at  d, just 0.05 d shorter than the maximum of the volume-corrected period distribution (Rucinski 2007). Stepien (2006) attempted to explain the period cut-off via the magnetic-wind driven angular momentum loss, the rate of which shows a progressive decay with the shortening of the period so that the period evolution takes progressively longer time. The period cut-off would then be due to a finite age of the binary population of several Gyr. Using the ASAS sample of binaries, Rucinski (2007) concluded that while no evidence exists for angular momentum evolution, the drop in numbers towards the cut-off still suffers from small number statistics and the cut-off itself remains unexplained. Hence, improving the statistics at the short-period end of contact binaries is important, where high-cadence transit search programs could play an important role. In our sample, there are four contact binaries out of 218 in the range of  d, which fall on the short-period end of the distribution but do not improve the statistics near the cut-off (the present record holder in the Galactic field has a period of 0.2178 d; Rucinski 2007).

It is also possible to use the periods and light curve morphologies to identify close eclipsing binaries that are potentially composed of low-mass components. Identifying low-mass stars in eclipsing binaries is extremely important for accurately deriving the fundamental stellar parameters of mass and radius that are crucial for constraining low-mass stellar formation and evolution models. Following the method of Weldrake et al. (2007), we select those contact binaries with periods  d, and Algol-type detached eclipsing binaries with no obvious out-of-eclipse variations and periods  d as good candidates for low-mass eclipsing binary systems. We find four contact binaries (the same four located near the period cut-off) and 31 detached binaries matching these criteria, listed in Table 3. There are quite a few bright () objects in this list which would make excellent targets for spectroscopic follow up.

ID RA(J2000.0) Dec(J2000.0) (mag) P (d)
Contact eclipsing binaries
UNSW-V-077 09 30 05.4 14 02 17.9 12.64 0.2480
UNSW-V-219 16 55 48.1 60 19 08.7 13.50 0.2456
UNSW-V-659 21 07 03.6 65 56 42.0 11.08 0.2472
UNSW-V-662 21 10 21.6 66 54 53.3 12.37 0.2492
Detached eclipsing binaries
UNSW-V-003 04 53 48.2 29 53 49.2 12.71 0.6956
UNSW-V-090 11 56 40.6 35 43 43.8 12.53 1.1870
UNSW-V-097 11 59 53.9 36 13 26.1 12.18 0.9025
UNSW-V-143 16 57 00.6 60 29 51.9 12.17 0.8117
UNSW-V-156 17 00 06.2 60 17 02.4 13.43 1.5618
UNSW-V-192 17 09 18.1 60 01 43.5 13.45 1.4877
UNSW-V-198 17 10 18.2 59 46 08.9 13.20 1.2275
UNSW-V-205 17 13 55.3 60 12 15.3 11.83 0.9610
UNSW-V-301 17 16 37.3 58 09 40.4 11.04 1.4828
UNSW-V-312 00 00 06.0 59 44 48.3 13.63 1.0574
UNSW-V-353 04 04 51.2 24 11 30.4 13.73 0.7065
UNSW-V-379 09 22 49.7 25 12 40.8 13.35 0.4975
UNSW-V-386 09 14 14.5 24 53 31.6 11.43 1.2956
UNSW-V-410 09 13 47.8 22 48 23.5 13.07 0.9690
UNSW-V-472 13 08 49.2 44 47 55.2 12.46 0.5484
UNSW-V-527 14 45 10.0 39 25 47.3 10.99 1.363
UNSW-V-528 14 45 19.4 38 08 48.0 11.26 1.211
UNSW-V-536 14 47 27.5 38 31 36.7 11.56 0.2303
UNSW-V-540 14 49 09.1 38 38 10.1 12.92 0.5216
UNSW-V-598 18 21 14.1 64 30 03.1 11.77 0.8492
UNSW-V-617 18 38 00.5 65 06 07.7 10.66 1.5092
UNSW-V-621 18 11 49.9 67 06 13.8 13.76 1.0194
UNSW-V-624 18 18 48.4 67 27 54.1 12.76 1.482
UNSW-V-644 18 34 50.6 67 24 09.9 12.96 1.4238
UNSW-V-683 20 58 55.7 67 02 12.8 13.21 1.0740
UNSW-V-696 21 08 04.4 68 57 53.1 11.55 0.9428
UNSW-V-706 20 58 35.9 69 04 01.4 12.03 1.025
UNSW-V-722 23 25 23.1 70 01 48.5 12.20 1.3523
UNSW-V-740 08 04 02.0 66 28 02.6 13.63 1.2634
UNSW-V-746 08 08 27.0 68 19 07.5 12.68 1.0324
UNSW-V-846 15 03 44.5 68 40 02.6 12.13 1.5804
Table 3: Eclipsing binary systems potentially composed of low-mass components.

5.2 Pre-main-sequence eclipsing binaries

Detached eclipsing binaries provide one of the most accurate (largely model-independent) sources of fundamental stellar parameters, notably masses and radii. These can be used to put the strongest constraints on stellar evolutionary models, which in turn can improve our understanding of the formation and evolution of individual stellar populations. On the pre-main-sequence, the calibration of stellar parameters is presently extremely scarce below 1 M where only six eclipsing binaries are known, all located in the Taurus-Orion region (Irwin et al. 2007). Comparison of these systems to different stellar models have indicated difficulties in fitting both components of the binaries simultaneously, which shows our current models of low-mass stars are seriously challenged by the known systems (see also Aigrain et al. (2007)).

Figure 8: Detached binaries and the location of five known pre-main-sequence eclipsing systems (data taken from Stassun et al. 2004, Covino et al. 2004, Hebb et al. 2006, Stassun, Mathieu & Valenti 2007 and Irwin et al. 2007).
ID RA(J2000.0) Dec(J2000.0) (mag) P (d)
UNSW-V-097 11 59 53.9 36 13 26.1 12.18 0.90251
UNSW-V-107 12 03 06.8 35 37 47.5 12.40 6.23034
UNSW-V-299 17 16 10.8 57 20 37.5 12.33 5.97542
UNSW-V-312 00 00 06.0 59 44 48.3 13.63 1.05762
UNSW-V-491 13 12 31.5 46 04 08.8 12.76 3.83246
UNSW-V-518 13 07 31.8 45 12 42.0 12.59 2.75743
UNSW-V-617 18 38 00.5 65 06 07.7 10.66 1.50906
UNSW-V-676 21 20 45.9 66 22 18.5 13.50 3.28503
UNSW-V-722 23 25 23.1 70 01 48.5 12.20 1.01274
UNSW-V-746 08 08 27.0 68 19 07.5 12.68 1.03214
Table 4: Candidate PMS detached binaries

One possibility for identifying pre-main-sequence binaries is by using colour-colour diagrams, such as the one depicted in Figure 8. Here we plot the location of the detached binaries in our sample, using 2MASS magnitudes, the intrinsic stellar loci from Bessell & Brett (1988) and the position of five PMS binaries with published photometry (mostly from 2MASS). We use two dashed lines as boundaries for defining a PMS candidate: the vertical and horizontal lines are at  mag and  mag, respectively. In total we extract 17 Algol-type systems from our sample that have redder colours than the boundary lines (i.e. located around the known PMS systems in Figure 8). Investigation of higher spatial resolution archived images from the Digitized Sky Surveys3 (DSS) demonstrates that in six cases, the APT photometry aperture contains a single central 2MASS source. In four additional cases, the aperture contains a single bright source and up to half a dozen additional sources several magnitudes fainter, where the depth of the eclipse ( mag) precludes the fainter sources from producing binary signal.

These ten are listed in Table 4: some could be heavily reddened main-sequence stars, but since the majority of our fields are located above a Galactic altitude of 15, there is the distinct possibility of genuine PMS binaries in the sample. There is also the possibility of composite colours of unresolved blends in the 2MASS catalogue. One method of confirmation would be obtaining high-resolution spectroscopy to look for PMS signatures, such as strong Li absorption (Irwin et al. 2007)

5.3 The Blazhko-effect in RR Lyrae stars

RR Lyrae stars are horizontal branch stars showing high-amplitude pulsations driven by the -mechanism, with typical periods of about 0.5 days. It is known that a large fraction of RR Lyrae stars (20–30% of the fundamental mode RRabs and 2% of the first-overtone RRcs, Kovács 2001) exhibit periodic amplitude and/or phase modulations, the so-called Blazhko-effect, which is one the greatest mysteries in classical pulsating star research. Currently, two classes of models are usually put forward as possible explanations, both assuming the presence of non-radial oscillations (note, that RR Lyrae stars have long been considered as the prototypes of purely radially pulsating stars): the resonance models, in which resonance effects excite non-radial modes in addition to the main radial mode, and the magnetic models, which are essentially oblique rotating pulsator models (see Kolenberg et al. 2006 and references for more details). Recently, Stothers (2006) published a new explanation, in which turbulent convection inside the hydrogen and helium ionization zones becomes cyclically weakened and strengthened owing to the presence of a transient magnetic field that is generated by some kind of a dynamo mechanism.

ID RA(J2000.0) Dec(J2000.0) (mag) P (d)
Blazhko-effect
UNSW-V-101 12 00 49.6 36 11 59.2 14.26 0.6264
UNSW-V-203 17 12 35.0 60 29 32.1 11.99 0.4933
UNSW-V-384 09 24 25.5 24 05 03.4 11.45 0.5169
UNSW-V-442 12 50 44.3 44 41 20.3 13.08 0.5853
UNSW-V-614 18 34 41.2 65 27 08.1 11.74 0.4769
UNSW-V-773 14 40 40.8 68 23 16.8 11.03 0.5518
Double-mode
UNSW-V-358 09 16 04.3 -23 36 08.0 13.49 0.3602
0.4840
UNSW-V-532 14 46 35.8 -39 33 31.7 12.86 0.6540
0.5012
UNSW-V-577 14 52 42.1 -41 41 55.3 9.60 0.8735
0.6682
UNSW-V-758 08 16 09.3 -66 44 46.3 11.94 0.3850
0.5170
UNSW-V-810 14 50 13.3 -69 45 18.5 11.70 0.7372
1.0576
Table 5: RR Lyrae stars with detected Blazhko-effect and double-mode pulsation.

With a variety of competing models, theory is in desperate need for further empirical constraints, most notably ones that are capable of detecting non-radial oscillations and/or magnetic fields, for example high-resolution spectroscopy. Hence, the discovery of bright to moderately faint RR Lyrae stars with well-expressed Blazhko-effect could be of great interest. In our sample we find six RR Lyrae stars out of 52, listed in Table 5, that demonstrate the Blazhko-effect. Four were previously known variables, and two are new discoveries. The Blazhko-period of modulation for RR Lyrae stars typically ranges from tens to hundreds of days (see, for example, fig. 4 of Szczygiel & Fabrycky (2007)), although they can be as short as a few days (Jurcsik et al., 2006). Due to the comparable baseline of our observations, we could not determine the Blazhko-period for any of the stars, but the four objects brighter than mag, one of which is a new discovery, are good candidates for further studies. We also find five double-mode RR Lyrae stars, for which the period ratios suggest the well-known mixture of fundamental+first radial overtone pulsation. Three of these are new discoveries, with two previous published in the ASAS-3 catalogue, thus raising the total number of field double-mode RR Lyrae stars known in the Galaxy to 30; see Szczygiel & Fabrycky (2007).

6 Online access to light curves

All APT images from 2002 July until present day, including those used in the creation of this catalogue, are stored in a publicly available archive. This archive can be accessed using a web browser and the conventional web interface located at:

Alternatively, the archive can be accessed via the Simple Image Access Protocol (SIAP) (Tody & Plante 2004) as defined by the International Virtual Observatory Alliance (IVOA). The SIAP defines a standard for retrieving images from a repository using simple URL-based queries. For example, a request made to the following URL will return a list of APT images that intersect with the 1 degree square region centred on (75,-30) with RA and Dec expressed in decimal degrees:

The POS parameter is mandatory and defines the centre of the search region. The SIZE parameter is optional (default is SIZE=1) and determines the size of the search region. The TEL parameter distinguishes between images from different telescopes, and is specific to the UNSW implementation of the SIAP service.

The list of images returned by the SIAP query is in VOTable format (Ochsenbein et al. 2004). Each item in the list contains a set of attributes describing a particular image that satisfies the search criteria. Also included is a URL which can be used to download the associated image.

The catalogue of light curves discussed in this paper is available from the UNSW archive via the Simple Spectral Access Protocol (SSAP) (Tody et al 2007). The SSAP is an IVOA standard for accessing archives of one dimensional spectra, including time series data such as light curves. The format of an SSAP query is very similar to the format of an SIAP query. For example, the query specified in the following URL will search for light curves of stars within a circle of diameter 1 degree centred at the point (75,-30).

The REQUEST parameter is the only mandatory parameter. The optional parameters include POS, SIZE, BAND and TIME, which are used to constrain the search by region (degrees), bandpass (metres) and time of observation (ISO 8601).

The list of light curves returned by the SSAP query is in VOTable format, and each item in the list contains a set of metadata describing a particular light curve, including a URL for downloading the data. The light curve data itself is also presented in VOTable format and follows the structure of the Spectrum Data Model (McDowell 2007). Consequently, these light curve VOTables may be examined with any VO-compliant tool, such as TOPCAT (Taylor 2005).

7 Summary

We have presented a catalogue of 850 variable stars, compiled from 32 months of observations obtained for the University of New South Wales Extrasolar Planet Search. Of these stars, 659 are new discoveries that have not been previously reported in the GCVS, NSV or ASAS-3 catalogues. This catalogue of well-sampled high precision light curves, each spanning 1–4 months, has significant potential for astrophysically interesting data mining. We have nominated several possibilities, including eclipsing binary systems with low mass ratios, low-mass components or pre-main sequence components, and RR Lyrae stars demonstrating the curious Blazhko-effect. The data have been made publicly available on the University of New South Wales VO server in a standard format for retrieval and for analysis with standard VO tools.

Acknowledgments

We thank the referee for helpful comments. This project has been supported by the Australian Research Council and the Australian Research Collaboration Service (ARCS). JLC and AD are supported by Australian Postgraduate Research Awards.

Appendix A Cross-identifications with the GCVS, ASAS and ROSAT catalogues

The positions of the stars in this catalogue were correlated with the General Catalog of Variable Stars (GCVS) and All Sky Automated Survey (ASAS) variable star catalogues, using the Vizier online database (Ochsenbein et al., 2000). The photometry aperture used in our data reduction pipeline has a radius of 28.2, and so a simple cone search with a radius of 30 was performed. The results of the correlation are shown in Table 6. 191 of the 850 stars presented in this catalogue are positionally coincident with previously published variable stars. The columns in the table are the UNSW identifier from this catalogue, the right ascension and declination (J2000.0), the mean -band magnitude, -band amplitude of variation, period, epoch, the identifier from either the GCVS, NSV or ASAS catalogues, and our classification, which was found to be in good agreement with the published classification in more than 90% of the cases with a few exceptions, like V717 Ara (EB), which is listed as an RR Lyr in the GCVS or V500 Ara (EW), also RR Lyr in the GCVS. However, these are the classes with highly sinusoidal, i.e. indistinguishable light curve shapes, and it is therefore not surprising that single-filtered light curves are not enough in doubtful cases.

The positions were also correlated with x-ray sources in the ROSAT 1RXS (Voges et al., 1999, 2000) and 2RXP (ROSAT 2000) catalogues, similarly to Norton et al. (2007). The search was again performed through Vizier using a 30 cone search, and the results are shown in Table 9. The columns are as for Table 61, although in this case the second identifier column contains the ROSAT source identifier. 22 of the 850 stars were found to be spatially coincident with ROSAT sources, although we note that since the majority have been classified as pulsating variables, which are not expected to be strong X-ray sources (Makarov 2003), it is doubtful how much of the coincidence is real.

ID RA Dec Period Epoch Alternate ID Type
(J2000.0) (J2000.0) (mag) (mag) (d) HJD-2450000.0
UNSW-V-002 04 53 38.1 29 06 38.0 11.23 0.200 0.38555 3289.1020 ASAS 045338-2906.6 EW
UNSW-V-007 04 58 03.5 29 55 59.1 10.21 0.128 3.06827 3324.2200 ASAS 045804-2956.0 EA
UNSW-V-011 05 00 55.6 30 07 54.1 12.06 0.127 0.84183 3291.1200 ASAS 050056-3007.9 EW
UNSW-V-013 05 02 46.6 29 44 04.2 8.41 0.026 - - ASAS 050247-2944.1 LPV
UNSW-V-016 04 51 51.6 29 34 22.4 11.65 0.209 0.38150 3289.3880 ASAS 045151-2934.3 EW
UNSW-V-018 04 42 00.0 25 28 45.5 13.52 0.134 0.58747 3289.1000 ASAS 044200-2528.8 RRL
UNSW-V-019 04 42 16.3 25 49 32.1 11.46 0.220 0.25489 3285.3350 ASAS 044216-2549.5 EW
UNSW-V-021 04 42 25.4 26 17 31.3 11.52 0.087 0.45552 3285.2000 ASAS 044225-2617.5 EW
UNSW-V-026 04 48 00.9 25 31 23.1 12.23 0.200 0.54883 3285.1700 ASAS 044801-2531.4 RRL
UNSW-V-035 09 03 32.6 14 52 45.5 11.12 0.070 0.29358 3376.9850 ASAS 090333-1452.8 EW
UNSW-V-043 09 05 55.6 14 51 23.4 10.90 0.069 0.56636 3377.1500 ASAS 090556-1451.4 EW
UNSW-V-046 09 07 07.5 14 07 37.7 9.84 0.041 - - ASAS 090708-1407.7 LPV
UNSW-V-050 09 08 20.6 13 35 43.2 11.14 0.068 15.44279 3389.0400 ASAS 090821-1335.8 EA
UNSW-V-055 08 59 54.2 15 15 22.9 11.57 0.186 0.39362 3371.1350 ASAS 085954-1515.4 EW
UNSW-V-057 09 22 21.2 13 38 50.4 12.50 0.188 0.53220 3377.9800 IV HYA/ASAS 092220-1338.8 RRL
UNSW-V-061 09 23 55.7 14 20 22.1 8.65 0.026 35.06803 3393.0000 ASAS 092356-1420.4 LPV
UNSW-V-062 09 24 55.9 13 11 58.2 12.92 0.180 0.61124 3382.1500 ASAS 092456-1312.0 RRL
UNSW-V-066 09 26 41.0 13 45 06.5 9.66 0.239 0.44976 3377.2500 EZ HYA/ASAS 092641-1345.1 EW
UNSW-V-070 09 28 20.1 12 50 51.2 11.70 0.248 0.50182 3377.0970 ASAS 092820-1250.9 EB
UNSW-V-071 09 28 28.7 13 26 33.2 11.68 0.205 0.73146 3378.4340 ASAS 092829-1326.5 EB
UNSW-V-074 09 28 57.5 13 07 12.4 8.16 0.029 45.49365 3402.0000 ASAS 092857-1307.2 LPV
UNSW-V-075 09 29 15.3 14 05 57.2 12.27 0.263 0.33259 3377.2450 ASAS 092915-1405.9 EW
UNSW-V-081 09 20 17.4 14 05 09.7 8.61 0.099 - - ASAS 092018-1405.1 LPV
UNSW-V-084 11 55 44.0 36 26 19.8 11.78 0.134 0.53132 3415.1900 ASAS 115544-3626.3 EW
UNSW-V-086 11 56 12.9 35 59 29.9 11.50 0.266 0.37801 3415.1180 V0576 CEN/ASAS 115613-3559.5 EW
UNSW-V-087 11 56 20.4 35 28 45.5 11.39 0.141 0.29374 3415.3250 ASAS 115620-3528.8 EW
UNSW-V-090 11 56 40.6 35 43 43.8 12.53 0.102 1.18713 3415.1300 V577 CEN EA
UNSW-V-091 11 57 20.2 36 40 23.3 13.52 0.133 0.58688 3422.9900 V0580 CEN RRL
UNSW-V-092 11 57 57.2 36 06 10.3 13.41 0.266 0.34622 3415.1670 V0581 CEN EW
UNSW-V-097 11 59 53.9 36 13 26.1 12.18 0.057 0.90251 3422.1700 NSV 05410 EA
UNSW-V-101 12 00 49.6 36 11 59.2 14.26 0.170 0.62644 3423.0900 V0582 CEN RRL
UNSW-V-105 12 02 27.6 35 26 39.3 14.04 0.143 0.67441 3423.0800 EF HYA RRL
UNSW-V-107 12 03 06.8 35 37 47.5 12.40 0.078 6.23034 3415.0100 NSV 05437 EA
UNSW-V-115 11 53 52.2 35 26 55.4 13.57 0.084 0.58367 3415.1200 DS HYA RRL
UNSW-V-116 11 54 06.9 35 14 54.8 11.22 0.082 3.30294 3423.1900 ASAS 115407-3514.9 EA
UNSW-V-122 11 56 27.7 38 08 51.3 8.85 0.077 84.16663 - ASAS 115628-3808.9 LPV
UNSW-V-131 12 01 25.6 37 24 52.6 8.35 0.583 135.44203 - V0583 CEN/ASAS 120125-3724.9 LPV
UNSW-V-132 12 02 19.3 38 46 22.3 12.86 0.264 0.45906 3415.0900 V0584 CEN/ASAS 120219-3846.4 RRL
UNSW-V-137 12 06 53.6 37 37 36.0 12.51 0.157 0.39816 3415.3260 ASAS 120654-3737.7 EW
UNSW-V-138 11 53 40.7 38 22 00.5 10.55 0.063 0.82146 3423.5700 ASAS 115341-3822.1 EW
UNSW-V-140 16 56 35.0 59 04 41.2 10.44 0.580 184.16278 - CG ARA LPV
UNSW-V-143 16 57 00.6 60 29 51.9 12.17 0.020 0.81166 3510.1600 V0805 ARA EA
UNSW-V-144 16 57 15.2 60 08 05.3 12.71 0.224 0.92708 3499.5400 V0705 ARA EW
UNSW-V-145 16 57 50.6 59 07 20.0 12.60 0.187 0.57442 3509.1100 V0414 ARA RRL
UNSW-V-147 16 57 43.8 60 41 16.4 9.33 0.086 1.20159 3499.8030 ASAS 165744-6041.3 EW
UNSW-V-154 16 59 07.2 60 05 25.4 8.55 0.903 174.95479 - LS ARA/ASAS 165907-6005.4 LPV
UNSW-V-171 17 02 35.4 59 30 48.7 9.84 0.302 118.49562 - V0810 ARA LPV
UNSW-V-178 16 54 36.1 60 09 04.8 10.68 0.375 - - V0696 ARA LPV
UNSW-V-184 17 05 59.3 59 40 34.1 12.63 0.033 0.51838 3499.1100 V0464 ARA RRL
UNSW-V-186 17 06 48.2 59 03 20.0 8.68 0.864 152.13318 - CH ARA/ASAS 170648-5903.3 LPV
UNSW-V-187 17 07 32.4 60 58 45.6 11.47 0.117 2.32845 3504.1400 ASAS 170732-6058.8 EA
UNSW-V-194 17 09 43.0 60 31 14.9 12.27 0.170 0.35894 3499.2330 ASAS 170944-6031.2 EW
UNSW-V-195 17 10 07.8 60 39 45.7 10.51 0.134 2.52195 3499.2000 V0617 ARA/ASAS 171008-6039.8 CEP
UNSW-V-198 17 10 18.2 59 46 08.9 13.20 0.072 1.22754 3555.2400 V0485 ARA EA
UNSW-V-199 17 11 16.1 60 20 42.8 8.69 0.637 184.16278 - CN ARA LPV
UNSW-V-200 17 11 32.5 60 14 35.9 9.46 0.273 152.13318 - NSV 08245 LPV
UNSW-V-203 17 12 35.0 60 29 32.1 11.99 0.045 0.49332 3504.2800 CS ARA RRL
UNSW-V-207 17 13 52.6 59 05 16.1 9.56 0.137 75.56063 - V0733 ARA/ASAS 171352-5905.2 LPV
UNSW-V-214 17 15 58.6 60 04 06.2 11.25 0.129 0.40006 3499.1600 ASAS 171559-6004.1 EW
UNSW-V-215 17 16 19.3 59 55 07.2 12.77 0.072 0.77541 3504.2500 DG ARA RRL
UNSW-V-217 17 16 35.5 60 21 01.4 11.65 0.204 0.36364 3499.1060 V0791 ARA/ASAS 171636-6021.1 EW
UNSW-V-218 17 17 40.0 60 31 19.1 11.77 0.066 0.59981 3499.1400 MT ARA EB
UNSW-V-229 17 01 00.5 58 35 18.1 12.12 0.066 1.28635 3504.2500 V717 ARA EW
UNSW-V-231 17 01 23.8 58 16 46.1 12.90 0.036 0.58292 3504.1000 V0441 ARA RRL
UNSW-V-232 17 01 33.8 58 18 35.1 13.47 0.047 2.04759 3558.1000 V0442 ARA EA
UNSW-V-233 17 01 49.3 57 59 33.5 10.25 0.712 166.85367 - V0779 ARA LPV
UNSW-V-238 17 02 59.7 57 06 42.8 9.49 0.080 0.98892 3510.2800 V0722 ARA EA
UNSW-V-248 16 57 56.7 57 52 46.4 10.94 0.471 - - V0776 ARA LPV
UNSW-V-252 17 06 37.5 58 07 40.2 11.02 0.124 0.89702 3504.2500 ASAS 170637-5807.7 EW
UNSW-V-254 17 06 33.9 57 42 51.4 8.71 0.163 129.34859 - NSV 08172/ASAS 170634-5742.9 LPV
UNSW-V-255 17 06 46.4 58 31 15.2 12.05 1.066 175.19339 - V0780 ARA LPV
UNSW-V-259 16 58 20.3 57 19 38.9 11.20 0.031 0.11082 3504.2200 V0709 ARA PULS
UNSW-V-262 17 09 25.7 57 44 43.4 12.60 0.099 0.66760 3504.5700 V0817 ARA EB
UNSW-V-264 17 09 44.2 57 53 42.2 11.57 0.075 0.45536 3504.0700 ASAS 170944-5753.7 EW
UNSW-V-265 17 09 55.7 58 43 14.5 12.32 0.014 0.90689 3504.2800 V0783 ARA EA
UNSW-V-267 17 10 00.8 58 10 08.9 11.82 0.154 0.41460 3504.0800 CL ARA/ASAS 171000-5810.2 EW
UNSW-V-268 17 10 01.4 57 58 26.0 9.87 0.017 8.56234 3581.5000 ASAS 171002-5758.4 EA
UNSW-V-269 17 10 15.8 57 26 47.2 10.05 0.087 58.40231 - V0731 ARA/ASAS 171016-5726.7 LPV
Table 6: UNSW variable stars coincident with GCVS/ASAS records.
ID RA Dec Period Epoch Alternate ID Type
(J2000.0) (J2000.0) (mag) (mag) (d) HJD-2450000.0
UNSW-V-271 17 11 13.0 57 14 03.1 13.04 0.230 0.96374 3504.1900 V0785 ARA EB
UNSW-V-274 17 11 24.2 57 00 47.6 12.30 0.129 0.49237 3503.9700 V0492 ARA EW
UNSW-V-277 17 11 58.9 57 36 53.2 10.39 0.918 166.85367 - V0493 ARA/ASAS 171200-5737.2 LPV
UNSW-V-278 17 12 09.1 58 34 15.1 10.66 0.543 166.85367 - CQ ARA/ASAS 171209-5834.2 LPV
UNSW-V-280 17 12 34.4 57 24 11.1 11.23 0.823 122.58292 - CT ARA LPV
UNSW-V-285 17 13 33.5 57 55 15.3 10.57 0.389 119.22419 - V0732 ARA/ASAS 171334-5755.2 LPV
UNSW-V-287 17 14 24.3 58 46 39.6 10.39 0.918 140.15567 - V0498 ARA LPV
UNSW-V-289 17 14 28.1 57 26 15.9 12.97 0.177 0.39302 3504.2300 V0500 ARA EW
UNSW-V-295 17 15 33.2 57 05 15.1 10.46 1.149 175.19339 - DE ARA LPV
UNSW-V-301 17 16 37.3 58 09 40.4 11.04 0.032 1.48306 3531.2800 ASAS 171638-5809.7 EA
UNSW-V-307 17 18 45.7 57 46 29.7 11.17 0.208 0.79384 3510.2070 NSV 08452/ASAS 171846-5746.5 EB
UNSW-V-308 17 18 45.1 57 26 20.8 8.03 0.045 1.80572 3499.6000 V0858 ARA PUL
UNSW-V-310 23 57 02.7 58 26 01.3 13.42 0.223 0.68809 3582.2300 ASAS 235702-5826.0 RRL
UNSW-V-317 00 04 15.9 58 15 53.5 9.11 0.060 143.47872 - ASAS 000416-5815.9 LPV
UNSW-V-327 00 01 47.5 57 14 30.4 10.09 0.082 0.47036 3579.1760 ASAS 000147-5714.5 EW
UNSW-V-329 00 02 29.3 56 53 49.9 8.79 0.042 13.21167 3591.5000 ASAS 000229-5653.9 CEP
UNSW-V-336 23 51 57.4 57 25 20.8 10.01 0.088 0.39260 3577.1370 ASAS 235157-5725.4 EW
UNSW-V-337 23 54 23.7 57 56 27.6 10.40 0.199 0.58423 3577.5100 ASAS 235424-5756.5 EW
UNSW-V-352 04 13 38.9 24 33 32.5 12.05 0.066 - - ASAS 041339-2433.5 EW
UNSW-V-367 09 18 55.4 25 16 44.1 9.14 0.398 59.06429 - Z PYX/ASAS 091855-2516.7 LPV
UNSW-V-369 09 19 41.8 24 18 38.5 9.96 0.081 36.01291 - ASAS 091942-2418.6 LPV
UNSW-V-370 09 20 00.7 23 38 42.9 8.51 0.034 52.60413 - ASAS 092000-2338.7 LPV
UNSW-V-377 09 22 37.7 25 27 06.4 12.18 0.212 0.48368 3740.2300 SS PYX/ASAS 092238-2527.1 1 EW
UNSW-V-384 09 24 25.5 24 05 03.4 11.45 0.076 0.51693 3742.0900 ASAS 092425-2405.1 RRL
UNSW-V-390 09 25 51.5 24 00 39.4 7.98 0.242 59.06429 - LP HYA LPV
UNSW-V-396 09 10 29.0 22 44 34.4 8.71 0.025 35.67026 - ASAS 091029-2244.6 LPV
UNSW-V-400 09 11 03.1 23 27 16.3 11.69 0.168 0.62330 3743.0300 ASAS 091103-2327.3 EW
UNSW-V-421 09 17 26.3 22 48 10.7 9.03 0.120 59.06429 - ASAS 091726-2248.2 LPV
UNSW-V-450 12 54 31.3 46 07 36.5 8.68 0.014 1.04272 3805.1500 NSV 06020 CEP
UNSW-V-461 12 47 23.7 45 35 03.3 9.26 0.075 - - ASAS 124724-4535.1 LPV
UNSW-V-473 13 08 47.6 45 56 58.3 8.35 0.084 67.33070 - ASAS 130848-4557.3 LPV
UNSW-V-477 13 10 17.1 44 25 59.7 8.08 0.025 48.60485 - ASAS 131017-4426.2 LPV
UNSW-V-480 13 10 31.5 45 19 31.4 9.30 0.085 49.98603 - NSV 06118 LPV
UNSW-V-494 13 13 20.7 45 38 13.4 8.21 0.115 48.60485 - ASAS 131321-4538.5 LPV
UNSW-V-495 13 13 33.0 44 49 31.8 9.10 1.206 57.39314 - ASAS 131333-4449.8 LPV
UNSW-V-500 13 10 18.5 45 08 59.8 11.38 0.031 5.35927 3787.9950 ASAS 131018-4509.2 EA+DSCT
UNSW-V-503 13 15 39.9 45 51 14.9 12.18 0.151 0.33926 3788.1100 ASAS 131540-4551.5 EW
UNSW-V-508 13 16 49.0 45 48 47.2 8.82 0.195 0.40969 3788.1050 ASAS 131649-4549.0 EW
UNSW-V-516 13 18 21.8 44 43 28.1 12.08 0.074 0.30021 3788.0000 ASAS 131823-4443.9 RRL
UNSW-V-520 13 21 15.1 45 13 21.9 8.36 0.018 29.35691 4000.0000 ASAS 132115-4513.6 LPV
UNSW-V-524 14 43 52.6 39 54 40.2 10.07 0.265 31.46413 - V0549 CEN LPV
UNSW-V-535 14 47 23.2 39 06 22.6 11.80 0.158 0.31378 3846.9350 ASAS 144723-3906.4 EW
UNSW-V-537 14 48 14.5 38 18 32.7 8.44 0.795 29.14202 - V0557 CEN/ASAS 144815-3818.6 LPV
UNSW-V-551 14 52 30.1 38 24 33.2 10.44 0.082 30.28946 - ASAS 145230-3824.6 LPV
UNSW-V-556 14 54 44.7 38 56 47.8 9.81 0.135 31.77751 - V0566 CEN LPV
UNSW-V-557 14 42 20.5 38 40 32.9 12.76 0.103 3.09517 3848.9600 V0544 CEN EA
UNSW-V-558 14 43 27.3 41 02 05.7 8.07 0.050 - - V0642 CEN LPV
UNSW-V-562 14 45 49.0 41 26 12.4 8.51 0.013 - - V0551 CEN LPV
UNSW-V-564 14 46 58.5 41 17 50.6 12.82 0.083 0.69187 3847.2000 NSV 06795 RRL
UNSW-V-567 14 48 05.4 41 45 23.6 10.47 0.186 - - V0555 CEN/ASAS 144805-4145.4 LPV
UNSW-V-570 14 49 00.4 41 26 55.0 13.17 0.226 0.62177 3847.1400 V0558 CEN RRL
UNSW-V-571 14 49 31.6 40 06 29.1 9.01 0.074 0.79930 3847.1250 ASAS 144932-4006.4 EW
UNSW-V-572 14 49 24.8 40 04 27.9 9.14 0.042 - - V0560 CEN LPV
UNSW-V-574 14 50 28.2 40 56 15.0 11.99 0.208 0.47623 3847.1100 ASAS 145028-4056.3 EW
UNSW-V-577 14 52 42.1 41 41 55.3 9.60 0.032 0.87351 3847.0400 ASAS 145242-4141.9 RRL
UNSW-V-583 14 42 34.7 40 27 17.4 10.00 0.184 0.32503 3846.9750 V0677 CEN/ASAS 144235-4027.2 EW
UNSW-V-584 14 42 55.3 41 18 47.4 9.70 0.088 - - V0545 CEN LPV
UNSW-V-591 18 18 43.6 64 37 59.6 9.55 0.043 - - ASAS 181843-6437.9 LPV
UNSW-V-592 18 19 04.5 65 35 35.3 9.18 0.473 - - DF PAV/ASAS 181904-6535.6 LPV
UNSW-V-595 18 20 32.2 64 17 23.7 8.51 0.033 - - ASAS 182031-6417.3 LPV
UNSW-V-599 18 21 32.3 64 15 58.1 8.59 0.416 - - NSV 10616 LPV
UNSW-V-601 18 22 36.6 65 30 18.4 9.63 0.076 - - ASAS 182236-6530.3 LPV
UNSW-V-603 18 24 38.7 65 11 02.0 10.49 0.059 2.41936 3879.1400 ASAS 182438-6511.0 EA
UNSW-V-606 18 26 23.7 64 57 45.9 12.68 0.072 2.49162 3872.8300 DP PAV EA
UNSW-V-607 18 29 37.0 64 54 43.1 7.39 1.814 - - NSV 10827/ASAS 182937-6454.7 LPV
UNSW-V-609 18 13 35.1 65 14 13.1 10.86 0.463 - - NW PAV/ASAS 181335-6514.2 CEP
UNSW-V-610 18 30 35.7 64 51 33.7 8.69 0.031 - - ASAS 183034-6451.5 LPV
UNSW-V-614 18 34 41.2 65 27 08.1 11.74 0.291 0.47690 3874.1000 BH PAV/ASAS 183441-6527.0 RRL
UNSW-V-615 18 35 24.0 64 57 04.4 9.24 0.300 - - ASAS 183523-6457.0 LPV
UNSW-V-623 18 18 32.0 67 19 48.5 9.21 0.078 - - ASAS 181833-6719.9 LPV
UNSW-V-627 18 21 15.4 66 38 47.7 11.42 0.078 2.32625 3879.1100 ASAS 182117-6638.8 EA
UNSW-V-633 18 25 26.2 67 34 42.4 10.90 0.231 0.42713 3866.2150 ASAS 182528-6734.8 EW
UNSW-V-639 18 30 46.4 67 08 15.2 12.43 0.293 1.85125 3886.2200 NSV 10858 EA
UNSW-V-641 18 33 32.9 66 54 00.5 9.35 0.011 1.92981 3867.2800 ASAS 183333-6654.0 PUL
UNSW-V-643 18 34 13.5 66 07 10.0 11.98 0.340 - - ASAS 183414-6607.2 LPV
UNSW-V-645 18 35 38.8 66 55 52.6 8.94 0.032 0.84381 3866.2300 ASAS 183540-6656.0 EB
UNSW-V-646 18 36 25.9 67 56 03.1 8.52 1.293 - - DG PAV/ASAS 183627-6756.0 LPV
Table 7: continued
Table 8: continued
ID RA Dec Period Epoch Alternate ID Type
(J2000.0) (J2000.0) (mag) (mag) (d) HJD-2450000.0
UNSW-V-656 21 04 47.3 66 46 16.6 10.02 0.032 0.42922 3937.2050 ASAS 210447-6646.3 EW
UNSW-V-658 21 06 49.1 66 33 51.0 11.00 0.119 3.02652 3937.8800 ASAS 210649-6633.8 EA
UNSW-V-665 21 11 35.9 66 12 49.8 12.46 0.152 0.37251 3937.1700 ASAS 211136-6612.8 EW
UNSW-V-672 21 17 04.8 67 01 47.3 8.97 0.129 45.25221 - ASAS 211705-6701.8 LPV
UNSW-V-674 21 18 53.4 67 16 14.8 8.48 0.051 41.88170 - ASAS 211855-6716.2 LPV
UNSW-V-675 21 20 21.8 65 46 45.6 13.57 0.148 0.46078 3937.0900 NSV 13650 RRL
UNSW-V-677 21 20 51.0 65 50 15.5 8.84 0.033 32.99997 3046.0000 ASAS 212051-6550.3 LPV
UNSW-V-690 21 02 58.2 68 45 12.8 10.66 0.166 0.51007 3937.3190 ASAS 210258-6845.2 EW
UNSW-V-695 21 06 59.9 68 21 03.5 8.88 0.052 - - ASAS 210700-6821.0 LPV
UNSW-V-714 23 48 48.7 69 46 53.4 11.32 0.101 0.39326 3991.2520 ASAS 234849-6946.9 EW
UNSW-V-716 23 30 40.7 69 53 33.5 10.68 0.097 0.63150 3991.1660 ASAS 233041-6953.5 EW
UNSW-V-719 23 33 56.1 69 11 14.1 11.20 0.101 0.95375 3990.9800 ASAS 233356-6911.2 EW
UNSW-V-723 07 55 30.6 66 59 44.7 11.03 0.114 1.10830 4086.7700 ASAS 075530-6659.7 EW
UNSW-V-724 07 55 14.7 68 08 11.9 10.85 0.196 97.58038 - ASAS 075514-6808.2 LPV
UNSW-V-726 07 57 00.3 66 35 51.3 9.32 0.052 1.29478 4087.1300 ASAS 075700-6635.8 EB
UNSW-V-733 08 00 55.4 66 41 11.1 9.96 0.023 46.64445 - ASAS 080055-6641.2 LPV
UNSW-V-735 08 01 35.6 68 19 36.3 8.14 0.097 66.31470 - ASAS 080135-6819.6 LPV
UNSW-V-736 08 01 59.7 68 17 39.5 9.14 0.198 94.34667 - ASAS 080200-6817.7 LPV
UNSW-V-739 08 04 10.3 67 54 56.5 11.53 0.161 0.41287 4085.9850 ASAS 080410-6755.0 EW
UNSW-V-741 08 04 53.8 66 48 49.3 11.19 0.102 0.48046 4086.0050 ASAS 080454-6648.8 EW
UNSW-V-745 08 07 15.2 67 12 17.1 9.21 0.027 28.64224 4110.0000 ASAS 080715-6712.3 LPV
UNSW-V-755 08 12 59.1 67 14 44.3 11.61 0.124 0.34062 4085.9850 ASAS 081259-6714.7 EW
UNSW-V-756 08 14 09.5 68 02 13.0 11.31 0.088 0.41789 4086.1350 ASAS 081409-6802.2 EW
UNSW-V-758 08 16 09.3 66 44 46.3 11.94 0.139 0.38501 4086.1000 ASAS 081610-6644.8 RRL
UNSW-V-761 07 52 35.7 67 15 11.1 8.22 0.067 1.01055 - ASAS 075236-6715.2 LPV
UNSW-V-768 14 36 15.8 69 51 10.3 12.34 0.165 - - XZ CIR LPV
UNSW-V-770 14 39 01.4 69 22 43.2 11.15 0.168 - - NSV 06732 LPV
UNSW-V-801 14 47 44.1 68 54 05.6 9.57 0.072 7.24716 4175.8000 ASAS 144744-6854.1 CEP
UNSW-V-806 14 49 45.1 69 35 31.7 9.16 0.080 - - ASAS 144945-6935.6 LPV
UNSW-V-807 14 49 56.5 69 20 50.8 11.93 0.521 - - BL CIR LPV
UNSW-V-824 14 56 41.9 68 34 47.9 10.92 0.129 1.05497 4170.2500 ASAS 145643-6834.8 EA
UNSW-V-825 14 57 20.7 69 45 02.2 10.45 0.473 - - ASAS 145720-6945.0 LPV
UNSW-V-826 14 56 33.0 68 08 35.2 9.47 0.212 2.13147 4173.1100 EM TRA/ASAS 145633-6808.6 EA
UNSW-V-842 15 02 19.0 68 16 01.1 11.67 0.027 1.78869 4184.2700 NSV 06882 EA
UNSW-V-844 15 03 08.6 69 50 58.3 11.29 0.192 0.36855 4170.0500 ASAS 150308-6950.9 EW
UNSW-V-847 15 05 11.1 68 45 56.7 11.01 0.154 0.33718 4170.1500 ASAS 150511-6845.9 EW
ID RA Dec Period Epoch ROSAT ID Type
(J2000.0) (J2000.0) (mag) (mag) (d) HJD-2450000.0
UNSW-V-005 04 57 28.8 29 09 48.3 8.75 0.005 3.26837 3289.1200 1RXS J045728.9-290953 EB
UNSW-V-040 09 05 22.3 15 03 42.9 9.60 0.007 0.62883 3377.0800 1RXS J090522.2-150302 PUL
UNSW-V-362 09 16 44.1 24 47 42.9 9.56 0.012 2.59901 3744.1000 1RXS J091644.7-244735 CEP
UNSW-V-450 12 54 31.3 46 07 36.5 8.68 0.014 1.04272 3805.1500 1RXS J125430.7-460735 CEP
UNSW-V-468 12 47 55.7 44 57 34.1 8.94 0.059 - - 1RXS J124757.9-445735 PUL
UNSW-V-470 12 48 07.6 44 39 17.5 8.59 0.053 1.04962 3788.7300 1RXS J124807.6-443913 CEP
UNSW-V-493 13 13 07.4 45 37 30.3 9.56 0.013 6.85120 3791.4000 1RXS J131306.7-453740 CEP
UNSW-V-494 13 13 20.7 45 38 13.4 8.21 0.115 48.60485 0.0000 1RXS J131306.7-453740 LPV
UNSW-V-506 13 16 38.9 45 46 56.0 8.88 0.003 0.08857 3788.0200 1RXS J131651.3-454905 PUL
UNSW-V-508 13 16 49.0 45 48 47.2 8.82 0.195 0.40969 3788.1050 1RXS J131651.3-454905 EW
UNSW-V-510 13 17 24.2 45 28 17.4 9.84 0.070 72.84351 0.0000 1RXS J131717.7-452541 LPV
UNSW-V-514 13 17 46.5 44 56 39.3 10.19 0.013 0.48342 3788.0700 1RXS J131747.3-445707 PUL
UNSW-V-521 13 22 04.2 45 03 10.8 9.03 0.022 1.44852 3787.8500 1RXS J132204.7-450312 CEP
UNSW-V-525 14 44 14.2 39 10 15.9 10.29 0.003 0.09340 3847.0150 1RXS J144357.0-390847 PUL
UNSW-V-538 14 40 47.7 38 47 05.7 9.21 0.007 0.19950 3847.1700 1RXS J144037.4-384658 PUL
UNSW-V-541 14 49 26.1 39 50 48.4 9.67 0.012 3.74574 3847.3000 1RXS J144925.7-395042 CEP
UNSW-V-559 14 44 04.4 40 59 23.9 8.15 0.017 0.50383 3846.9200 1RXS J144405.2-405940 EW
UNSW-V-568 14 48 13.2 41 03 00.0 9.72 0.015 - - 1RXS J144812.6-410310 PUL
UNSW-V-577 14 52 42.1 41 41 55.3 9.60 0.032 0.87351 3847.0400 1RXS J145240.7-414206 RRL
UNSW-V-582 14 42 16.0 41 00 19.0 9.43 0.020 2.57400 3848.3000 1RXS J144214.5-410026 CEP:
UNSW-V-718 23 32 37.1 69 54 31.2 8.61 0.018 - - 1RXS J233239.5-695432 PUL
UNSW-V-760 07 51 49.4 68 14 04.3 10.70 0.026 0.19789 4086.0500 2RXP J075145.0-681416 PUL
Table 9: UNSW variable stars coincident with ROSAT X-ray sources.

Footnotes

  1. pagerange: The University of New South Wales Extrasolar Planet Search: a catalogue of variable stars from fields observed 2004–2007The University of New South Wales Extrasolar Planet Search: a catalogue of variable stars from fields observed 2004–2007
  2. pubyear: 2007
  3. The Digitized Sky Surveys were produced at the Space Telescope Science Institute under U.S. Government grant NAG W-2166. The images of these surveys are based on photographic data obtained using the Oschin Schmidt Telescope on Palomar Mountain and the UK Schmidt Telescope.

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