Variables in the Southern Polar region Evryscope 2016 dataset

Variables in the Southern Polar region Evryscope 2016 dataset

Jeffrey K. Ratzloff11affiliation: Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3255, USA , Henry T. Corbett11affiliation: Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3255, USA , Nicholas M. Law11affiliation: Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3255, USA , Brad N. Barlow22affiliation: Department of Physics and Astronomy, High Point University, High Point, NC 27268, USA , Amy Glazier11affiliation: Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3255, USA ,
Ward S. Howard11affiliation: Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3255, USA , Octavi Fors11affiliation: Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3255, USA 33affiliation: Institut de Ciències del Cosmos (ICCUB), Universitat de Barcelona, IEEC-UB, Martí i Franquès 1, E08028 Barcelona, Spain , Daniel del Ser11affiliation: Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3255, USA 33affiliation: Institut de Ciències del Cosmos (ICCUB), Universitat de Barcelona, IEEC-UB, Martí i Franquès 1, E08028 Barcelona, Spain , and Trifon Trifonov44affiliation: Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117 Heidelberg, Germany
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Abstract

The regions around the celestial poles offer the ability to find and characterize long-term variables from ground-based observatories. We used multi-year Evryscope data to search for high-amplitude ( 5% or greater) variable objects among 160,000 bright stars (mv 14.5) near the South Celestial Pole. We developed a machine learning based spectral classifier to identify eclipse and transit candidates with M-dwarf or K-dwarf host stars - and potential low-mass secondary stars or gas giant planets. The large amplitude transit signals from low-mass companions of smaller dwarf host stars lessens the photometric precision and systematics removal requirements necessary for detection, and increases the discoveries from long-term observations with modest light curve precision among the faintest stars in the survey. The Evryscope is a robotic telescope array that observes the Southern sky continuously at 2-minute cadence, searching for stellar variability, transients, transits around exotic stars and other observationally challenging astrophysical variables. The multi-year photometric stability is better than 1% for bright stars in uncrowded regions, with a 3-sigma limiting magnitude of g=16 in dark time. In this study, covering all stars 9 mv 14.5, in declinations to , and searching for high-amplitude variability, we recover 346 known variables and discover 303 new variables, including 168 eclipsing binaries. We characterize the discoveries and provide the amplitudes, periods, and variability type. A 1.7 RJ planet candidate with a late K-dwarf primary was found and the transit signal was verified with the PROMPT telescope network. Further followup revealed this object to be a likely grazing eclipsing binary system with nearly identical primary and secondary K5 stars. Radial velocity measurements from the Goodman Spectrograph on the 4.1 meter SOAR telescope of the likely-lowest-mass targets reveal that six of the eclipsing binary discoveries are low-mass (.06 - .37 ) secondaries with K-dwarf primaries, strong candidates for precision mass-radius measurements.

 E-mail: ]jeff215@live.unc.edu

1. Introduction

Variable star discoveries provide information on stellar properties, formation, and evolution, and are critical for determining distances and ages of astronomical objects. Eclipsing binaries allow the measurement of masses, radii, and temperatures, and can be used to test stellar formation theory predictions. Lower mass eclipsing binaries are observationally challenging due to the low intrinsic brightness of the star, and more systems are needed to properly characterize the mass/radius relationship in stellar models (Torres, 2010; Mann et al., 2015; Kraus et al., 2017). Ground-based surveys such as the Palomar Transient Factory (Law et al., 2009), ATLAS (Tonry, 2011), HAT (Bakos et al., 2004), HAT-South (Bakos, 2018), SuperWASP (Pollacco et al., 2006), KELT (Pepper et al., 2007), CSTAR (Wang et al., 2015), and many others are very successful in detecting variables (including transiting exoplanets) and adding to known variable star catalogs such as the Variable Star Index111http://www.aavso.org/vsx/ (VSX). These surveys either observe at day or longer time-scale cadences, or observe dedicated sky areas to reach fast cadence at the expense of all sky coverage. In contrast, the Evryscope is optimized for shorter-timescale observations with continuous all sky coverage and a multi-year period observation strategy. The continuous, fast-cadence, all-sky Evryscope light curves are sensitive to variations (including transits and eclipses) lasting only a few minutes, and provide fine sampling for ten minute level variations or longer.

The Evryscope is a robotic camera array mounted into a 6 ft-diameter hemisphere which tracks the sky (Law et al., 2015). The telescope is located at CTIO in Chile and observes continuously, covering 8150 sq. deg. in each 120s exposure. The Evryscope was deployed with 22 cameras and can accommodate 27 total cameras (with a corresponding increased field of view of 10,000 sq. deg). Each camera features a 29MPix CCD providing a plate scale of 13”/pixel. The Evryscope monitors the entire accessible Southern sky at 2-minute cadence, and the Evryscope database includes tens of thousands of epochs on 16 million sources. In this paper, we limited the search field to the region around the South Celestial Pole, and chose the brighter stars in order to maximize the number of epochs per source and minimize systematics.

The Southern Polar sky area is less explored than other parts of the sky, primarily due to the difficulty in reaching it. This is evidenced by the comparatively low number of planet, eclipsing binary, and variable star discoveries in this region. For example, the sky area in the declination region of to comprises 3.4% of the southern sky’s total area; however the VSX catalog of known variables in the same region accounts for only 1.2% of the southern sky total. Surveys of the Southern Polar sky region typically either use a telescope located at a low latitude South American site or an instrument in the Antarctic. The former choice can be challenging depending on the airmass of the target region, while the second poses engineering difficulties due to the harsh environment (Law et al., 2013; Wang et al., 2015).

We use the Evryscope to explore the Southern Polar region (declinations to ). While the airmass is non-optimal ( 1.7 average), the Evryscope monitors the Southern Polar region continuously every night for the entire night at 2 minute cadence, with the same camera for multiple years. This long-term, same-camera coverage at short cadence results in many continuous data points with consistent airmass, and minimizes systematics. Targets in this region average over 60,000 epochs per year. Our observing strategy results in several hundred thousand light curves with targets ranging in brightness from 9 mv 15. The light curves have the precision necessary to potentially detect eclipsing binaries, variable stars, transiting gas-giant planets around small-cool host stars, and short-transit-time planets around small compact stellar remnants including white dwarfs and hot subdwarfs. With additional filtering, the light curves are precise enough to potentially detect gas-giant planets around bright solar type stars; we will address this in future work. These Evryscope light curves also facilitate searches with wide period ranges (for the Polar Search we searched from 3-720 hours), longer periods, and wide amplitude ranges. Long-period discoveries are typically non-interacting stars and are challenging to detect due to the low number of transits.

The primary target of this paper’s search is eclipsing binaries, particularly low-mass and long-period systems. The secondary target of this paper’s search is gas-giant planets around M-dwarf or late K-dwarf primaries. This survey relies on detection power to narrow the candidates and uses observations from mid 2016 to early 2017. The more challenging transiting exoplanet detections will be conducted with additional systematics removal steps, additional candidate filtering to push to lower power detections, and will use the full three plus year data set (Ratzloff et al., in prep).

Eclipsing binaries are the best calibrators for determining relations between mass, radius, luminosity, and temperature. Relatively few low-mass (M-dwarf or late-K-dwarf secondary) eclipsing binaries have been discovered (Kraus et al., 2011; Law et al., 2012; Chaturvedi et al., 2018), and many are too faint for easy radial-velocity followup measurements. This has limited our ability to measure the mass/radius relation at low masses, where many low-mass systems suggest larger radii than stellar models predict (Torres, 2010; Mann et al., 2015; Kraus et al., 2017). This is particularly important for the determination of transiting planet radii around low-mass single stars, where some of the most exciting nearby planets are likely to be discovered (Ricker et al., 2014; Christ et al., 2018; Stassun et al., 2018).

In this paper, we report the discovery of 303 new variables including seven eclipsing binaries with low-mass secondary stars. We perform spectroscopic followup on select eclipsing binaries to confirm the stellar type and secondary size. Radial velocity measurements reveal that seven of the eclipsing binary discoveries are low-mass (.06 - .34 ) secondaries with K-dwarf primaries.

In § 2 we describe the Evryscope photometric observations that led to the discoveries as well as our analysis of the light curves and detection algorithms for identifying variables. In § 3 we describe the followup observations performed for the low-mass eclipsing binaries including PROMPT (Reichart et al., 2005) followup photometry, identification spectra and radial velocity followup using the Goodman (Clemens et al., 2004) spectrograph on the 4.1 meter SOAR telescope and the CHIRON (Tokovinin et al., 2013) echelle spectrometer on the CTIO/SMARTS 1.5 meter telescope. In § 4 we present and characterize our discoveries. We also detail our analysis of the radial velocity followup work including the Monte-Carlo simulation to fit the masses, radii, and other parameters. We conclude in § 5.

2. Observations and Variability Search

2.1. Evryscope Photometry

All eclipsing binary and variable discoveries were detected in a transit search of the polar region (declinations to ). The observations were taken from August 9, 2016 to April 4, 2017. The exposure time was 120s through a Sloan-g filter and each source typically had 16,000 epochs. We briefly describe the calibration and reduction of images and the construction of light curves; further details will be presented in an upcoming Evryscope instrumentation paper. Raw images are filtered with a quality check, calibrated with masterflats and masterdarks, and have large-scale backgrounds removed using the custom Evryscope pipeline. Forced photometry is performed using APASS-DR9 (Henden et al., 2015) as our master reference catalog. Aperture photometry is performed on all sources using multiple aperture sizes; the final aperture for each source is chosen to minimize light curve scatter. The primary systematics challenges are: background and airmass changes and the subsequent effects on stars of different magnitude and color, the ratchet observing cycle causing the targets to switch cameras and appear in different positions on the CCD chips over the observing season, daily aliases, source blending, PSF distortions, and vignetting. We use the quality filter, calibrations, aperture photometry, along with a custom implementation of the SysRem (Tamuz et al., 2005) algorithm to remove the systematics challenges described above.

2.2. Detection of Variables

Filtering by declination and magnitude returns 239,991 initial targets from the Evryscope light curve database. 76,407 are eliminated by an additional quality filter based on non-blended sources. The remaining 163,584 are analyzed using Box Least Squares (BLS) (Kovacs et al., 2002; Ofir, 2014) with the pre-filtering, daily-alias masking, and settings described in § 2.4. The light curves are then sorted by BLS detection power, in terms of Signal Detection Efficiency (SDE) (Kovacs et al., 2002). Figure 1 shows the BLS SDE distribution for the targets along with the distribution of detected periods. Targets with an SDE 10 and with nearby reference stars gives 9104 suspects for further inspection. The 10-SED cutoff is chosen to: 1) limit the number of targets to an amount that is reasonable for human followup (in this case 10,000), 2) ensure a reasonable chance of detecting high-amplitude candidates without accumulating excessive false-positives, and 3) reach three percent level signal depths on bright stars to potentially detect low-mass secondaries and gas-giant planets around M-dwarfs or late K-dwarfs.

We compare the target light curve (both unfolded and folded to the best BLS period) to two nearby reference stars of similar magnitude looking for any signs that the detected variation is present in the references indicating systematics (see § 2.5). The folded plots are colored by time to check how well-mixed the detection is, since a transit or eclipse with only a single or few occurrences is more likely to be an artifact of the detection algorithm. The light curves are also folded on the second and third best BLS periods to check for aliases, as well as the best Lomb-Scargle (LS) (Lomb, 1975; Scargle, 1982) period to check for sinusoidal variability. From visual inspection, we identify 649 variables from the machine filtered 9104 suspects. 346 are known variables and 303 are new discoveries.

Figure 1.— Detection characteristics from the BLS results of the polar search. The top panel shows the BLS power in SDE vs. magnitude (15% of the points are shown for better visualization), the lower left panel is the histogram of BLS power in SDE, the lower right is the histogram of periods found. Targets with an SDE 10 are selected for further inspection.

2.3. Machine-Learning Stellar Classification

We developed a machine-learning based classifier that uses publicly available catalog data to estimate stellar size from a B-V color/magnitude space, and to estimate spectral type from multiple color-differences. The discovery candidates were matched to APASS-DR9 (Henden et al., 2015) and PPMXL (Roeser et al., 2010) catalogs to obtain reduced proper motion (RPM) and color differences (B-V, V-K, J-H, H-K) for each target. Modifying the method in Jones (1972) with a two step machine learning process described below, we classify stars based on B-V and RPM to identify stellar size - main sequence, giants, white dwarfs, or sub dwarfs. The RPM and B-V combination provides a high return on our target catalog (99% of our targets are classified as demonstrated below) and captures spectral information using available data. After the stellar size estimation is completed, the four color differences are used to approximate the spectral type.

In the first step of the machine learning process, we use a support vector machine (SVM) from the SKYLEARN python module (Pedregosa et al., 2011) to identify likely hot subdwarfs (HSD) from all other stars. The HSD are challenging to separate since they can be close to main sequence O/A stars in this parameter space. We find the SVM to be an effective way to segregate the HSD, shown in the top panel of Figure 2 as the small confined area enclosed in the black border. This is done by using a training set of HSD from (Geier et al., 2017) and other types of stars from SIMBAD (Wenger et al., 2000), filtering the outliers, then computing the contour boundaries. The SVM method is a non-probabilistic two-class classifier that computes a hard boundary (decision boundary) by minimizing the distance (or margin) between the points closest to the boundary. As with any classifier there are missed targets and contaminants, and there are physical reasons the results can be skewed (reddening for example). Our goal in this step is to separate the most challenging class (the HSD) from all the other classes while providing a boundary with a reasonable contingency space to the nearby white dwarf and main sequence regions.

Once the HSD are identified, all remaining objects are classified using a Gaussian Mixture Model (GMM) (Pedregosa et al., 2011) with three classes to identify white dwarfs, main sequence, and giants. We again use an outlier filtered training set of stars of each type from SIMBAD (20,972 main sequence, 1515 white dwarfs (WD), and 10,000 giants). The GMM classifier results are shown in the bottom panel of Figure 2. The GMM method is a best fit to 2-D Gaussian function (probability density function), using the training points to adjust the Gaussian centers, orientations, and elongations. Our application of this method uses three dimensions (WD, main sequence, and giants). Although more dimensions are possible, overlapping or poorly separated classes tend to give poor results (part of the motivation of using the SVM for the HSD step). The GMM produces contour lines with Negative-log-likelihood (NLL) values that can be converted () to give an estimate of the confidence level the data point belongs in the class.

Figure 2.— The Evryscope Target Classification - We use B-V color differences and reduced proper motion (RPM) data with a two step machine learning algorithm to classify star size. Top: the training data (gold squares=hot subdwarfs, grey=all others) for the support vector machine (SVM) which returns the resulting hot subdwarf classification region (the area inside the black border). Bottom: the training data (blue stars=white dwarfs, green=main sequence, red diamonds=giants) for the Gaussian Mixture Model (GMM) which returns the resulting classification contours. Negative log likelihood plot-lines 1, 1.7, 2.8 are shown.

We use the spectral type and temperature profiles in (Pecaut & Mamajek, 2013) to derive a function (using 1-D interpolation) that uses available color differences to derive an estimate for spectral type (Figure 3). If only B-V is available, we classify simply by the letter (O,B,A,F,G,K,M); if multiple colors are available we average the fits and choose the closest spectral type (G9, K4, M3 for example). For main sequence stars we add the luminosity class V. The code produces a function with RPM and color differences inputs and outputs the star size, star type, and NLL score for the GMM step. We used this to classify all of our discoveries, with the added requirement that the HSD also be apparent spectral type O or B and that the WD have a NLL score of less than 4.0. The added requirements help filter contaminants from main sequence A stars for the HSD, and borderline WD stars. Candidates identified as likely K or M-dwarfs with shallow (typically less than 10%) eclipses or transits are identified as potentially high value targets and analyzed in more detail.

Figure 3.— The Evryscope Target Classification - We use (B-V, V-K, J-H, H-K) color differences to estimate temperature and spectral type using the data in (Pecaut & Mamajek, 2013) to interpolate profiles for each color difference. The data are the grey points and the interpolations are the colored lines in the figures. We average the four results and pick the closest spectral type.

The Evryscope classifier is designed to: 1) facilitate identification of as many of the target light curves as practical, 2) identify targets to be included in Evryscope transit searches (white dwarfs, hot subdwarfs, K and M-dwarfs), and 3) classify variability discoveries helping to identify those as potentially interesting for further followup. For the Polar Search, 98.5% of our targets have B-V and RPM data available, and 91.0% have all four color differences and RPM data. Tests using 485,000 targets spread across the entire southern sky (all RA and declinations to ) have demonstrated very high returns - 99% of Evryscope targets have all four color differences and RPM data available for classification. Once the catalogs were compiled and matched, the classifier took only a few minutes to classify the 485,000 test targets, making it practical for use on the full Evryscope database. All discoveries in this work are classified using the APASS-DR9 (Henden et al., 2015) and PPMXL (Roeser et al., 2010) catalogs as described above. A similar approach using the GAIA-DR2 (Gaia Collaboration et al., 2018) catalog will be used as an additional target filter for the transiting exoplanet searches (Ratzloff et al., in prep).

We tested the Evryscope classifier in several ways. We chose known WD from APASS (Raddi et al., 2017) and high confidence (.80) WD suspects from ATLAS (Pietro Gentile Fusillo et al., 2017) and SDSS (Pietro Gentile Fusillo et al., 2015) with mv 16.5, for a total of 211 classifier test targets. Using Geier et al. (2017) with mv 15.0, we obtain 1560 HSD classifier test subjects (which may include WD due to the difficulty in separating the two groups). We use Lépine & Gaidos (2011) to obtain 3764 high-confidence M-dwarfs. Using Kharchenko (2001) and filtering out the bright stars we have 999 main sequence, 452 giants, and 895 K-dwarfs for classifier testing.

Table 1 shows the performance of the classifier to correctly determine star size (ms/giant/WD/HSD). Table 2 shows the performance of the classifier to correctly determine letter spectral type (O,B,A,F,G,K,M). Table 3 shows the performance of the classifier to correctly determine full spectral type (O0 - M9).

Test group Star size ES Classifier % correct
M-dwarfs ms 95.3%
K-dwarfs ms 86.2%
M-giants giant 98.7%
Main Sequence ms 94.1%
HSD HSD 54.5 (77.7 w/WD)%
WD WD 87.0%
Table 1Evryscope Classifier star size (ms/giant/WD/HSD) performance.
Test group letter spectral type ES Classifier % correct
M-dwarfs M 95.2%
K-dwarfs K 81.1%
M-giants M 97.9%
Main Sequence O-M 69.5%
HSD O,B 76.5%
Table 2Evryscope Classifier letter spectral type (O,B,A,F,G,K,M) performance.
ES Classifier
Test group spectral type mean variance % +/-3
M-dwarfs M0-M9 -.50 1.9 95.3%
K-dwarfs K0-K9 -.98 2.7 81.6%
M-giants M0-M9 -2.0 1.7 78.0%
Main Sequence O0-M9 -.87 3.7 63.1%
222Shown is the mean difference and variance in classifier performance numerical class versus the known class. The last column shows the percent of the test group that is classified correctly to within 3 of the known numerical class.
Table 3Evryscope Classifier full spectral type (O0 - M9) performance.

We also compared the classifier results to SOAR ID spectra taken for the low-mass eclipsing binaries (§ 4.7). 7 of the 8 were classified as the correct spectral type (K for example), and within +/- 1 numeric class (K5 or K6 for example) (Table 4).

ID (EVR+) SOAR ID Sptp ES Classifier Sptp
J053513.22-774248.2 G7V K1V
J06456.10-823501.0 G8V G9V
J103938.18-872853.8 K7V K6V
J110815.96-870153.8 K4V K3V
J165050.23-843634.6 K5V K4V
J180826.26-842418.0 G5V G6V
J184114.02-843436.8 K2V K3V
J211905.47-865829.3 K5V K6V
Table 4Comparison of the Evryscope Classifier to SOAR ID spectra.

2.4. Variability search algorithms

We selected sources in the polar region with mv 14.5 and with light curves that passed quality tests to eliminate sources with blending, narrow time coverage, or low number of epochs (§ 2.2). Light curves (with MJD timestamps) were pre-filtered with a Gaussian smoother to remove variations on periods greater than 30 days, and a 3rd order polynomial fit was subtracted to remove long-term variations. Light curves were then searched for transit-like, eclipse-like, and stellar variability signals using the Box Least Squares (BLS) (Kovacs et al., 2002; Ofir, 2014) and Lomb-Scargle (LS) (Lomb, 1975; Scargle, 1982) algorithms.

We tested the recovery rates on Evryscope light curves with different BLS settings - with periods ranging from 2-720 hours, 10,000-100,000 periods tested, and transit fractions from .001 to 0.5. Recovery rate tests were run on known eclipsing binaries in our magnitude range with different transit depths ranging from .01 to .25, and on simulated few-percent level transit signals injected onto Evryscope light curves representative of low-mass secondaries. The tests showed that a very wide BLS test period range (2-720 hours) led to decreased detections as the periodogram becomes biased to long periods or spikes in longer periods arise from data gaps. This challenge combined with the survey 6-month time coverage (§ 1), shows too aggressive of a period range can detect fewer eclipsing binary candidates. Based on these tests, the final BLS settings used on the Evryscope Polar Search were a period range 3-250 hours with 25,000 periods tested and a transit fraction of .01 to .25.

Period detections of 24-hours and corresponding aliases (4, 6, 8, 16, 36, 48, and 72 hours) were masked in +/- .1 hour widths. The results were sorted by BLS signal detection strength - BLS periodogram peak power in terms of sigmas above the mean power. Targets with peak power greater than 10-sigma were verified visually with a panel detection plot. We use the Lomb-Scargle (LS) algorithm to identify sinusoidal variables. For LS, we used a period range 3-720 hours to include sensitivity to longer period variables. We recover slightly lower amplitude variables (minimum discovery amplitude in this work ) than eclipsing binaries (minimum discovery depth in this work ) as shown in the Appendix.

Figure 4 shows the phase-folded Evryscope light curve for EVRJ110815.96-870153.8 a K4V primary and .21 secondary with a BLS detected period of 12.28 hours. Figure 5 shows the light curve for EVRJ032442.50-780853.9 a variable star with a LS detected period of 4.67 hours.

Figure 4.— An example low mass eclipsing binary discovery (EVRJ110815.96-870153.8) from this survey. The Evryscope light curve phased on its period of 12.277 hours is shown on the top panel. Grey points = 2 minute cadence, blue points = binned in phase. The bottom panel shows the BLS power spectrum with the highest peak at the 12.277 hour detection.
Figure 5.— An example variable discovery (EVRJ032442.50-780853.9) from this survey. The Evryscope light curve phased on its period of 4.676 hours is shown on the top panel. Grey points = 2 minute cadence, blue points = binned in phase. The bottom panel shows the LS power spectrum with the highest peak at the 4.676 hour detection.

2.5. False Positive Tests

We performed several tests to verify the variability signals were not false positives. First, we compared the candidate light curve with several nearby reference star light curves looking for similar variation to test for systematics or PSF blending. The nearest reference stars within 0.2 degrees of the reference star were filtered by magnitude and light curve coverage. The nearest three with magnitudes within 0.5 mag of the target star and with a light curve coverage and number of data points within 20% of the target light curve are chosen for comparison. The references are folded at the same period as the detected period of the candidate, and are inspected visually for signs of similar signals, offsets, or outliers. Candidates with references showing similar variability are assumed to be systematics and thrown out.

Next we tested how well-mixed in phase the observations were, with poor mixing potentially indicating matched-filter fits to systematics or data gaps instead of astrophysical signals. This is performed by folding the candidate on the detected period and color coding the points by time (ranging from a blue-to-red scheme mapped to early-to-late times) and visually inspecting the resulting plot. For each discovery, we also compared the phased light curve of the first and the second half of the data looking for inconsistency. Candidates with marginal results from these tests were reviewed by an additional person and thrown out if both agreed the target is suspect.

Eclipsing binary light curves that did not reveal a secondary eclipse or out-of-transit ellipsoidal variation were tested further. For these candidates, we folded the light curves at twice the detected period, looking for differences in odd/even transit depths to rule out finding half of the actual period. Candidates passing these tests were then flagged as probable variable discoveries and analyzed further as detailed in § 4.

3. Followup Observations

Followup observations for select eclipsing targets were made with the PROMPT telescopes (Reichart et al., 2005) in order to confirm the Evryscope detection. We used the SOAR Goodman spectrograph (Clemens et al., 2004) for stellar classification and intermediate-resolution radial velocity measurements. We used the CHIRON (Tokovinin et al., 2013) spectrograph for high-resolution radial velocity measurements to measure the companion masses of select suspected low-mass secondaries.

3.1. SOAR Goodman ID Spectroscopy

We observed the low mass candidates on April 29, 2018 on the SOAR 4.1 m telescope at Cerro Pachon, Chile with the Goodman spectrograph. We used the red camera with the 400 1/mm grating with a GG-455 filter in M1 and M2 preset mode with 2x2 binning and the 1” slit (R 825). The red camera 333http://www.ctio.noao.edu/soar/content/goodman-red-camera is optimized for the optical red part of the spectrum and when used with the M1 and M2 presets provides a wavelength coverage of 3500-9000 Angstroms. The Goodman spectra are 2-D, single order. We took eight consecutive 60s spectra for each of the targets and for the standard LTT3864. For calibrations, we took 3 x 60s FeAr lamps, 10 internal quartz flats using 50% quartz power and 10s integrations, and 10 bias spectra.

We processed the spectra with a custom pipeline written in Python by the Evryscope team; this pipeline is described in detail here. The eight spectra for each target are median-combined, bias-subtracted, and flat-corrected. A 3rd-order polynomial is fit to the brightest pixels in each row; the spectra are then extracted in a 10-pixel range and background subtracted. We identify 8 prominent lamp emission lines for each preset (including 3749, 4806, 6965 Angstroms and many others spread across the entire wavelength range) and compare with the known lines of the Iron-Argon arc lamp using a Gaussian fit of each feature. We use a 4th-order polynomial to fit the Gaussian peaks and wavelength-calibrate each spectrum. We used the standard star LTT3864 to flux-calibrate by first removing prominent absorption features then fitting a 7th-order polynomial to the continuum. The resulting SOAR standard star spectra was visually matched to the template from the ESO library and verified to fit within the template precision. The spectra were normalized and the results from the M1 and M2 presets were combined for each target with a wavelength coverage of 3500-9000 Angstroms.

Errors in the SOAR spectra arise from instrumentation systematics, observational conditions, and the extraction pipeline. Instrumentation error sources are dominated by flexure, component alignment, and limitations in optical quality due to manufacturing constraints; see Clemens et al. (2004) for an elaborate discussion of these contributions. Observational sources of errors are primarily due to background noise, airmass, and atmospheric effects. Errors in the spectra from the extraction process are discussed in detail in Fuchs et al. (2017); the chosen standard, normalization process, and resolution are the error sources relevant to this work. The Goodman spectrograph has been operating consistently for over 15 years, and we use the accumulated knowledge to minimize errors from instrumentation, observation, and processing sources. In § 4.2 we compare the SOAR ID spectra to the spectra of stars with known stellar types. The known spectra are from different instruments, observational strategies, and pipelines; additionally the available known spectra are limited to an accuracy of 1-2 in the luminosity class. The combined errors in the high SNR SOAR ID spectra are less than this limitation. We demonstrate this in § 4.2 by comparing the results from different stellar classification methods, which are consistent to 1-2 in the luminosity class.

3.2. PROMPT Photometry

EVRJ114225.51-793121.0, EVRJ06456.10-823501.0, EVRJ184114.02-843436.8, and EVRJ211905.47-865829.3 were observed with the PROMPT P8 60cm telescope located at CTIO Chile. All observations were taken with Johnson B and Johnson R filters, interleaved. Table 5 summarizes the PROMPT followup work.

ID (EVR+) Date Images B/R(s)
J06456.10-823501.0 Dec 10, 2017 412 40/20
J114225.51-793121.0 Oct 30, 2017 190 90/60
J114225.51-793121.0 Feb 16, 2018 288 90/60
J184114.02-843436.8 Dec 19, 2017 202 100/45
J211905.47-865829.3 Nov 21, 2017 120 130/90
Table 5PROMPT observations of select targets.

The PROMPT followup observations confirm the candidate variability is astrophysical and not an Evryscope systematic by observing the Evryscope detection signal with a separate instrument and different eclipse time. The PROMPT telescopes have a 100 times larger aperture than the Evryscope cameras, giving the PROMPT light curves a lower root-mean-square (RMS) scatter and improved signal-to-noise-ratio (SNR) compared to the Evryscope discovery light curves. The amount of improvement depends on many factors including target brightness and sky background; here we show a representative example, EVRJ211905.47-865829.3 in Figure 6. The light curve RMS (after removing the eclipse) for this target is .006 in PROMPT and .108 in Evryscope (unbinned 2-minute cadence). This corresponds to a SNR of for the PROMPT single transit light curve and for the Evryscope one year light curve. These results compare nicely to estimated theoretical SNR of 175 and 12 for PROMPT and Evryscope respectively, using reasonable values for sky background, throughput, and airmass for these telescopes observing an 14.0 magnitude target. We point out that the Evryscope binned light curve can reach the SNR of the PROMPT light curve, in this example with reduced sampling. In an upcoming white dwarf / hot subdwarf fast binary discovery paper (Ratzloff et al., in prep) we demonstrate the ability to reach higher than PROMPT SNR with multiple year binned Evryscope data. In this work, we use PROMPT to verify the Evryscope candidates and better characterize the eclipse depth and shape to reduce the error in the companion radii calculation. For this target, we also observed the secondary eclipse for comparison with the primary eclipse shown in Figure 6. The PROMPT data also provides an additional eclipse time (several months past the latest Evryscope eclipse), and by phase-folding both light curves, the period accuracy is increased.

Figure 6.— Top: Combined light curves of EVRJ211905.47-865829.3. This object was flagged as a potential 9.3 hour transiting gas giant planet as the transit depths are unchanged by color and in odd/even phase. There is a slight out of phase ellipsoidal variation when folded at the 18.6 hour period indicating it is most likely a grazing eclipsing binary with nearly identical primary and secondaries. Bottom: A detailed view of the transit in the PROMPT light curve with 1 errors shown.

The PROMPT images were processed with a custom aperture photometry pipeline written in Python. The images were dark and bias-subtracted and flat-field-corrected using the master calibration frames. Five reference stars of similar magnitude are selected and aperture photometry is performed using a range of aperture sizes. The background is estimated using a sigma clipped annulus for each star scaled by the aperture size. A centroid step Gaussian fits the PSF to calculate the best center and ensures each aperture center is consistent regardless of pixel drift. The light curve rms variation is computed for the range of apertures, and the lowest-variation aperture size is chosen. A final detrending step using a 3rd order polynomial is applied to remove remaining systematics. Photometric errors are calculated per epoch using the estimated CCD aperture photometry noise in Merline & Howell (1995) and the atmospheric scintillation noise approach in Young (1967). A detailed summary of the photometric error calculation is given in Collins et al. (2017). We combined the PROMPT and Evryscope light curves for final inspection. An example of a grazing eclipsing binary originally flagged as a 1.7 RJ planet candidate is shown in Figure 6. Radial velocity follow-up with the HARPS (Mayor et al., 2003) spectrograph on the ESO La Silla 3.6m telescope combined with the detailed light curve analysis confirms the candidate is a grazing eclipsing binary.

3.3. Intermediate-resolution Spectroscopy and Radial Velocity

EVRJ114225.51-793121.0, EVRJ06456.10-823501.0, EVRJ053513.22-774248.2, EVRJ184114.02-843436.8, and EVRJ211905.47-865829.3 were observed on November 15 and 19, 2017 and December 15 and 16, 2017 on the SOAR 4.1 m telescope at Cerro Pachon, Chile with the Goodman spectrograph. EVRJ110815.96-870153.8, EVRJ180826.26-842418.0, EVRJ165050.23-843634.6, and EVRJ103938.18-872853.8 were observed on February 12, 2018 and March 3, 2018. We used the blue camera with the 2100 1/mm grating in custom mode with 1x2 binning and the 1” slit (R 5500). We took four 300-360s spectra depending on the target and conditions. For all targets, we took 3 x 60s FeAr lamps after each group of science images. We took 10 internal quartz flats with 80% quartz lamp power and 60s integration, and 10 bias spectra.
The spectra are processed using a modified version of the Python code described in § 3.1 and radial velocity measurements are calculated (§ 4.3). The SOAR spectra return radial velocity precision of 10 km/s for our targets, which allows us to characterize the secondary mass for small late M-dwarf stars. This also allowed us to rule out potential planetary-mass secondaries - the case in several of the grazing eclipses.

3.4. High-resolution Radial Velocity

EVRJ06456.10-823501.0 and EVRJ053513.22-774248.2 were observed between January 28, 2018 and March 25, 2018 on seven nights (one data point per night) with the SMARTS 1.5 m telescope at CTIO, Chile with the CHIRON spectrograph. EVRJ184114.02-843436.8 was observed on March 23, 2018. Spectra were taken in image slicer mode (R 80000). One 1500 to 1800 second spectrum was taken depending on the target and conditions. Spectra of RV standard HD131977 were taken to verify processing results.

Spectra were wavelength calibrated by the CHIRON pipeline, which we processed using a custom python code to measure radial velocity. We visually inspected the spectral orders and chose the top seven by SNR and with prominent atmospheric absorption features. The orders are spread throughout the wavelength range, and we select the most prominent atmospheric feature per order. Within each of the selected orders, for each observation, we clip a small section (typically 20 Angstroms) encompassing the best absorption feature. For example order nine uses the 4957 Angstrom feature, order fourteen uses the 5328 Angstrom feature, and order thirty-seven uses the 6563 Angstrom feature. We fit a Lorentzian to the absorption features and measure the wavelength shift of each observation in each order. For each observation, we sigma clip any outlier orders and use the average shift to calculate the velocity. Using the standard deviation of the measured shifts between the orders, we place error limits. The error in the RV standard is measured to 200 m/s, while the errors in the fainter targets are 1km/s. An example is shown in Figure 7; the best fit RV amplitude from the CHIRON data is 69.0 km/s and for the SOAR data is 64.7 km/s.

Figure 7.— Combined Radial Velocity curves for target EVRJ06456.10-823501.0. The red data points are from CHIRON RV data, and the blue points are SOAR data with the yellow and green curves of best fit.

4. Discoveries

In this section we present the discoveries beginning with the eclipsing binaries and variables. We measure the amplitudes of the variation, and for select targets we use the radial velocity measurement to estimate the companion mass. We show distributions of the periods, amplitudes, and magnitudes of the discoveries and summarize the important statistics of the search. All results are summarized in Tables 8-11.

4.1. Discovery candidates parameter estimations

Candidates passing the false positive checks (§ 2.5) are separated by variation type (eclipse-like or sinusoidal variable-like) and measured. The eclipsing binary light curves are folded on the best period and fit with a Gaussian using the approximate phase and depth from the visual inspection plot as the prior. For the variable candidates, we use the best sinusoidal fit from the LS detection. Given the large number of candidates, fitting the light curve amplitude consistently and automatically is key. An additional challenge is the degeneracy due to orbital angle, limb darkening, and orbital eccentricity. We find the Gaussian (for eclipsing binaries) and best sinusoidal fit from the LS detection (for variables) methods to be effective and efficient to measure the variability of the discoveries, while select targets with followup data can be fit with more complicated tools (see § 4.3). Figure 8 shows an example eclipsing binary and variable star fit.

Figure 8.— Top: Eclipsing binary discovery EVRJ131324.31-792126.3 folded on its 33.7 hour period representative of 100’s of Evryscope variable discoveries. Gray points are two minute cadence and yellow is the best Gaussian fit to measure depth. Bottom: variable star discovery EVRJ131228.85-782429.2 folded on its 136.665 hour period representative of 100’s of Evryscope variable discoveries. Gray points are two minute cadence and yellow is the best LS fit to measure amplitude.

4.2. Identification Spectra

For the discoveries with potential low-mass secondaries, we compare the SOAR ID spectra to ESO template spectra (available at www.eso.org), see Figure 9. After finding the closest matching spectra, we compare the results from the color differences classifier described in the previous section. Finally, we use the PyHammer (Covey et al., 2007) spectra fitting tool to confirm our fits. PyHammer uses empirical templates of known spectral types and performs a weighted least squares best fit to the input spectra and returns the estimated spectral type. For the the low-mass secondary eclipsing binaries, the results from the three methods are in agreement to within 1-2 in the luminosity class. The spectral types are shown in Table 8.

Figure 9.— An example low mass eclipsing binary discovery (EVRJ103938.18-872853.8) ID spectra taken with the Goodman Spectrograph on the 4.1m SOAR telescope at CTIO, Chile. The green line is a K5V template from the ESO library.

4.3. Radial Velocity - SOAR Data

We cross-correlate the SOAR spectra and measure the velocity shift throughout the period found in the Evryscope photometry. Using the color differences in § 2.3, and the stellar type, radii, and mass profiles from Pecaut & Mamajek (2013), we derive functions (using 1-d interpolation) to estimate the primary radius and mass. The secondary radius and mass are then determined using Keplarian/Newtonian calculations described in the following section. For this step, we assume a circular orbit, zero inclination angle, and no limb-darkening. We run a Monte Carlo (MC) simulation to estimate the radius and mass ranges. Due to the simplifying zero-inclination angle assumption and the uncertainty in the SOAR RV measurements, our mass calculations for the secondaries are lower limits. More detailed modeling will be addressed in future work. We discuss our final solutions in § 4.7. The results are listed in Table 8 and plots of the photometric and radial velocity light curves are shown in the appendix.

4.3.1 Secondary mass and radius determination

Photometry: From visual inspection of the candidate light curves, an initial guess is made for the transit phase and depth and fit with a Gaussian (Figure 10). The data is fit with a least squares minimization using scipy to measure the amplitude and phase.

Radial Velocity: An initial sine curve fit is made using a guess for the amplitude and zero point, while the phase and period are controlled by the transit time and the period found in the photometric light curve (Figure 10). The amplitude and zero point are used as inputs to a sine fitting function that uses a sine curve with a fixed phase and period. The function fits the data with a least squares fit; this is the gold line and it returns an RV of 56 km/s for target EVRJ110815.96-870153.8. This assumes a circular orbit and edge on geometry. We leave more detailed analysis with additional variables to future work.

4.3.2 MC best fit of mass and radius

Using the methods described in the previous section, we perform a Monte Carlo simulation (as described in Press et al. (2007)) to determine the best fit and distribution of the primary and secondary mass and radius.

From the Evryscope photometry, we use a bootstrap technique to leverage the very large number of epochs. We randomly choose half of the data points for each iteration with 5000 trials, and fit the data with a least squares minimization for each iteration. We also vary the radius of the primary for each trial by the range in (Pecaut & Mamajek, 2013) (spanning +/- 1 in numeric class). From the radial velocity data, we choose a random number in the error bar range of each of the data points (red) and fit the best sine curve (the silver curves) shown in Figure 10. We vary the mass of the primary for each trial by the error range in the estimated mass. The propagated results are shown in Figure 11.

Figure 10.— EVRJ110815.96-870153.8 K-dwarf eclipsing binary eclipse and radial velocity fit. Top: The best fit (yellow) to the Evryscope photometry using a Gaussian with an initial guess to measure the depth and determine secondary radius. Bottom: The best fit (green) to the SOAR RV data (red points) using a sine curve with an initial guess to measure the velocity and determine the secondary mass. The silver lines are the MC simulation to determine the best fit and error range.
Figure 11.— Primary and secondary mass and radius determined from our MC simulation. The top panels are the mass and radius of the primary in solar units, the bottom panels are the mass and radius of the secondary. The y-axis is the counts from the MC simulation totaling 5000 trials.

4.4. Search Statistics

Sorting by BLS sigma power and choosing only the top candidates greater than 10 sigma narrows the candidates to 5.6% (9104/163,584) of the filtered list. Visual inspection yields 7.3% (649/9104) actual variables from the BLS 10 sigma power list. The fraction of all discoveries to all searched is .40% (649/163,584). The false positive BLS rate is 5.2% (8455/163,584). Of 649 total variables detected, 346 are known in VSX. The total known periodic variables listed in VSX for the same sky area as the Evryscope Polar Search is 1928, giving a return of 17.9% (346/1928). There are 1050 known variables in the widest period ranges (3-720 hours) we searched, giving 33.0% return. There are 858 known variables in the period ranges (3-240 hours) we searched with BLS, giving 40.3% return. We add 303 new variables or 29% to the known variables in the region.

4.5. Eclipsing Binaries and Variables - Distribution of results

Histograms of the eclipsing binary discoveries are shown in Figure 12. We discovered a total of 168 eclipsing binaries; most periods found are 75 hours or less, and most amplitudes found are 5-25%. The results of the variables are shown in Figure 13, we found 135 total and most are smaller amplitudes and shorter periods.

Figure 12.— Histogram plots summarizing the eclipsing binary discovery results. We are sensitive to periods of several hundred hours and a large fraction of our discoveries are greater than 10% amplitude.
Figure 13.— Histogram plots summarizing the variable discovery results. A larger fraction of the variable star discoveries are small amplitude and short period.

4.6. Classification

The discovery classification results are shown in Figure 14. We find 267 are main sequence, 34 are giants, and two are not classified. Spectral type G is the most common, with the spectral types shown in Table 6. We find more giant variables (24) than giant eclipsers (10) as shown in Figure 14. Also shown are the discoveries by star size and spectral type compared to total targets searched (Table 7).

Classifier Spectral Type Number of Discoveries Percent
B 2 0.7
A 14 4.6
F 89 29.4
G 109 36.0
K 76 25.1
M 11 3.6
none 2 0.7
Total 303 100
Table 6Classification discovery results - spectral type
Classification Total Searched Number of Discoveries Percent
ms 114585 267 0.23
giant 40775 34 0.08
HSD 335 0 0.00
WD 21 0 0.00
O 20 0 0.00
B 331 2 0.60
A 4110 14 0.34
F 26102 89 0.34
G 49560 109 0.22
K 60964 76 0.12
M 14629 11 0.08
Table 7Classification discovery results - compared to total searched
Figure 14.— Classification results of the eclipsing binary and variable discoveries - Negative log likelihood plot-lines 1, 1.7, 2.8 shown. Top: Eclipsing Binaries. Bottom: Variables.

4.7. Eclipsing Binaries with low-mass secondaries

We identified seven of the eclipsing binary discoveries as hosting potential low-mass secondaries and found that four are less than .25 solar mass. Three of the systems are fully eclipsing binaries (p = 12.3 to 25.9 hours) with dwarf primaries (SpTp = G5V, K4V, K5V) and M-dwarf secondaries (mass = .06 - .20 ). The other three systems are grazing eclipses with (p = 20.8 to 137.1 hours) with dwarf primaries (SpTp = G8V, K2V, K7V), M-dwarf secondaries (mass = .24 - .37 ) and minimum radii (r = .20 to .26 ). Table 8 presents a list of all low-mass secondary targets. Also included is a likely visual binary EVRJ114225.51-793121.0 (separated in SOAR observations) and EVRJ211905.47-865829.3 a grazing EB with nearly identical primary and secondaries.

5. Summary

The Evryscope was deployed to CTIO in May 2015 and has been operational since that time. We conducted a variability search of the southern polar area using the first 6-months of available data and by selecting the brighter stars (mv 14.5) and limiting the declination range ( to ). We sorted by detection power and visually searched the top 5 % for variability. We recovered 346 known variables and discovered 303 new variables, including 168 eclipsing binaries six of which we identify as low-mass (.06 - .37 ) secondaries with K-dwarf primaries. We encourage the community to followup further on these targets. We measured amplitudes, periods, and variability type and provide a catalog of all discoveries in the Appendix.

This research was supported by the NSF CAREER grant AST-1555175 and the Research Corporation Scialog grants 23782 and 23822. HC is supported by the NSFGRF grant DGE-1144081. BB is supported by the NSF grant AST-1812874. OF and DdS acknowledge support by the Spanish Ministerio de Economía y Competitividad (MINECO/FEDER, UE) under grants AYA2013-47447-C3-1-P, AYA2016-76012-C3-1-P, MDM-2014-0369 of ICCUB (Unidad de Excelencia ’María de Maeztu’). The Evryscope was constructed under NSF/ATI grant AST-1407589.

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Note: Columns 1-4 are identification numbers, right ascension and declination, and magnitude. Columns 5-6 are the reduced proper motion (RPM) and color difference (B-V) which we use to estimate the star size and spectral type (see Section 4.2.1). Columns 7-8 are the spectral type from the classifier and from the SOAR ID spectra. Column is 9 the period found in hours, columns 10-17 are the mass of the primary and secondary derived from SOAR radial velocity, along with the one sigma error.

Appendix A
List of Eclipsing Binary discoveries with low-mass secondaries

\textESID \textRA \textDec \textMv \textRPM \textB-V \textsptp \textSOAR \textperiod \textmp \text+/- \textrp \text+/- \textms \text+/- \textrs \text+/-
\text \text(J2000) \text(J2000) \text \text \text \text \text \text(hours) \text \text \text \text \text \text \text \text

EVRJ06456.10-823501.0
101.2754 -82.5836 11.80 8.19 1.12 G9V G8V 63.53 0.90 .04 .876 .059 .37 .02 .26 .01
EVRJ103938.18-872853.8 159.9091 -87.4816 13.78 12.09 1.07 K6V K7V 20.85 0.63 .04 .655 .037 .24 .02 .20 .01
EVRJ110815.96-870153.8 167.0665 -87.0316 12.68 9.77 0.84 K3V K4V 12.28 0.72 .04 .725 .028 .20 .03 .35 .02
EVRJ165050.23-843634.6 252.7093 -84.6096 13.84 11.54 1.09 K4V K5V 25.86 0.68 .05 .699 .037 .06 .01 .40 .02
EVRJ180826.26-842418.0 272.1094 -84.405 13.51 9.81 0.57 G6V G5V 17.31 0.98 .01 .983 .043 .19 .01 .56 .01
EVRJ184114.02-843436.8 280.3084 -84.5769 13.34 10.19 0.91 K3V K2V 137.16 0.78 .07 .764 .052 .28 .03 .24 .02

Table 8Eclipsing Binary discoveries with low-mass secondaries
\textESID \textRA(J2000) \textDec(J2000) \textMv \textRPM \textB-V \textsptp \textSOAR \textperiod \textnote
EVRJ053513.22-774248.2 83.8051 -77.7134 12.05 9.00 1.03 K1V G7V 50.96 Nearly identical primary and secondary
EVRJ114225.51-793121.0 175.6063 -79.5225 12.81 12.66 0.95 K3V 91.82 Visual Double in SOAR image
EVRJ211905.47-865829.3 319.7728 -86.9748 13.97 10.60 0.95 K6V K5V 18.61 Nearly identical primary and secondary
Table 9Peculiar Eclipsing Binary discoveries

Appendix B
List of all variable discoveries

Note: Columns 1-5 are identification numbers, right ascension and declination, and magnitude. Columns 6-9 are the reduced proper motion (RPM) and color difference (B-V) which we use to estimate the star size and spectral type (see Section 4.2.1). Columns 10 and 11 are the period found in hours, and the amplitude of the variability in magnitudes.

\textESID \textAPASSID \textRA \textDec \textMv \textRPM \textB-V \textsize \textspec \textperiod \textamplitude
\text \text \text(J2000) \text(J2000) \text \text \text \text \text \text(hours) \text( mag)
EVRJ000411.09-862200.5 36032704 1.0462 -86.3668 12.91 10.27 1.00 ms K3V 3.7893 0.048
EVRJ004033.86-852556.3 36034219 10.1411 -85.4323 13.88 8.56 0.57 ms G0V 11.9673 0.108
EVRJ010354.79-845024.4 36034985 15.9783 -84.8401 13.07 9.70 1.06 ms K6V 66.0947 0.067
EVRJ010428.68-752821.7 36397673 16.1195 -75.4727 12.74 8.80 0.44 ms F1V 4.4661 0.056
EVRJ010628.13-821135.5 36109108 16.6172 -82.1932 10.79 6.12 0.29 ms A1V 11.5043 0.021
EVRJ011610.92-853620.5 36033299 19.0455 -85.6057 13.56 9.95 0.82 ms G8V 4.0396 0.061
EVRJ013112.26-754727.2 36147371 22.8011 -75.7909 12.69 7.03 1.18 giant K6 565.2369 0.057
EVRJ014744.62-750722.8 36149487 26.9359 -75.1230 13.43 9.75 0.34 ms F2V 7.9793 0.022
EVRJ015507.25-842951.4 36036929 28.7802 -84.4976 12.03 1.82 1.13 giant K4 207.4862 0.048
EVRJ020014.59-824041.2 36042924 30.0608 -82.6781 13.68 11.05 0.66 ms F3V 3.8633 0.048
EVRJ020627.84-854259.4 36017206 31.6160 -85.7165 13.10 5.95 0.58 ms F1V 4.2580 0.049
EVRJ021301.18-850613.7 36035338 33.2549 -85.1038 14.18 8.74 0.74 ms F9V 14.5239 0.044
EVRJ022525.78-871212.6 36013743 36.3574 -87.2035 13.72 5.85 0.58 ms F9V 5.4833 0.128
EVRJ023633.12-841813.7 36036143 39.1380 -84.3038 12.99 8.31 1.55 giant M 3.0339 0.026
EVRJ024105.09-852056.8 36017369 40.2712 -85.3491 10.84 6.65 1.16 giant M0 383.3562 0.031
EVRJ031048.98-774610.2 36092849 47.7041 -77.7695 13.43 9.01 0.44 ms F1V 5.5437 0.107
EVRJ031437.25-812625.4 36050275 48.6552 -81.4404 11.83 10.24 0.65 ms G6V 22.5360 0.038
EVRJ032340.58-833811.4 36027122 50.9191 -83.6365 11.73 5.37 0.74 giant G8 3.6540 0.115
EVRJ032442.50-780853.9 36092101 51.1771 -78.1483 11.38 6.51 0.45 ms F5V 4.6764 0.022
EVRJ034027.74-771628.9 36093941 55.1156 -77.2747 11.26 8.02 0.95 ms K4V 527.5689 0.041
EVRJ035648.91-810628.4 36049584 59.2038 -81.1079 12.66 8.40 0.28 ms A9V 10.7653 0.035
EVRJ040145.72-794920.3 36086352 60.4405 -79.8223 13.50 7.80 0.45 ms F1V 4.3002 0.063
EVRJ041253.57-812919.7 36066969 63.2232 -81.4888 13.91 12.69 0.81 ms G 5.5612 0.061
EVRJ041719.75-803105.5 36068185 64.3323 -80.5182 11.38 6.23 1.61 giant M 583.9396 0.066
EVRJ042003.70-803034.9 36068172 65.0154 -80.5097 12.35 5.20 1.21 giant M0 423.7755 0.158
EVRJ042008.90-790539.1 36088224 65.0371 -79.0942 13.38 8.98 0.58 ms G2V 5.0229 0.085
EVRJ044109.58-774556.5 36081406 70.2899 -77.7657 10.19 5.99 1.55 giant M 426.2952 0.046
EVRJ044130.53-761227.0 36099120 70.3772 -76.2075 13.13 8.14 0.96 ms K3V 3.3209 0.065
EVRJ044735.35-775219.9 36081082 71.8973 -77.8722 11.30 8.69 0.50 ms G1V 3.3276 0.026
EVRJ050012.05-811743.4 36061636 75.0502 -81.2954 12.48 8.09 0.86 ms G9V 3.4901 0.076
EVRJ051213.46-761626.0 36201329 78.0561 -76.2739 12.99 9.39 0.60 ms G1V 6.3173 0.074
EVRJ051707.51-851934.3 36019388 79.2813 -85.3262 14.56 11.54 -0.09 ms F0 17.4600 0.138
EVRJ053531.56-792945.6 36073410 83.8815 -79.4960 13.45 10.57 0.75 ms K0V 4.8621 0.061
EVRJ053802.81-775651.0 36077655 84.5117 -77.9475 12.60 9.47 0.50 ms G0V 6.8030 0.048
EVRJ053857.96-840027.0 36021952 84.7415 -84.0075 13.18 8.45 0.69 ms K2V 3.7923 0.120
EVRJ053921.36-823350.0 36059589 84.8390 -82.5639 11.37 7.67 1.15 giant K5 294.1877 0.042
EVRJ055148.46-761552.6 36198063 87.9519 -76.2646 11.87 4.41 0.89 giant K4 98.4013 0.048
EVRJ055157.14-810831.9 36062821 87.9881 -81.1422 12.74 9.85 0.69 ms F5V 4.9623 0.208
EVRJ055856.16-785946.3 36075508 89.7340 -78.9962 13.06 9.65 0.98 ms K4V 3.1286 0.078
EVRJ060627.19-841156.4 37958830 91.6133 -84.1990 14.67 9.83 0.01 ms F0 6.8412 0.142
EVRJ061823.95-780812.1 38123485 94.5998 -78.1367 10.11 1.02 1.65 giant M 544.3723 0.064
EVRJ062714.64-794039.0 38117589 96.8110 -79.6775 13.19 8.31 0.66 ms G2V 5.1073 0.080
EVRJ064843.42-793349.3 38116489 102.1809 -79.5637 13.01 9.15 1.24 giant M1 534.1532 0.095
EVRJ070444.11-752812.7 39299363 106.1838 -75.4702 13.39 8.69 0.69 ms G6V 4.9315 0.098
EVRJ070553.98-813347.5 38085679 106.4749 -81.5632 13.28 12.32 0.78 ms G 208.1137 0.080
EVRJ070751.77-861600.8 37950806 106.9657 -86.2669 13.88 10.26 0.70 ms G6V 4.4384 0.137
EVRJ071040.15-854213.0 37951603 107.6673 -85.7036 13.28 10.46 0.68 ms G7V 14.0116 0.042
EVRJ071054.50-775214.5 38120922 107.7271 -77.8707 12.75 8.38 0.98 ms K6V 119.7744 0.061
EVRJ072150.93-814705.3 38084504 110.4622 -81.7848 12.48 3.69 1.02 giant K4 569.9960 0.073
EVRJ072411.33-865020.0 37932897 111.0472 -86.8389 12.06 7.72 0.80 ms G8V 3.8106 0.036
EVRJ074325.18-780127.5 38108272 115.8549 -78.0243 11.58 7.86 0.50 ms F7V 3.3874 0.029
EVRJ080401.49-824005.9 37961948 121.0062 -82.6683 11.55 7.70 0.97 giant K6 4.2669 0.028
EVRJ083403.17-811922.4 37975768 128.5132 -81.3229 13.54 -1.46 0.93 giant K4 5.1269 0.047
EVRJ084757.72-781627.1 38100581 131.9905 -78.2742 13.17 12.13 1.00 ms K4V 266.6131 0.064
EVRJ085910.42-813844.9 37967866 134.7934 -81.6458 13.69 9.23 0.41 ms A4V 6.5406 0.073
EVRJ090816.25-840058.0 37945248 137.0677 -84.0161 13.73 9.56 0.60 ms G1V 4.1451 0.159
EVRJ092130.62-780552.1 38047442 140.3776 -78.0978 12.06 7.58 1.17 giant K8 14.2823 0.036
EVRJ092934.58-883002.5 37925391 142.3941 -88.5007 12.70 11.83 1.04 ms K4V 14.9276 0.072
EVRJ094546.82-845901.7 37943780 146.4451 -84.9838 11.40 4.88 0.34 ms F4V 10.5364 0.023
EVRJ094914.47-765810.9 38050263 147.3103 -76.9697 11.94 7.38 0.67 ms G1V 4.7866 0.047
EVRJ095103.38-775148.6 38042145 147.7641 -77.8635 12.64 8.52 0.39 ms A9V 3.3413 0.052
EVRJ103805.95-823919.8 37947694 159.5248 -82.6555 12.25 9.15 1.12 ms K5V 188.7851 0.037
EVRJ103843.68-841342.6 37941994 159.6820 -84.2285 14.34 9.32 0.82 ms K1V 3.4514 0.095
EVRJ104338.88-812945.6 37997111 160.9120 -81.4960 13.10 12.81 1.05 ms K 9.2306 0.071
EVRJ104928.30-840709.1 37941961 162.3679 -84.1192 14.41 10.25 0.66 ms G5V 4.3652 0.071
EVRJ110736.10-801214.8 37995054 166.9004 -80.2041 13.20 8.60 0.65 ms G6V 5.2520 0.103
EVRJ112926.09-790251.7 38014623 172.3587 -79.0477 12.82 10.01 0.74 ms K3V 3.2616 0.032
EVRJ113221.60-773934.2 38021372 173.0900 -77.6595 12.62 7.52 0.64 ms F6V 2.2946 0.089
EVRJ113648.94-770820.8 38274528 174.2039 -77.1391 13.07 10.70 0.87 ms K2V 40.9161 0.049
EVRJ113950.33-823313.7 37986809 174.9597 -82.5538 12.70 8.23 0.69 ms G9V 12.2413 0.045
EVRJ121247.35-782517.8 47439342 183.1973 -78.4216 12.77 8.67 0.75 ms G8V 7.1346 0.071
EVRJ124042.22-852021.1 47199888 190.1759 -85.3392 12.33 7.11 0.73 ms K1V 51.6660 0.093
EVRJ124521.96-772053.5 47445563 191.3415 -77.3482 13.16 8.11 0.84 ms G8V 5.0777 0.089
EVRJ124614.88-851715.4 47199917 191.5620 -85.2876 13.75 12.43 0.71 ms G8V 3.6741 0.109
EVRJ124711.98-784313.8 191.7999 -78.7205 4.0535 0.100
EVRJ125757.58-773925.9 47442656 194.4899 -77.6572 12.88 9.03 0.97 ms K2V 13.6397 0.065
EVRJ130148.17-831417.9 47209493 195.4507 -83.2383 12.92 8.20 1.49 giant M3 121.8445 0.068
EVRJ130916.78-775637.7 47433337 197.3199 -77.9438 13.61 9.88 0.99 ms K4V 7.8242 0.153
EVRJ131216.99-803701.6 47370231 198.0708 -80.6171 11.08 5.79 0.72 ms G9V 7.9778 0.008
EVRJ131228.85-782429.2 198.1202 -78.4081 136.7678 0.047
EVRJ131248.91-794104.9 47374655 198.2038 -79.6847 10.48 7.50 1.04 giant K4 5.5061 0.015
EVRJ134036.82-810805.3 47361846 205.1534 -81.1348 13.08 10.74 0.90 ms K0V 3.4605 0.038
EVRJ134909.43-795116.2 47384377 207.2893 -79.8545 12.69 9.02 0.96 ms K1V 4.4504 0.032
EVRJ135636.96-852904.9 47198519 209.1540 -85.4847 13.04 9.29 0.67 ms G8V 5.5375 0.039
EVRJ135847.35-753616.9 47466750 209.6973 -75.6047 12.64 4.90 0.55 ms F8V 13.0043 0.081
EVRJ135948.38-773732.9 47408549 209.9516 -77.6258 12.25 7.12 0.59 ms G0V 6.4798 0.063
EVRJ142647.47-774451.4 47396223 216.6978 -77.7476 13.36 11.36 0.39 ms F0 3.0402 0.110
EVRJ143601.82-832825.3 47204308 219.0076 -83.4737 13.62 9.82 0.52 ms F2V 6.8145 0.117
EVRJ150519.75-753054.0 47509935 226.3323 -75.5150 10.53 4.96 1.03 giant K3 6.7618 0.036
EVRJ151320.26-833642.1 47215664 228.3344 -83.6117 11.52 7.98 0.94 ms K3V 202.2009 0.027
EVRJ152213.03-850853.2 47177228 230.5543 -85.1481 12.59 7.32 0.80 ms K0V 44.3809 0.081
EVRJ152422.85-765355.7 47316062 231.0952 -76.8988 10.58 5.74 0.37 ms A7V 3.3395 0.042
EVRJ154618.22-824405.3 47194254 236.5759 -82.7348 13.77 11.09 0.70 ms F6V 4.5592 0.091
EVRJ155137.18-824810.8 47194265 237.9049 -82.8030 12.70 8.58 0.44 ms F5V 8.0204 0.052
EVRJ155320.98-824654.8 47193653 238.3374 -82.7819 12.98 5.06 0.49 ms F8V 10.0283 0.044
EVRJ155645.07-802819.2 47288797 239.1878 -80.4720 11.75 6.08 0.03 ms B9V 116.9858 0.032
EVRJ155942.19-824514.8 47193923 239.9258 -82.7541 12.12 5.33 0.58 ms G0V 14.6855 0.038
EVRJ162259.47-805820.3 47254401 245.7478 -80.9723 11.73 8.04 0.55 ms F1V 4.8019 0.048
EVRJ162554.07-854342.2 47181390 246.4753 -85.7284 13.37 11.38 0.53 ms F5V 5.0865 0.083
EVRJ163031.56-834849.7 47191010 247.6315 -83.8138 13.35 8.31 0.63 ms G0V 12.2352 0.089
EVRJ163252.27-832748.2 47191300 248.2178 -83.4634 12.51 6.51 0.94 giant K6 150.4504 0.092
EVRJ163852.99-843744.0 47182121 249.7208 -84.6289 13.21 10.49 0.80 ms K2V 32.2767 0.048
EVRJ165216.22-825416.6 47186184 253.0676 -82.9046 13.94 9.91 0.45 ms F8V 15.1777 0.367
EVRJ165457.58-775615.7 47277017 253.7399 -77.9377 12.87 5.49 0.30 ms F1V 7.4973 0.069
EVRJ171344.76-825649.9 47243917 258.4365 -82.9472 13.44 7.62 0.51 ms F3V 5.2027 0.075
EVRJ171929.86-800819.0 47250893 259.8744 -80.1386 12.85 8.54 0.66 ms F3V 4.5325 0.071
EVRJ171939.84-852016.4 47180802 259.9160 -85.3379 14.67 13.00 1.39 ms K6V 3.2852 0.072
EVRJ172044.45-811413.2 47249474 260.1852 -81.2370 10.80 7.87 0.71 ms G1V 12.2459 0.019
EVRJ173256.64-833249.9 47242117 263.2360 -83.5472 14.11 10.70 0.42 ms F8V 7.1155 0.135
EVRJ173854.53-825617.2 47243150 264.7272 -82.9381 13.40 11.05 0.34 ms F3 6.2448 0.087
EVRJ174904.92-853647.9 47180335 267.2705 -85.6133 13.89 7.33 0.69 ms G1V 4.3006 0.093
EVRJ175053.26-794941.5 47264410 267.7219 -79.8282 11.96 8.79 0.84 ms K0V 134.4649 0.051
EVRJ175230.22-785048.8 47268452 268.1259 -78.8469 12.01 7.88 0.32 ms F1V 8.3917 0.041
EVRJ175340.68-753831.9 47710653 268.4195 -75.6422 12.35 7.83 0.72 ms G8V 5.0187 0.089
EVRJ175347.33-854135.5 47180309 268.4472 -85.6932 12.81 8.92 0.54 ms F9V 4.4685 0.051
EVRJ180742.10-824651.6 57161419 271.9254 -82.7810 14.02 10.10 0.39 ms F0V 7.0683 0.224
EVRJ181807.06-800525.4 57185282 274.5294 -80.0904 11.04 4.80 1.59 giant M 370.8165 0.025
EVRJ182044.62-754759.6 57415585 275.1859 -75.7999 11.50 5.52 0.50 ms F7V 3.4102 0.026
EVRJ182608.74-864925.3 57090707 276.5364 -86.8237 14.41 10.07 0.90 ms K2V 4.2908 0.130
EVRJ183802.93-811827.4 57166241 279.5122 -81.3076 10.90 10.24 0.83 ms K1V 48.6808 0.051
EVRJ192029.45-860534.4 57091324 290.1227 -86.0929 13.27 9.80 0.35 ms A7V 6.0642 0.025
EVRJ195449.01-840216.4 57097667 298.7042 -84.0379 12.73 10.22 0.73 ms G9V 5.5082 0.063
EVRJ200551.67-820620.5 57105858 301.4653 -82.1057 13.63 8.88 0.71 ms G9V 5.5203 0.087
EVRJ203404.49-871549.0 57072262 308.5187 -87.2636 13.87 9.50 0.36 ms F1V 4.8050 0.095
EVRJ204931.97-845034.4 57076995 312.3832 -84.8429 14.21 15.14 0.72 ms G 4.4090 0.078
EVRJ205037.49-774637.2 57175393 312.6562 -77.7770 11.00 6.99 0.46 ms F8V 3.3040 0.023
EVRJ205225.92-855742.5 57074554 313.1080 -85.9618 14.07 12.40 0.97 ms K3V 133.5587 0.062
EVRJ205802.14-793349.7 57118550 314.5089 -79.5638 12.97 8.41 0.61 ms F7V 6.2271 0.048
EVRJ210729.26-763906.5 57215979 316.8719 -76.6518 13.58 10.43 0.23 ms F3 4.7894 0.024
EVRJ210937.03-785828.2 57118801 317.4043 -78.9745 12.99 6.25 1.07 giant K4 180.8028 0.051
EVRJ213403.58-865953.5 57070601 323.5149 -86.9982 12.69 8.97 0.60 ms F9V 4.7182 0.082
EVRJ215744.06-790828.7 57141740 329.4336 -79.1413 13.51 7.75 1.11 giant K5 10.6690 0.148
EVRJ220737.90-813510.0 57090539 331.9079 -81.5861 11.93 14.35 0.90 ms G 14.9246 0.016
EVRJ223616.97-773616.2 57136977 339.0707 -77.6045 13.55 8.89 1.15 giant K6 2074.7965 0.299
EVRJ235019.03-840248.8 57081420 357.5793 -84.0469 11.40 7.79 0.39 ms F5V 5.8564 0.020
Table 10Variable Star discoveries

Note: Columns 1-5 are identification numbers, right ascension and declination, and magnitude. Columns 6-9 are the reduced proper motion (RPM) and color difference (B-V) which we use to estimate the star size and spectral type (see Section 4.2.1). Columns 10 and 11 are the period found in hours, and the fractional eclipse depth from normalized flux.

\textESID \textAPASSID \textRA \textDec \textMv \textRPM \textB-V \textsize \textspec \textperiod \textdepth
\text \text \text(J2000) \text(J2000) \text \text \text \text \text \text(hours) \text(fractional)

EVRJ002445.62-784031.1 36136315 6.1901 -78.6753 11.78 6.21 0.56 ms G9V 109.3650 0.118
EVRJ004748.46-754942.6 36397363 11.9519 -75.8285 11.79 5.20 0.52 ms F9V 157.4220 0.229
EVRJ005637.85-782127.0 36134521 14.1577 -78.3575 12.39 8.65 0.63 ms G5V 74.0640 0.273
EVRJ010726.33-774753.2 36135199 16.8597 -77.7981 12.96 8.26 0.30 ms A5V 15.7654 0.148
EVRJ012740.66-841645.1 36035232 21.9194 -84.2792 13.25 7.92 0.65 ms G1V 20.0793 0.109
EVRJ013849.70-842426.6 36037250 24.7071 -84.4074 13.24 14.24 0.90 ms K 198.5830 0.172
EVRJ014115.60-800737.9 36113485 25.3150 -80.1272 14.97 9.44 0.23 ms G2V 22.1010 0.187
EVRJ023605.38-852430.6 36017318 39.0224 -85.4085 12.75 7.47 0.54 ms F1V 118.7710 0.225
EVRJ024203.26-750224.0 36161150 40.5136 -75.0400 11.98 6.76 0.31 ms F4V 30.9563 0.243
EVRJ024438.52-835122.7 36036709 41.1605 -83.8563 12.50 8.28 0.72 ms G8V 25.9882 0.303
EVRJ030147.71-761211.5 36155585 45.4488 -76.2032 10.14 2.93 0.92 giant K3 68.3947 0.065
EVRJ032000.70-760821.5 36156333 50.0029 -76.1393 13.15 10.03 0.59 ms F9V 79.5734 0.202
EVRJ032206.70-752842.6 36157171 50.5279 -75.4785 12.91 9.86 1.00 ms K4V 7.3698 0.147
EVRJ032355.42-783922.7 36091552 50.9809 -78.6563 11.40 8.26 0.62 ms F8V 22.0561 0.215
EVRJ033317.16-792812.7 36087183 53.3215 -79.4702 13.30 9.82 0.38 ms A8V 21.1649 0.095
EVRJ043634.42-863132.9 36012543 69.1434 -86.5258 14.30 11.43 0.76 ms F1V 8.1408 0.221
EVRJ043913.51-855448.6 36019521 69.8063 -85.9135 10.23 8.34 0.82 ms G3V 36.8604 0.054
EVRJ043932.02-794339.0 36070451 69.8834 -79.7275 11.53 6.84 0.59 ms F9V 61.6244 0.117
EVRJ044501.22-771324.6 36081663 71.2551 -77.2235 11.33 10.78 0.59 ms F9V 85.9685 0.217
EVRJ044545.98-770625.6 36081683 71.4416 -77.1071 13.16 4.63 0.30 ms F1V 17.5505 0.387
EVRJ045203.19-853702.6 36019627 73.0133 -85.6174 12.49 10.39 0.09 ms F9 30.2652 0.092
EVRJ045807.78-772037.7 36082386 74.5324 -77.3438 12.58 8.92 0.69 ms G6V 38.1567 0.379
EVRJ050731.01-760919.8 36201646 76.8792 -76.1555 13.38 10.14 0.64 ms G2V 10.7099 0.065
EVRJ052042.43-753131.8 36202184 80.1768 -75.5255 11.06 3.14 0.05 ms A1V 64.8150 0.322
EVRJ053006.26-811232.4 36062995 82.5261 -81.2090 11.95 7.32 0.49 ms F8V 45.1688 0.217
EVRJ053504.90-834045.5 36058479 83.7704 -83.6793 12.40 9.23 0.41 ms F9V 35.7837 0.130
EVRJ053541.69-753728.6 36199669 83.9237 -75.6246 12.75 8.21 0.81 ms K2V 31.2876 0.113
EVRJ054814.83-772912.5 36195270 87.0618 -77.4868 13.85 9.49 0.47 ms G3V 17.3382 0.611
EVRJ055000.48-780018.7 36076437 87.5020 -78.0052 13.60 8.79 0.44 ms G0V 22.1010 0.243
EVRJ055918.00-861604.1 36018864 89.8250 -86.2678 12.51 8.16 0.54 ms G6V 16.0350 0.101
EVRJ060300.82-763227.2 39301921 90.7534 -76.5409 13.01 9.37 0.63 ms G6V 35.3178 0.368
EVRJ060956.86-842635.9 37958393 92.4869 -84.4433 12.32 8.02 0.03 ms F1 64.7228 0.106
EVRJ061730.58-853507.8 37952210 94.3774 -85.5855 13.76 9.17 -0.23 ms A0 19.5420 0.064
EVRJ061942.89-872037.7 37932270 94.9287 -87.3438 12.77 8.30 0.53 ms G0V 11.7925 0.176
EVRJ062614.11-812323.6 38088026 96.5588 -81.3899 12.84 9.76 0.45 ms F8V 22.1397 0.057
EVRJ065136.84-775609.6 38124833 102.9035 -77.9360 11.32 4.24 0.66 giant G5 154.2250 0.158
EVRJ065350.93-840014.0 37957964 103.4622 -84.0039 12.91 9.75 0.60 ms K0V 45.7400 0.212
EVRJ065609.43-810853.5 38088326 104.0393 -81.1482 13.49 12.36 0.71 ms K0V 13.4842 0.116
EVRJ070327.70-813323.4 38085967 105.8654 -81.5565 13.29 9.59 0.45 ms A9V 22.2891 0.357
EVRJ071503.55-792949.6 38091930 108.7648 -79.4971 13.08 7.32 0.41 ms G1V 35.9922 0.599
EVRJ071744.33-854505.4 37951607 109.4347 -85.7515 13.09 6.75 0.77 ms K3V 101.6850 0.314
EVRJ071748.29-844104.6 37953026 109.4512 -84.6846 14.52 10.35 0.39 ms G3V 7.6682 0.184
EVRJ071938.74-794442.4 38091354 109.9114 -79.7451 12.18 5.14 0.39 ms F2V 20.8352 0.107
EVRJ072710.08-815757.2 38084205 111.7920 -81.9659 13.73 5.29 0.54 ms G9V 41.0657 1.000
EVRJ073157.14-815943.4 38084148 112.9881 -81.9954 9.93 6.74 0.19 ms F1V 60.0174 0.074
EVRJ074851.14-844938.3 37953692 117.2131 -84.8273 13.18 8.90 0.56 ms F 23.3998 0.115
EVRJ075512.70-831036.1 37960834 118.8029 -83.1767 12.40 8.05 0.54 ms F8V 68.9536 0.237
EVRJ080959.06-765721.2 38113121 122.4961 -76.9559 12.33 8.08 0.52 ms F8V 109.3650 0.257
EVRJ082431.85-771708.5 38110818 126.1327 -77.2857 10.99 6.53 0.55 ms G1V 46.0730 0.162
EVRJ083235.69-814208.3 37974452 128.1487 -81.7023 13.27 10.91 0.48 ms G7V 22.5004 0.273
EVRJ083610.66-822751.1 37966550 129.0444 -82.4642 12.08 7.46 1.30 giant K 15.4668 0.066
EVRJ084853.45-755536.1 38163662 132.2227 -75.9267 10.01 5.34 0.28 ms F8V 59.1296 0.081
EVRJ085629.66-833101.6 37964619 134.1236 -83.5171 12.47 4.96 0.51 ms F1V 59.1383 0.250
EVRJ090851.91-835702.5 37945252 137.2163 -83.9507 13.29 8.89 0.42 ms F1V 10.8306 0.337
EVRJ091345.72-822820.3 37965615 138.4405 -82.4723 11.75 6.95 0.60 ms G5V 43.7381 0.182
EVRJ092241.74-833802.0 37945406 140.6739 -83.6339 13.77 8.53 0.54 ms G0V 4.4717 0.145
EVRJ093342.00-865534.0 37929506 143.4250 -86.9261 13.03 8.34 0.75 ms G9V 106.1730 0.347
EVRJ093554.48-763543.8 38051623 143.9770 -76.5955 13.59 4.56 0.67 giant F6 35.6159 0.248
EVRJ093619.37-811153.2 37970069 144.0807 -81.1981 13.13 10.33 0.82 ms K1V 129.2850 0.239
EVRJ094641.04-781309.8 38041815 146.6710 -78.2194 13.00 7.60 0.56 ms G2V 33.1735 0.231
EVRJ095515.41-830705.9 37948497 148.8142 -83.1183 12.88 9.48 0.59 ms G5V 151.7600 0.345
EVRJ100205.04-814503.2 37950302 150.5210 -81.7509 12.79 6.15 0.30 ms F5V 67.4233 0.196
EVRJ100426.40-803846.0 38032563 151.1100 -80.6461 13.09 7.01 0.59 ms F6V 28.3768 0.095
EVRJ100649.61-801046.9 38033725 151.7067 -80.1797 12.65 12.88 0.51 ms F4 44.7694 0.205
EVRJ101423.47-774932.5 38040775 153.5978 -77.8257 13.29 13.24 1.05 ms K 35.1543 0.083
EVRJ103443.51-775813.1 38026295 158.6813 -77.9703 10.88 10.79 0.64 ms G4V 20.9396 0.029
EVRJ105421.24-782234.7 38024299 163.5885 -78.3763 12.41 10.76 1.00 ms K6V 20.3265 0.081
EVRJ105445.86-785351.4 38023795 163.6911 -78.8976 12.69 10.56 0.61 ms G9V 62.0270 0.169
EVRJ110105.30-864038.6 37928838 165.2721 -86.6774 13.73 9.45 0.58 ms G7V 42.5378 0.096
EVRJ110815.96-870153.8 37928658 167.0665 -87.0316 12.68 9.77 0.84 ms K3V 12.2767 0.230
EVRJ111244.66-830219.7 37987113 168.1861 -83.0388 13.18 7.43 0.49 ms G7V 17.3342 0.277
EVRJ111447.02-811836.7 37992322 168.6959 -81.3102 12.41 10.99 0.62 ms G6V 35.0246 0.171
EVRJ112755.49-842109.7 37940976 171.9812 -84.3527 13.81 9.72 0.34 ms F8V 8.4533 0.252
EVRJ114502.30-771447.0 38272180 176.2596 -77.2464 12.29 7.73 0.78 ms G4V 39.2816 0.209
EVRJ114706.02-835834.7 37940896 176.7751 -83.9763 12.90 9.39 0.87 ms K3V 54.3094 0.091
EVRJ120501.68-852738.9 47205316 181.2570 -85.4608 12.73 9.41 0.48 ms F6V 103.6910 0.144
EVRJ120856.86-770450.9 48482910 182.2369 -77.0808 13.21 6.55 0.92 giant K2 40.7350 0.275
EVRJ122230.55-772324.4 48480777 185.6273 -77.3901 11.60 8.26 0.77 ms K0V 100.8010 0.248
EVRJ125134.08-790133.2 47428800 192.8920 -79.0259 10.32 9.09 0.57 ms G8V 114.7710 0.157
EVRJ125505.76-851321.7 47199929 193.7740 -85.2227 11.73 6.37 0.38 ms F3V 30.5570 0.083
EVRJ131324.31-792126.3 47375486 198.3513 -79.3573 12.28 6.37 0.31 ms F7V 33.7030 0.228
EVRJ131504.46-763140.1 47445061 198.7686 -76.5278 12.08 7.84 0.69 ms G9V 21.5120 0.055
EVRJ131906.34-840040.3 47206948 199.7764 -84.0112 11.58 5.87 1.07 giant K3 23.8310 0.086
EVRJ131909.89-834711.0 47207423 199.7912 -83.7864 12.74 8.79 0.40 ms F0V 15.7910 0.368
EVRJ132210.78-790543.1 47376460 200.5449 -79.0953 13.29 9.33 0.48 ms F8V 37.6460 0.108
EVRJ132915.46-763040.0 47457701 202.3144 -76.5111 12.65 8.42 0.53 ms F 19.3470 0.205
EVRJ133026.14-852532.2 47199044 202.6089 -85.4256 12.55 8.51 1.08 giant K5 12.6020 0.038
EVRJ133347.26-833757.7 47210054 203.4469 -83.6327 13.39 8.35 0.56 ms G3V 17.8470 0.057
EVRJ133848.86-834425.4 47209861 204.7036 -83.7404 13.58 10.16 0.58 ms G7V 71.4170 0.455
EVRJ134321.74-845650.6 47199233 205.8406 -84.9474 12.78 8.18 0.55 ms G5V 9.7290 0.365
EVRJ135211.09-844337.9 47201544 208.0462 -84.7272 13.73 9.53 0.60 ms G8V 27.8460 0.382
EVRJ135212.36-785333.7 47399995 208.0515 -78.8927 12.07 6.17 0.59 ms F9V 61.0300 0.443
EVRJ135431.18-815457.2 47214874 208.6299 -81.9159 11.19 6.69 0.26 ms A4V 79.1580 0.050
EVRJ135907.01-842606.7 47203065 209.7792 -84.4352 13.05 7.80 0.42 ms F7V 14.1710 0.324
EVRJ140102.93-823656.9 47212126 210.2622 -82.6158 10.79 9.06 0.75 ms K0V 43.2894 0.188
EVRJ140546.42-835919.0 47203290 211.4434 -83.9886 11.29 8.38 0.76 ms G9V 33.0580 0.044
EVRJ141018.94-830332.8 47211232 212.5789 -83.0591 11.89 4.11 0.21 ms F1V 18.8112 0.123
EVRJ141043.54-834026.4 47204533 212.6814 -83.6740 12.19 10.85 0.86 ms G3V 13.7081 0.045
EVRJ142633.19-825021.5 47205241 216.6383 -82.8393 12.92 11.70 0.75 ms G9V 13.7050 0.176
EVRJ143328.56-833750.5 47202745 218.3690 -83.6307 12.77 8.89 0.68 ms G5V 129.9480 0.420
EVRJ143842.46-813049.0 47228767 219.6769 -81.5136 13.07 8.39 0.67 ms G1V 16.4483 0.153
EVRJ143916.73-844909.5 47178809 219.8197 -84.8193 12.18 6.11 0.55 ms F 26.6630 0.082
EVRJ144001.37-842946.7 47201869 220.0057 -84.4963 12.67 7.87 0.42 ms F6V 18.0120 0.126
EVRJ144437.08-775109.4 47397018 221.1545 -77.8526 12.23 4.31 0.30 ms F1V 40.6480 0.151
EVRJ144607.85-835647.8 47202209 221.5327 -83.9466 12.20 8.33 0.73 ms G6V 16.1201 0.081
EVRJ145302.21-823117.0 47219133 223.2592 -82.5214 12.80 8.93 0.87 ms K0V 52.0750 0.236
EVRJ145519.22-761940.8 47504010 223.8301 -76.3280 13.50 5.89 0.71 ms G7V 20.7599 0.242
EVRJ150051.41-823800.6 47219888 225.2142 -82.6335 11.69 7.50 1.42 giant M2 50.1160 0.071
EVRJ150534.08-873605.8 47165613 226.3920 -87.6016 12.70 8.88 0.55 ms F7V 178.9330 0.127
EVRJ151657.05-782900.6 47306697 229.2377 -78.4835 11.51 8.78 0.60 ms G7V 155.4880 0.187
EVRJ152229.16-855039.1 47176529 230.6215 -85.8442 12.70 11.49 0.76 ms K0V 55.5406 0.319
EVRJ152858.46-781119.0 47305454 232.2436 -78.1886 12.08 7.47 0.53 ms F2V 37.9090 0.124
EVRJ152933.41-755721.2 47507129 232.3892 -75.9559 9.95 5.49 0.27 ms A6V 27.9790 0.099
EVRJ153920.90-820531.2 47222149 234.8371 -82.0920 13.86 8.80 0.64 ms G2V 21.6105 0.093
EVRJ154507.37-793351.1 47291578 236.2807 -79.5642 12.75 7.16 0.66 ms G5V 12.6404 0.244
EVRJ160954.46-851858.3 47181489 242.4769 -85.3162 12.76 7.60 0.33 ms F4V 44.7430 0.189
EVRJ161508.66-835940.6 47187461 243.7861 -83.9946 13.18 11.16 0.73 ms K0V 144.5400 0.403
EVRJ162303.89-810023.4 47253636 245.7662 -81.0065 12.34 3.25 0.56 ms G7V 62.7963 0.313
EVRJ163423.28-841700.6 47182229 248.5970 -84.2835 13.63 9.48 0.99 ms K2V 4.7610 0.397
EVRJ163736.60-853222.2 47181298 249.4025 -85.5395 12.70 6.11 0.67 ms G2V 25.4995 0.076
EVRJ164003.19-835758.7 47185009 250.0133 -83.9663 12.91 9.45 0.57 ms G6V 41.2660 0.247
EVRJ164326.83-861143.8 47180996 250.8618 -86.1955 14.13 7.55 0.86 ms G 9.7203 0.336
EVRJ164433.50-860655.4 47181009 251.1396 -86.1154 12.22 5.87 0.59 ms F1V 63.6382 0.219
EVRJ165236.34-775955.0 47279936 253.1514 -77.9986 12.57 6.78 0.53 ms F9V 45.8782 0.215
EVRJ170835.06-804042.2 47250036 257.1461 -80.6784 13.36 10.77 0.64 ms G6V 11.7251 0.185
EVRJ171020.11-794022.8 47266105 257.5838 -79.6730 13.38 9.62 0.60 ms G0V 34.9601 0.224
EVRJ173306.41-841023.5 47184009 263.2767 -84.1732 11.94 7.48 0.01 ms B8V 62.0777 0.107
EVRJ175021.89-833451.2 47241898 267.5912 -83.5809 12.90 10.55 0.45 ms G1V 97.8154 0.670
EVRJ175151.98-770054.7 47705441 267.9666 -77.0152 12.99 7.95 0.60 ms F1V 14.0583 0.312
EVRJ175821.86-823023.4 47246340 269.5911 -82.5065 13.32 8.36 0.68 ms G6V 32.6439 0.272
EVRJ181441.14-755531.1 57414649 273.6714 -75.9253 11.71 9.14 0.66 ms G3V 70.3589 0.235
EVRJ181713.66-870538.4 57073210 274.3069 -87.0940 11.79 12.45 0.66 ms K0V 8.4675 0.138
EVRJ181714.18-773047.2 57406134 274.3091 -77.5131 13.02 7.02 0.51 ms G1V 36.9321 0.603
EVRJ181841.30-822733.5 57161826 274.6721 -82.4593 12.83 3.05 0.62 giant G2 76.6038 0.070
EVRJ184428.49-823046.8 57160536 281.1187 -82.5130 13.66 9.22 0.54 ms G8V 71.0168 0.238
EVRJ184600.48-775012.5 57197233 281.5020 -77.8368 12.93 8.55 0.56 ms G1V 37.1489 0.192
EVRJ185649.78-802355.0 57169339 284.2074 -80.3986 12.77 8.40 0.71 ms K1V 11.4201 0.128
EVRJ190936.96-825733.8 57102741 287.4040 -82.9594 12.21 9.17 0.81 ms K1V 20.9770 0.097
EVRJ191030.65-852500.5 57093523 287.6277 -85.4168 13.72 7.03 0.40 ms F8V 69.7130 0.580
EVRJ191957.17-815827.8 57163432 289.9882 -81.9744 11.82 8.40 0.76 ms G9V 49.7553 0.190
EVRJ195938.52-800854.6 57171413 299.9105 -80.1485 13.38 8.73 0.66 ms G6V 11.8792 0.362
EVRJ200844.69-841100.6 57097190 302.1862 -84.1835 12.36 6.21 0.74 ms G8V 25.0280 0.183
EVRJ204029.23-792542.2 57173726 310.1218 -79.4284 12.04 7.83 0.74 ms G8V 27.8260 0.149
EVRJ211629.93-755719.1 57218408 319.1247 -75.9553 11.50 8.69 0.41 ms F4V 15.3512 0.213
EVRJ213512.74-762221.4 57148511 323.8031 -76.3726 13.06 9.38 0.70 ms G4V 21.3854 0.100
EVRJ214611.09-783816.4 57142128 326.5462 -78.6379 13.01 7.87 0.74 ms K1V 58.0007 0.138
EVRJ215221.58-830052.9 57089418 328.0899 -83.0147 12.85 3.57 1.43 giant M2 14.9228 0.069
EVRJ215337.54-775041.6 57143831 328.4064 -77.8449 12.46 10.23 0.66 ms G4V 4.7476 0.086
EVRJ215538.66-830823.6 57089350 328.9111 -83.1399 12.97 9.49 0.77 ms G9V 14.9228 0.095
EVRJ223125.73-803027.7 57127400 337.8572 -80.5077 11.82 5.48 0.58 ms F9V 41.7888 0.068
EVRJ223812.41-780109.1 57136882 339.5517 -78.0192 13.50 10.29 0.38 ms A8V 15.0893 0.256
EVRJ225743.22-782429.2 57136245 344.4301 -78.4081 11.86 8.36 0.49 ms G6V 19.8070 0.071
EVRJ225936.22-830757.0 57083769 344.9009 -83.1325 11.22 9.34 0.69 ms K0V 12.8218 0.064
EVRJ230355.18-773000.7 57137831 345.9799 -77.5002 12.58 8.47 0.59 ms G2V 34.7999 0.138
EVRJ231712.07-790607.6 57132830 349.3003 -79.1021 11.51 8.15 0.66 ms G6V 195.0140 0.154
EVRJ232322.32-771336.5 57135188 350.8430 -77.2268 12.43 5.89 0.71 ms G6V 66.0742 0.196
EVRJ233038.69-760536.6 57261482 352.6612 -76.0935 12.56 9.29 0.60 ms G0V 84.7218 0.390
EVRJ234021.77-780703.4 57134048 355.0907 -78.1176 13.66 8.99 0.47 ms F8V 11.6461 0.136
EVRJ234130.91-790731.8 57133414 355.3788 -79.1255 13.36 7.75 0.53 ms F5V 90.6906 0.291
Table 11Eclipsing Binary discoveries
Figure 15.— Low mass secondary discoveries. Top panels are the Evryscope light curves with the best transit fit. Bottom panels are the SOAR RV points (red) with the best sinusoidal fit. Primary and secondary mass and radius values are shown in Table 2.
Figure 16.— Variable star discoveries. Y-axis is instrument magnitude, x-axis is the phase, p = period found in hours, a = amplitude change in magnitude. Gray points are two minute cadence, yellow is the best LS fit.
Figure 16.— Variable star discoveries continued. Y-axis is instrument magnitude, x-axis is the phase, p = period found in hours, a = amplitude change in magnitude. Gray points are two minute cadence, yellow is the best LS fit.
Figure 16.— Variable star discoveries continued. Y-axis is instrument magnitude, x-axis is the phase, p = period found in hours, a = amplitude change in magnitude. Gray points are two minute cadence, yellow is the best LS fit.
Figure 16.— Variable star discoveries continued. Y-axis is instrument magnitude, x-axis is the phase, p = period found in hours, a = amplitude change in magnitude. Gray points are two minute cadence, yellow is the best LS fit.
Figure 16.— Variable star discoveries continued. Y-axis is instrument magnitude, x-axis is the phase, p = period found in hours, a = amplitude change in magnitude. Gray points are two minute cadence, yellow is the best LS fit.
Figure 16.— Variable star discoveries continued. Y-axis is instrument magnitude, x-axis is the phase, p = period found in hours, a = amplitude change in magnitude. Gray points are two minute cadence, yellow is the best LS fit.
Figure 17.— Eclipsing Binary discoveries. Y-axis is normalized flux, x-axis is the phase, p = period found in hours, a = eclipse depth. Gray points are two minute cadence, yellow is the best fit.
Figure 17.— Eclipsing Binary discoveries continued. Y-axis is normalized flux, x-axis is the phase, p = period found in hours, a = eclipse depth. Gray points are two minute cadence, yellow is the best fit.
Figure 17.— Eclipsing Binary discoveries continued. Y-axis is normalized flux, x-axis is the phase, p = period found in hours, a = eclipse depth. Gray points are two minute cadence, yellow is the best fit.
Figure 17.— Eclipsing Binary discoveries continued. Y-axis is normalized flux, x-axis is the phase, p = period found in hours, a = eclipse depth. Gray points are two minute cadence, yellow is the best fit.
Figure 17.— Eclipsing Binary discoveries continued. Y-axis is normalized flux, x-axis is the phase, p = period found in hours, a = eclipse depth. Gray points are two minute cadence, yellow is the best fit.
Figure 17.— Eclipsing Binary discoveries continued. Y-axis is normalized flux, x-axis is the phase, p = period found in hours, a = eclipse depth. Gray points are two minute cadence, yellow is the best fit.
Figure 17.— Eclipsing Binary discoveries continued. Y-axis is normalized flux, x-axis is the phase, p = period found in hours, a = eclipse depth. Gray points are two minute cadence, yellow is the best fit.
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