Near-IR Field Variable Stars in Cygnus OB7
aa r X i v : . [ a s t r o - ph . S R ] F e b Accepted by AJ
Near-IR Periodic and Other Variable Field Stars in the Field of the CygnusOB7 Star Forming Region
Scott J. Wolk
Harvard–Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138
Thomas S. Rice
Harvard–Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138
Colin A. Aspin
Institute for Astronomy, University of Hawaii at Manoa, 640 N Aohoku Pl, Hilo, HI 96720
ABSTRACT
We present a subset of the results of a three season, 124 night, near-infrared moni-toring campaign of the dark clouds Lynds 1003 and Lynds 1004 in the Cygnus OB7 starforming region. In this paper, we focus on the field star population. Using three seasonsof UKIRT J, H and K band observations spanning 1.5 years, we obtained high-qualityphotometry on 9,200 stars down to J=17 mag, with photometric uncertainty betterthan 0.04 mag. After excluding known disk bearing stars we identify 149 variables –1.6% of the sample. Of these, about 60 are strictly periodic, with periods predominantly < ∼
60 stars showed variations which appear to be purely stochastic.
Subject headings: stars: eclipsing binaries – stars: variables – infrared: stars
1. Introduction
Two crucial experiments for identifying Young Stellar Objects (YSOs) are near-infrared (NIR)disk studies and variability studies. We have combined these two techniques in a three season, 124night NIR monitoring campaign of the star forming region Cyg OB7 (Rice et al. 2012; hereafterPaper I). The first result of this experiment was the identification of 30 YSOs in the region and thediscovery that about a quarter of them were seen to transition in JHK color-color space from the 2 –“photospheric” region of the NIR color-color diagram to the region of the diagram correspondingto disk bearing stars (Lada & Adams 1992). In a follow-up paper, we study the periodic nature ofthe YSOs and the observed systematic color changes (Wolk et al. 2013; hereafter Paper III).Disk-bearing young stars, also known as classical T Tauri stars (cTTs), have long been iden-tified as optically variable (Joy 1945; Herbig 1962). At a minimum the variability is due to acombination of starspots, accretion and circumstellar disk occultations (Herbst et al. 1994). Thestudy of near-infrared variability in young stars allows us to directly study changes in those diskstructures. Studies of the Orion A and Chameleon I molecular clouds established that variability isrelated to the presence of an inner accretion disk (Carpenter et al. 2001, 2002). In Orion, as manyas 93% of the variable stars are identified to be young stars, and a strong connection is establishedbetween variability and near-infrared excess. Studies of individual YSOs such as AA Tau andits analogs reveal insights into magnetospheric accretion processes linked to inner-disk dynamics(Bouvier et al. 2003, 2007; Donati et al. 2010). Other types of stars such as EX Lup (Aspin et al.2010) and V1118 Ori (Audard et al. 2010) exhibit large, eruptive mass-accretion events due tomass infall events of
M > . M ⊕ that are easily studied in the near-infrared; during outburst, theirnear-infrared emission is dominated by hotspot radiation and emission reprocessed in the disk.Recent mid-IR variability surveys such in the IC 1396A and Orion star forming regions provide keyinsights into physical processes of young stars over short ( ∼
40 day) timescales, finding that 70% ofYSOs with infrared excess are variable (Morales-Calder´on et al. 2009, 2011). Scholz (2012) studiedthe NIR variability of several young clusters including the ONC, NGC 1333, IC 348 and σ Orionis.He finds variability amplitudes are largest in NGC 1333, presumably because it has the youngestsample of YSOs. The frequency of highly variable objects also increases with the time window ofthe observations.Although the focus of our observing campaign was to study YSOs, our observed field containsabout 9200 background and foreground stars for which variability analysis were carried out. Ofthese, 158 were found to be variable via the Stetson index (Stetson 1996). Variable field starsprovide important information about the nature and evolution of stars in various regions of ourgalaxy. For example, eclipsing binary stars provide us information about the masses and radii ofstars (see e.g. Huang & Struve 1956; Popper et al. 1985). Pulsating variables, such as Cepheids andRR Lyrae, serve as distance indicators (see e.g. Benedict et al. 2007). A common type of pulsatorsknown as δ Scuti stars offer unique insight into the internal structure and evolution of main-sequenceobjects (Thompson et al. 2003). Pietrukowicz et al. (2009) recently performed a deep four nightmonitoring campaign of about 50,000 stars in Carina with the VLT and found a 0.7% variabilityfraction. The main goal of their program was the optical follow-up of OGLE planetary transitcandidates. They found 43% of the variables were eclipsing systems while a statistically identicalnumber were pulsating systems. About 2/3 of the pulsating systems were δ Scuti stars.The goal of this paper is simple: to document the rate, and to a lesser extent the types, of NIRvariability seen in the field stars. We will then use the result of the field star analysis as a pointof comparison for the results found in star forming regions. In the next section ( §
2) we will briefly 3 –review the source data. In § χ minimizationalgorithm and Lomb-Scargle periodogram analysis. In § §
2. Observation and Data Reduction
The constellation Cygnus contains many rich and complex star-forming regions, including theNorth America and Pelican nebulae and the Cygnus X star forming region (Reipurth & Schneider2008). In the Cygnus region, nine OB associations have been found. Cygnus OB7 is the nearestat a distance of around 800 pc (Aspin et al. 2009, distance modulus µ = 9.5). Cygnus OB7 isalong the line-of-sight of the large dark cloud Kh 141 (Khavtasi 1955) – also called the northerncoal sack; this cloud is over 5 o across. While a physical connection has not been confirmed, thedark clouds Lynds 1003-1004, the target of this study, lie near the middle of Kh 141. The darkclouds were first studied in the context of star formation by Cohen (1980) who found a diffusered nebula he named RNO 127. This nebula was later determined to be a bright Herbig-Haro(HH) object by Melikian & Karapetian (HH 428 2001, 2003). Further study in the optical andnear-infrared identified a number of Herbig-Haro objects (Devine et al. 1997; Movsessian et al.2003) and multiple IRAS sources (Dobashi et al. 1996) that reveal the presence of a young stellarpopulation and significant star formation activity. The field of view of the study is about a 1 degreesquare centered near 21h00m +52 o ′ (J2000.) near the “Braid Nebula Star”. While Aspin et al.(2009) and Rice, Wolk & Aspin (2012) have identified over 40 disked YSOs in the region, to dateno deep X-ray studies have been made to identify the disk-free young stellar population.The dataset used here was fully described in Paper I. In brief, J , H , and K observations ofthe Cygnus OB7 region were obtained using the Wide Field Camera (WFCAM) instrument onthe United Kingdom InfraRed Telescope (UKIRT), an infrared-optimized 3.8 meter telescope atopMauna Kea, Hawaii at 4200 meters elevation. Our data consist of WFCAM observations taken fromMay 2008 to October 2009 in three observing seasons as part of a special observation program.Data were taken on a total of 124 nights during this period. For each night, we extracted datafrom the archive for all stars with photometric uncertainties less than 0.1 mag in J band and noprocessing error flags. Starting from about 100,000 detected objects, over 38,000 stars met thiscriterion every night. The typical errors of these stars for one night are shown in Figure 1. Out ofthe 124 nights in the original survey, 24 nights were rejected due to deviations in the mean color of & J . . ≤
4% at J; we also excluded starsbrighter than J =11 as bright stars would saturate in epochs with especially good seeing or if thestar brightened. We also required no quality error bit flags on any of the observations. In the end 4 –we obtained high-quality photometry on 9,200 stars.Fig. 1.— Errors vs. magnitude in all three bands shown for one night of data. This is about 38,000stars with J errors < S ; Stetson 1996) .This is a method for quantifying variability within a sample which includes multiple colors eachwith different error characteristics. The resultant value is zero for a constant source and exceeds 1for a source with significant correlated variability; high values indicate greater variability. This index is referred to by the letter J in the original article, we use S to avoid confusion with the filter band. Carpenter et al. (2001) compared the Stetson index to χ fits and concluded that S > .
55 was sufficient to confirmvariability. In Paper I we concluded that
S > . Table 1. Observing LogEpoch Start Date End Date Number of observationsSeason 1 26-Apr-2008 22-May-2008 19Season 2 19-Sep-2008 27-Nov-2008 39Season 3 28-Aug-2009 13-Oct-2009 41Total 26-Apr-2008 13-Oct-2009 100 5 –
3. Period Analysis3.1. Automated Period Searches
To investigate whether the variable stars were periodic, we used two period-finding algorithms:the Lomb-Scargle periodogram (also knonwn as the Lomb Normalized Periodogram – LNP; cf.Press & Rybicki 1989) and the Fast χ (F χ ) algorithm (Palmer 2009), both suited for periodanalysis on unevenly sampled data such as ours. The Lomb-Scargle method is a useful and pop-ular way to analyze periodicity with an easily interpreted periodogram, which identifies multiplecandidate periods and their relative probability. This method essentially takes the Fast FourierTransform of the input signal and reinterprets it (with certain restrictions) as the periodogram.The signal in the periodogram with the maximum power is generally interpreted as the true signal.The method uses the number and spacings of samples to determine the frequency domain to beexplored. Spurious detections can be produced at frequencies where the sample times have signif-icant periodicity (i.e. nightly samples); power from harmonics above the fundamental sine wave islost, and the algorithm ignores point-to-point variations in the measurement error.Given the long baselines in our data, the LNP method can determine very precise periods.The formal error on derived (linear) frequencies is: δf = 3 σ N / T A √ N (Horne & Baliunas 1986),where σ N is the variance of noise remaining after the periodic signal is subtracted, N is the numberof independent points (about 100), T is the length of time baseline (about 500 days), and A is theamplitude of signal (typically 10%). For our data, the formal error is typically less than 0.001 days,an indication of the tremendous leverage gained by long term monitoring of very stable periods.On the other hand the step size of the period search algorithms is a function of the square of theperiod and while the step size is ∼ < ∼
15 minutes)which is well within the precision for periods <
10 days and reasonable for all listed periods. Thisprecision is consistent with our purposes, which is to identify the field variables, not to physicallyevaluate each one in detail.Among non-variables in our data, our LNP analysis often found spurious periods near 0.5and 1.0 days, likely due to the ∼ χ algorithm. The F χ method uses atruncated Fourier expansion. Since a limited number of harmonics are explored, it allows thefrequencies searched to have arbitrary range and density so that periods are not missed by the 6 –Table 2. Period search parameters.Description ParameterJ Range ∼ ∼ ∼ ∼ σ P eriod < .
01 days 7 –search. F χ is sensitive to power in higher harmonics of the fundamental sinusoid meaning itcan identify structures more complex than sinusoids. Further, there is insensitivity to the sampletiming including periods less than one day and near one day. The frequency search is gridded moretightly than the traditional integer number of cycles over the span of observations, eliminatingpower loss from peaks that fall between the grid points. Using the (F χ ) algorithm we were able toexplore periods as short as 0.1 days and found very stable results across the three bands. However,simulations with test data indicated that period near 0.1 days still tended to get lost in aliasingwith the sampling rate, especially in the single season data. Empirically, the F χ method is morereliable for complex variables such pulsating stars and for short-period ( . χ and LNPtechniques. This analysis was followed by analysis of the combined three epochs of data (4 seasonaldivisions × × Even when it appears a period is present, the algorithms often disagree on the preferred period.We manually graded the light curves of each star on a 5 point empirical scale. A grade of 5 wasgiven to the subset of periods which appeared beyond question; usually these were clear eclipsingsystems or other systems which varied sinusiodally with periods under a day. A grade of 4 wasgiven to stars with periods of similar consistency in results, but in which the noise in the individualmeasurements was nearly at the level of the signal and hence less reliable. A grade of 3 was givenwhen the various techniques returned clear aliases of each other an hence the positive determinationof the true period was not possible. Periods longer than 60 days often fall into this category. Agrade of 2 was given when the various methods returned different periods of similar quality andhence the period list was not considered to be reliable. A grade of 1 was assigned to cases in whichthe rise and fall in the data were clear, but no period could be established, perhaps simply dueto unlucky sampling. Finally, a 0 is used for stars which are variable, but have no evidence ofperiodicity.For the intermediate cases, Figure 2 shows an example of the follow-up analysis. The individualperiodograms show multiple peaks. In this case, they are near 0.78 days, 1.55 days and 3.55 days.The peaks are sharpest in the combined three season data, indicating a very stable long termsignal. Though the signal peaks are similar in size, inspection of the three fits shows the 1.55 dayperiod has the smallest deviations from a smooth fit to the data. Further, we note, in the 1.55 day 8 –decomposition there are two peaks of different sizes and hence when they are folded on top of eachother the apparent noise is enhanced. This is a contact eclipsing system, similar to W UMa.This approach does not work when the data are too symmetric. Many of the minimum χ phase-folded light curves showed a single fall and rise (see the third panel of Figure 2). This issurprising as we expected eclipses to be common but most eclipse lightcurves have both a primarya secondary eclipse. In addition, when we compared the periods of systems with a single rise fallto those that showed two, the periods of the single cycle objects where shorter than those whichshowed both a primary a secondary eclipse – this indicated a bias in the period finding. Formally,the difference in χ between a lightcurve showing a single event and dual events very small. Hencethe period finding codes will tend to favor, incorrectly, the shorter period showing a single eclipse.Because of this, we manually reviewed all periodic sources and doubled the period of those whichindicated a single eclipse. We corrected only systems that showed highly stable periods consistentwith eclipse induced variations. We do not apply the correction to more stochastic systems sinceother phenomena, especially pulsation and star spots, could induce a periodic single dip modulation.
4. Results
We divided the field variable stars into four groups based on their periodic signatures. About60 appeared to be eclipsing systems with strong regular variations on time scales usually less thantwo days. Around 20 had weak modulation on the order of a few days – consistent with starspots. Three appear to have long term cycles of between 20 and 60 days. The remainder, about60 variables, showed no signs of periodic behavior. We labeled these stochastic variables. About90% of these had
S <
S > .
7. A cursory examination found 17 lay along theLynds 1003-1004 dust ridge, consistent with a random spatial distribution. There was an overabundance of the stochastic sources south of the dust lane, compared to north, but this was notfound to be statistically significant.In Tables 3–5, we quantify the variability observed in these sources. The first two columnsof these tables list the position of the sources. Later, we will refer to particular star by theposition, contracted by the removal of the colons. Columns 3-5 list the median of the 10 brightestmeasurements of the star in the respective filters, J , H and K . This gives the relevant brightnessof the star out of eclipse while minimizing observational biases. Columns 6 and 7 list the rangeseen in the J and K filters. Similarly, columns 8 and 9 list the peak to floor change in the J − H and H − K colors. Column 10 lists the Stetson index for the star. Tables 3 and 5 have an eleventhcolumn listing the period of the star. Table 3 adds a final column to indicate the classification of the binary which we discuss in § grade of thatperiod which we discussed in § K -band lightcurvefor all three seasons. Top-right: The raw K -band lightcurve for season 2 shown for clarity. Second-left: The result of folding over the strongest signal in the K -band LNP using data for all threeseasons LNP is best suited to finding single sinusoidal periods. Second-Right: The result of foldingover the strongest signal in the K -band data found in the χ minimization data, this is also one ofthe harmonics in the LNP (bottom-left). Note the RMS on the curve exceeds the nominal error.Third-Left: Now folded on the intermediate harmonic – season 3 only. Double sine wave pattern isevident with very low residuals. Third-Right: The result of folding all K -band data over 1.56 days.Note that the peaks are unequal indicative of an eclipsing contact binary system. Bottom-left:The periodogram of the K -band data for all three seasons, the peaks labeled “1”,“2” and “3” referto the peaks of 0.78, 1.56, 3.56 days respectively. Bottom-right: Phased color-magnitude diagramshows little color dependence as a function of brightness or phase. 10 –Table 3. Strongly periodic stars in the monitored field. R.A. Decl.
J H K ∆ J ∆ K ∆ J − H ∆ H − K S
Period Type(J2000) (J2000) mag mag mag mag mag mag mag (days)
Simple eclipse
Continual change
11 –Table 3—Continued
R.A. Decl.
J H K ∆ J ∆ K ∆ J − H ∆ H − K S
Period Type(J2000) (J2000) mag mag mag mag mag mag mag (days)21:02:31.31 +52:11:08.9 16.04 15.13 14.72 0.34 0.30 0.12 0.11 8.84 0.36 EW21:02:40.60 +52:34:48.4 12.39 11.86 11.57 0.49 0.45 0.11 0.07 16.91 0.94 EW21:03:05.18 +52:55:01.1 16.86 15.96 15.57 0.43 0.31 0.24 0.35 4.14 0.24 EW
Other periodic δ Scu
Lower quality ∼
12 –Table 4. Stochastically variable stars in the monitored field.
R.A. Decl.
J H K ∆ J ∆ K ∆ J − H ∆ H − K S (J2000) (J2000) mag mag mag mag mag mag mag20:57:33.38 +52:02:51.9 15.65 14.81 14.34 0.18 0.22 0.13 0.19 2.1220:57:33.82 +52:16:57.1 14.04 13.51 13.30 0.10 0.09 0.07 0.09 1.0420:57:47.82 +52:35:24.6 13.35 12.60 12.23 0.07 0.08 0.10 0.07 1.1620:57:51.99 +52:52:33.7 13.91 13.08 12.75 0.05 0.08 0.05 0.08 1.0420:58:08.27 +52:22:24.4 16.39 15.48 15.07 0.13 0.11 0.14 0.10 1.3020:58:18.83 +52:13:07.4 15.68 14.65 14.20 0.10 0.14 0.09 0.07 1.6020:58:18.92 +52:10:10.1 16.26 15.44 15.13 0.12 0.14 0.12 0.12 1.1820:58:22.06 +52:26:47.8 14.83 14.18 13.90 0.07 0.12 0.07 0.09 1.0720:58:27.66 +52:18:31.9 15.55 14.47 13.94 0.11 0.05 0.09 0.07 1.5720:58:35.47 +52:42:41.0 14.05 13.06 12.65 0.11 0.06 0.09 0.07 1.1120:58:35.74 +52:43:53.1 15.20 14.59 14.26 0.12 0.09 0.09 0.07 1.2620:58:37.31 +52:28:08.2 16.91 15.52 14.82 0.23 0.14 0.20 0.12 1.6420:58:41.61 +52:42:45.8 15.62 14.64 14.24 0.14 0.07 0.14 0.10 1.0220:58:45.02 +52:42:59.0 15.39 14.63 14.37 0.17 0.10 0.10 0.10 1.2820:58:46.47 +52:29:41.6 13.20 11.83 11.07 0.20 0.12 0.10 0.10 1.3420:58:46.95 +52:08:23.5 14.70 14.09 13.87 0.09 0.08 0.10 0.13 1.2820:58:47.02 +52:41:40.6 16.69 16.13 15.62 0.97 0.57 1.01 0.44 1.3520:58:48.88 +52:22:31.6 16.89 14.86 13.77 0.22 0.07 0.19 0.10 1.3820:58:52.78 +52:19:39.1 15.98 15.24 14.95 0.10 0.11 0.10 0.10 1.2020:58:53.16 +52:42:46.7 14.88 14.07 13.72 0.16 0.09 0.11 0.09 1.0520:59:03.46 +52:02:33.9 15.16 14.27 13.92 0.08 0.15 0.06 0.10 1.1820:59:04.29 +52:33:02.6 16.94 14.21 12.78 0.21 0.05 0.19 0.06 1.0520:59:11.42 +52:46:41.9 14.82 13.87 13.45 0.11 0.08 0.07 0.07 2.2020:59:12.17 +52:09:42.0 14.78 14.21 14.02 0.08 0.07 0.05 0.05 1.0720:59:13.54 +52:03:59.1 16.45 15.20 14.65 0.16 0.10 0.14 0.10 1.2720:59:17.76 +52:46:12.3 15.75 14.81 14.36 0.11 0.09 0.11 0.07 1.8020:59:22.91 +52:35:13.8 14.55 13.63 13.25 0.08 0.08 0.08 0.12 1.0120:59:32.26 +52:32:47.4 13.93 13.31 12.97 0.07 0.08 0.05 0.05 1.2120:59:33.88 +52:10:16.8 13.15 12.67 12.60 0.07 0.07 0.04 0.05 1.1120:59:41.00 +52:23:49.2 16.98 15.26 14.23 0.23 0.06 0.21 0.10 1.2620:59:49.14 +52:04:42.7 16.53 15.39 14.94 0.13 0.09 0.11 0.09 1.1320:59:57.42 +52:38:57.2 15.82 14.20 13.36 0.09 0.04 0.09 0.11 1.2020:59:59.71 +52:10:43.1 16.17 15.34 15.10 0.12 0.12 0.13 0.14 1.1421:00:01.78 +52:25:45.3 16.56 14.98 14.10 0.17 0.07 0.17 0.09 1.1121:00:13.31 +52:02:24.6 16.30 15.24 14.69 0.14 0.18 0.19 0.19 1.4321:00:15.00 +52:30:06.0 14.07 13.43 13.30 0.12 0.10 0.08 0.07 1.1821:00:19.16 +52:27:16.0 17.15 15.13 14.02 0.20 0.07 0.20 0.08 1.0521:00:20.48 +52:31:08.4 14.19 12.70 11.89 0.11 0.13 0.08 0.08 1.1421:00:22.53 +52:42:53.7 15.94 15.14 14.79 0.27 0.17 0.14 0.15 1.4321:00:22.81 +52:07:33.7 15.86 14.96 14.68 0.11 0.12 0.12 0.13 1.4821:00:23.12 +52:04:32.7 16.10 14.59 13.77 0.12 0.10 0.12 0.06 1.0321:00:23.32 +52:42:58.4 15.79 14.62 14.12 0.21 0.13 0.12 0.13 1.4721:00:23.49 +52:54:18.2 15.83 14.91 14.50 0.11 0.12 0.12 0.10 1.1021:00:23.97 +52:42:20.5 15.09 14.00 13.55 0.18 0.15 0.11 0.12 1.0521:00:37.50 +52:07:04.1 16.88 15.65 15.03 0.60 0.12 0.60 0.13 2.40
13 –
Eclipsing binaries are among the easiest periodic objects to detect due to their highly stableperiodicity and their conspicuous sudden drops by 0.2 mag or more. Eclipsing binaries can providefundamental mass and radius measurements for the component stars (see the extensive review byAndersen 1991). These mass and radius measurements allow for accurate tests of stellar evolutionmodels (e.g., Pols et al. 1997; Schroder, Pols, & Eggleton 1997; Guinan et al. 2000; Torres & Ribas2002). In their Kepler survey, Prˇsa et al. (2011) identify 1879 such objects in the 105 o2 field. Thereare over 100 variability types listed in the Variable Star Catalog (GCVS) (Samus & Durlevich,2009). It is not our purpose to precisely identify each variable with its exact type; however, theirshort periods, large amplitudes and stability generally rule out most varieties of pulsating stars.Because our observations span a year and a half, we can highly constrain the shapes of these stars’periodic light curves despite not observing with a cadence of more than once per night.Among the 149 diskless variable stars, we identified about 60 candidate eclipsing binaries. Thedetermining factors for the assignment of eclipsing as the reason for the variation were a) strongperiod detections which were consistent between the seasons with no phase shift and b) generallyshort periods. We visually inspected each folded light curve and found 51 of these with very clearperiods, consistent, season to season and filter to filter. Since their periods tended to be short,usually F χ fitting was more appropriate. The GCVS divides eclipsing binary systems into fourobservational classes based on the shape of their light curves. The general classes are: 1) ‘E’ foreclipsing systems in which the stars are well separated with very sharp eclipses. 2) ‘EA’ for Algol-like system in which we can notice some rounding of the edges near the time of the eclipses. 3)‘EB’ for β Lyrae-like system in which the eclipse edges are so rounded as to make it difficult to tellwhen the eclipses begin and end, and they have one minimum much deeper than the other. 4) ‘EW’for W UMa-like near contact systems with nearly sinusoidal signals and period . ∼
500 day photometric base-line. In some cases, the signal levels were close to thenoise, so single-peaked and double-peaked signals were difficult to distinguish. While some of thereported periods are below our formal sensitivity for periods given the sampling of our data, theperiod-folded lightcurves are convincing enough that we are confident enough to include them. Wedo not make classifications for Table 5 as the quality of the folded lightcurves was not high enough.Just under 40% of the eclipsing systems showed clean, sharp eclipses, in which the star deviatedfrom its nominal flux level for a fixed time period in which it dropped sharply and then returnedto the original flux. These are detached systems where the separation of both components is largecompared to their radii. The stars interact gravitationally, but the distortion of their surfaces due 14 –Table 4—Continued
R.A. Decl.
J H K ∆ J ∆ K ∆ J − H ∆ H − K S (J2000) (J2000) mag mag mag mag mag mag mag21:01:03.61 +52:20:42.8 15.58 14.56 14.13 0.11 0.09 0.06 0.06 1.4521:01:06.83 +52:04:32.1 17.11 16.27 15.79 0.76 0.23 0.71 0.22 2.1521:01:13.48 +52:09:01.3 14.72 13.92 13.54 0.07 0.10 0.04 0.05 1.2221:01:36.23 +52:21:44.2 13.61 12.86 12.46 0.09 0.15 0.07 0.11 2.6221:01:41.63 +52:02:17.7 15.17 13.60 12.90 0.19 0.14 0.18 0.11 1.1021:01:55.68 +52:26:59.6 17.21 14.07 12.42 0.26 0.07 0.23 0.08 1.2621:01:57.61 +52:04:30.3 16.87 15.85 15.44 0.24 0.17 0.27 0.23 1.2521:02:06.63 +52:07:31.0 16.32 15.31 14.70 0.11 0.12 0.16 0.16 1.0221:02:17.82 +52:07:31.5 16.87 15.97 15.61 0.18 0.42 0.38 0.29 1.4021:02:19.35 +52:04:46.1 13.57 12.85 12.55 0.07 0.08 0.05 0.09 1.4021:02:34.21 +52:09:33.8 14.66 13.76 13.43 0.07 0.07 0.06 0.05 1.3021:03:14.00 +52:03:37.9 16.85 15.40 14.77 0.30 0.19 0.24 0.13 1.0921:03:14.92 +52:03:58.5 14.66 13.90 13.49 0.09 0.16 0.12 0.13 1.1221:03:17.92 +52:16:38.0 12.91 12.41 12.34 0.13 0.14 0.07 0.11 1.0421:03:18.27 +52:17:46.6 15.48 14.23 13.64 0.09 0.14 0.08 0.09 1.0721:03:18.42 +52:03:36.9 15.55 14.12 13.46 0.11 0.18 0.17 0.16 1.0021:03:18.56 +52:15:06.5 14.09 13.38 13.11 0.15 0.14 0.11 0.09 1.09Note. — Typical photometric errors are ∼
15 –Table 5. Other possibly periodic stars in the monitored field.
R.A. Decl.
J H K ∆ J ∆ K ∆ J − H ∆ H − K S
Period Gr.(J2000) (J2000) mag mag mag mag mag mag mag (days) 1 − Long period
Moderate period † ∼ ∗ Noted stars did not have a unique period identified. † Period only noted in Season 2.
16 –to tidal deformation and rotation is minimal. These constitute the first group of stars in Table 3,identified as “simple eclipse”. These systems have periods ranging from around 0.4 days to a littleover 13 days. For the best examples, these stars ranged from an average J magnitude of 13.3 to 16.8mag with a median average J ∼ .
2. Eclipse depth ranges from 0.15 to 0.92 mag with a medianof 0.45 mag. The color changes are small, but measurable, with the median shift in J − K colorof about 0.18 and and a maximum shift of about ∆ J − K ∼ .
5. We show 10 of these in Figure 3.The nominal level when the system is out of eclipse indicates limited tidal distortion effects – witha few exceptions, 210149.79+525235.9 being the most conspicuous.
Slightly over 60% of the lightcurves in this sample had continuous flux variations. There havebeen several recent surveys focused on finding variable stars. A long baseline study covering upto 8 years is a galactic field monitoring program by Paczy´nski et al. (2006). It is more commonthat these studies last an observing season (e.g.Weldrake & Bayliss 2008 and Miller et al. 2010).Pietrukowicz et al. (2009) and Pietrukowicz et al. (2011) focused on deep monitoring program ofa few days which included NIR and optical photometry. The results of these programs are thatbetween 0.06 and 0.30 % of the field stars show systems with continuous flux variations, indicativeof contact binaries or elliptical stars. An exception is the result from the
Kepler mission whichindicates the rate of such stars may be as high as 1.2% when the precision in the photometric ishigh enough (e.g.mmag, Prˇsa et al. 2011).While it is possible the continuous signal variation is due to the stars being ellipsoidal in shape,it is more likely that two stars are in or near contact with each other and there is a continuousvariation of flux. In contact binary systems all cycles contain two maxima and two minima and thiswas enforced by the period finding algorithm. We show 10 of these in Figure 4. We would expectthat the periods of these contact binaries are shorter than other binaries. Indeed the periods rangefrom about 0.2 days to 3 days for these kinds of systems. The median period found was around 0.4days. Periodic phenomena are also expected in young spotted stars, but the short periods and andlong term stability argue against this interpretation. Some of the qualitatively weaker sources, suchas 205922.81+522338.4, 205746.71+520302.3 and 210058.93+523810.7, were explicitly examined athigh order peaks in their periodograms, but no convincing signal was found. The periods shown inFigure 4 here are the strongest periods for these stars, but the two algorithms occasionally returnedalias frequencies. These stars ranged from an average J magnitude of 12.5 to 17.3 with a median of J ∼ .
1. Eclipse depth ranges from ∆ J = 0.1 to 0.9 with a median of 0.35. The color changes aresmall but measurable with the median shift in J − H color of about 0.2 and and a maximum shift ofabout ∆ J − H ∼ .
4. A few systems had clear color patterns. 205945.69+520716.3 tends to becomeredder as it becomes fainter and bluer as it become brighter; 210029.43+521147.1 showed a similarpattern. 205905.47+520711.3 and 205746.71+520302.3 appear reddest when they are faintest, butthe effect is weak, perhaps 0.02 in H − K . A detailed statistical analysis would be needed to prove 17 –the color patterns noted in these stars were significant.Most of the contact systems are probably W UMa type systems, consisting of a pair of starsin a tight, circular orbit, with each star in contact with and eclipsing the other. W UMa typecontact binary stars are surprisingly common in the solar neighborhood. The apparent relativefrequency of W UMa type stars was estimated at about 1/130 of the frequency of the commonsolar-neighborhood FGK-type disc population dwarfs with an age of 5 − × years (OGLERucinski 1998). This is similar to the detection rate found here, which was about 32/9200 ( ≈ About 20 stars showed sinusoidal variations on timescales of about 2 to 25 days. These areindicated as “moderate period/ weak signal” variables in Table 5. Many of these show significantlymore deviation from the computed signal than the typical short period system. This is not an effectof low peak to trough signal (and hence higher noise) – the best examples of stars with period from2-25 days ranged from and average J magnitude of 12.4 to 15.5 with a median average J ∼ . <
15% 18 –Fig. 3.— Some examples of detached systems. K -band data are shown. Phase 0 is arbitrary basedon the time of the first observation. Names are indicated in each panel. In the bottom two rows,ellipsoidal effects are clearly seen. 19 –Fig. 4.— Some example contact systems. Names are indicated in each panel. 20 –(Cohen, Herbst, & Williams 2004). The implied color change due to a lower effective temperatureis < ∼ J − H and H − K , however color measurements are more sensitive to measurement precisionso we do not consider this result inconsistent with the spot interpretation – at least for some of thesources. A small number of stars showed very strong periodic light curves which neither correspondedto eclipse events or symmetric signals. These are indicated as “other periodic” stars in Table 3.Two examples are shown in Figure 6. Star 210145.39+524059.9 has a period of just 6.8 hours with aone flat minima which lasts about an hour ∆ J ∼ ∆ K ≈ .
33. There is a clear pattern in the color-magnitude diagram indicating the star changes color, becoming bluer the longer it is in the faintstate, reddening somewhat as it brightens and then becoming fainter with little noticeable colorchange. This appears to be an eclipsing binary. Star 205931.39+522802.1 has a period of about14.7 hours with a smooth rapid rise coming about 1/3 of the cycle and the decline lasting the other2/3 ∆ J = 0 .
48 and ∆ J − K ∼ .
15. This appears to be an RR Lyrae sub-type known as an RRabstar which has a large amplitude, hours long, asymmetric light curve. Star 210223.22+524951.0shows a combination of these effects: a short ( ∼ J = 0 .
64 and ∆ J − K ∼ .
10. This may be a δ Scuti star.There are a few stars which appear to vary on longer times scales. Because there are onlythree, and the periods are at least of order the duration of the observing season, it is hard to tellif they share any characteristics. There are also a few objects with very regular, non-sinusoidalvariations. Some of these may instead be tidally distorted stars or perhaps pulsating stars.Since our period detection algorithms were designed to find stable periods we may have missed δ Scuti stars - these are among the most common pulsating stars (Pietrukowicz et al. 2009). One ofthe key distinguishing characteristic of common pulsating stars is irregular change signal strengthon a regular period. The typical δ Scuti star has an amplitude of 0.003 - 0.9 magnitudes. Thevariations in luminosity are due to both radial and non-radial pulsations of the star’s surface. Theysometimes have beat patterns which make the light curves difficult to fold as the amplitude ofadjoining cycles is often modulated. Further the periods are typically < δ Scutis are early A to late F range and hence are generally warm with respectto the near-IR studied here, with color changes more obvious in optical wavelengths. The J-K colorshouldn’t change much at all. Given the 0.2% fraction observed in Carina (Pietrukowicz et al. 2009)we expect about 18 of these stars in our dataset; however, given their typical periods of 0.25 daysand typical amplitudes, including amplitude modulation, many of these may have gone undetectedor noted as stochastic systems. 21 –Fig. 5.— Three–season K band period-folded lightcurves of stars with quasi-sinusoidal periodslonger than 2 days which were stable across the three seasons including 2 long period variables.Names are indicated in each panel. 22 –Fig. 6.— Three stars with very short, but stable periods. Names are given in each panel. Top-to-bottom are: an eclipsing system, an RRab star and a probable δ Scuti star. For each the left panelis the full K -band data set folded on the period. The right panels show the K vs. H − K colormagnitude diagrams. 23 –
5. Conclusions
In this contribution, we present the results of an approximately 100 night, NIR photometricmonitoring campaign. While the program itself was focused on the pre-main sequence populationof the very young star forming region Cyg OB7, we report here on the field star population. The ∼ < J < .
3, 11 < H < .
5, and 11 < K <
16. About 1.6% of the non-disked stars variable. This is significantly less than the rate for diskedYSO’s which have variability rates >
90% (Carpenter et al. 2001, 2002; Morales-Calder´on et al.2009, 2011; Rice, Wolk & Aspin 2012; Scholz 2012). Of the variables, about 1/3 are weakly variablewith stochastic changes.We used two period finding algorithms on the variables, Fast χ and the Lomb-Scargle peri-odogram. About one-third of the variables are clearly eclipsing systems with highly stable periods.The greater half of the eclipsing systems are contact or near contact binaries with continous fluxchanges. The detection rate of contact/ellipsoidal binary systems is 0.4%, about 25% higher thanother ground-based surveys, but about one-third the rate found by Kepler .Among the remaining variables, some of these have very stable, very short periods and appearto be pulsators. There are intermediate length periodic variables with less stable variability, withnearly colorless luminosity changes of about 0.1 magnitudes. These stars have periods between 2and 25 days and may be spotted. Follow-up deep X-ray or optical H α observations could be usedto ascertain the evolutionary status of these stars and the likelihood that these changes do indeedarise from spots.
6. Acknowledgements
S.J.W. is supported by NASA contract NAS8-03060 (Chandra). T.S.R. was supported byGrant
REFERENCES
Andersen, J. 1991, A&A Rev., 3, 91Aspin, C., et al. 2009, AJ, 137, 431Aspin, C., Reipurth, B., Herczeg, G. J., & Capak, P. 2010, ApJ, 719, L50Attridge, J. M., & Herbst, W. 1992, ApJ, 398, L61Audard, M., et al. 2010, A&A, 511, A63Benedict, G. F., et al. 2007, AJ, 133, 1810Bouvier, J., et al. 2003, A&A, 409, 169Bouvier, J., et al. 2007, A&A, 463, 1017Carpenter, J. M., Hillenbrand, L. A., & Skrutskie, M. F. 2001, AJ, 121, 3160Carpenter, J. M., Hillenbrand, L. A., Skrutskie, M. F., & Meyer, M. R. 2002, AJ, 124, 1001Cohen, M. 1980, AJ, 85, 29Cohen, R. E., Herbst, W., & Williams, E. C. 2004, AJ, 127, 1602Devine, D., Reipurth, B., & Bally, J. 1997, Herbig-Haro Flows and the Birth of Stars, 182, 91PDobashi, K., Bernard, J.-P., & Fukui, Y. 1996, ApJ, 466, 282Donati, J.-F., et al. 2010, MNRAS, 409, 1347Feigelson, E.D., & Montmerle, T. 1999, ARAA, 37, 363Grankin, K. N., Bouvier, J., Herbst, W., & Melnikov, S. Y. 2008, A&A, 479, 827Guinan, E. F., Ribas, I., Fitzpatrick, E. L., Gim´enez, ´A., Jordi, C., McCook, G. P., & Popper,D. M. 2000, ApJ, 544, 409Herbig, G. H. 1962, Advances in Astronomy and Astrophysics, 1, 47Herbst, W., Herbst, D. K., Grossman, E. J., & Weinstein, D. 1994, AJ, 108, 1906Horne, J.H., Baliunas S.L. 1986, ApJ, 302, 757Hodgkin, S. T., Irwin, M. J., Hewett, P. C., & Warren, S. J. 2009, MNRAS, 394, 675Huang, S. S., & Struve, O. 1956, AJ, 61, 300 25 –Joy, A. H. 1945, ApJ, 102, 168Khavtasi, D. S. 1955, Abastumanskaia Astrofizicheskaia Observatoriia Byulleten, 18, 29Lada, C. J., & Adams, F. C. 1992, ApJ, 393, 278Melikian, N. D., & Karapetian, A. A. 2003, Astrophysics, 46, 282Melikian, N. D., & Karapetian, A. A. 2001, Astrophysics, 44, 216Miller, V. R., Albrow, M. D., Afonso, C., & Henning, Th. 2010, A&A, 519, A12Morales-Calder´on, M., et al. 2011, ApJ, 733, 50Morales-Calder´on, M., et al. 2009, ApJ, 702, 1507Movsessian, T., Khanzadyan, T., Magakian, T., Smith, M. D., & Nikogosian, E. 2003, A&A, 412,147Paczy´nski, B., Szczygie l, D. M., Pilecki, B., & Pojma´nski, G. 2006, MNRAS, 368, 1311Palmer, D. M. 2009, ApJ, 695, 496Parihar, P., Messina, S., Distefano, E., Shantikumar, N. S., & Medhi, B. J. 2009, MNRAS, 400, 603Pietrukowicz, P., et al. 2009, A&A, 503, 651Pietrukowicz, P., et al. 2011, arXiv:1110.3453Pols, O. R., Tout, C. A., Schroder, K.-P., Eggleton, P. P., & Manners, J. 1997, MNRAS, 289, 869Popper, D. M., Andersen, J., Clausen, J. V., & Nordstrom, B. 1985, AJ, 90, 1324Press, W. H., & Rybicki, G. B. 1989, ApJ, 338, 277Prˇsa, A., et al. 2011, AJ, 141, 83Reipurth, B., & Schneider, N. 2008, Handbook of Star Forming Regions, Volume I, 36Rice, T. S., Wolk, S. J., & Aspin, C. 2012, ApJ, 755, 65Rieke, G. H., & Lebofsky, M. J. 1985, ApJ, 288, 618Rucinski, S. M. 1998, AJ, 115, 1135Samus, N. N., Durlevich, O. V., & et al. 2009, VizieR Online Data Catalog, 1, 2025Scholz, A. 2012, MNRAS, 420, 1495Schroder, K.-P., Pols, O. R., & Eggleton, P. P. 1997, MNRAS, 285, 696 26 –Stetson, P. B. 1996, PASP, 108, 851Tokunaga, A. T., Simons, D. A., & Vacca, W. D. 2002, PASP, 114, 180Torres, G., & Ribas, I. 2002, ApJ, 567, 1140Thompson, S. E., Clemens, J. C., van Kerkwijk, M. H., & Koester, D. 2003, ApJ, 589, 921Vrba, F. J., Rydgren, A. E., Zak, D. S., & Schmelz, J. T. 1985, AJ, 90, 326Weldrake, D. T. F., & Bayliss, D. D. R. 2008, AJ, 135, 649Wolk, S. J., Rice, T. S., & Aspin, C. 2013, in prep.