DRAFTVERSION JULY 31, 2018 Typeset using LATEX twocolumn style in AASTeX61

REVEALING THE VARIABILITY OF NAKED-EYE WITH K2 HALO PHOTOMETRY

MICHAEL GREKLEK-MCKEON1 AND DANIEL HUBER2

1Department of Astronomy, University of Maryland, College Park, MD 20742, USA 2Institute for Astronomy, 2680 Woodlawn Dr, Honolulu, HI 96822, USA

ABSTRACT Using the technique of halo photometry, we analyze the brightest stars observed by K2 in campaigns 11-14, searching for previously unidentified variability. We correct and detrend the lightcurves, calculate power spectra for each , and perform frequency analysis to extract the dominant oscillation modes. We discover seven new classical pulsators, 28 new pulsation modes for previously known pulsators, and 24 new oscillating red giants. We also find evidence for granulation-like signatures in the power spectra of the classical pulsators in the sample, and set constraints on the radii and of potentially observable exoplanets around the bright K dwarf 36 Ophiuchi, with typical lower limits of 0.8 RE radius and 40 day period.

1. INTRODUCTION as described by (Van Cleve et al. 2016). Yet, improved data The brightest stars on the ecliptic, visible to the naked processing pipelines have increased K2’s photometric pre- eye, have been observed by astronomers for millenia. Be- cision to almost the level of Kepler. Many pipelines have fore the K2 mission, they had never been observed for an been developed to correct the systematics present in the raw extended period with high-precision space-based photome- K2 photometry, including: K2SFF (Vanderburg & Johnson try. Revealing the inherent variability of stars is essential 2014), K2P2 (Lund et al. 2015), K2SC (Aigrain et al. 2016), not only for proper classification, but for understanding their K2VARCAT (Armstrong et al. 2015, 2016), K2PHOT (Eylen interior structures and evolution using tools such as astero- et al. 2016), and EVEREST (Luger et al. 2016; Luger et al. seismology (Handler 2012). By examining high-precision 2017). photometry from K2, which observed many stars of Kepler While these tools are available to alleviate the problem of magnitude 6 or brighter, it is possible to determine variability systematic pointing drift errors in the lightcurves, our sam- in these systems that was previously unseen due to the lim- ple has a much greater problem - being so bright, each of the itations of ground based observations. While there have of targets is saturated and hence suffers from significant bleed course been observations of bright ecliptic stars from space columns. In theory, the flux of the star could be recovered observatories in the past, never before have we had access to by analyzing the entire bleed column of the target, but K2’s continuous long-cadence data, collected every half hour for limited bandwidth and large distance from makes it in- an entire campaign of approximately 80 days. feasible to download such a large amount of pixels. Instead, a When the original Kepler mission (Borucki et al. 2010) circular aperture around the saturated target can be requested, ended due to to the loss of two reaction wheels and by ex- ignoring the full length of the bleed column. This aper- tension the loss of the stable pointing that the spacecraft was ture has an economically feasible number of pixels, and the originally capable of, K2 was born. K2, Kepler’s extended pioneering technique of halo photometry from White et al. mission, uses the balancing pressure of sunlight of the satel- (2017) can be used to recover the flux and variability of the lite’s carefully aligned solar panels, as well as the two re- target even without access to all of the saturated pixels. Halo maining reaction wheels, to achieve pseudo-stable pointing photometry uses the flux from unsaturated pixels in the cir- (Howell et al. 2014). Still, K2 experiences systematic point- cular aperture surrounding the target to recover the total flux ing errors due to the thruster fires used to orient the space- by weighting these pixels to minimize the total variation in craft’s roll axis. This mechanism gives typical roll motion flux between successive measurements for the entire time se- in the focal plane of 1.0 pixels peak-to-peak over 6 hours at ries. It then corrects the reconstructed data with the K2SC the edges of the field, two orders of magnitude greater than pipeline. More detail can be found in White et al.(2017). In typical 6 hour pointing errors in the Kepler primary mission this work, we use the photometry corrected with this method to determine the power spectral density for 75 bright stars 2 GREKLEK-MCKEON &HUBER observed over K2 campaigns 11, 12, 13, and 14. From the the entire campaign, showing a constant increase in bright- power spectral density, we use iterative sinusoid fitting to ex- ness unlikely to be caused by true stellar variability. The tract the most dominant signals of variation from each star, majority of the lighcurves also exhibited significant non- and compare these variable modes to ones in the literature physical trends in flux at the start of each campaign, either that have been previously identified. We also search for os- displaying a steep rise or a steep drop in flux before leveling cillation signatures in the power spectra of the red giants in off for the remaining time in the campaign (see figure 2a & the sample. 2b). These features often took place on a scale of days and Our sample contains both "classical pulsators", objects that displayed up to a 40% change in relative flux, and are caused show a high amount of power at a few dominant frequen- by thermal effects as the CCD stabilized at the start of each cies and very low power elsewhere, as well as red giants, campaign. Data exhibiting clear systematic signatures such which are "solar-like oscillators". These red giants have as these even after running through the correction pipelines many frequencies over which the power is spread. Instead were simply trimmed away. of one dominant mode with power high above the noise, they Next, a convolution was found for each system and divided have a frequency where the power is highest among the en- out to remove any remaining long term trends unlikely to be velope around the nearby spread, commonly referred to as true stellar variability (these can be seen in figure 2 frames c νmax. We identify many previously unknown variabilities in & d). This was performed with astropy’s convolution model these bright stars, and detail several corrections to "known" (http://astropy.org/convolution), which uses a 2D Gaussian stellar pulsation periods. Furthermore, we analyze the rela- kernel run over the data at each point to produce the convo- tionship of low-frequency, 8hr granulation noise to the color lution function. For the Gaussian kernel, in an effort to pass and in the red giants of our sample, as well as a wide filter and retain the short-period stellar signals spe- in the prewhitened power spectra of the dwarfs. This rela- cific to each system, we varied between a 96 data point or tionship was initially established by Kallinger & Matthews 2-day standard deviation and a 240 data point or 5-day stan- (2010) and explored further by Bastien(2015), and Bastien dard deviation. The first half of the dataset was mirrored back et al.(2016). Finally, we examine the system of 36 Ophiuchi, from the start of the time series, and the second half mirrored the only K dwarf in this sample of bright stars, and inject ex- forward from the end of the time series, to remove the edge oplanet transits modeled with BATMAN (Kreidberg 2015) effects of the convolution function (visible in figure 2 frames to constrain the parameters of a detectable planet given the c & d as the blue points). After dividing out the convolu- star’s inherent variability. tion function to remove the long term variations, we began analyzing the stars in frequency space. 2. METHODOLOGY The stars in this sample include 11 targets from cam- paign 11, 8 from campaign 12, 35 from campaign 13, and 21 from campaign 14. Their visual magnitudes range from 1.62 to -0.62. In order to understand the distribution of the sample, we used each target’s EPIC ID to retrieve its co- ordinates from MAST (https://archive.stsci.edu/), and then match this to a known object in the SIMBAD database (https://archive.stsci.edu/), from which we collected information, and V and B magnitudes. We used these quan- tities to calculate the distance to each system, the absolute visual magnitude, and the color, to create a color-magnitude diagram of our sample, shown in figure 1, and described fur- ther in the results section. 2.1. Light Curve Detrending For each star in this sample, we used data from the halo photometry routine described in the introduction as our cor- rected beginning flux time series. Data from campaigns 11 and 12 was split into two distinct observing periods, which we joined together. Then, each lightcurve was examined in- dividually, and edited to remove systematic effects such as Figure 1. Color-Magnitude Diagram of the stars observed in this clear steep drop-offs followed by near instantaneous correc- study, with the shown in purple. tion due to thruster fires, or an overall ramping effect through VARIABILITYOF BRIGHT ECLIPTIC STARS 3

2.2. Frequency Analysis is the result of individual convection cells reaching a stel- After detrending each star’s time series, a Lomb-Scargle lar surface in close proximity. As the hot plasma rises in a periodogram was calculated from the corrected flux (cor- convection cell and reaches the surface, it cools and begins rected flux visible in figure 2 frame e, periodogram in frame to descend back into the stellar interior, streaming along the f). Python’s scipy.signal.lombscargle module was used to edges of the convection cell as the charged particles follow calculate the periodogram. The Lomb-Scargle periodogram the magnetic field lines. Because of this, the tops of these determines the relative power of weak periodic signals over convection cells, which is what we observe with photome- a range of frequencies from 0 to the Nyquist frequency, in a try, have centers that are relatively brighter than their edges sample with uneven temporal sampling, with total power nor- - the material rising from inside the star is hotter than the malized to 1, as detailed by Lomb(1976)& Scargle(1982). material falling back inside. When we observe the many in- The full example of the starting flux after systematic correc- dividual granules (the solar surface has 4 million granules, tion and halo photometry, followed by the convolution, and for example), the pattern of their edges creates a grainy ap- the resulting corrected flux and power spectrum, is shown pearance, and this manifests as a frequency-dependent sig- in figure 2, with the steps of photometric correction and nal in the power spectrum. Measurement of granulation can frequency analysis in the frames progressing alphabetically. lead to useful information, such as (Bastien The Lomb-Scargle periodogram was also plotted in log space et al. 2013) and interior structure characteristics derived from (figure 2 frame h) for easier identification of the frequency of asteroseismic models, which has previously been done with maximum power in stars. Further calculations done red giants in the Kepler sample (Mathur et al. 2011). Red on this reduced data are discussed in the results section. giants are known to exhibit granulation, while intermediate After finding the power spectrum, we were able to iden- mass classical pulsators (e.g. A type stars) are commonly tify the dominant frequencies of variability for each system not predicted to exhibit, and currently lack a wide body of in the sample. By compiling a table of the previously iden- observational evidence for, granulation. This is because the tified frequencies of variation in these stars from both the dominant mode of energy transport at their surface is theo- General Catalog of Variable Stars (Samus’ et al. 2017) and rized to be radiative, so they would lack the convection cells the SIMBAD Astronomical Database (Wenger et al. 2000), necessary to produce a granulation signature. we were able to identify which specific stellar variabilities We examine this potential further by taking the subset of were new discoveries made possible by the enhanced photo- our sample that only includes dwarf stars. The cut used for metric capability of K2. These specific cases are described this determination is a B-V index less than 0.6, and a spectral in the following section. type of O, B, or A. This is noteworthy because these types After comparing our detected variabilities to those doc- of dwarf stars are not expected to show signs of granula- umented in prior literature, we sought to quantify the fre- tion, a minute- to day-scale recycling of hot and cold mate- quencies, amplitudes, and phases of the strongest oscillation rial manifesting at the stellar surface and causing brightness modes of the classical pulsators observed in our sample. To fluctuations, because they are theorized to be dominated by do this, we used SIGSPEC (Reegen 2007), a program that purely radiative energy transport at the surface (Kallinger & employs an iterative prewhitening sequence of sinusoid com- Matthews 2010). We remove only the most dominant fre- ponent fitting to calculate the significance spectrum of our quencies from these systems, defined through trial and er- corrected time series. For each sample, SIGSPEC returns the ror by us as any signal with a significance higher than 12. significance of each dominant frequency in the time series, We then take the residual time series after these frequen- which we can then compare to those observed in our Lomb- cies are removed, and recalculate the power spectra. These Scargle periodograms, considering that some of the other prewhitened power spectra are then used, along with the orig- strong frequencies present in the time series may have been inal power spectra of the red giants, to determine the power washed out by the comparatively larger power of the most of the system near the low frequency of 8 hours (this is typi- ± µ dominant oscillations. SIGSPEC defines "significance" in the cal of granulation), or 3.86 1 Hz, an analogous measure following way: if a frequency has a significance of 5, there of potential granulation based "flicker" noise examined by is a 1 in 105 chance that the amplitude corresponding to this Bastien(2015). The relationship of the power in this region frequency is due to noise alone. We compute all frequencies to the luminosity of the system was then examined, after we with a significance greater than 5 for each classical pulsator obtained Luminosity values for each system using the VIZIER in the sample. catalog (McDonald et al. 2017).

2.3. Measurement of Granulation 2.4. Exoplanet Signal Injection - 36 Ophiuchi Granulation is a surface feature of stars with convective The vast majority of the stars in this sample are either envelopes that can be observed from photometric data. It bright dwarfs of spectral type O/B/A, or red giants of spec- 4 GREKLEK-MCKEON &HUBER

Figure 2. An example of the convolution correction and power spectrum calculation process with kappa Piscium. tral type F/G/K (see figure 1). There is one notable exception, spheres (Gardner et al. 2006). 36 Oph is one of the brightest however - 36 Ophiuchi. This is a nearby K dwarf triple sys- K dwarfs in the sky and would be an optimal target for JWST, tem, whose main component (and the one observed by Ke- but TESS does not observe in the ecliptic, so setting limits on pler), has been the specific subject of several previous studies transiting planets is especially important. (Cayrel de Strobel et al. 1989; Irwin et al. 1996). It has even We use BATMAN to model the transit light curve of var- been examined in a previous work that determined the lim- ious possible planets. We take the inclination to be 90 de- its of detectable planet parameters (Wittenmyer et al. 2006). grees, eccentricity to be 0, and obtain quadratic limb darken- This previous study used radial velocities to determine min- ing values from the VIZIER-hosted table of Claret & Bloemen imum mass limits for 36 Oph over various orbital periods (2011), using a temperature from Taylor(2005) and calcu- and eccentricities. We perform a similar analysis, exploring lated a logg value from mass and radius values reported in the parameter space of planetary radius and orbital period Demory et al.(2009) to query. We also use the mass value around 36 Oph using photometry, which is also what TESS to calculate the semimajor axis of each injected planet’s or- will use. TESS was designed to discover the closest transit- bit, given its orbital period, using Newton’s version (Newton ing planets to Earth (Sullivan et al. 2015), while JWST can 1687) of Kepler’s third law (Kepler 1619). perform follow-up observations to characterize their atmo- VARIABILITYOF BRIGHT ECLIPTIC STARS 5

We explore potential planets with radii ranging from sub- oscillation modes upon inspection of the periodogram in log Earth to super-Jupiter, and periods between 0.5 days and 40 space, and report the center frequency of these oscillations. days (the longest that would be detectable from a single cam- The identification of new variability in this sample of paign in K2). We make the modeled transit, and then impose bright stars can also be visualized in the color-magnitude di- it at a random phase on the corrected and deconvolved light agram of the sample. Figure 3 shows the CMD again, but curve of 36 Ophiuchi, and then calculate the periodogram us- where each system in which new variability was detected is ing the same procedure described in section 2.2. We search highlighted in red. It is apparent from this figure that the the region of the power spectra near where the frequency of majority of the stars in this sample did not previously have the planet is known to be (the injected frequency ± 2%), and recorded variability (even though they have been studied ex- if the power here rises above the noise floor, we count the tensively from the ground), although this is less true of the planet as detectable. An example of this process is shown in classical pulsators than it is of the red giants. This is to be figure 3, where the corrected light curve with injected tran- expected, because classical pulsators exhibit coherent, phase sits and corresponding power spectra are shown for planets of stable pulsations over much of their lifetimes, and these rel- 0.5, 1, and 2 Earth radii. These radii span the range of clearly atively high-power oscillations are more easily discernible detectable, to detectable, to not detectable given the inherent with lower resolution in the power spectrum. variability of the star. We further explore the full range of radii and periods, monitoring whether each combination is 3.2. Granulation in Classical Pulsators detectable or not, and create a plot showing the detectability Beyond the primary results of documenting the newly de- distribution, described further in the results section. tected variabilities, we examine the relationship between the power spectra of the red giants, which show a clear frequency 3. RESULTS dependent noise that ramps up towards lower frequencies, common evidence for granulation, and compare these to the 3.1. New Variable Stars power spectra of our prewhitened bright dwarfs, mostly A The most fundamental results of this study are the discov- stars, after their dominant pulsation modes have been re- ery of new variability in the majority of the bright stars in this moved. This line of analysis was initially unplanned. While sample. We split the sample into two categories - red giants, we were performing the frequency analysis for the classical and bright dwarf stars (O, B, A, and some F types), with the pulsators, we noticed that many exhibit frequency-dependent notable exception of the single K dwarf, 36 Ophiuchi. For noise in their power spectra after prewhitening, which resem- the dwarf stars, which exhibited in their power spectra clear bled granulation in red giants. A detailed comparison indeed classical pulsation modes, we use the prewhitening sequence revealed a striking resemblance between the red giant and described in the methodology to extract the most significant prewhitened pulsator power spectra. Figure 4 shows two such frequencies of oscillation for each star. Table 1 lists these stars plotted on top of each other. systems, along with the 5 most significant oscillation fre- Most of the other prewhitened classical pulsators showed quencies for each star, along with the exact significant values the same significant similarity to the seemingly convection- of these frequencies (defined in the same way as in the in- influenced power spectra of the red giants. While it is not troduction), a flag indicating whether this star is a previously widely accepted that granulation is typical of classical pul- known variable (0 for known, 1 for unknown), and a listing sators, potential evidence for granulation in A stars has been of the reference for the variability if it is known. As is clear put forth before (Kallinger & Matthews 2010), and previous from the table, we discover seven new classical pulsator stars works have established power law relationships between the - d Ophiuchi, HD 154779, 14 Psc, tau Tau, 80 Tau, k Tau, and low-frequency granulation power at 8 hours and the luminos- l Leo. Each of these stars has at least four oscillation modes ity (Bastien 2015) as well as the surface gravity (Bastien et al. with significances greater than 10. For each of the 14 classi- 2016) of the stars. We adopted a similar approach to verify cal pulsators that were already known, we discover at least 2 the nature of the relationship between the background signal new and significant oscillation frequencies for the star. of the classical pulsators to their luminosity, by summing the Table 2 provides similar results, but for the red giants in the power in a frequency bin near the 8 hour mark, described in sample. For each system, the name is listed, along with the the methodology. Figure 5 shows the H-R diagram diagram frequency of maximum power (νmax), a corresponding flag to of the sample, but color coded for 8hr power intensity, or indicate whether the system was known to be an oscillating 8hr "flicker". When looking at the red giants, it is clear that red giant or not (0 for known, 1 for unknown prior to this the general trend is towards greater power in the low gran- study), and the appropriate reference if it was known. In this ulation frequencies the more luminous the giant is (dots get grouping, 12 stars were previously identified as oscillating lighter further towards the top right corner). Interestingly, the red giants. We identify an additional 24 red giants with clear prewhitened classical pulsators have a similar general trend. 6 GREKLEK-MCKEON &HUBER

Figure 3. Injected transits for 36 Ophicuhi and the resulting power spectra, which are used to determine if the planet is detectable or not. VARIABILITYOF BRIGHT ECLIPTIC STARS 7

Figure 6. H-R diagram, color-coded for 8hr flicker power. Clas- sical pulsators are plotted as circles, while red giants are plotted as squares.

Figure 4. A color-magnitude diagram of the sample where each of the newly discovered variable stars are highlighted in red.

Figure 7. 8-hour "flicker" power versus luminosity for the red gi- ants and prewhitened classical pulsators in the sample. CP stands for Classical Pulsator, and RG for Red Giant.

expected to correlate with luminosity, which has previously been shown empirically (Kjeldsen & Bedding 2011). We find that this relationship between 8hr granulation power and lu- Figure 5. Power spectra of red giant HD 34579 plotted on top of minosity does indeed have a positive correlation (although it the prewhitened power spectra of classical pulsator k Tau. is quite loose) for both red giants and prewhitened classical pulsators in our sample, as can be seen in figure 6. This may be the first evidence from a large sample of stars that shows granulation in A, F, and possibly even B type stars. 3.3. Transit Observation Constraints To more closely examine this relationship, we obtain the lu- There is one star in this sample that stands out from the minosities and temperatures of these stars from the VIZIER rest - 36 Ophiuchi, a triple whose main compo- catalog (McDonald et al. 2017), and plot the relationship be- nent (and the one observed by K2) is a K dwarf that hap- tween flicker intensity and luminosity in both the red giants pens to be bright enough to saturate the CCD because of its and the prewhitened pulsators. Convection, the process re- 5.98 distance. This is one of the closest and bright- sponsible for granulation, is expected to be more vigorous est stars that is similar to the Sun, and is of particular inter- the more luminous a star is, and thus granulation power is est for potential exoplanet transit observations and follow-up 8 GREKLEK-MCKEON &HUBER

high-precision space-based photometry for an extended pe- riod. Our main conclusions are as follows: • We discover 7 new classical pulsators - d Ophiuchi, HD 154779, 14 Psc, tau Tau, 80 Tau, k Tau, and l Leo.

• We discover new oscillation modes in 28 known clas- sical pulsators.

• We discover 24 new oscillating red giants.

• We find evidence for (somewhat unexpected) granula- tion in A and B stars, with the largest sample yet to explore this feature.

– We show that granulation power in the classical pulsators of the sample is positively correlated to Figure 8. Detectability of synthetic exoplanet transit injections for their luminosity. 36 Ophiuchi. • We set constraints on the radii and orbital periods of with JWST. We set constraints on the radius and period of potentially observable exoplanets around the K dwarf transiting planets that could potentially be observed around 36 Ophiuchi. 36 Ophiuchi given the stellar background signal from K2’s space based photometry. We probe radii from sub-Earth to – Planets as small as 0.8 RE with 40-day periods super-Jupiter size, and orbital periods from 0.1 days to 40 are easily detectable, and set the lower limit. days (the longest transit that could be observed twice by a single K2 campaign). We model transits using BATMAN The main results of this study, the discovery of new pul- (Kreidberg 2015). Each model assumes an inclination of 90 sating stars and new pulsation modes, would not be possi- degrees, 0 eccentricity, a 90 degree longitude of periastron. ble without halo photometry. These bright stars represent We use quadratic limb-darkening parameters obtained from K2 campaigns 11-14, but with K2 campaign 19 beginning the temperature and logg values described in the methodol- soon, and halo photometry apertures selected for all naked- ogy. Transits are modeled over the entire K2 time series and eye brightness targets since campaign 11, there is a lot more then injected at a random phase. We probe the previously halo photometry analysis to be done. Many more new vari- defined period/radii space at 2500 individual points, each of able stars can be discovered, and the signature of granulation which has a modeled transit injected into the lightcurve at in prewhitened classical pulsators can be explored further 100 random phases. The fraction of injections out of 100 for with more data. As it stands, the correlation is loose, and it a given period-radii pair that is detected in the power spec- will be beneficial to accumulate more bright targets to further trum is taken to be the detection rate. A plot of the detection confirm that these prewhitened classical pulsators exhibit fre- rates for given periods and radii is shown in figure 7. The quency dependent noise. With upcoming data from TESS, colorbar on the right represents the detection rate. It is clear halo photometry could also be applied to an ever greater that even at radii as small as 0.5 RE , short period planets (1- number of stars, vastly increasing the number of known vari- day) will be detectable around 36 Oph. The smallest planet able stars in the sky. that we can typically rule is around 0.8 RE . This size planet is essentially undetectable at periods greater than 20 days. 5. ACKNOWLEDGEMENTS MGM acknowledges support from Research Experience 4. CONCLUSIONS & DISCUSSION for Undergraduate program at the Institute for Astronomy, We have used halo photometry to analyze 75 stars ob- University of Hawaii-Manoa funded through NSF grant served by the K2 mission in campaigns 11-14. These stars 6104374. MGM would like to thank the Institute for As- are all of naked-eye brightness, magnitude 6 or less. This tronomy for their kind hospitality during the course of this is the first time these bright stars have been observed with project, and Daniel Huber for his mentorship and guidance.

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Table 1. Frequencies of maximum power for the red giants in the sample, and a flag indicating if they are newly discovered. References are listed by their identifier in the General Catalogue of Variable Stars reference list, which can be found at www.sai.msu.su/gcvs.

Red Giant νmax New Flag Reference

omi Oph A 1.20446 1

HD 157527 0.222633 1

HD 154783 0.109753 1

191 Oph 2.37582 1

26 Oph 0.801018 1

36 Oph 0.145636 0 1145

h Aqr 4.81473 1

24 Psc 0.688173 1

HD 217563 0.990573 1

HD 221081 0.365922 1

81 Aqr 1.05 1

HD 220406 0.132121 1

alf Tau 0.255233 0 03000

eps Tau 1.40674 1

tet01 Tau 13.6919 0 1619

del03 Tau 0.112658 0 04447

75 Tau 0.465727 0 0098

15 Ori 0.472544 1

HD 28527 0.00432158 0 1033

HD 33554 0.416976 1

HD 31539 0.751319 1

81 Tau 0.360645 0 0284

99 Tau 0.126395 1

51 Tau 0.705905 1

HD 28226 0.127201 1

105 Tau 2.55515 0 HIP

HD 31373 0.691244 1

HD 32482 0.318139 1 VARIABILITYOF BRIGHT ECLIPTIC STARS 11

Table 2. (continued)

Red Giant νmax New Flag Reference

HD 34579 29.1283 1

HD 30912 6.96059 1

HD 27901 2.07968 1

HD 34810 0.940048 1

V1116 Tau 0.754465 0 73326

85 Tau 0.126766 0 0948

58 Leo 1.50001 1

48 Leo 4.63426 1 p04 Leo 18.0867 0 0820

35 Sex 1.23626 1

43 Leo 0.57509 1

HD 96710 2.17167 1

HD 94515 0.512844 0 1846

HD 92706 0.0910505 1

31 Sex 2.85687 1

HD 90125 6.09341 1

HD 90155 2.0482 1

HD 90572 1.29757 1

HD 93101 0.513852 1

HD 88802 18.3401 1

HD 93244 0.0986907 1

HD 93169 0.508905 1

HD 94030 0.52006 1

HD 95004 0.507018 1

HD 94460 1.55094 1

HD 94318 0.880614 1 12 GREKLEK-MCKEON &HUBER

Table 3. Dominant frequencies and significances for the observed classical pulsators, where "sig" corresponds to the significance level. Refer- ences are listed by their identifier in the General Catalogue of Variable Stars reference list, which can be found at www.sai.msu.su/gcvs. A flag of 1 means the star was a previously known variable.

Name ν1 ν2 ν3 ν4 ν5 sig1 sig2 sig3 sig4 sig5 Flag Reference

tet Oph 7.1159 7.3693 7.4674 7.7654 0.7118 428.8070 115.4970 125.1390 119.4640 86.5145 0 4207

b Oph 0.0363 0.7771 0.0766 0.7612 0.0884 48.9715 48.9804 38.1136 34.2763 34.8890 0 2313

d Oph 0.0289 0.0616 0.0112 0.5867 0.0757 292.3740 109.4990 109.3350 130.3590 105.3300 1

c Oph 0.1076 0.9277 0.0182 1.8538 0.0386 115.4200 108.3050 75.0756 66.2497 70.5523 0 0807

HD 154779 0.0974 0.1934 0.0836 0.0322 0.0498 357.9490 254.9310 163.9860 168.2180 115.9510 1

kap Psc 0.7067 1.4135 0.0322 0.1099 0.0147 408.9240 557.2580 93.6344 94.2151 83.8700 0 09692

14 Psc 0.0003 0.5097 0.4628 0.5218 0.4466 0.0002 27.0422 19.5126 11.3256 10.8080 1

tet02 Tau 13.2293 13.6926 10.8643 12.8295 12.3955 208.7500 274.5620 163.4170 139.5020 152.9010 0 10088

kap01 Tau 15.1209 17.3891 19.8612 0.0773 1.1350 252.5680 227.0500 46.7825 17.1012 14.0070 0 1033

ups Tau 6.8099 4.0555 4.0916 4.1051 1.9900 261.9960 23.1152 14.8974 13.8591 10.4599 0 10091

tau Tau 1.3273 0.3392 0.6760 0.1710 1.5009 145.3170 176.0610 109.6720 92.4976 105.1510 1

rho Tau 15.9702 0.5155 0.8864 2.5266 1.1391 70.8503 13.1482 10.4453 10.6419 10.7362 0 08569

11 Ori 0.2156 0.4310 0.0094 0.0569 0.0417 637.3730 557.3680 243.0240 179.8770 133.2410 0 04013

i Tau 11.2330 14.3348 9.1779 0.0673 11.6477 91.4380 26.2438 23.7643 24.0900 24.5872 0 05830

kap02 Tau 15.1204 17.3901 12.3793 4.1163 0.0004 36.9397 20.2007 19.8826 13.0826 0.0003 0 1033

56 Tau 0.6375 1.2749 0.6145 1.1366 1.1187 583.5740 270.8420 74.7657 19.1384 20.0515 0 08824

53 Tau 0.2232 1.2818 0.0925 0.0005 0.2232 102.1120 29.1406 18.7923 0.0004 102.1120 0 70123

80 Tau 0.1213 0.2477 0.3672 1.7801 0.0401 141.9370 144.7840 41.2731 35.9116 26.0028 1

89 Tau 13.6588 0.1080 0.5073 0.0119 2.4978 112.9320 37.0828 22.1039 20.9696 13.4942 0 0284

k Tau 1.1523 0.5765 0.1077 1.7289 0.0923 375.6010 460.8520 59.6990 51.9272 48.2473 1

l Leo 6.9504 5.4806 2.3387 3.1332 2.6596 111.9740 56.2938 53.2606 53.9871 39.9775 1