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1. INTRODUCTION two transit measurements including one incomplete light Thousands of transiting exoplanets have opened up a curve (the two light curves are refitted in this work – see wealth of opportunities to discern nuances of the plane- §3) in the discovery paper (Quinn et al. 2012). Further- tary formation and evolution processes. more, the mid-transit times of the light curves listed in 1 In favorable cases, the measurement of planetary the Exoplanet Transit Database (ETD) show substan- radii using transit photometry, combined with follow-up tial deviations from the linear ephemeris provided by Doppler velocimetery (RV) measurements to determine Quinn et al. (2012). As a consequence, follow-up obser- masses, can reveal the bulk densities of representatives vations are needed to consolidate the system parameters from the host of newly discovered super-Earths, deter- and to improve the overall characterization. mining, in turn, whether they are likely to be predomi- In this work, we present the first transit photometry nantly gaseous, watery or rocky worlds. Moreover, RV of HAT-P-25b since the discovery paper, covering eight observations taken during transit permit measurement transits. These new transits, when combined with the of projected spin-orbital misalignment angles, λ, of the published data from Quinn et al. (2012), allow us to re- transiting planets (e.g., Queloz et al. 2000; Winn et al. fine the physical and orbital parameters. Based on the 2005; Addison et al. 2014; Winn & Fabrycky 2015a; analysis of mid-transit times derived from all available Wang et al. 2018a). follow-up light curves (eight from this work, and two High-precision photometric follow-up observations from Quinn et al. 2012), we determine an updated lin- not only can confirm the planetary interpretation of ear ephemeris, as well as upper mass limits on potential a transit-signal detection, but they can also contribute nearby and long-term perturbers. to improve the accuracy of the planet’s physical and or- We proceed as follows. In § 2, we detail the observa- bital parameters (Wang et al. 2018b). Moreover, high- tions and data reduction. An analysis of the resulting precision photometric follow-up enables TTV assess- light curves is presented in § 3 and § 4 describes a dy- ments (e.g., Holman & Murray 2005; Agol et al. 2005), namical analysis of this system, and places the assess- which offer the prospect of detecting dynamically inter- ment of HAT-P-25 into the broader context provided esting perturbers. The architectures of systems that by the galactic planetary census, before segueing into contain hot Jupiters provide clues that can poten- the final section which contains a brief summary and tially distinguish between competing formation theo- overview. ries for hot Jupiters (Batygin et al. 2016) and can add 2. OBSERVATIONS AND DATA REDUCTION to the knowledge of general statistical trends of multi- Between September 2013 and November 2016, we planetary systems (e.g., Steffen et al. 2012; Huang et al. observed eight transits of HAT-P-25b with three tele- 2016). Moreover, high-precision multi-band transit pho- scopes at two different sites. Three of transits were ob- tometry permits exploration of the atmospheric prop- served with the 1-m telescope at the Weihai Observatory erties of close-in planets, notably conditions related to (WHOT; 122 02 58.6 E, 37 32 09.3 N) of the Shandong the presence and potentially the compositions of clouds ◦ ′ ′′ ◦ ′ ′′ University in China, and others were observed with the and hazes (e.g., Sing et al. 2016). Hence, we initialized 60 cm telescope and the 60/90 cm Schmidt telescope at the Transiting Exoplanet Monitoring Project (TEMP, Xinglong Station (117 34 30 E, 40 23 39 N) of the Na- Wang Y. et al. 2017) to study dozens exoplanet sys- ◦ ′ ′′ ◦ ′ ′′ tional Astronomical Observatories of China (NAOC). tems which have a lack of follow-up observations and/or show interesting TTV signals. We refine their system 2.1. Weihai Observatory parameters, and orbital ephemerides, and characterize Three Johnson V band transits of HAT-P-25b were their dynamical histories by collecting and analyzing obtained at Weihai Observatory in 2013, on September high-precision photometric light curves. 25, October 17 and November 30. With a 2K×2K imag- The transiting hot Jupiter HAT-P-25b was discov- ing array (13.5×13.5 µm pixel 1), the telescope provides ered by Quinn et al. (2012) under the auspices of the − a field of view of 12 × 12 , and a pixel scale of 0.35 HATNet project. The system comprises a G5 dwarf ′ ′ ′′ pixel 1. The full technical details of this telescope are star and a hot Jupiter, which has a transit period of − given Hu et al. (2014). P =3.652836±0.000019days. The host star (V =13.19) In each run, exposure times were held fixed to avoid has an effective temperature of T=5500±80 K and a adverse systematic effects on the measurements of mid- mass of 1.010±0.032 M . The mass and radius of the ⊙ +0.081 transit times. The time on the telescope and CCD con- planet were found to be 0.567±0.022 MJ and 1.190 0.056 − RJ. As one of the first targets in TEMP, the photo- metric characterization of HAT-P-25 relies primarily on 1 http://var2.astro.cz/ETD/index.php 3
Table 1. Log of Observations
Date Time Telescope FilterNumberofexposuresExposuretime Airmass Moon Phase Distancea Scatter a
(UTC) (UTC) (second) degree
2013 Sep 25 16:40:25-20:53:39 Weihai 1 m V 221 50 1.02-1.16 0.00 26.00 0.0035 2013 Oct 17 14:58:49-18:32:24 Weihai 1 m V 202 40 1.02-1.18 0.98 43.80 0.0053 2013 Nov 19b 12:15:18-16:21:57 Xinglong 60/90 cm Schmidt R 95 120 1.06-1.37 0.96 27.71 0.0030 2013 Nov 30 10:31:55-14:40:40 Weihai 1 m V 229 50 1.02-1.59 0.09 160.20 0.0029 2015 Feb 04 10:56:25-15:02:29 Xinglong 60/90 cm Schmidt R 136 80 1.04-1.92 0.99 170.00 0.0034 2016 Jan 17 11:07:08-15:29:06 Xinglong 60 cm R 121 100 1.04-1.55 0.59 20.20 0.0027 2016 Jan 28b 11:47:06-15:02:28 Xinglong 60/90 cm Schmidt R 65 150 1.06-1.67 0.80 125.67 0.0039 2016 Nov 04 16:44:43-21:12:31 Xinglong 60/90 cm Schmidt R 84 180 1.04-2.00 0.25 133.33 0.0020 a Distance is the mean of the distance between the target and the moon on the sky during the observation. Scatter represents the RMS of the residuals from the best-fitting transit model. b Due to bad weather, the observations were interrupted, resulting in two partial transit light curves.
trol computers were GPS-synchronized at a one-minute Table 2. Photometry of HAT-P-25 cadence. The HJD time stamps in each FITS header a log the mid-exposure time, and were logged from the BJDTDB Relative Flux Uncertainty Filter synchronized system time following the UTC time stan- 2456561.199442 1.0018 0.0028 R dard. For the accurate timing studies presented later 2456561.200288 1.0018 0.0028 R in this work, we converted the HJD time stamps in 2456561.201028 0.9954 0.0028 R the UTC time standard to BJD time stamps valid in 2456561.201881 0.9932 0.0028 R the TDB time standard. The precise time management 2456561.202627 0.9954 0.0028 R techniques that we used are described in Eastman et al. 2456561.203472 1.0008 0.0028 R (2010). 2456561.204225 1.0008 0.0028 R Using standard procedures, all data were debiased 2456561.205068 0.9954 0.0028 R and flat-fielded. Aperture photometry was then ob- 2456561.205809 1.0014 0.0028 R tained using the Source Extractor Software Package ...... (Bertin & Arnouts 1996). Transit light curves were ob- a Time stamps throughout the paper have been tained using differential photometry. The choice of aper- converted to BJDTDB. tures and photometric comparison stars were adjusted manually in order to produce the lowest scatter among observations taken out of transit. Due to bad weather, the observations on Nov 19, 2013, and Jan 28, 2016, were interrupted, which resulted in two partial transit light curves. 2.2. Xinglong Station, National Astronomical We used the same strategy described in §2.1 to han- Observatory dle the data from the observations performed at Xin- Between 2013 November and 2016 November, four glong station. A detailed summary of observations is Johnson R band transits of HAT-P-25b were obtained presented in Table 1. The resulting transit curves are with the 60/90 cm Schmidt telescope at Xinglong Sta- listed in Table 2, and are shown in Figures 1 and 2. tion of the NAOC of China, and an additional transit was monitored with the R-band using the 60 cm tele- 3. LIGHT CURVE ANALYSIS scope. The 60/90 cm Schmidt telescope is equipped To re-estimate the global parameters, we employed with a 4K×4K CCD, which gives a 94 × 94 field of ′ ′ Multi-EXOFAST2 (Eastman et al. 2013), a speed- view with a pixel scale of 1.38 pixel 1. The 60 cm tele- ′′ − optimized suite of exoplanet model-fitting software writ- scope is equipped with a 1K×1K CCD, which provides a 17′ ×17′ effective field of view with a pixel scale of 1.00′′ 1 2 pixel− . More instrumental details for these telescopes A description of the procedure can be found at can be found in Zhou et al. (1999) and Yang (2009). http://astroutils.astronomy.ohio-state.edu/exofast/. 4 ten in IDL which can fit multiple follow-up transit light to accurately measure mid-transit times of the eight curves in different filter bands and multiple RV telescope new light curves from this work and the two acquired data sets (Collins et al. 2017). The package is able to fit from the discovery paper (Quinn et al. 2012). In the transit data and RVs simultaneously, which can improve JKTEBOP fitting, we fixed all the parameters derived the quality of both fit types and provide a clearer pic- from previously global fitting and just considered the ture of the system under consideration (Eastman et al. mid-transit times and baseline fluxes as free parame- 2013). In our treatment, each data set was first fitted ters for each light curve to float. Using Levenberg- separately to scale the uncertainties and to derive a pre- Marquardt optimization, we derived mid-transit times. liminary best fit. Then, based on the Differential Evolu- Furthermore, through comparing uncertainties gener- tion Markov Chain Monte Carlo (DE-MCMC; ter Braak ated with the residual-permutation algorithm and with 2006), the package was employed to carry out a global fit Monte Carlo simulations (10,000 trials), we consistently to all data sets. This permitted refinement of the best chose the larger of the obtained uncertainties of mid- joint fit. Stellar parameters were calculated with the transit times to secure a conservative estimate. The help of Torres relations (Torres et al. 2008). By using mid-transit times derived using JKTEBOP are listed in standard MCMC techniques to evaluate the posterior Table 5. density, we determined robust uncertainty estimates for the parameters. As described in Eastman et al. (2013), 4. RESULT AND ANALYSIS a given Markov chain is considered to be converged when 4.1. System Parameters both the number of independent draws is greater than 1000 and the Gelman-Rubin statistic is less than 1.01 Based on the analysis described in §3, the updated for all parameters. During fitting, Markov chains that physical and orbital parameters for the HAT-P-25 sys- satisfy these criteria six consecutive times are considered tem, along with their errors, are presented in Tables 3. to be well-mixed. The tables also include estimates stemming from the For comparison purposes, we performed global fitting previous work (Quinn et al. 2012). The best-fitting for three different incarnations of the joint data set. In model of transit and RV are shown in Figures 1 and 3. the first incarnation, we used the eight new light curves The results of the three different global fits are mu- obtained from this work in conjunction with the RVs tually consistent. Moreover, compared with the qual- from discovery work (a set we call “8 new light curves ity of the parameters in the other two fittings, the “All + RVs”). In the second incarnation we used the two ex- light curves + RVs” fit indicates that the accuracy of tant light curves from the literature in conjunction with 28 of the 46 parameters has improved; all important RVs from the discovery work (a set we call “2 literature parameters were improved except stellar mass, effective light curves + RVs”). In the third incarnation we used temperature, metallicity, argument of periastron, plan- Multi-EXOFAST to deal with all ten above-mentioned etary mass, ingress/egress duration (details can be seen light curves in three different filter bands and the RVs in Table 3). Therefore, the result of the global fitting, from discovery work (a set we call “All light curves + the “All light curves + RVs” model, is regarded as the RVs”). The parameters we used in order to initialize the best global fitting result. Extrapolating from the RMS fits are from Quinn et al. (2012). The results from the residuals of individual fits shown in Figure 2 and the three different global fittings and their errors are pre- observation logs, we can conclude that with more favor- sented in Table 3. After making a comparison between able sky conditions and with the use longer exposure the fits (details can be seen in §4.1), we selected the pa- times (in conjunction with appropriate defocusing) one 4 rameters from the “All light curves + RVs” set as the might be able to obtain lower RMS . We have also car- best representation of the global parameters. The fitting ried out a similar model calculation that retains only results are shown in Figure 2. the V/Sloani/R-filter data. For that case, the plane- tary and stellar radius ratios RP /R in each band were With the best global parameters derived from the ∗ “All light curves + RVs” data set in hand, we ap- found to be within the radius measurement presented in plied the JKTEBOP routine3 (Southworth et al. 2004a; Table 4. From this we conclude that the current data Southworth et al. 2004b), a fast procedure that can ana- do not allow for differentiation between pass-band de- lyze light curves of detached eclipsing binaries and tran- siting extra-solar planetary systems. This allowed us 4 Because the light curves of ‘2013 Nov 19’ and ‘2016 Jan 28’ are partial, we performed a Pearson correlation analysis the corpus of data exclusive of these light curves. The Pearson correlation 3 Code is available in its entirety at coefficient between exposure times and RMS is r = −0.74, whereas http://www.astro.keele.ac.uk/jkt/codes/jktebop.html. that between the moon-target distance and the RMS is r = −0.36. 5
pendent radius measurements. In summary, our work provides the following updates to the characterization of the system provided in the discovery paper: Given that the same RVs were used, it comes as no surprise that we find all Doppler velocimetric proper- ties consistent with those of discovery work (Quinn et al. 2012). The RV parameters derived from our work are all