OGLE-2018-BLG-1428Lb: a Jupiter-mass planet beyond the snow line of a dwarf star
Yun-Hak Kim, Sun-Ju Chung, Andrej Udalski, Andrew Gould, Michael D. Albrow, Youn Kil Jung, Kyu-Ha Hwang, Cheongho Han, Yoon-Hyun Ryu, In-Gu Shin, Jennifer C. Yee, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Hyoun-Woo Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge, Przemek Mróz, Radek Poleski, Marcin Wrona, Patryk Iwanek, Micha? K. Szyma?ski, Jan Skowron, Igor Soszy?ski, Szymon Koz?owski, Pawe? Pietrukowicz, Krzysztof Ulaczyk, Krzysztof Rybicki
MMNRAS , 1–5 (2020) Preprint 1 March 2021 Compiled using MNRAS L A TEX style file v3.0
OGLE-2018-BLG-1428Lb: a Jupiter-mass planet beyond the snow line ofa dwarf star
Yun-Hak Kim, , Sun-Ju Chung, , (cid:63) † Andrej Udalski, Andrew Gould, , Michael D. Albrow, Youn Kil Jung, Kyu-Ha Hwang, Cheongho Han, Yoon-Hyun Ryu, In-Gu Shin, Yossi Shvartzvald, Jennifer C. Yee, Weicheng Zang, Sang-Mok Cha, , Dong-Jin Kim, Hyoun-Woo Kim, , Seung-Lee Kim, , Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, , Byeong-Gon Park, and Richard W. Pogge (KMTNet Collaboration)Przemek Mr´oz, , Radek Poleski, Marcin Wrona, Patryk Iwanek, Michał K. Szyma´nski, Jan Skowron, Igor Soszy´nski, Szymon Kozłowski, Paweł Pietrukowicz, Krzysztof Ulaczyk , and Krzysztof Rybicki (The OGLE collaboration) Korea Astronomy and Space Science Institute, 776 Daedeokdae-ro, Yuseong-Gu, Daejeon 34055, Republic of Korea University of Science and Technology, Korea, (UST), 217 Gajeong-ro, Yuseong-gu, Daejeon 34113, Republic of Korea Warsaw University Observatory, AI. Ujazdowskie 4, 00-478 Warszawa, Poland Department of Astronomy, Ohio State University, 140 W. 18th Avenue, Columbus, OH 43210, USA Max-Planck-Institute for Astronomy, K¨onigstuhl 17, D-69117 Heidelberg, Germany Department of Physics and Astronomy, University of Canterbury, Private Bag 4800 Christchurch, New Zealand Department of Physics, Chungbuk National University, Cheongju 361-763, Republic of Korea Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot 76100, Israel Center for Astrophysics I Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA Department of Astronomy and Tsinghua Centre for Astrophysics, Tsinghua University, Beijing 100084, China School of Space Research, Kyung Hee University, Giheung-gu, Yongin, Gyeonggi-do, 17104, Republic of Korea Department of Astronomy, Chungbuk National University, Cheongju 361-763, Republic of Korea Division of Physics, Mathematics, and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA Department of Physics, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, UK accepted XXX. Received YYY; in original form ZZZ
ABSTRACT
We present the analysis of the microlensing event OGLE-2018-BLG-1428, which has a short-duration ( ∼ / host mass ratio q = . × − . Thanks to the detectionof the caustic-crossing anomaly, the finite source e ff ect was well measured, but the microlens parallax was not constraineddue to the relatively short timescale ( t E =
24 days). From a Bayesian analysis, we find that the host star is a dwarf star M host = . + . − . M (cid:12) at a distance D L = . + . − . kpc and the planet is a Jovian-mass planet M p = . + . − . M J with aprojected separation a ⊥ = . + . − . au. The planet orbits beyond the snow line of the host star. Considering the relative lens-source proper motion of µ rel = . ± .
38 mas yr − , the lens can be resolved by adaptive optics with a 30m telescope in thefuture. Key words: gravitational lensing: micro - planets and satellites: detection
The core accretion model proposes that gas giant planets originatedbeyond the snow line of their host stars, such as Jupiter and Saturnin the Solar system (Mizuno 1980; Pollack et al. 1996; Inaba et al.2003). This model predicts that it takes ∼ (cid:63) Corresponding author † E-mail: [email protected] to form such giant planets (Ida & Lin 2004; Laughlin et al. 2004;Boss 2006). For example, the formation time of gas giant planets fora 0 . M (cid:12) dwarf is (cid:38)
10 Myr, whereas its disk lifetime is <
10 Myr(Boss 2006). On the other hand, the disk instability model is thoughtto be more likely to form gas giants around beyond the snow lineof M dwarfs (Boss 2006). Actual detections of Jupiter-mass plan-ets orbiting low-mass stars (Boss 2006 and references therein) areconsistent with the disk instability model. Hence, these two planetformation models may actually complement one another, althoughthis remains uncertain.Currently, the majority of host stars with exoplanets are Sun-likestars, and their planets are mostly located inside the snow line. Those © a r X i v : . [ a s t r o - ph . E P ] F e b Kim Y.-H. et al. planets have been mostly discovered by the radial-velocity and tran-sit methods. However, most of stars in the Galaxy are low-mass Mdwarf stars, which are di ffi cult to observe with the two methods. Onthe other hand, the microlensing method typically detects low-massM dwarfs hosting planets located beyond the snow line. This is be-cause the microlensing depends only the mass of objects, not thelight. Therefore, microlensing provides very important samples toconstrain planet formation models including the core-accretion andgravitational instability models.However, a majority of masses of microlensing planets were notdirectly measured but estimated from a Bayesian analysis, whichassumes that the planet hosting probability is independent of the hoststar mass (Vandorou et al. 2020; Bhattacharya et al. 2020). The lensmasses estimated from the Bayesian analysis can be confirmed fromhigh-resolution follow-up observations. This is because the lens andsource stars are typically separated each other within ∼
10 yearsafter the peak time of event, thus making it possible to discriminatethe two stars. Until now, the masses of 18 planetary lens systems(e.g., Bennett et al. 2006, 2015; Batista et al. 2015; Fukui et al.2015; Vandorou et al. 2020; Bhattacharya et al. 2020) have beenmeasured from high-resolution follow-up observations with Keck,VLT, Subaru, or HST.In addition, the masses of lens systems can be directly measuredfrom the measurement of two parameters of angular Einstein radius( θ E ) and microlens parallax ( π E ). However, it is usually hard to mea-sure the two parameters. This is because θ E can be measured fromevents with high-magnification or caustic-crossing features, while π E can be measured from the detection of the distortions induced bythe orbital motion of Earth on a standard microlensing light curve(Gould 1992). In general, the measurement of the microlens paral-lax is limited to events with long timescale t E (cid:38)
60 days or large π E to detect the light-curve distortion induced by the orbital motion ofEarth. This means that for short timescale events induced by low-mass objects (e.g., M dwarfs or brown dwarfs), it is di ffi cult to mea-sure the microlens parallax. The microlensing parallax measurementwas first reported in 1995 (Alcock et al. 1995), and it was due toa long timescale of the event, t E =
110 days. For the measurementof the microlens parallax for all events, it is required a simultaneousobservation of an event from Earth and a satellite (Refsdal S. 1966;Gould 1994). Then, the microlens parallax is measured from the dif-ference in the light curves as seen from the two observatories (Refs-dal S. 1966; Gould 1994). Over 900 events so far have been detectedfrom ground-based observations and the
Spitzer satellite, which isfor studying the Galactic distribution of planets (Zhu et al. 2017 andthe references therein). Also, the
Nancy Grace Roman ( Roman , for-merly
WFIRST ) satellite will be launched in near future (Spergel etal. 2015). With this satellite, it is expected to detect ∼ ∼
250 free-floating planets (John-son et al. 2020). Hence, the masses of over 1000 planetary systemscan be measured from the
Roman together with ground-based obser-vations, such as the Korea Microlensing Telescope Network (KMT-Net; Kim et al. 2016). However, we note that the main mass mea-surement method for the
Roman
Galactic Exoplanet Survey will bethe detection of the exoplanet host stars in the
Roman imaging data.The microlensing parallax between the Earth and
Roman will be dif-ficult to measure for most events for two reasons. First, most events The first event with a microlens parallax measurement was MACHO-LMC-5, which was discovered in 1993 (Alcock et al. 1997), but the parallaxmeasurement was only reported eight years after the event (Alcock et al.2001). detected by
Roman will be too faint to observe from the ground, par-ticularly with small telescopes. Second, for events without caustic-crossings, the separation between the Earth and
Roman ’s orbit atL2 will not be large enough to reveal a microlensing parallax mea-surement. Fortunately, for events with caustic-crossings the Earth-L2 separation yields a useful microlensing parallax measurement asWyrzykowski et al. (2020) demonstrate. Moreover, for events withanomalies due to terrestrial planets, the microlens parallax may bemeasurable even in the absence of caustic-crossing features (Gouldet al. 2003).Recently, planetary systems composed of low-mass dwarfs and agiant planet beyond the snow line of the dwarfs have been routinelydetected from the KMTNet microlensing survey, even though mostof masses of the host stars were estimated from a Bayesian anal-ysis. OGLE-2018-BLG-1428 is one such planetary system. In thispaper, we present the analysis of the planetary event OGLE-2018-BLG-1428, which has a short-duration caustic-crossing anomaly.Although the finite source e ff ect was measured from the caustic-crossing feature, the microlens parallax was not measured. There-fore, the physical parameters of the lens system are estimated froma Bayesian analysis. The planetary lensing event OGLE-2018-BLG-1428 is lo-cated at equatorial coordinates (RA , decl . ) J2000 = (17:42:11.69, − l , b ) = (1 . , . . field ofview (FOV) at the La Campanas Observatory in Chile. The eventlies in the OGLE-IV field BLG652 with a low cadence of Γ (cid:39) . − . − . In addition, the event is very near the edge of theOGLE chip and therefore has many missing data points due to smallpointing variations. In spite of this fact, OGLE alerted it at HJD-245000 (HJD (cid:48) ) = ∼ (cid:48) = FOV, which areindividually located at CTIO in Chile (KMTC), SAAO in SouthAfrica (KMTS), and SSO in Australia (KMTA). The event lies inthe KMT field BLG18 with cadence of Γ (cid:39) − . With this ca-dence, the anomaly was well covered by KMTNet. While most ofKMTNet data were taken in the I band, some of them were takenin the V band in order to characterize the source star. However, wefound that the extinction toward the event, A I = .
07, is high, andthus it is di ffi cult to use the V band data to constrain the source color.To estimate the source color ( I − H ), we used the H -band data of theVVV microlensing survey (Navarro et al. 2017, 2018), which willbe described in Section 4. The KMTNet data were reduced by py-SIS based on Di ff erence Image Analysis (DIA; Tomaney & Crotts(1996); Alard & Lupton (1998); Albrow et al. (2009)). MNRAS , 1–5 (2020) upiter-mass planet orbiting a dwarf star OGLE-2018-BLG-1428 is a binary lensing event with a clearcaustic-crossing anomaly, which lasts ∼ t , u , t E ), three binarylensing parameters ( s , q , α ), and the source radius normalized to theangular Einstein radius of the lens θ E ( ρ = θ (cid:63) /θ E ). Here, t is thepeak time of the event, u is the separation (in units of θ E ) betweenthe lens and the source at t , t E is the crossing time of the Ein-stein radius, s is the star-planet separation in units of θ E , q is theplanet-star mass ratio, and α is the angle between the source trajec-tory and the binary axis. In the binary lensing modeling process, theobserved fluxes of each observatory at a given time t are modeledas F i ( t ) = A i ( t ) f s , i + f b , i , where A i is the magnification at the i th ob-servatory and f s , i and f b , i are the source and blended fluxes at the i thobservatory, respectively. The ( f s , i , f b , i ) are obtained from a linear fit.In order to find the best-fit solution, we conduct a grid search over( s , q , α ), which have the ranges of − (cid:54) log s (cid:54) − (cid:54) log q < (cid:54) α < π , respectively. During the grid search, the ( s , q ) arefixed, and the other parameters ( t , u , t E , α, ρ ) are allowed to vary ina Markov Chain Monte Carlo (MCMC) chain. From the grid search,we find three local solutions including binary and planetary lensmodels with ( s , q , α ) = (1 . , . , . , (0 . , . , . . , . , . s , q ) = (1 . , . ∆ χ =
493 relative to the binary lens model. In this case, there is no s ↔ / s degeneracy. Figure 1 shows the light curve of the best-fitplanetary lens model. The best-fit lensing parameters are listed inTable 1.Because the source crosses the caustic, we should consider thelimb darkening of the finite source star in the modeling. Consideringthe source type (discussed in Section 4), we assume that the sourcehas solar metallicity, e ff ective temperature T e ff = g (cid:39) .
0, and microturbulent velocity v t = . − . Wethus adopt the limb darkening coe ffi cient Γ I = .
51 (Claret 2000)and use Equation (7) of Chung et al. (2019) for the source brightnessprofile.
Because the event timescale of t E =
24 days is relatively short andthe source is relatively faint ( I s = π E = ( π E , N , π E , E ),while the lens orbital motion is described by ds / dt and d α/ dt , whichare the instantaneous changes of the binary separation and the ori-entation of the binary axis, respectively. From this, we find that al-though the parallax + orbital model is improved by ∆ χ =
123 rela-tive to the standard model, it yields a weirdly high parallax magni-tude π E ∼ . χ improvement and checkfor systematics, we build the cumulative distribution of ∆ χ betweenthe two models as a function of time. As shown in Figure 2, the χ improvement comes from KMTC and KMTS (especially the former)while there is essentially no improvement for OGLE and KMTA. Wethus check the systematics of KMT data by binning them to 1 perday. These investigations show that the ∆ χ improvement primarilycomes from structures in the KMTC and KMTS data that are notseen in KMTA or OGLE. Thus, they are likely due to correlatednoise rather than a real signal.Hence, we conduct the parallax + orbital remodeling with partialdata sets for KMTC and KMTS and full data sets for KMTA andOGLE. We restrict KMTC and KMTS data to data taken over theanomaly, i.e., in the range 8330 . < HJD (cid:48) < ∆ χ between the standard and the parallax + orbital modelsis 16. No orbital motion, ( ds / dt , d α/ dt ) = (0 ,
0) is within 3 σ of thebest-fit values, meaning these parameters are not significantly de-tected. By contrast, ( π E , N , π E , E ) = (0 ,
0) is more than 3 σ from thebest-fit values, implying that there could be some real signal due toparallax. However, this does not mean the parallax has to be large.The contours are broad, allowing for a wide range of parallax val-ues. For example, the parallax values of ( π E , N , π E , E ) = (0 . , − . ∆ χ =
6. See Figure 3. Since thesevalues are not unreasonable, the parallax could be real. ff ect However, the parallax-like e ff ects could be due to xallarap (sourceorbital motion). We thus check the xallarap model. Figure 4 showsthe χ distribution for the best-fit xallarap solutions as a function of afixed binary source orbital period P . If the estimated parallax is real,the best-fit xallarap solution should appear at P = . P = . P = . P = . χ nearthe best solution. The ∆ χ between the best-fit parallax and xallarapsolutions is ∆ χ =
34. This suggests that the parallax solution iswrong and the large (and so, suspicious) parallax value is actuallydue to xallarap e ff ects or systematics in the data.We first check that all xallarap solutions are physically reasonable.For each xallarap solution, we have two key parameters: the orbitalperiod of binary source motion P and the counterpart of the parallax ξ E . The ξ E is defined as ξ E = a s / ˆ r E , where a s is the semimajor axis ofthe source and ˆ r E is the Einstein radius projected to the source plane.Thus, the source semimajor axis is a s = ξ E ˆ r E , ˆ r E / AU = θ E D S . (1)As discussed in Section 4, the source is a G-type giant in the bulge, θ E = .
377 mas, and we assume D S = . ∼ M (cid:12) , and then ˆ r E = .
02 AU.According to Kepler’s third law, M tot M (cid:12) (cid:32) P yr (cid:33) = (cid:18) a tot AU (cid:19) , (2)where M tot = M s + M comp and a s / a tot = M comp / M tot . Here M s and M comp are the masses of the source and source companion, respec-tively. We can parameterize Equation (2) by Q = M comp / M s . Equa-tion (2) then becomes(1 + Q ) Q = M s M (cid:12) ( P / yr) a . (3)Using the estimated ˆ r E , it is(1 + Q ) Q = .
036 ( P / yr) ξ . (4) MNRAS , 1–5 (2020)
Kim Y.-H. et al.
We solve this cubic equation for Q . Then if 0 . < Q < .
0, thesolution is “physically reasonable”. That is, the companion will be a“typical main-sequence star”.The results for each P and ξ E are Q = . , . , . , . , . , . , . , . , and 1456806 . P = . , . , . , . , . , . , . , .
6, and2 .
0, respectively. This means that the companion would be a verymassive black hole. Since the xallarap solution is not “physicallyreasonable”, the xallarap “signal” is certainly not due to realxallarap. Hence, it is due to systematics. This investigation providesfurther evidence that systematics cause the parallax “signal”.Therefore, we cannot measure the microlens parallax in this event(as was already anticipated due to its short Einstein timescale, t E =
24 days). As a result, we need a Bayesian analysis to estimatephysical parameters of the lens system, which will be discussed inSection 5.
KMTNet data were taken in the I - and V -bands in order to measurethe instrumental source color. Usually, the source color ( V − I ) ismeasured from the linear regression of the V on I flux. However,because there is only one V point that is su ffi ciently magnified togive a significant signal due to high extinction A I = .
07, we cannotmeasure a reliable ( V − I ).In order to determine a reliable source color, we use the VVV H -band catalog. Because the source is bright and the best-fit modelshows negligible blending ( f s (cid:29) f b ), we can attempt to measure theo ff set between the baseline object and the clump in the instrumen-tal color magnitude diagram (CMD). Here, we assume the baselineobject corresponds to the source star due to negligible blending.Figure 5 shows the calibrated ( I − H , I ) and ( V − I , I ) CMDs.The CMDs have been calibrated by first applying the OGLE-IV cal-ibration constants to the OGLE-IV data and then transforming theinstrumental KMTC pyDIA data to the calibrated OGLE-IV sys-tem, in which the KMTC data are already matched to the VVVstars before the calibration. From the ( I − H , I ) CMD, we find that( I − H , I ) cl = (3 . , .
55) and ( I − H , I ) s = (3 . , . ∆ ( I − H ) = − .
15. Using Bessel & Brett (1988), we find that thiscorresponds to ∆ ( V − I ) = − .
11. From the ( V − I , I ) CMD, wefind that ( V − I , I ) cl = (3 . , .
57) and ( V − I , I ) s = (3 . , . ∆ ( V − I ) = − .
26. Note that this baseline measurement derivesfrom a stacking of several dozen images and so is more reliable thanthe regression method, which relies on a single magnified V point.We finally adopt that ∆ ( V − I ) = − .
19 by taking the average ofthese two values. The instrumental source magnitude is I = . I = .
99 and f s = . θ (cid:63) is estimated from the intrinsic color and magnitude of the source, inwhich are determined from( V − I , I ) = ( V − I , I ) cl , + ∆ ( V − I , I ) . (5)With the measured ∆ ( V − I ) = − . I = .
99, and ( V − I , I ) cl , = (1 . , .
37) (Bensby et al. 2011, Nataf et al. 2013), we find that( V − I , I ) = (0 . , . VIK color-color relation of Bessel & Brett (1988) and the color-surfacebrightness relation of Kervella et al. (2004). From this, it is foundthat θ (cid:63) = . ± . µ as . With the θ (cid:63) and ρ , the Einstein angularradius of the lens is determined by θ E = θ (cid:63) /ρ = . ± .
026 mas (6) and the relative lens-source proper motion is µ rel = θ E / t E = . ± .
38 mas yr − . (7) Because the parallax measurement is unreliable, we perform aBayesian analysis to estimate physical properties of the lens, i.e., themass and distance. The Bayesian analysis implicitly assumes that allstars have an equal probability to host a planet of the measured massratio. The Bayesian analysis is carried out with the same proceduresas Jung et al. (2018) did, but we use a new Galactic model basedon more recent data and scientific understanding. The new Galac-tic model includes the bulge mean velocity and dispersions takenfrom
Gaia , disk density profile and disk velocity dispersion from theRobin-based model in Bennett et al. (2014), while the bulge meanvelocity is generally zero and the bulge density profile is the same asthe one in Jung et al. (2018). However, we know the proper motionof the source ( µ α , µ δ ) = ( − . ± . , − . ± . Gaia ,even though its error is big. We thus use the proper motion value asthe mean velocity of the source for bulge-bulge events.In addition, we should consider the extinction at a given distancefor the lens brightness. For the extinction to the lens A L , we use thefollowing equation (Bennett et al. 2015; Batista et al. 2015) A i , L = − e −| D L / ( h dust sin b ) | − e −| D S / ( h dust sin b ) | A i , S , (8)where the index i denotes the passband: V , I , or K , and the dust scaleheight is h dust =
120 pc. Here we adopt the extinction to the sourceof A I , S = .
98 and A K , S = .
35 from the VVV / KMTC CMD analysisand
VIK color-color relation of Bessel & Brett (1988), which werediscussed in Section 4.Figure 6 shows the results of the Bayesian analysis. From this,we find that the lens is a sub Jupiter-mass planet M p = . + . − . M J orbiting a star M h = . + . − . M (cid:12) at a distance D L = . + . − . kpc,and the projected star-planet separation is 3 . + . − . AU. This indi-cates that OGLE-2018-BLG-1428L is likely to be an M dwarf starhosting a sub Jupiter-mass planet beyond the snow line based on a snow = . M / M (cid:12) ) (Kennedy & Kenyon 2008). However, it couldbe also a K or a G dwarf. The lens distribution in Figure 6 showsthat the lens is located in the disk and bulge with equal probability.This is consistent with the relative proper motion of 5 . − .Figure 6 also shows the Bayesian distributions for the brightnessof host star. The distributions show that if the host star is a main-sequence star, its brightness is I L = . + . − . and K L = . + . − . .Considering the brightness of the giant source star with K = . ∼
240 times fainter than the source. This high con-trast between the source and the lens makes it di ffi cult to resolvethe two stars by follow-up observations. However, for both MOA-2007-BLG-400 (Bhattacharya et al. 2020) and MOA-2013-BLG-220 (Vandorou et al. 2020), the lens mass measured from Keckis much closer to the 2 σ upper limit from the Bayesian analy-sis than the median. Thus, considering the 2 σ upper limit of thelens brightness, the lens with K = . K = .
9, which is ∼
10 times fainter than the source and is ∼
50 mas away from the source, can be detected at a separation of0 .
53 FWHM with Keck. For this event, because the proper motionis 5 . − , the lens will be separated from the source by 56 masin 2028. Hence, it seems plausible that a lens at K ∼
17 would bedetectable by Keck, while for a lens at K ∼
21 it would be hard todetect with Keck. If the lens is a very faint star at K ∼
21, the lens
MNRAS , 1–5 (2020) upiter-mass planet orbiting a dwarf star can be resolved by a 30m telescope equipped with a state of the artlaser guide star adaptive optics system, even though the contrast be-tween source and lens is high. Such a measurement can resolve thenature of the lens and confirm the results of the Bayesian analysis. We analyzed the event OGLE-2018-BLG-1428 with a caustic-crossing feature. From the Bayesian analysis, it is found that thelens is a star M L = . + . − . M (cid:12) hosting a sub Jupiter-mass planet M p = . + . − . M J , at a distance D L = . + . − . kpc, and the pro-jected separation between the star and the planet is 3 . + . − . AU, sug-gesting that the planet orbits beyond the snow line of the host. Thelens distance distribution and the proper motion µ rel = . − indicate that the lens is located in the disk and bulge with equalprobability. The lens can be resolved by adaptive optics of a 30mtelescope in the future. ACKNOWLEDGEMENTS
Work by Y.-H. Kim and S.-J. Chung was supported by the KASI(Korea Astronomy and Space Science Institute) grant 2021-1-830-08. Work by A.G. was supported by JPL grant 1500811. Work byC.H. was supported by the grant of National Research Founda-tion of Korea (2019R1A2C2085965 and 2020R1A4A2002885). TheOGLE project has received funding from the National Science Cen-tre, Poland, grant MAESTRO 2014 / / A / ST9 / DATA AVAILABILITY
The data underlying this article will be shared on reasonable requestto the corresponding author.
REFERENCES
Alard, C., & Lupton, R. H. 1998, ApJ, 503, 325Albrow, M. D., Horne, K., Bramich, D. M., et al. 2009, MNRAS, 397, 2099Alcock, C., Allsman, R. A., Axelord, T. S., et al. 1995, ApJ, 454, L125Alcock, C., Allsman, R. A., Alves, D. R. et al. 1997 ApJ, 486, 697Alcock, C., Allsman, R. A., Alves, D. R. et al. 2001, Nature, 414, 617Batista, V., Gould, A., Dieters, S., et al. 2011, A&A, 529, 102Batista, V., Beaulieu, J.-P., Bennett, D. P., et al. 2015, ApJ, 808, 170Bennett, D. P., Anderson, J., Bond, I. A., et al. 2006, ApJ, 647, L171Bennett, D. P., Batista, V., Bond, I. A., et al. 2014, ApJ, 785, 155Bennett, D. P., Bhattacharya, A., Anderson, J., et al. 2015, ApJ, 808, 169Bensby, T., Ad´en, D., Mel´endez, J., et al. 2011, A&A, 533, 134Bessell, M. S., & Brett, J. M. 1988, PASP, 100, 1134Bhattacharya, A., Bennett, D. P., Beaulieu, J. P., et al. 2020,arXiv:2009.02329Boss, Alan P., 2006, ApJ, 643, 501Cameron, A. G. W. 1978, Moon Planets, 18, 5Chung, S.-J., Gould, A., Skowron, J. et al. 2019, ApJ, 871, 179Claret, A. 2000, A&A, 363, 1081Dong, S., Bond, I., Gould, A. et al. 2009, ApJ, 698, 1826Fukui, A., Gould, A., Sumi, T. et al., 2015, ApJ, 809, 74Gould, A. 1992, ApJ, 392, 442Gould, A. 1994, ApJ, 421, L75Gould, A. 2000, ApJ, 542, 785 Gould, A., Gaudi, B. S., Han, C. 2000, ApJL, 591, L53Ida, S., & Lin, D. N. C. 2004, ApJ, 604, 388Inaba, S., Wetherill, G. W., Ikoma, M. 2003, Icarus, 166, 46Johnson, S. A., Penny, M., Gaudi, B. S., et al. 2020, AJ, 160, 123Jung, Y. K., Udalski, A., Gould, A., et al. 2018, AJ, 155, 219Kennedy, G. M., & Kenyon, S. J. 2008, ApJ, 673, 502Kennedy, G. M., Kenyon, S. J., & Bromley, B. C. 2006, ApJ, 650, L139Kervella, P., Th´evenin, F., Di Folco, E., & S´egransan, D. 2004, A&A, 426,297Kim, D.-J., Kim, H.-W., Hwang, K.-H., et al. 2018, AJ, 155, 76Kim, H.-W., Hwang, K.-H., Shvartzvald, Y., et al. 2018b, arXiv:1806.07545Kim, S.-L., Lee, C.-U., Park, B.-G., et al. 2016, JKAS, 49, 37Laughlin, G., Bodenheimer, P., Adams, F. C. 2004, ApJ, 612, L73Mizuno, H. 1980, Progress of Theoretical Physics, 64, 544Nataf, D. M., Gould, A., Fouqu´e, P., et al. 2013, ApJ, 769, 88Navarro, M. G., Minniti, D., & Contreras-Ramos, R. 2017, ApJ, 851, L13Navarro, M. G., Minniti, D., & Contreras-Ramos, R. 2018, ApJ, 865, L5Penny, M. T., Gaudi, B. S., Kerins, E., et al. 2019, ApJ, 241, 3Pollack, J. B., Hubickyj, O., Bodenheimer, P., et al. 1996, Icarus, 124, 62Refsdal, S. 1966 MNRAS, 134, 315Shan, Y., Yee, J. C., Udalski, A., et al. 2019, ApJ, 873, 30Skowron J., Udalski, A., Gould, A., et al. 2011, ApJ, 738, 87Spergel, D., Gehrels, N., Baltay, C., et al. 2015, arXiv:1503.03757Tomaney, A. B. & Crotts, A. P. S. 1996, ApJ, 112, 2872Udalski, A., Szyma´nski, M. K., and Szyma´nski, G. 2015, AcA, 65, 1Vandorou, A., Bennett, D. P., Beaulieu, J. P., et al. 2020, AJ, 160, 121Yee, J. C., Han, C., Gould, A., et al. 2014, ApJ, 790, 14Yee, J. C., Shvartzvald, Y., Gal-Yam, A., et al. 2012, ApJ, 755, 102Wyrzykowski, L, & Mandel, I. 2020, A&A, 636, 20Yoo, J., DePoy, D. L., Gal-Yam, A., et al. 2004, ApJ, 603, 139Zhu, W., Udalski, A., Calchi Novati, S., et al. 2017, AJ, 154, 210MNRAS , 1–5 (2020)
Kim Y.-H. et al.
Table 1.
Best-fit lensing parameters.Parameter χ / dof 2902 . / t (HJD (cid:48) ) 8339 . ± . u . ± . t E (days) 24 . ± . s . ± . q (10 − ) 1 . ± . α (rad) 1 . ± . ρ . ± . f s , kmt . ± . f b , kmt − . ± . f s , ogle . ± . f b , ogle . ± . (cid:48) = HJD - 2450000.
Table 2.
Physical lens parameters.Parameter M host ( M (cid:12) ) 0 . + . − . M p ( M J ) 0 . + . − . D L (kpc) 6 . + . − . a ⊥ (au) 3 . + . − . θ E . ± . µ rel (mas yr − ) 5 . ± .000
Physical lens parameters.Parameter M host ( M (cid:12) ) 0 . + . − . M p ( M J ) 0 . + . − . D L (kpc) 6 . + . − . a ⊥ (au) 3 . + . − . θ E . ± . µ rel (mas yr − ) 5 . ± .000 , 1–5 (2020) upiter-mass planet orbiting a dwarf star OGLEKMTC18KMTS18KMTA188300 8350 8400-0.200.2 HJD-2450000
Figure 1.
Lightcurve of the best-fit lensing model. The right inset shows the source trajectory crossing the planetary caustic.MNRAS , 1–5 (2020)
Kim Y.-H. et al.
Figure 2.
Cumulative ∆ χ between the standard and the parallax + orbital models. This shows that the χ improvement for the parallax + orbital model comesfrom KMTC and KMTS data sets.MNRAS000
Cumulative ∆ χ between the standard and the parallax + orbital models. This shows that the χ improvement for the parallax + orbital model comesfrom KMTC and KMTS data sets.MNRAS000 , 1–5 (2020) upiter-mass planet orbiting a dwarf star Figure 3. χ distributions of the parallax + orbital model with partial data sets of the anomaly range (restricted to the anomaly) for KMTC and KMTS and fulldata sets for KMTA and OGLE. MNRAS , 1–5 (2020) Kim Y.-H. et al.
Figure 4. χ distribution for the best-fit xallarap solutions as a function of a fixed binary source orbital period P . The red dot is the χ of the best-fit paral-lax + orbital model.MNRAS000
Figure 4. χ distribution for the best-fit xallarap solutions as a function of a fixed binary source orbital period P . The red dot is the χ of the best-fit paral-lax + orbital model.MNRAS000 , 1–5 (2020) upiter-mass planet orbiting a dwarf star Figure 5.
Calibrated ( I − H , I ) and ( V − I , I ) color-magnitude diagrams (CMDs). The red and blue dots are the red clump giant centroid and source position.MNRAS , 1–5 (2020) Kim Y.-H. et al.
Figure 6.
Bayesian distributions for physical parameters of the host star. The vertical solid line indicates the median value, while the two vertical dotted linesindicate the confidence intervals of 68%.MNRAS000