Issue 
A&A
Volume 529, May 2011



Article Number  A102  
Number of page(s)  16  
Section  Planets and planetary systems  
DOI  https://doi.org/10.1051/00046361/201016111  
Published online  12 April 2011 
MOA2009BLG387Lb: a massive planet orbiting an M dwarf^{⋆}
^{1} Probing Lensing Anomalies NETwork (PLANET)
^{2}
Institut d’Astrophysique de Paris, Université Pierre et Marie
Curie, CNRS UMR7095, 98bis
Boulevard Arago, 75014
Paris,
France
email: batista@iap.fr; beaulieu@iap.fr; cassan@iap.fr; marquett@iap.fr
^{3} Microlensing Follow Up Network ( μ FUN)
^{4}
Department of Astronomy, Ohio State University,
140 W. 18th Ave., Columbus, OH
43210,
USA
email: gouldsimkoz@ astronomy.ohiostate.edu; gaudisimkoz@ astronomy.ohiostate.edu; jdeastsimkoz@ astronomy.ohiostate.edu; jyeesimkoz@ astronomy.ohiostate.edu; poggesimkoz@ astronomy.ohiostate.edu; simkoz@ astronomy.ohiostate.edu; nick.morgan@alum.mit.edu
^{5}
Institute for Advanced Study, Einstein Drive, Princeton, NJ
08540,
USA
email: dong@ias.edu
^{6} Sagan Fellow
^{7} Microlensing Observations in Astrophysics (MOA)
^{8}
Institute of Information and Mathematical Sciences, Massey
University, Private Bag 102904,
North Shore Mail Centre, Auckland, New Zealand
email: i.a.bond@massey.ac.nz; l.skuljan@massey.ac.nz; w.lin@massey.ac.nz; c.h.ling@massey.ac.nz; w.sweatman@massey.ac.nz
^{9}
School of Physics and Astronomy and Wise Observatory, TelAviv
University, TelAviv
69978,
Israel
email: shai@wise.tau. ac.il; dani@wise.tau. ac.il; david@wise.tau. ac.il; shporer@wise.tau. ac.il; odedspec@wise.tau. ac.il
^{10}
Bronberg Observatory, Centre for Backyard Astrophysics,
Pretoria, South
Africa
email: lagmonar@nmisa.org
^{11}
Auckland Observatory, Auckland, New
Zealand
email: gwchristie@christie.org.nz
^{12}
Farm Cove Observatory, Centre for Backyard Astrophysics, Pakuranga,
Auckland, New
Zealand
email: farmcoveobs@xtra.co.nz
^{13} University of Canterbury, Department of Physics and
Astronomy, Private Bag 4800, Christchurch 8020, New Zealand
email: Michael.Albrow@canterbury.ac.nz
^{14} The RoboNet Collaboration
^{15} SUPA School of Physics and Astronomy, Univ. of St Andrews,
Scotland KY16 9SS, UK
email: md35@standrews.ac.uk; nk87@standrews.ac.uk; ep41@standrews.ac.uk; kdh1@standrews.ac.uk
^{16} Microlensing Network for the Detection of Small Terrestrial
Exoplanets (MiNDSTEp)
^{17}
Niels Bohr Institutet, Københavns Universitet,
Juliane Maries Vej 30,
2100
København Ø,
Denmark
^{18}
Centre for Star and Planet Formation, Københavns
Universitet, Øster Voldgade
57, 1350
København Ø,
Denmark
^{19}
McDonald Observatory, 16120 St Hwy Spur 78 #2, Fort Davis, TX
79734,
USA
email: caldwell@astro.as.utexas.edu
^{20}
European Southern Observatory, Casilla 19001, Santiago 19, Chile
email: sbrillan@eso.org; dkubas@eso.org
^{21}
Department of Physics, Institute for Basic Science Research,
Chungbuk National University, Chongju
361763,
Korea
email: cheongho@astroph.chungbuk.ac.kr
^{22}
University of Notre Dame, Department of Physics,
225 Nieuwland Science Hall,
Notre Dame, IN
465565670,
USA
email: bennett@nd.edu
^{23}
SolarTerrestrial Environment Laboratory, Nagoya
University, Nagoya,
4648601,
Japan
email: sumi@stelab.nagoya u.ac.jp; abe@stelab.nagoya u.ac.jp; afukui@stelab.nagoya u.ac.jp; furusawa@stelab.nagoya u.ac.jp; itow@stelab.nagoya u.ac.jp; kkamiya@stelab.nagoya u.ac.jp; kmasuda@stelab.nagoya u.ac.jp; ymatsu@stelab.nagoya u.ac.jp; nmiyake@stelab.nagoya u.ac.jp; mnagaya@stelab.nagoya u.ac.jp; okumurat@stelab.nagoya u.ac.jp; sako@stelab.nagoya u.ac.jp
^{24}
Department of Physics, University of Auckland,
Private Bag 92019, Auckland, New
Zealand
email: c.botzler@auckland.ac.nz; p.yock@auckland.ac.nz; yper006@auckland.ac.nz
^{25}
Mt. John Observatory, PO Box 56, Lake Tekapo
8780, New
Zealand
^{26}
School of Chemical and Physical Sciences, Victoria
University, Wellington, New Zealand
email: a.korpela@niwa.co.nz; denis.sullivan@vuw.ac.nz
^{27}
Department of Physics, Konan University,
Nishiokamoto 891,
Kobe
6588501,
Japan
^{28}
Nagano National College of Technology,
Nagano
3818550,
Japan
^{29}
Tokyo Metropolitan College of Industrial Technology,
Tokyo
1168523,
Japan
^{30}
University of the Free State, Faculty of Natural and Agricultural
Sciences, Department of Physics, PO
Box 339, Bloemfontein
9300, South
Africa
email: HoffmaMJ.SCI@mail.uovs.ac.za
^{31}
University of Tasmania, School of Mathematics and
Physics, Private Bag 37,
GPO, Hobart,
Tas
7001,
Australia
email: John.Greenhill@utas.edu.au; Andrew.Cole@utas.edu.au
^{32} Lawrence Livermore National Laboratory, Institute of
Geophysics and Planetary Physics, PO Box 808, Livermore, CA 945510808 USA
email: kcook@llnl.gov
^{33}
CEA/Saclay, 91191
GifsurYvette Cedex,
France
email: coutures@iap.fr
^{34}
Department of Physics, University of Rijeka,
Omladinska 14, 51000
Rijeka,
Croatia
^{35}
Technische Universitaet Wien, Wieder Hauptst. 810, 1040
Wienna,
Austria
email: donatowicz@tuwien.ac.at
^{36}
LATT, Université de Toulouse, CNRS, France
^{37}
Instituto Nacional de Pesquisas Espaciais,
Sao Jose dos Campos, SP, Brazil
^{38}
NASA Exoplanet Science Institute, Caltech, MS 10022, 770 south Wilson Avenue,
Pasadena, CA
91125,
USA
email: skane@ipac.caltech.edu
^{39}
Perth Observatory, Walnut Road, Bickley, Perth 6076, WA, Australia
email: Ralph.Martin@dec.wa.gov.au; Andrew.Williams@dec.wa.gov.au
^{40}
South African Astronomical Observatory,
PO box 9, Observatory
7935, South
Africa
^{41}
Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD
21218,
USA
^{42}
Astronomisches RechenInstitut, Zentrum für Astronomie der
Universität Heidelberg (ZAH), Mönchhofstr. 1214, 69120
Heidelberg,
Germany
^{43}
Institute of Astronomy, University of Zielona Góra,
Lubuska st. 2, 65265
Zielona Góra,
Poland
^{44}
Vintage Lane Observatory, Blenheim, New
Zealand
email: whallen@xtra.co.nz
^{45}
Perth, Australia
email: gbolt@iinet.net.au
^{46}
Molehill Astronomical Observatory, Auckland, New
Zealand
email: molehill@ihug.co.nz
^{47}
Department of Physics and Astronomy, Texas A&M
University, College
Station, TX,
USA
email: depoy@physics.tamu.edu
^{48}
Possum Observatory, Patutahi, New
Zealand
email: john_drummond@xtra.co.nz
^{49}
Department of Particle Physics and Astrophysics, Weizmann
Institute of Science, 76100
Rehovot,
Israel
email: avishay.galyam@weizmann.ac.il
^{50}
Hunters Hill Observatory, Canberra, Australia
email: dhi67540@bigpond.net.au
^{51}
Department of Physics, Technion, Haifa
32000,
Israel
^{52}
KoreaAstronomy and Space Science Institute,
Daejon
305348,
Korea
email: leecu@kasi.re.kr; bgpark@kasi.re.kr
^{53}
Campo Catino Austral Observatory, San Pedro de Atacama,
Chile
email: francomallia@campocatinobservatory.org; alain@spaceobs.com
^{54}
Kumeu Observatory, Kumeu, New Zealand
email: acrux@orcon.net.nz; guy.thornley@gmail.com
^{55}
AUT University, Auckland, New Zealand
email: tim.natusch@aut.ac.nz
^{56}
Palomar Observatory, California, USA
email: eran@astro.caltech.edu
^{57}
Einstein Fellow
^{58}
Southern Stars Observatory, Faaa, Tahiti, French
Polynesia
email: obs930@southernstarsobservatory.org
^{59}
School of Physics, University of Exeter,
Stocker Road, Exeter
EX4 4QL,
UK
^{60}
Astrophysics Research Institute, Liverpool John Moores
University, Liverpool
CH41 1LD,
UK
^{61}
European Southern Observatory, KarlSchwarzschildStraße 2, 85748
Garching bei München,
Germany
^{62}
Deutsches SOFIA Institut, Universität Stuttgart,
Pfaffenwaldring 31,
70569
Stuttgart,
Germany
^{63}
SOFIA Science Center, NASA Ames Research Center, Mail Stop
N2113, Moffett Field
CA
94035,
USA
^{64}
Jodrell Bank Centre for Astrophysics, The University of
Manchester, Oxford
Road, Manchester
M13 9PL,
UK
email: Eamonn.Kerins@manchester.ac.uk
^{65}
Max Planck Institute for Solar System Research,
MaxPlanckStr. 2, 37191
KatlenburgLindau,
Germany
^{66}
Astronomy Unit, School of Mathematical Sciences, Queen Mary,
University of London, Mile End
Road, London,
E1 4NS,
UK
^{67}
Las Cumbres Observatory Global Telescope network,
6740 Cortona Drive, suite 102,
Goleta, CA
93117,
USA
^{68}
Dept. of Physics, Broida Hall, University of
California, Santa
Barbara
CA
931069530,
USA
^{69}
Università degli Studi di Salerno, Dipartimento di Fisica
“E.R. Caianiello”, via Ponte Don
Melillo, 84085
Fisciano ( SA), Italy
^{70}
Istituto Internazionale per gli Alti Studi Scientifici
(IIASS), via G. Pellegrino
19, 84019
Vietri sul Mare ( SA), Italy
^{71}
INFN, Gruppo Collegato di Salerno, Sezione di Napoli,
Italy
^{72}
Royal Society University Research Fellow
^{73}
Institut für Astrophysik, GeorgAugustUniversität,
FriedrichHundPlatz 1,
37077
Göttingen,
Germany
^{74}
Institut d’Astrophysique et de Géophysique,
Allée du 6 Août 17, Sart Tilman, Bât.
B5c, 4000
Liège,
Belgium
^{75}
Department of Physics & Astronomy, Aarhus
University, Ny Munkegade
120, 8000
Århus C,
Denmark
^{76}
Armagh Observatory, College Hill, Armagh, BT61
9DG, UK
^{77}
Department of Physics, Sharif University of
Technology, PO Box
11155–9161, Tehran,
Iran
^{78}
Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK
^{79}
School of Astronomy, IPM (Institute for Studies in Theoretical
Physics and Mathematics), PO Box
193955531, Tehran,
Iran
^{80}
Dipartimento di Ingegneria, Università del Sannio,
Corso Garibaldi 107,
82100
Benevento,
Italy
^{81}
National Astronomical Observatories, Chinese Academy of
Sciences, A20 Datun Road, Chaoyang
District, Beijing
100012, P.R.
China
email: jinan@nao.cas.cn
^{82}
School of Physics, University of Western Australia,
Perth, WA 6009 Australia
Received:
8
November
2010
Accepted:
17
January
2011
Aims. We report the discovery of a planet with a high planettostar mass ratio in the microlensing event MOA2009BLG387, which exhibited pronounced deviations over a 12day interval, one of the longest for any planetary event. The host is an M dwarf, with a mass in the range 0.07 M_{⊙} < M_{host} < 0.49 M_{⊙} at 90% confidence. The planetstar mass ratio q = 0.0132 ± 0.003 has been measured extremely well, so at the bestestimated host mass, the planet mass is m_{p} = 2.6 Jupiter masses for the median host mass, M = 0.19 M_{⊙}.
Methods. The host mass is determined from two “higher order” microlensing parameters. One of these, the angular Einstein radius θ_{E} = 0.31 ± 0.03 mas has been accurately measured, but the other (the microlens parallax π_{E}, which is due to the Earth’s orbital motion) is highly degenerate with the orbital motion of the planet. We statistically resolve the degeneracy between Earth and planet orbital effects by imposing priors from a Galactic model that specifies the positions and velocities of lenses and sources and a Kepler model of orbits.
Results. The 90% confidence intervals for the distance, semimajor axis, and period of the planet are 3.5 kpc < D_{L} < 7.9 kpc, 1.1 AU < a < 2.7 AU, and 3.8 yr < P < 7.6 yr, respectively.
Key words: gravitational lensing: micro / methods: data analysis / planets and satellites: detection / methods: numerical / instrumentation: adaptive optics / instrumentation: photometers
Photometric data is only available in electronic form at the CDS via anonymous ftp to cdsarc.ustrasbg.fr (130.79.128.5) or via http://cdsarc.ustrasbg.fr/vizbin/qcat?J/A+A/529/A102
© ESO, 2011
1. Introduction
Over the past decade, the gravitational microlensing method has led to detection of ten exoplanets (Bond et al. 2004; Udalski et al. 2005; Beaulieu et al. 2006; Gould et al. 2006; Gaudi et al. 2008; Bennett et al. 2008; Dong et al. 2009b; Janczak et al. 2010; Sumi et al. 2010), which permits the exploration of hoststar and planet populations whose mass and distance are not probed by any other method. Indeed, since the efficiency of the microlensing method does not depend on detecting light from the host star, it allows one to probe essentially all stellar types over distant regions of our Galaxy. In particular, microlensing is an excellent method to explore planets around M dwarfs, which are the most common stars in our Galaxy, but which are often a challenge for other techniques because of their low luminosity. Roughly half of all microlensing events toward the Galactic bulge stem from stars with mass ≲0.5 M_{⊙} (Gould 2000).
Determining the characteristics and frequency of planets orbiting M dwarfs is of interest not only because M dwarfs are the most common type of stars in the Galaxy, but also because these systems provide important tests of planet formation theories. In particular, the core accretion theory of giant planet formation predicts that giant planets should be less common around lowmass stars (Laughlin et al. 2004; Ida & Lin 2005; Kennedy & Kenyon 2008; D’Angelo et al. 2010), whereas the gravitational instability model predicts that giant planets can form around M dwarfs with sufficiently massive protoplanetary disks (Boss 2006). In fact, there is accumulating evidence from radial velocity surveys that giant planets are less common around lowmass primaries (Cumming et al. 2008; Johnson et al. 2010). However, these surveys are only sensitive to planets with semimajor axes of <2.5 AU. Since it is thought that the majority of the giant planets found by radial velocity surveys likely formed farther out in their protoplanetary disks and subsequently migrated close to their parent star, it is not clear whether the relative paucity of giant planets around lowmass stars found in these surveys is a statement about the dependence on stellar mass of migration or of formation.
Microlensing is complementary to the radial velocity technique in that it is sensitive to planets with larger semimajor axes, closer to their supposed birth sites. Indeed, based on the analysis of 13 wellmonitored highmagnification events with 6 detected planets, Gould et al. (2010) found that the frequency of giant planets at separations of ~2.5 AU orbiting ~0.5 M_{⊙} hosts was quite high and, in particular, consistent with the extrapolation of the frequencies of smallseparation giant planets orbiting solar mass hosts inferred from radial velocity surveys out to the separations where microlensing is most sensitive. This suggests that lowmass stars may form giant planets as efficiently as do higher mass stars, but that these planets do not migrate as efficiently.
Furthermore, of the ten previously published microlensing planets, one was a “supermassive” planet with a very high mass ratio: a m_{p} = 3.8 M_{Jup} planet orbiting an M dwarf of mass M = 0.46 M_{⊙} (Dong et al. 2009a). Given their high planettostar mass ratios q, such planets are expected to be exceedingly rare in the coreaccretion paradigm, so the mere existence of this planet may pose a challenge to such theories. Gravitational instability, on the other hand, favors the formation of massive planets (provided they form at all).
Current and future microlensing surveys are particularly sensitive to large q planets orbiting M dwarf hosts, for several reasons. As with other techniques, microlensing is more sensitive to planets with higher q. In addition, as the mass ratio increases, a larger fraction of systems induce an important subclass of resonantcaustic lenses. Resonant caustics are created when the planet happens to have a projected separation close to the Einstein radius of the primary (Wambsganss 1997). The range of separations that give rise to resonant caustics is quite narrow for small q, but grows as q^{1/3}. Furthermore, although the range of parameter space giving rise to resonant caustics is narrow, the caustics themselves and their cross sections are large and also grow as q^{1/3}. Thus the probability of detecting planets via these caustics is relatively high, and such systems contribute a significant fraction of all detected events, particularly for supermassive planets orbiting M dwarfs. Events due to resonant caustics are particularly valuable, as they allow one to further constrain the properties and orbit of the planet. This is because these events usually exhibit caustic features that are separated well in time. When combined with the fact that the precise shape of a resonant caustic is extremely sensitive to the separation of the planet from the Einstein ring, such light curves are particularly sensitive to orbital motion of the planet (see, e.g., Bennett et al. 2010).
Fig. 1
Top: light curve of MOA2009BLG387 near its peak in July 2009 and the trajectory of the source across the caustic feature on the right. The source is going upward. We show the model with finitesource, parallax and orbital motion effects. Middle: magnitude residuals. Bottom: zooms of the caustic features of the light curve. 

Open with DEXTER 
Here we present the analysis of the microlensing event MOA2009BLG387, a resonantcaustic event, which we demonstrate is caused by a massive planet orbiting an M dwarf. The light curve associated with this event contains very prominent caustic features that are well separated in time. These structures were very intensively monitored by the microlensing observers, so that the geometry of the system is quite well constrained. As a result, the event has high sensitivity to two higher order effects: parallax and orbital motion of the planet. In Sect. 4, we present the modeling of these two effects and our estimates of the event characteristics. This analysis reveals a degeneracy between one component of the parallax and one component of the orbital motion. We explain, for the first time, the causes of this degeneracy. It gives rise to very large errors in both the parallax and orbital motion, which makes the final results highly sensitive to the adopted priors. In particular, uniform priors in microlensing variables imply essentially uniform priors in lenssource relative parallax, whereas the proper prior for physical location is uniformity in volume element. These differ by approximately a factor , where D_{l} is the lens distance. In Sect. 5, we therefore give a careful Bayesian analysis that properly weights the distribution by correct physical priors. The highmass end of the range still permitted is eliminated by the failure to detect flux from the lens using highresolution NACO images on the VLT. Combining all available information, we find that the host is an M dwarf in the mass range 0.07 M_{⊙} < M_{host} < 0.49 M_{⊙} at 90% confidence.
2. Observational data
The microlensing event MOA2009BLG387 was alerted by the MOA collaboration (Microlensing Observations in Astrophysics) on 24 July 2009 at 15:08 UT, HJD′ ≡ HJD−2 450 000 = 5037.13, a few days before the first caustic entry. Many observatories obtained data of the event. The celestial coordinates of the event are α = 17^{h}53^{m}50.79^{s} and δ = −33°59′25′′ (J2000.0) corresponding to Galactic coordinates: l = +356.56, b = −4.097.
The lightcurve is overall characterized by two pairs of caustic crossings (entrance plus exit), which together span 12 days (see Fig. 1). This structure is caused by the source passing over two “prongs” of a resonant caustic (see Fig. 1 inset). Obtaining good coverage of these caustic crossings posed a variety of challenges.
The first caustic entrance (HJD′ = 5040.3) was detected by the PLANET collaboration using the South African Astronomical Observatory (SAAO) at Sutherland (Elizabeth 1 m) who then issued an anomaly alert at HJD′ 5040.4 calling for intensive followup observations, which in turn enabled excellent coverage of the first caustic exit roughly one day later.
The second caustic entrance occurred about seven days later (HJD′ = 5047.1, see Fig. 1). That the caustic crossings are so far apart in time is quite unusual in planetary microlensing events. Since roundtheclock intensive observations cannot normally be sustained for a week, accurate realtime prediction of the second caustic entrance was important for obtaining intensive coverage of this feature. In fact, the second caustic entrance was predicted 14 h in advance, with a fivehour discrete uncertainty due to the wellknown close/wide s ↔ s^{1} degeneracy, where s is the projected separation in units of the Einstein radius. The closegeometry crossing prediction was accurate to less than one half hour and the causticgeometry prediction was almost identical to the one derived from the best fit to the full lightcurve, which is shown in Fig. 1.
The extended duration of the lightcurve anomalies indicates a correspondingly large caustic structure. Indeed, the preliminary models found a planet/star separation (in units of Einstein radius) close to unity, which means that the caustic is resonant (see the caustic shape in the upper panel of Fig. 1, where the source is going upward).
The event was alerted and monitored by the MOA collaboration. It was also monitored by the Probing Lensing Anomalies Network collaboration (PLANET; Albrow et al. 1998) from three different telescopes: at the South African Astronomical Observatory (SAAO), as mentioned above, as well as the Canopus 1 m at Hobart (Tasmania) and the 60 cm of Perth Observatory (Australia).
The Microlensing Follow Up Network (μFUN; Yoo et al. 2004) followed the event from Chile (1.3 m SMARTS telescope at CTIO) (V, I and H band data), South Africa (0.35 m telescope at Bronberg observatory), New Zealand (0.40 m and 0.35 m telescopes at Auckland Observatory (AO) and Farm Cove (FCO) observatory, respectively, the Wise observatory (1.0 m at Mitzpe Ramon, Israel), and the Kumeu observatory (0.36 m telescope at Auckland, NZ).
The RoboNet collaboration also followed the event with their three 2 m robotic telescopes: the Faulkes Telescopes North (FTN) and South (FTS) in Hawaii and Australia (Siding Springs Observatory) respectively, and the Liverpool Telescope (LT) on La Palma (Canary Islands). And finally, the MiNDSTEp collaboration observed the event with the Danish 1.54 m at ESO La Silla (Chile).
Observational conditions for this event were unusually challenging, due in part to the faintness of the target and the presence of a bright neighboring star. Moreover, the full moon passed close to the source near the second caustic entrance. As a result, several data sets were of much lower statistical quality and had much stronger systematics than the others. We therefore selected seven data sets that cover the caustic features and the entire lightcurve: MOA, SAAO, FCO, AO, Danish, Bronberg, and Wise. They include 118 MOA data points in I band, 221 PLANET data points in I band, 262 μFUN data points in unfiltered, R and I bands, and 300 MiNDSTEp data points in I band. We also fit the μFUN CTIO I and V data to the final model, but solely for the purpose of determining the source size. And finally, we fit μFUN CTIO Hband data to the lightcurve in order to compare the Hband source flux with the latetime Hband baseline flux from VLT images (see Sect. 2.1). The SAAO, FCO, AO, Danish, Bronberg, and Wise data were reduced by MDA using the PYSIS3 software (Albrow et al. 2009). The FCO, AO, Bronberg, and Wise images were taken in white light and suffered from systematic effects related to the airmass. Such effects were corrected by extracting lightcurves of other stars in the field with similar colors to the lens, and assuming that these stars are intrinsically constant.
For each data set, the errors were rescaled to make χ^{2} per degree of freedom for the best binarylens fit close to unity. We then eliminated the largest outlier and repeated the process until there were no 3σ outliers.
2.1. VLT NACO Images
On 7 June 2010, we obtained highresolution Hband images using the NACO imager on the Very Large Telescope (VLT). Since this was approximately 7.7 Einstein timescales after the peak of the event, the source was essentially at the baseline. The reduction procedures were similar to those of MOA2008BLG310, which are described in detail by Janczak et al. (2010).
To identify the source on the NACO frame, we first performed image subtraction on CTIO Iband images to locate its position on the Iband frame. We then used the NACO image to find relatively unblended stars that could be used to align the Iband and NACO frames. There is clearly a source at the inferred position, but it lies only seven pixels (0.19′′) from an ambient star, which is 1.35 mag brighter than the “target” (source plus lens plus any other blended light within the aperture). This proximity induces a 94% correlation coefficient between the photometric measurements of the two stars. We therefore estimate the target error as 0.06 mag. In the NACO system (which is calibrated to 2MASS using comparison stars) the target magnitude is (1)We have an Hband light curve (taken simultaneously with V and I at CTIO), and so (once we have established a model fit the light curve in Sect. 4) we can measure quite precisely the source flux in the CTIO system, H_{source,CTIO} = 20.03 ± 0.02. To compare with NACO, we transform to the NACO system using 4 comparison stars that are relatively unblended, a process to which we assign a 0.03 mag error, finding (2)The difference, consisting of light from the lens as well as any other blended light in the aperture, is 0.10 ± 0.07.
This excessflux measurement could in principle be due to five physical effects. First, it is reasonably consistent with normal statistical noise. Second, it could come from the lens. As we show in Sect. 5, this would be consistent with a broad range of M dwarf lenses. Third, it could be a companion to the source, and fourth, a companion to the lens. Finally, it could be an ambient star unrelated to the event. The fundamental importance of this measurement is that, for all five of these possibilities, the measurement places an upper limit on the flux from the lens, hence its mass (assuming it is not a white dwarf).
3. Source properties from color–magnitude diagram and measurement of θ_{E}
To determine the dereddened color and magnitude of the microlensed source, we put the best fit color and magnitude of the source on an (I,V − I) instrumental color magnitude diagram (CMD) (cf. Fig. 2), using instrumental CTIO data. The magnitude and color of the target are I = 20.62 ± 0.04 and (V − I) = −0.42 ± 0.01. The mean position of the red clump is represented by an open circle at (I,V − I)_{RC} = (16.36,−0.16), with an error of 0.05 for both quantities.
Fig. 2
Instrumental color − magnitude diagram of the field around MOA2009BLG387. The clump centroid is shown by an open circle, while the CTIO I and V − I measurements of the source are shown by a filled circle. 

Open with DEXTER 
Fit parameters for finitesource binarylens models.
For the absolute clump magnitude, we adopt M_{I,RC} = −0.25 ± 0.05 from Bennett et al. (2010). We adopt the measured bulge clump color (V − I)_{0,RC} = 1.08 ± 0.05 (Fig. 5 of Bensby et al. 2010) and a Galactocentric distance R_{0} = 8.0 ± 0.3 kpc (Yelda et al. 2010). We further assume that at the longitude (l = −3.4), the bar lies 0.7 kpc more distant than R_{0} (D. Nataf et al., in prep.), i.e., 8.7 kpc. From this, we derive (I,V − I)_{0,RC} = (14.45,1.08) ± (0.10,0.05), so that the dereddened source color and magnitude are given by: (I,V − I)_{0} = Δ(I,V − I) + (I,V − I)_{0,RC} = (18.71,0.82). From (V − I)_{0}, we derive (V − K)_{0} = 1.78 ± 0.14 using the Bessel & Brett (1988) colorcolor relations.
The color determines the relation between dereddened source flux and angular source radius (Kervella et al. 2004) (3)giving θ_{∗} = 0.63 ± 0.06 μas. With the angular size of the source given by the limbdarkened extendedsource fit (model 5, see Table 1), ρ_{∗} = 0.00202 ± 0.00003, we derive the angular Einstein radius θ_{E}:θ_{E} = θ_{∗}/ρ_{∗} = 0.31 ± 0.03 mas.
4. Event modeling
4.1. Overview
The modeling proceeds in several stages. We first give an overview of these stages and then consider them each in detail. First, inspection of the lightcurve shows that the source crossed over two “prongs” of a caustic, or possibly two separate caustics, with a pronounced trough in between. The source spent 1−3 days crossing each prong and 7 days between prongs. This pattern strongly implies that the event topology is that of a source crossing the “back end” of a resonant caustic with s < 1, as illustrated in Fig. 1. We nevertheless conducted a blind search of parameter space, incorporating the minimal 6 standard staticbinary parameters required to describe all binary events, as well as ρ = θ_{∗}/θ_{E}, the source size in units of the Einstein radius. The parameters derived from this fit are quite robust. However, they yield only the planetstar mass ratio q, but not the planet mass m_{p} = qM, where M is the host mass. In principle, one can measure M from (e.g. Gould 2000) (4)where π_{E} is the “microlens parallax” and . However, while θ_{E} = θ_{∗}/ρ is also quite robustly determined from the static solution (and Sect. 3), π_{E} is not.
However, the event timescale is moderately long (~40 days). This would not normally be long enough to measure the full microlens parallax, but might be enough to measure one dimension of the parallax vector (Gould et al. 1994). Moreover, the large separation in time of the caustic features could permit detection of orbital motion effects as well (Albrow et al. 2000). We therefore incorporate these two effects, first separately and then together. We find that each is separately detected with high significance, but that when combined they are partially degenerate with each other. In particular, one of the two components of the microlensing parallax vector π_{E} is highly degenerate with one of the two measurable parameters of orbital motion. It is often the case that one or both components of π_{E} are poorly measured in planetary microlensing events. The usual solution is to adopt Bayesian priors for the lenssource relative parallax and proper motion, based on a Galactic model. We also pursue this approach, but in addition we consider separately Bayesian priors on the orbital parameters as well. We show that the results obtained by employing either set of priors separately are consistent with each other, and we therefore combine both sets of priors.
4.2. Static binary
A static binarylens pointsource model involves six microlensing parameters: three related to the lenssource kinematics (t_{0},u_{0},t_{E}), where t_{0} is the time of lenssource closest approach, u_{0} is the impact parameter with respect to the center of mass of the binarylens system and t_{E} is the Einstein timescale of the event, and three related to the binarylens system (q,s,α), where q and s are the planetstar mass ratio and separation in units of Einstein radius, respectively, and α is the angle between the trajectory of the source and the starplanet axis. For n = 7 observatories, there are 2n photometric parameters, n × (F_{s},F_{b}), which correspond to the source flux and blend flux for each data set. These are usually determined by linear regression. The radius of the source, ρ, in Einstein units, can also be derived from the model provided that the source passes over, or sufficiently close to, a caustic structure. To optimize the fit in terms of computing time, we adopt different methods for implementing finitesource effects, depending on the distance between the source and the caustic features in the sky plane. When the source is far from the caustic (in the wings of the lightcurve), we treat it as a point source. In the caustic crossing regions, we use a finitesource model based on the GreenStokes theorem (Gould & Gaucherel 1997). Numerical implementation of this method is adapted from the code that was originally devised for Albrow et al. (2001) and refined in An et al. (2002). This technique, which reduces the 2dimensional integral over the source to a 1dimensional integral over its boundary and so is extremely efficient, implicitly assumes that the source has uniform surface brightness, i.e., is not limb darkened. We then include limbdarkening in the final fit, as described in Sect. 4.6. Lastly, in the intermediary regions, we use the hexadecapole approximation (Pejcha & Heyrovsky 2009; Gould 2008), which consists of calculating the magnification of 13 points distributed over the source in a characteristic pattern. To fit the microlensing parameters, we perform a Markov Chain Monte Carlo (MCMC) fitting with an adaptive stepsize Gaussian sampler (Doran & Muller 2004; Dong et al. 2009a). After every 200 links in the chain, the covariance matrix between the MCMC parameters is calculated again. We proceed to five runs corresponding to five different configurations: without either parallax or orbital motion, with parallax only, with orbital motion only, with both effects, and finally with both effects and limbdarkening effects included. The results are presented in Sect. 4.7.
The static binary search without parallax leads to the following parameters: q = 0.0107, s = 0.9152, ρ = 0.00149, and then θ_{E} = 0.42 mas, implying (5)This product is consistent, for example, with a 1 M_{⊙} mass host in the Galactic bulge or a 0.025 M_{⊙} mass browndwarf star at 1 kpc, either of which would have very important implications for the nature of the q = 0.0107 planet. We therefore first investigate whether the microlens parallax can be measured.
Fig. 3
The π_{E} contours at 1, 2, 3, and 4σ in black, red, orange, and green, respectively. As a comparison, the gray points show the approximate 3σ region of Model 4, i.e., with both parallax and orbital motion effects, with the 1σ contour shown in black. The black cross shows the (0, 0) coordinates. 

Open with DEXTER 
4.3. Parallax effects
When observing a microlensing event, the resulting flux for each observatoryfilter i can be expressed as, (6)where F_{s,i} is the flux of the unmagnified source, F_{b,i} is the background flux and u(t) is the sourcelens projected separation in the lens plane. The sourcelens projected separation in the lens plane, u(t) of Eq. (6), can be expressed as a combination of two components, τ(t) and β(t), its projections along the direction of lenssource motion and perpendicular to it, respectively: (7)If the motion of the source, lens and observer can all be considered rectilinear, the two components of u(t) are given by (8)To introduce parallax effects, we use the geocentric formalism (An et al. 2002; Gould 2004) which ensures that the three standard microlensing parameters (t_{0},t_{E},u_{0}) are nearly the same as for the noparallax fit. Hence, two more parameters are fitted in the MCMC code, i.e., the two components of the parallax vector, π_{E}, whose magnitude gives the projected Einstein radius, and whose direction is that of lenssource relative motion. The parallax effects imply additional terms in Eq. (8) (9)where (10)and Δp_{ ⊙ } is the apparent position of the Sun relative to what it would have been assuming rectilinear motion of the Earth.
The configuration with parallax effects corresponds to Model 2 of Table 1, The resulting diagram showing the north and east components of π_{E} is presented in Fig. 3. Taking the parallax effect into account substantially improves the fit (Δχ^{2} = −52). The best fit allowing only for parallax is π_{E} = (−1.38,0.60). There is a hard 3σ lower limit π_{E} > 0.6 and a 3σ upper limit π_{E} < 1.9. If taken at face value, these results would imply 0.025 < M/M_{⊙} < 0.075, i.e., a brown dwarf host with a gas giant planet. However, as can be seen from Fig. 3, these results are inconsistent with the results from Model 4, which takes account of both parallax and orbital motion. This inconsistency reflects an incorrect assumption in Model 2, namely that the planet is not moving.
4.4. Orbital motion effects
For the planet orbital motion, we use the formalism of Dong et al. (2009a). The lightcurve is capable of constraining at most two additional orbital parameters that can be interpreted as the instantaneous velocity components in the plane of the sky. They are implemented via two new MCMC parameters ds/dt and ω, which are the uniform expansion rate in binary separation s and the binary rotation rate α, (11)These two effects induce variations in the shape and orientation of the resonant caustic, respectively. To ensure that the resulting orbital characteristics are physically plausible, we can verify for any trial solution that the projected velocity of the planet is not greater than the escape velocity of the system, v_{⊥} < v_{esc} for a given assumed mass and distance, where (Dong et al. 2009a) (12)and (13)The configuration with only orbital motion corresponds to the Model 3 of Table 1. The resulting diagram showing the solution for the two orbital parameters ω and ds/dt is presented in Fig. 4. Taking the orbital motion of the planet into account substantially improves the fit (Δχ^{2} = −67.5).
Fig. 4
Orbital parameters of solutions at 1, 2, 3, and 4σ in black, red, orange, and green, respectively. As a comparison, the gray points show the 3σ region of Model 4, i.e., with both parallax and orbital motion effects, with the 1σ contour shown in black. 

Open with DEXTER 
4.5. Combined parallax and orbital motion
In this section we model both parallax and orbital motion effects, which is called Model 4 in Table 1. Taking these two effects into account results in only a modest improvement in χ^{2} compared to the cases for which the effects are considered individually . The triangle diagram presented in Fig. 5 shows the 2parameter contours between the four MCMC parameters π_{E,N}, π_{E,E}, ω and ds/dt introduced in Sects. 4.3 and 4.4. The best fit is (π_{E,N},π_{E,E}) = (2.495,−0.311) and (ω,ds/dt) = (−0.738,−0.360). This would lead to a host star of 0.015 M_{⊙} at a distance D_{l} = 1.11 kpc and a 0.21 Jupiter mass planet with a projected separation of 0.32 AU.
Fig. 5
Parallax and orbital motion parameters of solutions contours at 1, 2, 3, and 4σ. The black crosses show the (0, 0) coordinates. 

Open with DEXTER 
This small improvement in χ^{2} can be explained by a degeneracy between the north component of π_{E} and the orbital parameter ω, as shown in Fig. 5. In fact, the actual degeneracy is between π_{E, ⊥ } and ω, where π_{E, ⊥ } (described by Gould 2004) is the component of π_{E} that is perpendicular to the instantaneous direction of the Earth’s acceleration, i.e., that of the Sun projected on the plane of the sky at the peak of the event. This acceleration direction is φ = 257.4° (north through east). Hence, the perpendicular direction is φ − 90° = 192.6°, which is quite close to the 195.7° degeneracy direction in the π_{E,N} and π_{E,E} diagram. Since π_{E, ⊥ } is very close (only 13°) from north, π_{E,N} is a good approximation for it.
Indeed, π_{E, ∥ } generates an asymmetry in the lightcurve because, to the extent that the sourcelens motion is in the direction of the SunEarth axis, the event rises faster than it falls (or vice versa). This effect is relatively easy to detect. But to the extent that the motion is perpendicular to this axis, the Sun’s acceleration induces a parabolic deviation in the trajectory. To lowest order, this produces exactly the same effect as rotation of the lens geometry (which is a circular deviation). Hence, the degeneracy between π_{E, ⊥ } and ω can only be broken at higher order. This degeneracy was discussed in the context of point lenses in Gould et al. (1994), Smith et al. (2003a), and Gould (2004). In the pointlens case, the π_{E, ⊥ } degeneracy appears nakedly (because the lens system is invariant under rotation). In the present case, the rotational symmetry is broken. In case orbital motion is ignored, it thus may appear that parallax is measured more easily in binary events, as originally suggested by An & Gould (2001). But in fact, as shown in the present case, once the caustic is allowed to “rotate” (lowest order representation of orbital motion), then the π_{E, ⊥ } degeneracy is restored.
4.6. Limbdarkening implementation
Most of the calculations in this paper are done using Stokes’ theorem, which greatly speeds up the computations by reducing a 2dimensional integral to one dimension. However, this method implicitly assumes that the source has uniform surface brightness, whereas real sources are limb darkened. In the linear approximation, the normalized surface brightness can be written (14)where Γ is the limbdarkening coefficient depending on the considered wavelength, and z is the position on the source divided by the source radius.
We adopt this approach because we expect that the solutions with and without limb darkening will be nearly identical, except thatthe uniform source should appear smaller by approximately a factor (15)because this ratio preserves the rms radial distribution of light.
To test this conjecture, we approximate the surface as a set of 20 equalarea rings, with the magnification of each ring still computed by Stokes’ method. The surface brightness of the ith ring is simply W(z_{i}) where z_{i} is the middle of the ring. The limbdarkening coefficients for the unfiltered data have been determined by interpolation, from V, R, I and H limbdarkening coefficients. We find from the CMD that the source star has (V − I)_{0} = 0.82, so roughly a G7 dwarf or slightly cooler. We adopt a temperature of T = 5500 K. We thus obtain the following limbdarkening parameters (u_{V},u_{R},u_{I},u_{H}) = (0.7117,0.6353,0.5507,0.3659), where u = 3Γ/(Γ + 2) (Afonso et al. 2000). Then (Γ_{V},Γ_{R},Γ_{I},Γ_{H}) = (0.6220,0.5373,0.4497,0.2778). For a given observatory/filter (or possibly unfiltered), we then compare (R_{observed} − I_{CTIO}) to (V_{CTIO} − I_{CTIO}), considering that I_{CTIO} = 0.07V + 0.93I and that approximately V = 2R − I and deduce empirical expression for the corresponding Γ coefficients. The Γ coefficients for all the observatories then become (Γ_{MOA},Γ_{SAAO},Γ_{FCO},Γ_{AO},Γ_{Danish},Γ_{Bronberg},Γ_{Wise}) = (0.493,0.45,0.52,0.51,0.45,0.53,0.49). Substituting, a mean Γ ~ 0.47 into Eq. (15), we expect ρ to be ~5% larger when limbdarkening is included.
4.7. Results summary
We summarize the bestfit results for the five different models presented in Sect. 4 in Table 1. The five models are Model 1: finitesource binarylens model with neither parallax nor orbital motion effects; Model 2: finitesource binarylens model with parallax effects only; Model 3: finitesource binarylens model with orbital motion effects only; Model 4: finitesource binarylens model with both parallax and orbital motion effects; and Model 5: finitesource binarylens model with both parallax and orbital motion effects and limbdarkening.
Note in particular that Models 4 and 5 agree within ~1σ for all parameters, except that ρ is ~7% greater in the limbdarkened case (Model 5).
5. Bayesian analysis
The Markov Chain used to find the solutions illustrated in Fig. 5 is constructed (as usual) by taking trial steps that are uniform in the MCMC variables, including t_{0}, u_{0}, and t_{E}. This amounts to assuming a uniform prior in each of these variables. In the case of the three variables t_{0}, u_{0}, and t_{E}, the solution is extremely well constrained, so it makes hardly any difference which prior is assumed. Whenever this is the case, Bayesian and frequentist orientations lead to essentially the same results. However, as shown in Fig. 5, π_{E} is quite poorly constrained: at the 2σ level, the magnitude of π_{E} varies by more than an order of magnitude. Since the lens distance is related to the microlens parallax by D_{l} = AU/(θ_{E}π_{E} + π_{S}), where π_{S} = AU/D_{s}, this amounts to giving equal prior weight to a tiny range of distances nearby and a huge range of distances far away. But the actual weighting should have the reverse sign, primarily because a fixed distance range corresponds to far more volume at large than small distances. In fact, a Galactic model should be used to predict the a priori expected rate of microlensing events, which depends not only on the correct volume element but also on the density and velocity distributions of the lens and the source as well.
Similarly, a Keplerian orbit can be equally well characterized by specifying the seven standard Kepler parameters or six phasespace coordinates at a given instant of time, plus the host mass. The latter parametrization is more convenient from a microlensing perspective because microlensing most robustly measures the two inskyplane Cartesian spatial coordinates (scosα and ssinα) and the two inplane Cartesian velocity coordinates (ds/dt and sω), while the mass is directly given by microlens variables M = θ_{E}/κπ_{E}. However, the former (Kepler) variables have simple wellestablished priors. By stepping equally in microlens parameters, one is effectively assuming uniform priors in these variables, whereas one should establish the priors according to the Kepler parameters.
In principle, one would simultaneously incorporate both sets of priors (Galactic and Kepler), and we do ultimately adopt this approach. However, it is instructive to first apply them separately to determine whether these two sets of priors are basically compatible or are relatively inconsistent.
Formally, we can evaluate the posterior distribution f(X  D), including both prior expectations from (Galactic and/or Keplerian) models and posterior observational data using Bayes’ Theorem: (16)Here f(D  X) is the likelihood function over the data D for a given model X, f(X) is the prior distribution containing all ex ante information about the parameters X available before observing the data, and f(D) = ^{∫}_{X}f(D  X)f(X)dX. In the present context, this standard Bayes formula is interpreted as follows: the density of links on the MCMC chain directly gives f(D  X), while f(X) encapsulates the parameter priors, including both the underlying rate of events in a “natural physical coordinate system” in which these priors assume a simple form and the Jacobian of the transformation from this “physical” system to the “natural microlensing parameters” that are directly modeled in the lightcurve analysis.
Density distribution for the bulge and disk models.
It is not obvious, but we find below that the coordinate transformations for Galactic and Kepler models actually factor, so we can consider them independently.
5.1. Galactic model
Applying the generic rate formula Γ = nσv to microlensing rates as a function of the independent physical variables (M,D_{l},μ), yields (17)where the spatial positions (x,y,z), the physical Einstein radius R_{E}, and the lens velocity relative to the observersource line of sight v_{rel} are all regarded as dependent variables of the four variables shown on the l.h.s., plus the two angular coordinates. Here ν(x,y,z) is the local density of lenses, g(M) is the mass function [we will eventually adopt g(M) ∝ M^{1}], and f(μ) is the twodimensional probability function for a given sourcelens relative proper motion, μ. Since v_{rel} = μD_{l} and R_{E} = D_{l}θ_{E}, this can be rewritten in terms of microlensing variables, where M = θ_{E}/κπ_{E}, D_{l} = AU/(π_{rel} + π_{s}), π_{rel} = θ_{E}π_{E}, and μ = θ_{E}/t_{E} are now regarded as dependent variables. We note that where the last evaluation follows from the general theorem: Finally, Eq. (17) reduces to (18)The variables on the l.h.s. of Eq. (18) are essentially the Markov chain variables in the microlensing fit procedure^{1}. The distribution of MCMC links applied to the data can be thought of as the posterior probability distribution of the Markovchain variables under the assumption that the prior probability distribution in these variables is uniform. In our case, the prior distribution is not uniform, but is instead given by the r.h.s. of Eq. (18). We therefore must weight the output of the MCMC by this quantity, which is the specific evaluation of f(X) in Eqs. (16) and (17).
As mentioned above, we adopt g(M) ∝ M^{1}, so the term in square brackets disappears. We evaluate ν(x,y,z) and f(μ) as follows.
5.1.1. Lenssource relative proper motion distribution f(μ)
To compute the relative proper motion probability, we assume that the velocity distributions of the lenses and sources are Gaussian f(v_{y},v_{z}) = f(v_{y})f(v_{z}) where (19)and a similar distribution for f(μ_{z}). Here v_{y} and v_{z} are components of the projected velocity v derived from the MCMC fit, which is expressed by v = μD_{l}, where (20)The expected projected velocity which appears in Eq. (19) is defined as (21)where D_{l}, D_{s} are respectively the lens and source distances from the observer and D_{ls} the lenssource distance. The velocity is expressed in the (x,y,z) coordinate system, centered on the center of the Galaxy, where x and z axes point to the Earth and the North Galactic pole, respectively. As given in Han & Gould (1995), we adopt v_{z,disk} = v_{z,bulge} = 0 and σ_{z,disk} = 20 km s^{1}, σ_{z,bulge} = 100 km s^{1} for the z component of the velocity. For the y direction, v_{y,disk} = 220 km s^{1}, v_{y,bulge} = 0 and σ_{y,disk} = 30 km s^{1}, σ_{y,bulge} = 100 km s^{1} depending on whether the lens is situated in the disk or in the bulge. We also consider the asymmetric drift of the disk stars by subtracting 10 km s^{1} from v_{y,disk}. The celestial north and east velocities of the Earth seen by the Sun at the time of the event are v_{E} = (v_{E,E},v_{E,N}) = (+22.95,−3.60) km s^{1}. In the Galactic frame, the galactic north and east components of the Earth velocity become The velocity of the Sun in the Galactic frame is v_{⊙} = (7, 12) km s^{1} + (0,v_{circ}), where v_{circ} = 220 km s^{1}, from which we deduce the velocity v_{o} of the observer in the Galactic frame by adding the Earth velocity from Eq. (22).
5.1.2. Density distribution ν(x,y,z)
The density distribution, ν(x,y,z), is given at the lens coordinates (x,y,z) in the Galactic frame. For this distribution, we adopt the model of Han & Gould (2003), which is based primarily on star counts, and, without any adjustment, reproduces the microlensing optical depth measured toward Baade’s window. The density models are given in Table 2. The disk parameters are H = 2.75 kpc, h_{1} = 156 pc, h_{2} = 439 pc, and β = 0.381, where R ≡ (x^{2} + y^{2})^{1/2}. For the barred (anisotropic) bulge model, r_{s} = ([(x′/x_{0})^{2} + (y′/y_{0})^{2}] ^{2} + (z′/z_{0})^{4})^{1/4}. Here the coordinates (x′,y′,z′) have their center at the Galactic center, the longest axis is the x′, which is rotated 20° from the SunGC axis toward positive longitude, and the shortest axis is the z′ axis. The values of the scale lengths are x_{0} = 1.58 kpc, y_{0} = 0.62 kpc and z_{0} = 0.43 kpc respectively. For the bulge, Han & Gould (2003) normalize the “G2” Kband integratedlightbased bar model of Dwek et al. (1995) using star counts toward Baade’s window from Holtzman et al. (1998) and Zoccali et al. (2000). For the disk, they incorporate the model of Zheng et al. (2001), which is a fit to star counts.
In the calculation, we sum the probabilities of disk and bulge locations for the lens. We set the limits of the disk range to be [0,7] kpc from us and [5,11] kpc for the bulge range. We also apply the bulge density distribution to the source, in the [6.5,11] kpc range. Rigorously, because we already know the dereddened flux of the source, we should have derived a distribution of sources from the luminosity distribution of bulge stars combined with their distance. However, as we do not know the precise distribution of bulge luminosities at fixed color, we only consider the density distribution of sources as a function of their position in the bulge only. Because the stellar density drops off very rapidly from the peak, the source is effectively localized as being close to the Galactocentric distance.
5.2. Orbital motion model
In addition to the Galactic model, we build a Keplerian model to put priors on the orbital motion of the planet. To extract the orbital parameters from the microlensing parameters, we refer to the appendix of Dong et al. (2009a). Given that from the light curve of the event we have access to the instantaneous projected velocity and position of the planet for only a short time, we consider a circular orbit to model the planet motion. The distortions of the light curve are modeled by ω and ds/dt, which then specify the variations in orientation and shape of the resonant caustic, respectively. These quantities are defined in Sect. 4.4. Since r_{⊥} = D_{l}θ_{E}d is the projected starplanet separation, we evaluate the instantaneous planet velocity in the sky plane, with r_{⊥}γ_{⊥} = r_{⊥}ω the velocity perpendicular to the planetstar axis and r_{⊥}γ_{ ∥ } = r_{⊥}(ds/dt)/s the velocity parallel to this axis. We define the directions as the instantaneous starplanet axis on the sky plane, the direction into the sky, and . In this frame, the planet is moving among two directions, defined by the angles θ and φ, which are effectively a (complement to a) polar angle and an azimuthal angle, respectively. Specifically, φ is the angle between the starplanetobserver (r_{⊥} = asinφ), and θ characterizes the motion in the direction of the velocity along . Then the instantaneous velocity of the planet is (24)where a is the semimajor axis. Thus we obtain and . The Jacobian expression to transform from P(s,γ_{⊥},γ_{ ∥ }) to P(a,φ,θ) is (25)As explained in Dong et al. (2009a), for one set of microlensing parameters, there are two degenerate solutions in physical space. In the orbital model, we consider the two solutions to constrain the light curve fit, each with its own separate probability.
From the definition of the two angles, the transformation of the polar system (a,π/2 − θ,φ) contains the quantity sinθ and so the Jacobian includes the factor cosθ from d(sinθ)dφ = dθdφcosθ. Moreover, we adopt a flat distribution on ln(a), implying the factor 1/a in the Jacobian expression. Then, (26)Note that the terms sinθ and cosθ in the denominators of Eq. (26) correct an error in Dong et al. (2009a).
5.3. Constraints from VLT
As foreshadowed in Sect. 2.1, the VLT NACO flux measurement places upper limits on the flux from the lens, hence on its mass (assuming it is not a white dwarf). However, we begin by assuming that the excess light is caused by the lens. We do so for two reasons. First, this is actually the most precise way to enforce an upper limit on the lens flux. Second, it is of some interest to see what mass range is “picked out” by this measurement, assuming the excess flux is due to the lens.
The first point to note is that, if the lens contributes any significant flux, then it lies behind most or all of the dust seen toward the source. For example, if the lens mass is just M = 0.15 M_{⊙} (which would make it quite dim, M_{H} > 8), then it would lie at distance kpc, where we have adopted the central values θ_{E} = 0.31 mas and D_{S} = 8.7 kpc for this exercise. More massive lenses would be farther.
Next we estimate A_{H} = 0.4 from the measured clump color (V − I)_{cl} = 2.10, assuming an intrinsic color of the red giant clump of (V − I)_{0,cl} = 1.08 (Bensby et al. 2010) and adopting for this line of sight A_{H}/E(V − I) = 0.40.
Finally, for the relation between M and M_{H}, we consult the library of empiricallycalibrated isochrones of An et al. (2007). We adopt the oldest isochrones available (4 Gyr), since there is virtually no evolution after this age for the mass range that will prove to be of interest M < 0.7 M_{⊙}. Moreover, in this mass range, the isochrones hardly depend on metallicity within the range explored (−0.3 < [Fe/H] < +0.2).
Fig. 6
Bayesian analysis results. Each panel shows host mass M versus lenssource relative parallax π_{rel}, with 1, 2, 3, and 4σ contours under two different conditions. The solid black contours are derived from the light curve alone, without any priors. The colored symbols show contour levels after applying various priors, respectively Galactic proper motion only, Kepler only, full Galactic and Kepler priors, and full Galactic and Kepler priors, plus VLT imaging constraints. The propermotion and Kepler priors are fully consistent with the light curve, but there is strong tension between between the distancerelated priors and the lightcurve, with the former favoring high masses and small lenssource separations. The highest part of this disputed mass range, M > 0.7 M_{⊙}, is essentially ruled out by the VLT imaging constraint (lower right). 

Open with DEXTER 
For each mass and distance considered below, we then calculate H_{L} = M_{H} + A_{H} + 5log (D_{L}/10 pc) and combine the corresponding flux with H_{S} = 18.35 to obtain H_{pred}. We then calculate a likelihood factor , where H_{obs} = 18.25 and σ_{H} = 0.07, as discussed in Sect. 2.1.
For fiducial values D_{S} = 8.7 kpc and θ_{E} = 0.31 mas, this likelihood peaks at M = 0.42 M_{⊙}, but it does so very gently. The suppression factor is just L_{H} ~ 0.7 at M = 0.21 M_{⊙} and M = 0.52 M_{⊙}. At lower masses, even if there were zero flux, the suppression would never get lower than L_{H} = 0.36, simply because the excessflux measurement is consistent with zero at 1.4σ. But at higher mass, the expected flux quickly becomes inconsistent. For example, L_{H}(0.65 M_{⊙}) = 0.07.
Hence, by treating the flux measurement as an excessflux “detection”, we impose the “upper limit” on mass in a graceful manner. Moreover, as regards the upper limit, this approach remains valid when we relax the assumption that the excess flux is solely due to the lens. That is, even if there are other contributors, the likelihood of a given highmass lens being compatible with the flux measurement can only go down.
However, the same reasoning does not apply at the lowmass limit. For example, if the excess flux came from a source companion or an ambient star, then a browndwarf lens would be fully compatible with the flux measurement. Nevertheless, this is quite a minor effect because, in any event, the suppression factor would not fall below 0.36. To account for other potential sources of light, we impose a minimum suppression factor L_{H,min} = 0.5 at the lowmass end.
5.4. Combining Galactic and Kepler priors and adding VLT constraints
In this section, we impose the priors from the Galactic and Kepler models and add the constraints from the VLT flux measurement. We defer the VLT constraints to the end because they do not apply to the special case of whitedwarf lenses.
We begin by examining the role of the various priors separately to determine the level of “tension” between these and the χ^{2} derived from the light curve alone. We do so because each prior involves different physical assumptions, and tension with the light curve may reveal shortcomings in these assumptions.
The Kepler priors involve two assumptions, first that the planetary system is viewed at a random orientation (which is almost certainly correct) and second that the orbit is circular (which is almost certainly not correct). We will argue further below that the assumption of circular orbits has a modest impact. In any event, we want to implement the Kepler priors by themselves.
The Galactic priors really involve two sets of assumptions. The more sweeping assumption is that planetary systems are distributed with the same physicallocation distribution and hostmass distribution as are stars in the Galaxy. We really have no idea whether this assumption is true or not. For example, it could be that bulge stars do not host planets. The assumptions about host mass and physical location are linked extremely strongly in a mathematical sense (even if they prove to be unrelated physically) because θ_{E} is wellmeasured, and . Thus, we must be cautious about this entire set of assumptions.
However, the Galactic priors also contain another factor f(μ), in which we can have greater a priori confidence. This prior basically assumes that planetary systems at a given distance (regardless of how common they are at that distance) will have similar kinematics to the general stellar population at the same distance. The scenarios in which this assumption would be strongly violated, while not impossible, are fairly extreme.
Therefore we begin by imposing propermotiononly and Kepleronly priors in the top two panels of Fig. 6, which plots host mass M versus lenssource relative parallax π_{rel}. We choose to plot π_{rel} rather than D_{L} because it is given directly by microlensing parameters π_{rel} = π_{E}θ_{E}. The 1, 2, 3, and 4σ contours from the χ^{2} based on the light curve only are shown in black. Each of these priors is consistent with the light curve at the 1σ level, so we combine them and find that they still display good consistency. In the lower left panel, we combine the full Galactic and Kepler priors. These tend to favor much heavier, more distant lenses, which are strongly disfavored by the lightcurve, primarily because of the factor in Eq. (18). Indeed masses M > 0.7 M_{⊙} will be effectively ruled out by highresolution VLT imaging, further below.
When combining Galactic and Kepler priors, we simply weight the output of the MCMC by the product of the factors corresponding to each. This is appropriate because, while the 6 × 6 matrix, transforming the full set of microlensing parameters (s,γ_{⊥},γ_{ ∥ },t_{E},θ_{E},π_{E}) to the full set of physical parameters (a,φ,θ,M,D_{L},μ), is not block diagonal, the Jacobian nevertheless factors as Hence, the full weight, f(X) in Eq. (16) is simply the product of the two found separately for the Galactic and orbital priors.
Fig. 7
Probability as a function of host mass after applying the Galactic and Kepler priors (red) and then adding the constraints from VLT observations (black). 

Open with DEXTER 
Physical parameters.
Figure 7 shows the hostmass probability distribution before (red) and after (black) applying the constraint from VLT imaging to the previous analysis incorporating both Galactic and Kepler priors. The 90% confidence interval is marked. The high mass solutions toward the right are strongly disfavored by the lightcurve (see Fig. 6), but the Galactic prior for them is so strong that they have substantial posterior probability. However, these solutions are heavily suppressed by the VLT flux limits. The hsot is most likely to be an M dwarf. The lower right panel of Fig. 6 shows the 2dimensional (M,π_{rel}) probability distribution for direct comparison with the results from applying various combinations of priors.
Fig. 8
Physical test of Bayesian results: physicality diagnostic β = E_{kin, ⊥ }/E_{pot, ⊥ } is plotted against host distance. Bound orbits must have β < 1, and we expect a priori 0.1 < β < 0.5. 

Open with DEXTER 
5.5. Bayesian results for physical parameters
Table 3 shows the median estimates and 90% confidence intervals for six physical parameters (plus one physical diagnostic) as more priors and constraints are applied. The bottom row, which includes full Galactic and Kepler priors, plus constraints from VLT photometry shows our adopted results. The six physical parameters are the host mass M, the planet mass m_{p}, the distance of the system D_{L}, the period P, the semimajor axis a, and the orbital inclination i. The last three assume a circular orbit. For rows 2 and 4 (which do not apply Kepler constraints), the values shown for (P,a,i) summarize the results restricted to links in the chain that are consistent with a circular orbit, while the first four columns summarize all links in the chain. The key results are (27)and corresponding to this, m_{p} = qM, where q = 0.00132 ± 0.00002, i.e., with the medians at M = 0.19 M_{⊙}, m_{p} = 2.6 M_{Jup}, P = 5.4 yr, a = 1.8 AU. That is, the host is an M dwarf with a superJovian massive planetary companion. For completeness, we note that in obtaining these results, we have implicitly assumed that the probability of a star having a planet with a given planetstar mass ratio q and semimajor axis a is independent of the host mass and distance.
5.6. White dwarf host?
When we applied the VLT flux constraint, we noticed that it would not apply to whitedwarf hosts. Is such a host otherwise permitted? In principle, the answer is “yes”, but as we now show, it is rather unlikely. The WD mass function peaks at about M ~ 0.6 M_{⊙}, which corresponds to an M_{prog} ~ 2 M_{⊙} progenitor. If the progenitor had a planet, it would have increased its semimajor axis by a factor a/a_{init} = M_{prog}/M ~ 3.3 as the host adiabatically expelled its envelope. We find that, for M = 0.6 M_{⊙}, the orbital semimajor axis is fairly tightly constrained to a = 2.3 ± 0.3 AU, implying a_{init} = 0.7 ± 0.1 AU. It is unlikely that such a close planet would survive the AGB phase of stellar evolution. Of course, a white dwarf need not be right at the peak. For lower mass progenitors, the ratio of initial to final masses is lower, which would enhance the probability of survival. But it is also the case that such white dwarfs are rarer.
5.7. Physical consistency checks of bayesian analysis
The results reported here have been derived with the aid of fairly complicated machinery, both in fitting the light curves and in transforming from microlensing to physical parameters. In particular, we have identified a strong mathematical degeneracy between the parameters π_{E,N} and ω, which arise from orbital motion of the Earth and the planet, respectively. When considering “MCMConly” solutions, this degeneracy led to extremely large errors in π_{E,N} in Fig. 5, which are then reflected in similarly large errors in the “lightcurveonly” contours for host mass and lenssource relative parallax in Fig. 6. Nevertheless, these large errors gradually shrink when the priors are applied in Fig. 6, and more so when the constraints from VLT observations are added in Fig. 7.
We have emphasized that the highπ_{E} (so lowD_{L}, lowM) solutions are very strongly, and improperly, favored by the MCMC when it is cast in microlensing parameters, and that the Galactic prior (Eq. (18)) properly compensates for this. But is this really true? The bestfit distance for the Galacticprior model is four times larger than for the MCMConly model, meaning that the term favors the Galactic model by a factor ~2500. Thus, even if the light curve strongly favored the nearby model, the Galactic prior could “trump” the light curve and enforce a larger distance. Indeed, this would be an issue if the Galactic prior were operating by itself. In fact, however, Fig. 6 shows that the finally adopted solution (including the VLT flux constraint) is disfavored by the light curve alone by just Δχ^{2} ~ 3, so, in the end there is no strong tension.
A second issue is that both parallax and orbital motion are fairly subtle effects that could, in principle, be affected by systematics. If this were the case, the principal lensing parameters, such as q and s, would remain secure, but most of the “higher order” information, such as lens mass, distance, and orbital motion would be compromised. It is always difficult to test for systematics, particularly in this case for which there are two effects that are degenerate with each other and in combination are detected at only Δχ^{2} < 100.
However, we can in fact test for such systematics using the diagnostic (31)where v_{⊥} and v_{esc, ⊥ } are defined in Eqs. (12) and (13). Bound orbits require β < 1. Circular orbits, if seen faceon, have β = 0.5 and otherwise β < 0.5. Of course, it is possible to have β ≪ 1, but it requires very special configurations to achieve this. For example, if the planet is close to transiting its host, or if the orbit is edgeon and the phase is near quadrature. Thus, a clear signature of systematics would be β > 1 for all lightcurve solutions with reasonable χ^{2}. And if β ≲ 0.1, one should be concerned about systematics, although this condition would certainly not be proof of systematics. With these considerations in mind, we plot D_{L} vs. β in Fig. 8.
The key point is that the 1σ region of the Galacticprior panel straddles the region β ≲ 0.5 (log β ≲ −0.7), which is characteristic of approximately circular, approximately faceon orbits. It is important to emphasize that no selection or weighting by orbital characteristics has gone into construction of this panel. This is a test which could easily have been failed if the orbital parameters were seriously influenced by systematics: β could have taken literally on any value.
Finally, we turn to the two righthand panels, which incorporate the orbital constraints. Since these assume circular orbits, they naturally eliminate all solutions with β > 0.5, and some smallerβ solutions as well, because when ds/dt ≠ 0, it is impossible to accommodate a β = 0.5 circular orbit. While this radical censoring of the highβ solutions is the most dramatic aspect of these plots, there is also the very interesting effect that lowβ solutions are also suppressed (though more gently). This is because, as mentioned above, these require special configurations and so are disfavored by the Kepler Jacobian, Eq. (25). Of course, radical censorship of β > 1 solutions is entirely appropriate (provided that β < 1 solutions exist at reasonable χ^{2}), but what about 0.5 ≲ β < 1? A more sophisticated approach would permit noncircular orbits and then suppress these solutions “more gently” using a Jacobian (as is already being for done lowβ solutions). However, as we have emphasized, the limited sensitivity of this event to additional orbital parameters does not warrant such an approach. Hence, radical truncation is a reasonable proxy in the present case for the “gentler” and more sophisticated approach.
Moreover, one can see by comparing Rows 2 and 3 of Table 3 that the addition of Kepler priors does not markedly alter the Galacticprior solutions.
6. Conclusions
We report the discovery of the planetary event MOA2009BLG387Lb. The planet/star mass ratio is very welldetermined, q = 0.0132 ± 0.0003. We constrain the host mass to lie in the interval. 0.07 < M_{host}/M_{⊙} < 0.49 at 90% confidence, which corresponds to the full range of M dwarfs. The planet mass therefore lies in the range 1.0 < m_{p}/M_{Jup} < 6.7, with its uncertainty almost entirely due to the uncertainty in the host mass. The host mass is determined from two “higherorder” microlensing parameters, θ_{E} and π_{E}, (i.e., M = θ_{E}/κπ_{E}).
The first of these, the angular Einstein radius is actually quite well measured, θ_{E} = 0.31 ± 0.03 mas, from four separate causticcrossings by the source during the event. On the other hand, from the lightcurve analysis alone, the microlensing parallax vector π_{E} is poorly constrained because one of its components is degenerate with a parameter describing orbital motion of the lens. That is, effects of the orbital motion of our planet (Earth) and the lens planet have a similar impact on the light curve and are difficult to disentangle.
Nevertheless, the closestlens (and so also lowestlensmass) solutions permitted by the light curve are strongly disfavored by the Galactic model simply because there are relatively few extremeforeground lenses that can reproduce the observed lightcurve parameters. Of course, we cannot absolutely rule out the possibility that we are victims of chance, so in principle it is possible that the host is an extremely lowmass brown dwarf, or even a planet, with a lunar companion.
On the other hand, the arguments against a higher mass lens rest on directly observed features of the light curve. That is, as mentioned above, θ_{E} is measured accurately from the four observed caustic crossings. And one component of π_{E}, the one in the projected direction of the Sun, is also reasonably well measured from the observed asymmetry in the light curve outside the caustic region. This places a lower limit on π_{E}, hence an upper limit on the mass.
However, for the latter parameter, the very strong prior from the Galactic model favoring more distant lenses would, by itself, “overpower” the lightcurve and impose solutions with M > 1 M_{⊙}, which are disfavored by the lightcurve at > 3σ. It is only because these highmass solutions are ruled out by flux limits from VLT imaging that the lightcurveonly χ^{2} is quite compatible with the final, posteriorprobability solution.
The relatively high planet/star mass ratio (implying a Jupitermass planet for the case of a very late Mdwarf host) is then difficult to explain within the context of the standard coreaccretion paradigm.
The 12day duration of the planetary perturbation, one of the longest seen for a planetary microlensing event, enabled us to detect two components of the orbital motion, basically the projected velocity in the plane of the sky perpendicular and parallel to the starplanet separation vector. While the first of these is strongly degenerate with the microlens parallax (as mentioned above), the second one (which induces a changing shape of the caustic) is reasonably well constrained by the two sets of wellseparated double caustic crossings. Moreover, once the Galacticmodel prior constrained the microlensing parallax, its correlated orbital parameter was implicitly constrained as well. With two orbital parameters, plus two position parameters from the basic microlensing fit (projected separation s, and orientation of the binary axis relative to the source motion α) plus the lens mass, there is enough information to specify an orbit, if the orbit is assumed circular. We are thus able to estimate a semimajor axis a = 1.8 AU and period 5.4 years.
We recognized that inferences derived from such subtle light curve effects could in principle be compromised by systematics. We therefore tested whether the derived ratio of orbital kinetic to potential energy was in the expected range, before imposing any orbital constraints. If the measurements were strongly influenced by systematic errors, this ratio could have taken on any value. In fact, it fell right in the expected range.
Acknowledgments
V.B. thanks Ohio State University for its hospitality during a six week visit, during which this study was initiated. We acknowledge the following support: Grants HOLMES ANR06BLAN0416 Dave Warren for the Mt Canopus Observatory; NSF AST0757888 (AG, SD); NASA NNG04GL51G (DD, AG, RP); Polish MNiSW N20303032/4275 (AU); HSTGO11311 (KS); NSF AST0206189 and AST0708890, NASA NAF513042 and NNX07AL71G (DPB); Korea Science and Engineering Foundation grant 2009008561 (CH); Korea Research Foundation grant 2006311C00072 (BGP); Korea Astronomy and Space Science Institute (KASI); Deutsche Forschungsgemeinschaft (CSB); PPARC/STFC, EU FP6 programme “ANGLES” (ŁW, NJR); PPARC/STFC (RoboNet); Dill Faulkes Educational Trust (Faulkes Telescope North); Grants JSPS18253002, JSPS20340052 and JSPS19340058 (MOA); Marsden Fund of NZ(IAB, PCMY); Foundation for Research Science and Technology of NZ; Creative Research Initiative program (2009008561) (CH); Grants MEXT19015005 and JSPS18749004 (TS). Work by S.D. was performed under contract with the California Institute of Technology (Caltech) funded by NASA through the Sagan Fellowship Program. J.C.Y. is supported by an NSF Graduate Research Fellowship. This work was supported in part by an allocation of computing time from the Ohio Supercomputer Center. J.A. is supported by the Chinese Academy of Sciences (CAS) Fellowships for Young International Scientist, Grant No.: 2009Y2AJ7.
References
 Afonso, C., Alard, C., Albert, J. N., et al. 2000, ApJ, 532, 340 [NASA ADS] [CrossRef] [Google Scholar]
 Albrow, M., Beaulieu, J.P., Birch, P., et al. 1998, ApJ, 509, 687 [NASA ADS] [CrossRef] [Google Scholar]
 Albrow, M. D., Beaulieu, J.P., Caldwell, J. A. R., et al. 2000, ApJ, 534, 894 [NASA ADS] [CrossRef] [Google Scholar]
 Albrow, M. D., An, J., Beaulieu, J.P., et al. 2001, ApJ, 549, 759 [NASA ADS] [CrossRef] [Google Scholar]
 Albrow, M. D., Horne, K., Bramich, D. M., et al. 2009, MNRAS, 397, 2099 [NASA ADS] [CrossRef] [Google Scholar]
 An, J. H., & Gould, A. 2001, ApJ, 563, 111 [NASA ADS] [CrossRef] [Google Scholar]
 An, J. H., Albrow, M. D., Beaulieu, J.P., et al. 2002, ApJ, 572, 521 [NASA ADS] [CrossRef] [Google Scholar]
 An, D., Terndrup, D. M., Pinsonneault, M. H., et al. 2007, ApJ, 655, 233 [NASA ADS] [CrossRef] [Google Scholar]
 Beaulieu, J. P., Bennett, D. P., Fouqué, P., et al. 2006, Nature, 439, 437 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
 Bennett, D. P., Bond, I. A., Udalski, A., et al. 2008, ApJ, 684, 663 [NASA ADS] [CrossRef] [Google Scholar]
 Bennett, D. P., Rhie, S. H., Nikolaev, S., et al. 2010, ApJ, 713, 837 [NASA ADS] [CrossRef] [Google Scholar]
 Bensby, T., Feltzing, S., Johnson, J. A., et al. 2010, A&A, 512, A41 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Bessel, M. S., & Brett, J. M. 1988, Astron. Pac. Soc., 100, 1134 [NASA ADS] [CrossRef] [Google Scholar]
 Boss, A. P. 2006, ApJ, 643, 501 [NASA ADS] [CrossRef] [Google Scholar]
 Cumming, A., Butler, R. P., Marcy, G. W., et al. 2008, PASP, 120, 531 [CrossRef] [Google Scholar]
 Bond, I. A., Udalski, A., Jaroszyński, M., et al. 2004, ApJ, 606, L155 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
 D’Angelo, G., Durisen, R. H., & Lissauer, J. J. 2010, Exoplanets, ed. S. Seager et al. (Tucson: University of Arizona) [Google Scholar]
 Dong, S., Bond, I. A., Gould, A., et al. 2009b, ApJ, 698, 1826 [NASA ADS] [CrossRef] [Google Scholar]
 Dong, S., Gould, A., Udalski, A., et al. 2009a, ApJ, 695, 970 [NASA ADS] [CrossRef] [Google Scholar]
 Doran, M., & Muller, C. M. 2004, JCAP, 09, 003 [NASA ADS] [CrossRef] [Google Scholar]
 Dwek, E., Arendt, R. G., Hauser, M. G., et al. 1995, ApJ, 445, 716 [NASA ADS] [CrossRef] [Google Scholar]
 Gaudi, B. S., Bennett, D. P., Udalski, A., et al. 2008, Science, 319, 927 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
 Gould, A. 2000a, ApJ, 535, 928 [NASA ADS] [CrossRef] [Google Scholar]
 Gould, A. 2000b, ApJ, 542, 785 [NASA ADS] [CrossRef] [Google Scholar]
 Gould, A. 2004, ApJ, 606, 319 [NASA ADS] [CrossRef] [Google Scholar]
 Gould, A. 2008, ApJ, 681, 1593 [NASA ADS] [CrossRef] [Google Scholar]
 Gould, A., & Gaucherel, C. 1997, ApJ, 477, 580 [NASA ADS] [CrossRef] [Google Scholar]
 Gould, A., MiraldaEscude, J., & Bahcall, J. N. 1994, ApJ, 423, 105 [NASA ADS] [CrossRef] [Google Scholar]
 Gould, A., Udalski, A., An, D., et al. 2006, ApJ, 644, L37 [NASA ADS] [CrossRef] [Google Scholar]
 Gould, A., Dong, S., Gaudi, B. S., et al. 2010, ApJ, 720, 1073 [NASA ADS] [CrossRef] [Google Scholar]
 Han, C., & Gould, A. 1995, ApJ, 447, 53 [NASA ADS] [CrossRef] [Google Scholar]
 Han, C., & Gould, A. 2003, ApJ, 592, 172 [NASA ADS] [CrossRef] [Google Scholar]
 Holtzman, J. A., Watson, A. M., Baum, W. A., et al. 1998, AJ, 115, 1946 [NASA ADS] [CrossRef] [Google Scholar]
 Ida, S., & Lin, D. N. C. 2005, ApJ, 626, 1045 [NASA ADS] [CrossRef] [Google Scholar]
 Janczak, J., Fukui, A., Dong, S., et al. 2010, ApJ, 711, 731 [NASA ADS] [CrossRef] [Google Scholar]
 Johnson, J. A., Aller, K. M., Howard, A. W., & Crepp, J. R. 2010, PASP, 122, 905 [NASA ADS] [CrossRef] [Google Scholar]
 Kennedy, G. M., & Kenyon, S. J. 2008, ApJ, 673, 502 [NASA ADS] [CrossRef] [Google Scholar]
 Kervella, P., Thévenin, F., Di Folco, E., & Ségransan, D. 2004, A&A, 426, 297 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Laughlin, G., Bodenheimer, P., & Adams, F. C. 2004, ApJ, 612, L73 [NASA ADS] [CrossRef] [Google Scholar]
 Pejcha, Q., & Heyrovsky, D. 2009 ApJ, 690, 1772 [Google Scholar]
 Smith, M., Mao, S., & Paczyński, B. 2003, MNRAS, 339, 925 [NASA ADS] [CrossRef] [Google Scholar]
 Sumi, T., Bennett, D. P., Bond, I. A., et al. 2010, ApJ, 710, 1641 [NASA ADS] [CrossRef] [Google Scholar]
 Udalski, A., Jaroszyński, M., Paczyński, B., et al. 2005, ApJ, 628, L109 [NASA ADS] [CrossRef] [Google Scholar]
 Yelda, S., Ghez, A. M., Lu, J. R., et al. 2010, The Galactic Center: A Window on the Nuclear Environment of Disk Galaxies, ed. M. Morris, D. Q. Wang, & F. Yuan, in press [Google Scholar]
 Wambsganss, J. 1997, MNRAS, 284, 172 [NASA ADS] [Google Scholar]
 Yoo, J., DePoy, D. L., GalYam, A., et al. 2004, ApJ, 603, 139 [NASA ADS] [CrossRef] [Google Scholar]
 Zheng, Z., Flynn, C., Gould, A., Bahcall, J. N., & Salim, S. 2001, ApJ, 555, 393 [NASA ADS] [CrossRef] [Google Scholar]
 Zoccali, M., Cassisi, S., Frogel, J. A., et al. 2000, ApJ, 530, 418 [NASA ADS] [CrossRef] [Google Scholar]
All Tables
All Figures
Fig. 1
Top: light curve of MOA2009BLG387 near its peak in July 2009 and the trajectory of the source across the caustic feature on the right. The source is going upward. We show the model with finitesource, parallax and orbital motion effects. Middle: magnitude residuals. Bottom: zooms of the caustic features of the light curve. 

Open with DEXTER  
In the text 
Fig. 2
Instrumental color − magnitude diagram of the field around MOA2009BLG387. The clump centroid is shown by an open circle, while the CTIO I and V − I measurements of the source are shown by a filled circle. 

Open with DEXTER  
In the text 
Fig. 3
The π_{E} contours at 1, 2, 3, and 4σ in black, red, orange, and green, respectively. As a comparison, the gray points show the approximate 3σ region of Model 4, i.e., with both parallax and orbital motion effects, with the 1σ contour shown in black. The black cross shows the (0, 0) coordinates. 

Open with DEXTER  
In the text 
Fig. 4
Orbital parameters of solutions at 1, 2, 3, and 4σ in black, red, orange, and green, respectively. As a comparison, the gray points show the 3σ region of Model 4, i.e., with both parallax and orbital motion effects, with the 1σ contour shown in black. 

Open with DEXTER  
In the text 
Fig. 5
Parallax and orbital motion parameters of solutions contours at 1, 2, 3, and 4σ. The black crosses show the (0, 0) coordinates. 

Open with DEXTER  
In the text 
Fig. 6
Bayesian analysis results. Each panel shows host mass M versus lenssource relative parallax π_{rel}, with 1, 2, 3, and 4σ contours under two different conditions. The solid black contours are derived from the light curve alone, without any priors. The colored symbols show contour levels after applying various priors, respectively Galactic proper motion only, Kepler only, full Galactic and Kepler priors, and full Galactic and Kepler priors, plus VLT imaging constraints. The propermotion and Kepler priors are fully consistent with the light curve, but there is strong tension between between the distancerelated priors and the lightcurve, with the former favoring high masses and small lenssource separations. The highest part of this disputed mass range, M > 0.7 M_{⊙}, is essentially ruled out by the VLT imaging constraint (lower right). 

Open with DEXTER  
In the text 
Fig. 7
Probability as a function of host mass after applying the Galactic and Kepler priors (red) and then adding the constraints from VLT observations (black). 

Open with DEXTER  
In the text 
Fig. 8
Physical test of Bayesian results: physicality diagnostic β = E_{kin, ⊥ }/E_{pot, ⊥ } is plotted against host distance. Bound orbits must have β < 1, and we expect a priori 0.1 < β < 0.5. 

Open with DEXTER  
In the text 
Current usage metrics show cumulative count of Article Views (fulltext article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 4896 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.