Issue 
A&A
Volume 568, August 2014



Article Number  A43  
Number of page(s)  11  
Section  Planets and planetary systems  
DOI  https://doi.org/10.1051/00046361/201323090  
Published online  11 August 2014 
Physical characterisation of nearEarth asteroid (1620) Geographos
Reconciling radar and thermalinfrared observations
^{1} Planetary and Space Sciences, Department of Physical Sciences, The Open University, Walton Hall, Milton Keynes, MK7 6AA, UK
^{2} Department of Earth and Planetary Sciences, University of Tennessee, Knoxville, TN 379961410, USA
email: brozitis@utk.edu
Received: 20 November 2013
Accepted: 28 June 2014
Context. The Yarkovsky (orbital drift) and YORP (spin state change) effects play important roles in the dynamical and physical evolution of asteroids. Thermophysical modelling of these observed effects, and of thermalinfrared observations, allows a detailed physical characterisation of an individual asteroid to be performed.
Aims. We perform a detailed physical characterisation of nearEarth asteroid (1620) Geographos, a potential meteor stream source and former spacecraft target, using the same techniques as previously used for (1862) Apollo.
Methods. We use the advanced thermophysical model (ATPM) on published lightcurve, radar, and thermalinfrared observations to constrain the thermophysical properties of Geographos. The derived properties are used to make detailed predictions of the Yarkovsky orbital drift and YORP rotational acceleration, which are then compared against published measurements to determine Geographos’s bulk density.
Results. We find that Geographos has a thermal inertia of 340_{100}^{+140} J m^{2} K^{1} s^{−1/2}, a roughness fraction of ≥50%, and a bulk density of 2100_{450}^{+550} kg m^{3} when using the lightcurvederived shape model with the radarderived maximum equatorial diameter of 5.04 ± 0.07 km. It is also found that the radar observations had overestimated the zaxis in Geographos’s shape model because of their nearequatorial view. This results in a poor fit to the thermalinfrared observations if its effective diameter is kept fixed in the model fitting.
Conclusions. The thermal inertia derived for Geographos is slightly higher than the typical values for a nearEarth asteroid of its size, and its derived bulk density suggests a rubblepile interior structure. Large uncertainties in shape model zaxes are likely to explain why radar and thermalinfrared observations sometimes give inconsistent diameter determinations for other asteroids.
Key words: radiation mechanisms: thermal / celestial mechanics / minor planets, asteroids: individual: (1620) Geographos / methods: data analysis / infrared: planetary systems
© ESO, 2014
1. Introduction
The asymmetric reflection and thermal reradiation of sunlight from an asteroid’s surface imposes a net force (Yarkovsky effect) and torque (YarkovskyO’KeefeRadzievskiiPaddack or YORP effect). The Yarkovsky effect results in a drift in the semimajor axis of an asteroid’s orbit, and the YORP effect changes its rotation period and the direction of its spin axis. Both effects are of fundamental importance for the dynamical and physical evolution of small asteroids in the solar system (see review by Bottke et al. 2006 and introduction of Rozitis et al. 2013). Furthermore, asteroid bulk densities can be determined from modeltomeasurement comparisons of the Yarkovsky semimajor axis drift. The Yarkovsky effect has been detected by sensitive radar ranging for (6489) Golevka and (101955) Bennu (Chesley et al. 2003, 2014), and by deviations from predicted ephemerides over a long time span for several tens of other nearEarth asteroids including (1620) Geographos (Vokrouhlický et al. 2008; Chesley et al. 2008; Nugent et al. 2012; Farnocchia et al. 2013). The YORP effect has been detected through observations of phase shifts in photometric lightcurves of five nearEarth asteroids, which include (54509) YORP (Lowry et al. 2007; Taylor et al. 2007), (1862) Apollo (Kaasalainen et al. 2007; Ďurech et al. 2008a), (1620) Geographos (Ďurech et al. 2008b), (3103) Eger (Ďurech et al. 2012a), and (25143) Itokawa (Lowry et al. 2014). Accurate predictions of the Yarkovsky and YORP effects must take into account various thermophysical properties, which include the asteroid’s size and shape, mass and moment of inertia, surface thermal properties, rotation state, and its orbit about the Sun. Recently, Rozitis et al. (2013) have produced a unified model which can simultaneously match both observed effects for (1862) Apollo using a single set of thermophysical properties derived from groundbased observations.
The object (1620) Geographos (hereafter referred to as just Geographos) is an Apollo and Stype nearEarth asteroid (Bus & Binzel 2002), and has detections of both Yarkovsky orbital drift and YORP rotational acceleration. Its semimajor axis was found to be decreasing at a rate of 27.4 ± 5.7 m yr^{1} (mean value from Chesley et al. 2008; Nugent et al. 2012; Farnocchia et al. 2013), and its rotation rate was found to be increasing at a rate of (1.5 ± 0.2) × 10^{3} rad yr^{2} (Ďurech et al. 2008b). Photometric investigations also revealed a retrograde rotation and a very high amplitude lightcurve (~2 mag) indicative of a highly elongated shape (Dunlap 1974; Michalowski et al. 1994; Kwiatkowski 1995; Magnusson et al. 1996). The highly elongated shape was confirmed by radar studies, which also find it to have tapered ends and for it to contain many ridges and concavities (Ostro et al. 1995, 1996; Hudson & Ostro 1999). Unfortunately, the radarderived shape model does contain a northsouth ambiguity because of the nearequatorial view when the radar observations were taken. Nevertheless, this unusual shape with high elongation is suggestive of a rubblepile asteroid that was tidally distorted during a close planetary encounter (Solem & Hills 1996; Bottke et al. 1999). Geographos’s shape and rotation period (~5.2 h) make it possible for loose material to be lofted away from the surface during close encounters with the Earth (Ryabova 2002a,b). It is not clear whether any meteors originating from Geographos using this lowvelocity ejection mechanism have been detected, but two meteor streams consistent with highvelocity ejection (up to 1 km s^{1}) have been identified in meteor catalogues (Ryabova 2002b). During its 1994 Earth flyby, Geographos was going to be visited by the Clementine spacecraft as the secondary mission target after the primary lunar mission was complete (Vorder Bruegge & Shoemaker 1993). Unfortunately, the spacecraft malfunctioned before leaving the Moon and never reached Geographos.
In addition to these studies, Geographos was observed in the thermalinfrared at 10.1 μm by Veeder et al. (1989) who obtained two lightcurves at this wavelength, and at 12, 25, and 60 μm by the IRAS satellite in a single snapshot measurement (Green 1985; Tedesco et al. 2004). These observations complete Geographos’s data set, and allow a full thermophysical analysis of its Yarkovsky and YORP effects to be performed using the same methodology as that presented in Rozitis et al. (2013) for (1862) Apollo. In the following sections, we present results from application of the advanced thermophysical model (ATPM; Rozitis & Green 2011, 2012, 2013a), which explicitly incorporates 1D heat conduction, shadowing, multiple scattering of sunlight, global selfheating, and rough surface thermalinfrared beaming, to this complete data set in order to constrain Geographos’s thermophysical properties and bulk density.
2. Thermophysical modelling
To determine Geographos’s thermophysical properties we combine the ATPM with the radarderived^{1} and lightcurvederived^{2} shape models and spin states (Hudson & Ostro 1999; Ďurech et al. 2008b), and compare the model outputs for various thermophysical properties with the thermalinfrared observations obtained from three occasions in 1983 (Green 1985; Veeder et al. 1989; Tedesco et al. 2004) via chisquared fitting. The methodology used here is exactly the same as that presented in Rozitis et al. (2013). To determine the asteroid thermal emission, the ATPM is used to compute the surface temperature variation for each shape model facet during a rotation by solving the 1D heat conduction equation with a surface boundary condition that includes direct and multiple scattered solar radiation, shadowing, and reabsorbed thermal radiation from interfacing facets. The model explicitly includes rough surface thermalinfrared beaming (i.e. reradiation of absorbed sunlight back towards the Sun at thermalinfrared wavelengths as a result of surface roughness) from each shape facet by including roughness facets that are arranged in the form of hemispherical craters. The degree of roughness and thermalinfrared beaming is characterised by the fraction of surface, f_{R}, covered by the hemispherical craters. A Planck function is applied to the derived temperatures and summed across visible shape and roughness facets to give the emitted thermal flux as a function of wavelength, rotation phase, and various thermophysical properties.
Summary of the (1620) Geographos thermalinfrared observations obtained in 1983.
Assumed and previously measured thermophysical modelling parameters for thermalinfrared flux fitting and Yarkovsky and YORP effect modelling.
For Geographos, the free parameters to be constrained by fits to the thermalinfrared observations are the effective diameter (i.e. the diameter of an equivalent volume sphere), D, geometric albedo, p_{v}, thermal inertia, Γ, and surface roughness, f_{R}. The effective diameter and geometric albedo are related to the absolute visual magnitude, H_{v}, by (1)and can be considered as a single free parameter (Fowler & Chillemi 1992). It is also possible to fix the diameter and albedo at the radarderived values (Hudson & Ostro 1999), and both possibilities of having a free and fixed diameter will be explored in the model fitting. The model thermal flux predictions, F_{MOD}(λ_{n},ϕ_{n},Γ,D,f_{R}), were compared with the observations, F_{OBS}(λ_{n},ϕ_{n}), and observational errors, σ_{OBS}(λ_{n},ϕ_{n}), by varying the effective diameter, thermal inertia, and roughness fraction to give the minimum chisquared fit (2)for a set of N observations with wavelength λ_{n} and rotation phase ϕ_{n}. The flux correction factor, FCF(D,f_{R}), is given by (3)where A_{B}(D,f_{R}) is the required Bond albedo for an asteroid with effective diameter D and roughness fraction f_{R}, and A_{B_MOD} is the model Bond albedo used in the ATPM. This saves a lot of computational effort by not running the model for every value of Bond albedo required, and the flux correction factor is typically within 10% of unity for an assumed value of A_{B_MOD} = 0.06.
The rotation phase of Geographos at the time of the thermalinfrared observations was calculated using the initial epoch, rotation period, and YORP rotational acceleration determined by Ďurech et al. (2008b) from lightcurve inversion. For the radar shape model, an additional rotation offset was required to account for the different coordinate systems used in the radar and lightcurve shape models. This offset was found by minimising the relativechisquared fit (see Eq. (7) of Kaasalainen & Torppa 2001) of the synthetic radar shape model lightcurves to the optical lightcurve observations. For comparison purposes, the radar shape model gave a minimised relativechisquare value of 26.6 whilst the lightcurve shape model gave 11.9, which indicates that the lightcurve shape model gave (perhaps unsurprisingly) a better fit to the optical lightcurve observations.
Separate thermophysical models were run for thermal inertia values ranging from 0 to 3000 J m^{2} K^{1} s^{−1/2} in equally spaced steps of 20 J m^{2} K^{1} s^{−1/2}. Similarly, the effective diameter and roughness fraction were stepped through their plausible ranges, which formed a 3D grid of model test parameters (or test clones) with the thermal inertia steps. A parameter region bounded by a constant Δχ^{2} value at the 3σ confidence value was chosen to define the range of possible parameters, and the three sets of thermalinfrared observations were fitted simultaneously in the ATPM chisquare fitting. Table 1 summarises the two sets of thermalinfrared observations, and Table 2 summarises the fixed model parameters, used to determine Geographos’s thermophysical properties.
In the ATPM fitting, the maximum equatorial diameter was initially kept fixed at the Dopplerradarderived value of 5.04 ± 0.07 km for both the radar and lightcurve shape models, as this was the most reliable diameter measurement available (Ostro et al. 1996). The corresponding effective diameters were then 2.56 ± 0.03 and 2.46 ± 0.03 km for the radar and lightcurve shape models, respectively. The logic was by using a fixed diameter in the ATPM fitting it would reduce the number of free parameters to 2 (instead of 3), and lead to tighter constraints on the derived thermal inertia and roughness fraction values. However, whilst a good fit (i.e. a reduced chisquared value of 0.50) was obtained for the lightcurve shape model, a bad fit (i.e. a reduced chisquared value of 2.42) was obtained for the radar shape model using this fixed diameter. A thermal inertia of 340 J m^{2} K^{1} s^{−1/2} was derived for the lightcurve shape model whereas a very high thermal inertia of 1320 J m^{2} K^{1} s^{−1/2} was suggested for the radar shape model (see Table 3). Indeed, the radar shape model value is much greater than the largest thermal inertia derived for any asteroid, i.e. nearEarth asteroid (25143) Itokawa has the highest measured thermal inertia of 750 J m^{2} K^{1} s^{−1/2} (Müller et al. 2005). A much better fit (i.e. a reduced chisquared value of 0.30) could be obtained for the radar shape model by leaving the diameter as a free parameter in the ATPM fitting. This required a smaller effective diameter of 2.21 ± 0.13 km and a more consistent thermal inertia of 320 J m^{2} K^{1} s^{−1/2} (see Table 3), which suggested that the radar observations had overestimated the size of Geographos. However, this produced a maximum equatorial diameter of 4.35 ± 0.26 km, which was inconsistent with that of 5.04 ± 0.07 km accurately measured by Dopplerradar observations.
ATPM derived thermophysical properties of (1620) Geographos using the thermalinfrared observations obtained in 1983 at the 3σ confidence level.
Fig. 1 Radar (left column; Hudson & Ostro 1999), flattenedradar (middle column), and lightcurve (right column; Ďurech et al. 2008b) shape models of (1620) Geographos. The shape models have been scaled so that they have the same maximum equatorial diameter of 5.04 km, and they have been illuminated by the Sun along their yaxes. 

Open with DEXTER 
Intriguingly, the lightcurve shape model with a free diameter still produced similar results to when its diameter was kept fixed in the ATPM fitting (see Table 3). It also produced a maximum equatorial diameter that agreed with the Dopplerradar observations. This led to suspicions that there was something wrong with the radar shape model. Indeed, by comparing the axial ratios of the two shape models, i.e. 2.51:1.00:1.07 for the radar shape model versus 3.08:1.22:1.00 for the lightcurve shape model, it is seen that the lightcurve shape model is significantly more oblate than the radar shape model (see Fig. 1). This meant that the radar shape model had more crosssectional area projected towards the observer, which leads to enhanced model flux for the same effective diameter and thermophysical properties. Therefore, the ATPM fitting tried to counter this by using a larger thermal inertia value to reduce the model flux. It seemed that the radar observations had overestimated the zaxis of Geographos, which is very plausible considering that the radar shape model contains a northsouth ambiguity (Hudson & Ostro 1999).
To check that this was the case, the radar shape model was flattened, i.e. the flattenedradar shape model (see Fig. 1 and Table 2), so that it had the same axial ratios as the lightcurve shape model. The radar shape model was flattened by multiplying its zaxis by a factor of 0.767, which reduced the effective diameter (i.e. 2.34 ± 0.03 km) of Geographos but maintained the same equatorial diameter. Performing the ATPM fitting with the flattenedradar shape model using a fixed diameter produced a much better fit (i.e. a reduced chisquared value of 0.20) and results that were consistent with the lightcurve shape model results, i.e. a thermal inertia of 260 J m^{2} K^{1} s^{−1/2} (see Table 3). Consistent results were also obtained when its diameter was left free in the ATPM fitting too. Furthermore, the flattenedradar shape model also produced a better fit to the optical lightcurve observations than the original radar shape model, i.e. a lightcurve relativechisquared value of 23.3 was obtained versus 26.6 originally. Therefore, simply reducing the zaxis of the radar shape model reconciled the differences seen between the radar and thermalinfrared observations.
Similar levels of surface roughness were derived for the three different shape models. These suggest a roughness greater than 50%, although the flattenedradar shape model that gave the best fit to the thermalinfrared observations, had the smoothest surface.
Fig. 2 Example ATPM fits to the (1620) Geographos thermalinfared observations (data points; Green 1985; Veeder et al. 1989; Tedesco et al. 2004) using the radar (dashed lines), flattenedradar (dotted lines), and lightcurve (solid lines) shape models. The thermalinfrared spectrum of the flattenedradar shape model in the lower right panel is almost identical to that of the lightcurve shape model. 

Open with DEXTER 
Figure 2 gives example ATPM fits to the thermalinfrared observations using the three different shape models with a fixed diameter, and Table 3 summarises the ATPM derived thermophysical properties at the 3σ confidence level. Figure 3 shows the distribution of possible thermal inertia values, and the covariance of the average roughness fraction with thermal inertia, derived using a fixed diameter. As in Rozitis et al. (2013), the thermal inertia distribution is obtained by counting each allowed test clone with a specific thermal inertia value and dividing by the total number of allowed test clones (~10^{4} clones). The covariance of the roughness fraction with thermal inertia is obtained by averaging the values of the allowed test clones in each thermal inertia bin (n.b. this format is also used in the Yarkovsky and YORP effect predictions presented later in this work).
Fig. 3 Summary of ATPM chisquare fitting results to the thermalinfrared observations of (1620) Geographos obtained in 1983 using a fixed maximum equatorial diameter of 5.04 ± 0.07 km. The possible thermal inertia distribution (left) and the covariance with thermal inertia of the average roughness fraction (right) derived at the 3σ confidence level for the three different shape models (legend). 

Open with DEXTER 
3. Yarkovsky and YORP modelling
The Yarkovsky and YORP effects acting on Geographos can be determined by computing the total recoil forces and torques from reflected and thermally emitted photons from the asteroid surface (see Rozitis & Green 2012, 2013a, for methodology). The inclusion of rough surface thermalinfrared beaming effects in the predictions, on average, enhances the Yarkovsky orbital drift whilst it dampens the YORP rotational acceleration by orders of several tens of per cent (Rozitis & Green 2012). Including global selfheating effects does not significantly affect the Yarkovsky orbital drift predictions (less than a few per cent difference) but can significantly affect the YORP rotational acceleration predictions in some cases (Rozitis & Green 2013a). Utilising the three shape models of Geographos, Yarkovsky and YORP effect predictions were made for the range of possible thermophysical properties (or allowed test clones) determined by the thermalinfrared flux fitting and compared against published measurements.
Like (1862) Apollo in Rozitis et al. (2013), Geographos has three measurements of Yarkovsky semimajor axis drift: –17.7 ± 5.9 m yr^{1} by Chesley et al. (2008), –37.4 ± 9.0 m yr^{1} by Nugent et al. (2012), and –27.2 ± 9.0 m yr^{1} by Farnocchia et al. (2013). We combined the three measurements to produce an average drift of –27.4 m yr^{1} with a standard error of 5.7 m yr^{1}, which we used to produce a normal distribution of possible drifts that has a mean and standard deviation equal to these values. For model comparison, 200 values of possible Yarkovsky drift were randomly selected from this distribution that ranged from –14.0 to –43.0 m yr^{1}, which ensured that the three measured values were encompassed (see Fig. 4). The overall predicted Yarkovsky drift, da/dt(Γ,D,f_{R},ρ), for a bulk density of ρ was determined from the predicted smooth surface drift, da/dt(Γ)_{smooth}, the rough surface drift, da/dt(Γ)_{rough}, and the seasonal drift, da/dt(Γ)_{seasonal}, using (4)
Fig. 4 Sampling distribution of the Yarkovsky semimajor axis drift (solid line) used in the analysis of (1620) Geographos compared against the three measured values and their uncertainties (circle – Chesley et al. 2008; square – Nugent et al. 2012; triangle – Farnocchia et al. 2013). 

Open with DEXTER 
Fig. 5 Summary of ATPM Yarkovsky effect modelling results for (1620) Geographos using its three different shape models (legend) and a fixed maximum equatorial diameter of 5.04 ± 0.07 km. a) Average Yarkovsky semimajor axis drift as a function of thermal inertia at a fixed bulk density of 2000 kg m^{3}. b) Average bulk density as a function of thermal inertia derived by comparing the model orbital drift against that measured. c) The distribution of possible bulk densities derived at the 3σ confidence level. 

Open with DEXTER 
where each component had been evaluated separately using the ATPM at an initial diameter D_{0} and bulk density ρ_{0}. It was model convenient to treat the three components separately this way (see Rozitis & Green 2012, 2013a for more details), as each component arose from surface temperature distributions occurring at different spatial scales (i.e. shape facet scale for smooth component, roughness facet scale for rough component, and seasonal thermal wave scale for seasonal component). The bulk density for a set of properties, ρ(Γ,D,f_{R}), could then be determined from the measured Yarkovsky drift, da/dt_{measured}, using (5)Yarkovsky/YORP modelling and bulk density determination were peformed for both a free and fixed diameter, however, the fixed diameter modelling was taken as the nominal result. Figure 5a shows the average Yarkovsky drift as a function of thermal inertia with fixed bulk density. Figure 5b shows the average bulk density required to match the observed orbital drift as a function of thermal inertia. Lastly, Fig. 5c shows the distribution of possible bulk densities derived separately for the three different shape models using a fixed diameter. The derived bulk densities have median values and 1σ spreads of 1450 , 2150 , and 2100 kg m^{3} for the radar, flattenedradar, and lightcurve shape models, respectively. Whilst the flattenedradar and lightcurve shape models produced very consistent results, the radar shape model produced a much lower bulk density because of its much higher thermal inertia value when derived using a fixed diameter. However, the radar shape model produced a more consistent bulk density of 2400 kg m^{3} when using a free diameter. A free diameter also produced consistent results for the flattenedradar and lightcurve shape models but with slightly larger uncertainties when compared to the fixed diameter results.
Ďurech et al. (2008b) has measured Geographos’s YORP rotational acceleration to be (1.5 ± 0.2) × 10^{3} rad yr^{2}, which we use for model comparison. As in Rozitis et al. (2013), the overall YORP rotational acceleration, dω/dt(D,f_{R},ρ), can be predicted using (6)where dω/dt_{smooth} and dω/dt_{rough} are the YORP rotational acceleration values for a smooth and rough surface, respectively, which are independent of thermal inertia. Similarly, the rate of YORP obliquity shift, dξ/dt(Γ,D,f_{R},ρ), can be predicted using (7)where dξ/dt(Γ)_{smooth} and dξ/dt(Γ)_{rough} are the rates of YORP obliquity shift for a smooth and rough surface, respectively, which are dependent on thermal inertia in this case.
Fig. 6 Summary of ATPM YORP effect modelling results for (1620) Geographos using its three different shape models (legend) and a fixed maximum equatorial diameter of 5.04 ± 0.07 km. a) Average YORP rotational acceleration as a function of thermal inertia. b) The distribution of possible YORP rotational acceleration values derived at the 3σ confidence level. The grey vertical lines represent the lower and upper bounds of the YORP rotational acceleration acting on (1620) Geographos as measured by Ďurech et al. (2008b). c) Average rate of YORP obliquity shift as a function of thermal inertia. d) The distribution of possible rates of YORP obliquity shift derived at the 3σ confidence level. 

Open with DEXTER 
Mass and spin change properties of (1620) Geographos derived by ATPM at the 3σ confidence level.
The bulk density values used here were those determined by the modeltomeasurement comparisons of the Yarkovsky drift from Eq. (4). Whilst it was conceivable to try and fit the bulk density using modeltomeasurement comparisons of both the Yarkovsky drift and YORP rotational acceleration simultaneously, it was not done so here because the error in the YORP effect prediction could be very large (see Sect. 4.1). Figure 6a shows the average YORP rotational acceleration as a function of thermal inertia, and Fig. 6b shows the distribution of possible YORP rotational accelerations derived separately for the three different shape models. The derived YORP rotational accelerations using a fixed diameter have median values and 1σ spreads of (–5.5 ), (–4.2 ), and (1.9 rad yr^{2} for the radar, flattenedradar, and lightcurve shape models, respectively. Only the lightcurve shape model produced a prediction range that agreed with the measured value of (1.5 ± 0.2) × 10^{3} rad yr^{2}, as the radar and flattenedradar shape models produced predictions with opposite sign to that observed. Figure 6c shows the average rate of YORP obliquity shift, and Fig. 6d shows the distribution of possible values. The derived rates of YORP obliquity shift have median and 1σ spreads of 1.0 ± 0.4, 4.2 , and 1.9 degrees per 10^{5} yr for the radar, flattenedradar, and lightcurve shape models, respectively. Again, a free diameter produced consistent results but with slightly larger uncertainties. Table 4 summarises Geographos’s density, mass, and spin change properties derived for the three different shape models investigated.
4. Discussion
4.1. Modelling critiques
The accuracy of the Yarkovsky and YORP effect analysis described in Sect. 3 depends strongly on the accuracy of the measured Yarkovsky semimajor axis drift, and for Geographos there are three different measurements giving three slightly different values. It is not clear where these differences arise but it will depend on the parameter fitting, the data weighting treament, and the data sets used in their respective astrometric studies. Fortunately, these measurement differences are not significant enough for them to be considered inconsistent with one another. For example, the largest difference arises between the Chesley et al. (2008) measurement and the Nugent et al. (2012) measurement, which is 1.8σ using their quoted uncertainties. The Farnocchia et al. (2013) measurement is consistent with the two other measurements at the 1σ level. For us to assume that they are inconsistent would require their differences to be greater than the 3σ level, which is clearly not the case here. In the nominal Yarkovsky and YORP effect analysis described above, Monte Carlo sampling from a normal distribution derived from all three measurements was utilised in an attempt to combine the three measurements in a statistically meaningful way (see Fig. 4). However, if one of the measurements could be rejected for a valid reason then the results of the analysis would change. The only reason to consider this is for removal of the Chesley et al. (2008) measurement, as it could be considered to be superseded by the updated analysis described in Nugent et al. (2012) and Farnocchia et al. (2013). A higher rate of Yarkovsky drift would then be preferred, which would lower the derived bulk density and increase the predicted value of the YORP rotational acceleration and obliquity shift (because dω/dt ∝ 1 /ρ ∝ da/dt).
In the YORP effect modelling, it was the lightcurve shape model that provided the best fit to the observations rather than the radar and flattenedradar shape models. In particular, the radar and flattenedradar shape models could not predict the correct sign of the YORP rotational acceleration. The opposite sign YORP rotational acceleration was also found by Ďurech et al. (2008b), and Rozitis & Green (2012) speculated that this is because the Hudson & Ostro (1999) shape model is not unique as the data set it was derived from contains northsouth ambiguities due to the nearequatorial view of the asteroid during the radar observations. The lightcurve observations were taken from multiple and different geometries, and the resulting shape model produced by lightcurve inversion contains no significant degeneracy. As demonstrated, flattening the radar shape model by reducing its zaxis did not reconcile the opposite sign prediction because it equally affected YORP driving shape features located on opposite sides of the asteroid. Reconciliation would require these shape features to be modified unevenly.
Like (1862) Apollo in Rozitis et al. (2013), it is surprising that the theoretical YORP rotational acceleration predicted by the lightcurve shape model agrees with the observed value quite well. Especially when previous studies have shown that the YORP effect can be highly sensitive to unresolved shape features and surface roughness (Statler 2009; Rozitis & Green 2012), the shape model resolution (Breiter et al. 2009), and internal bulk density distribution (Scheeres & Gaskell 2008; Lowry et al. 2014). However, Geographos has a relatively high YORPcoefficient of 0.01 (see Rossi et al. 2009 or Rozitis & Green 2013b for a definition), and Rozitis & Green (2013a) showed that asteroids with high values are less sensitive to the inclusion of concavities in their global shape model. Furthermore, the Geographos prediction is relatively insensitive to smallscale shape features, as when the roughness is allowed to vary in an extreme way across the surface it only introduced an uncertainty of ~30%. Similar findings were made by Ďurech et al. (2008b) and Kaasalainen & Nortunen (2013). In Ďurech et al. (2008b), they added smallscale topography from the spacecraftderived and highresolution shape model of (25143) Itokawa (Gaskell 2008) to the lightcurve shape model of Geographos and found no differences larger than ~5% between their YORP rotational acceleration predictions. In Kaasalainen & Nortunen (2013), they find that the YORP rotational acceleration prediction produced by the lightcurve shape model of Geographos is stable and semistable against local and global shape perturbations respectively.
4.2. Reconciling radar and thermalinfrared observations
The poor fit of the radar shape model with its nominal diameter highlights the importance of having a good shape model for thermophysical modelling of irregular shaped asteroids. Not every shape model, whether it is radarderived or lightcurvederived, produces results that are consistent with other types of observational data. During shape model inversion there is generally a range of similarlylooking shapes that fit equally well to a set of data of one type, and one nominal solution is usually given in publications. In certain circumstances, such as when there is limited data or the range of viewing geometries is small, acceptable shape models can differ significantly. This mainly manifests itself through a large uncertainty in the shape model zaxis.
For example, radar observations in some cases cannot determine whether an asteroid is a prograde or a retrograde rotator and there are two possible shape models, which is the case for nearEarth asteroids (4486) Mithra (Brozovic et al. 2010) and (29075) 1950 DA (Busch et al. 2007). In other cases, radarderived shape models sometimes contain a northsouth ambiquity because of a nearequatorial view during the time of the radar observations. Depending on the acquired data set, the resulting uncertainty in the shape model zaxis could be very large, e.g. as seen for Geographos (Hudson & Ostro 1999), or relatively small, e.g. as seen for (101955) Bennu (Nolan et al. 2013). Similar issues can also affect shape models derived from lightcurve observations. For example, Ďurech et al. (2008a) noted that the zaxis of the (1862) Apollo shape model was not well constrained from the lightcurve photometry, and their nominal shape model appeared to be too flat. Lowry et al. (2012) also demonstrated that even with a good lightcurve data set there can be some flexibility in the derived shape model, which resulted in an uncertainty of ~7% in the axial ratios determined for the nucleus of comet 67P/ChurymovGerasimenko.
Generally, Dopplerradar observations provide the most accurate measurement of an asteroid’s size through determination of its equatorial extent (Ostro et al. 2002). This is because the returned Dopplerbroadened signal depends only on the asteroids’s equatorial diameter, rotation period, and pole orientation. If the latter two asteroid properties are well known then the equatorial diameter can be accurately measured, and a shape model can be scaled to have this value. However, if the shape model zaxis is overestimated then the effective diameter is also overestimated despite it having the correct equatorial diameter. Likewise, if the shape model zaxis is underestimated then the effective diameter will also be underestimated. Thermalinfrared observations are more sensitive to the effective diameter rather than the equatorial diameter, as it is the crosssectional area projected towards the observer that is important in this case. If the spatial extent in the shape model zaxis is wrong then there will be a mismatch between the radarderived and thermalderived effective diameters. This was demonstrated to be the case here for Geographos, which had an overestimated zaxis for its radar shape model. It was also previously demonstrated for (1862) Apollo, which in that case had an underestimated zaxis in its lightcurvederived shape model (Rozitis et al. 2013). Large uncertainties in shape model zaxes are likely to explain why large diameter differences were obtained for nearEarth asteroids 2002 NY40 (Müller et al. 2004) and (308635) 2005 YU55 (Müller et al. 2013). It could potentially explain why some thermophysical model fits have high residuals despite producing consistent diameters, e.g. as seen for (341843) 2008 EV5 (AlìLagoa et al. 2014), because the error in the zaxis also alters the modelled surface temperatures away from the correct ones.
Ideally, the shape inversion and thermophysical modelling should be done simultaneously such that both aspects are optimised, as was done in a rudimentary way for (1862) Apollo in Rozitis et al. (2013). However, the combined modelling required might be too complex for current computational hardware but should be investigated in future work (e.g. Ďurech et al. 2012b).
4.3. Further implications for Geographos
The macroporosity for Geographos can be estimated by assuming a typical bulk density of ~3330 kg m^{3} for the ordinary chondrites that are associated with Stype asteroids (Carry 2012). The bulk densities of 1450 , 2150 , and 2100 kg m^{3} derived using a fixed diameter for the radar, flattenedradar, and lightcurve shape models give macroporosities of 56 , 35 , and 37 %, respectively. These values indicate that Geographos most likely has a rubblepile interior structure (Britt et al. 2002). This conclusion would remain valid if the Chesley et al. (2008) Yarkovsky drift measurement was rejected (see Sect. 4.1), as the bulk density would be lowered and the macroporosity would be increased (to ~1800 kg m^{3} and ~46% respectively) to take into account the increased rate of Yarkovsky drift (relative to our nominal value) of Geographos. The rubblepile nature is consistent with its irregular and highly elongated shape having formed from tidal distortion during a close planetary encounter (Solem & Hills 1996; Bottke et al. 1999). The measured bulk density is also lower than 2400 kg m^{3} assumed in the meteoroid ejection studies (Ryabova 2002a,b), which suggests that it is easier for loose material (as inferred from its thermal inertia value below) to be lofted away from Geographos’s surface during close encounters with the Earth than previously thought.
Taking the lightcurve shape model with a fixed diameter as the nominal result, Geographos’s thermal inertia of 340 J m^{2} K^{1} s^{−1/2} is slightly high for a nearEarth asteroid that is a few km in size. In particular, it is larger than 180 ± 50, 140 , and 120 ± 50 J m^{2} K^{1} s^{−1/2} determined for the nearEarth asteroids (1580) Betulia (Harris et al. 2005), (1862) Apollo (Rozitis et al. 2013), and (175706) 1996 FG3 (Wolters et al. 2011), respectively. It is also larger than the mean value of 200 ± 40 J m^{2} K^{1} s^{−1/2} determined for kmsized nearEarth asteroids (Delbo et al. 2007). It is most similar to that of 310 ± 70 and 400 ± 200 J m^{2} K^{1} s^{−1/2} determined for the subkm nearEarth asteroids (101955) Bennu (Emery et al. 2014) and (162173) 1999 JU3 (Müller et al. 2011), respectively. Its surface is therefore likely to consist of a mixture of fine grains and large rocks/boulders rather than just fine grains. The derived bulk density of 2100 kg m^{3} is very similar to that of 1950 ± 140 kg/m^{3} determined for the Stype nearEarth asteroid (25143) Itokawa (Abe et al. 2006). This suggests that Geographos could have a very similar interior structure to (25143) Itokawa despite being significantly larger, i.e. 2.46 km versus 0.33 km in effective diameter (Fujiwara et al. 2006).
Finally, the obliquity of Geographos is increasing for all three shape models, which indicates that it is approaching one of the YORP asymptotic states at 180° obliquity (Čapek & Vokrouhlický 2004). Like (1862) Apollo (Rozitis et al. 2013), the YORP effect will halve Geographos’s rotation period and shift its rotation axis to the 180° obliquity asymptotic state in just ~7 Myr, whilst in the same amount of time the Yarkovsky effect will decrease Geographos’s semimajor axis by just ~10^{3} AU. Therefore, the YORP effect will dominate Geographos’s long term evolution.
Acknowledgments
The authors acknowledge the financial support of the UK Science and Technology Facilities Council (STFC), and are grateful to the reviewer Marco Delbo for several suggested improvements to the paper.
References
 Abe, S., Mukai, T., Hirata, N., et al. 2006, Science, 312, 1344 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
 AlìLagoa, V., Lionni, L., Delbo, M., et al. 2014, A&A, 561, A45 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Bottke Jr., W. F., Richardson, D. C., Michel, P., & Love, S. G. 1999, AJ, 117, 1921 [NASA ADS] [CrossRef] [Google Scholar]
 Bottke Jr., W. F., Vokrouhlický, D., Rubincam, D. P., & Nesvorný, D. 2006, Ann. Rev. Earth Planet. Sci., 34, 157 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Breiter, S., Bartczak, P., Czekaj, M., et al. 2009, A&A, 507, 1073 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Britt, D. T., Yeomans, D., Housen, K., & Consolmagno, G. 2002, in Asteroids III, eds. W. F. Bottke Jr., A. Cellino, P. Paolicchi, & R. P. Binzel (Tucson: Univ. of Arizona Press), 485 [Google Scholar]
 Brozovic, M., Benner, L. A. M., Magri, C., et al. 2010, Icarus, 208, 207 [NASA ADS] [CrossRef] [Google Scholar]
 Bus, S. J., & Binzel, R. P. 2002, Icarus, 158, 146 [NASA ADS] [CrossRef] [Google Scholar]
 Busch, M. W., Giorgini, J. D., Ostro, S. J., et al. 2007, Icarus, 190, 608 [NASA ADS] [CrossRef] [Google Scholar]
 Čapek, D., & Vokrouhlický, D. 2004, Icarus, 172, 526 [NASA ADS] [CrossRef] [Google Scholar]
 Carry, B. 2012, Planet. Space Sci., 73, 98 [NASA ADS] [CrossRef] [Google Scholar]
 Chesley, S. R., Ostro, S. J., Vokrouhlický, D., et al. 2003, Science, 302, 1739 [NASA ADS] [CrossRef] [Google Scholar]
 Chesley, S. R., Vokrouhlický, D., Ostro, S. J., et al. 2008, Asteroids, Comets, Meteors 2008, Baltimore, LPI Co. No. 1405, 8330 [Google Scholar]
 Chesley, S. R., Farnocchia, D., Nolan, M. C., et al. 2014, Icarus, 235, 5 [NASA ADS] [CrossRef] [Google Scholar]
 Delbo, M., Dell’Oro, A., Harris, A. W., et al. 2007, Icarus, 190, 236 [NASA ADS] [CrossRef] [Google Scholar]
 Ďurech, J., Vokrouhlický, D., Kaasalainen, M., et al. 2008a, A&A, 488, 345 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Ďurech, J., Vokrouhlický, D., Kaasalainen, M., et al. 2008b, A&A, 489, L25 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Ďurech, J., Vokrouhlický, D., Baransky, A. R., et al. 2012a, A&A, 547, A10 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Ďurech, J., Delbo, M., & Carry, B. 2012b, Asteroids, Comets, Meteors 2012, Niigata, LPI Co. No. 1667, 6118 [Google Scholar]
 Dunlap, J. L. 1974, AJ, 79, 324 [NASA ADS] [CrossRef] [Google Scholar]
 Emery, J. P., Fernández, Y. R., Kelley, M. S. P., et al. 2014, Icarus, 234, 17 [NASA ADS] [CrossRef] [Google Scholar]
 Farnocchia, D., Chesley, S. R., Vokrouhlický, D., et al. 2013, Icarus, 224, 1 [NASA ADS] [CrossRef] [Google Scholar]
 Fowler, J. W., & Chillemi, J. R. 1992, in The IRAS Minor Planet Survey, eds. E. F. Tedesco, G. J. Veeder, J. W. Fowler, & J. R. Chillemi (Hanscom AF Base: Philips Laboratory), 17 [Google Scholar]
 Fujiwara, A., Kawaguchi, J., Yeomans, D. K., et al. 2006, Science, 312, 1330 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
 Gaskell, R. 2008, Gaskell Itokawa Shape Model V1.0. HAYAAMICA5ITOKAWASHAPEV 1.0, NASA Planetary Data System [Google Scholar]
 Green, S. F. 1985, Ph.D. Thesis, University of Leicester, UK [Google Scholar]
 Harris, A. W., Mueller, M., Delbo, M., & Bus, S. J. 2005, Icarus, 179, 95 [NASA ADS] [CrossRef] [Google Scholar]
 Hudson, R. S., & Ostro, S. J. 1999, Icarus, 140, 369 [NASA ADS] [CrossRef] [Google Scholar]
 Kaasalainen, M., & Nortunen, H. 2013, A&A, 558, A104 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Kaasalainen, M., & Torppa, J. 2001, Icarus, 153, 24 [NASA ADS] [CrossRef] [Google Scholar]
 Kaasalainen, M., Ďurech, J., Warner, B. D., et al. 2007, Nature, 446, 420 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
 Kwiatkowski, T. 1995, A&A, 294, 274 [NASA ADS] [Google Scholar]
 Lowry, S. C., Fitzsimmons, A., Pravec, P., et al. 2007, Science, 316, 272 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
 Lowry, S. C., Duddy, S. R., Rozitis, B., et al. 2012, A&A, 548, A12 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Lowry, S. C., Weissman, P. R., Duddy, S. R., et al. 2014, A&A, 562, A48 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Magnusson, P., Dahlgren, M., Barucci, M. A., et al. 1996, Icarus, 123, 227 [NASA ADS] [CrossRef] [Google Scholar]
 Michalowski, T., Kwiatkowski, T., Borczyk, W., & Pych, W. 1994, Acta Astron., 44, 223 [NASA ADS] [Google Scholar]
 Müller, T. G., Sterzik, M. F., Schütz, O., Pravec, P., & Siebenmorgen, R. 2004, A&A, 424, 1075 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Müller, T. G., Sekiguchi, T., Kaasalainen, M., et al. 2005, A&A, 443, 347 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Müller, T. G., Ďurech, J., Hasegawa, S., et al. 2011, A&A, 525, A145 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Müller, T. G., Miyata, T., Kiss, C., et al. 2013, A&A, 558, A97 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Nolan, M. C., Magri, C., Howell, E. S., et al. 2013, Icarus, 226, 629 [NASA ADS] [CrossRef] [Google Scholar]
 Nugent, C. R., Margot, J.L., Chesley, S. R., & Vokrouhlický, D. 2012, AJ, 144, 60 [NASA ADS] [CrossRef] [Google Scholar]
 Ostro, S. J., Rosema, K. D., Hudson, R. S., et al. 1995, Nature, 375, 474 [NASA ADS] [CrossRef] [Google Scholar]
 Ostro, S. J., Jurgens, R. F., Rosema, K. D., et al. 1996, Icarus, 121, 46 [NASA ADS] [CrossRef] [Google Scholar]
 Ostro, S. J., Hudson, R. S., Benner, L. A. M., et al. 2002, in Asteroids III, eds. W. F. Bottke Jr., A. Cellino, P. Paolicchi, & R. P. Binzel (Tucson: Univ. of Arizona Press), 151 [Google Scholar]
 Rossi, A., Marzari, F., & Scheeres, D. J. 2009, Icarus, 202, 95 [NASA ADS] [CrossRef] [Google Scholar]
 Rozitis, B., & Green, S. F. 2011, MNRAS, 415, 2042 [NASA ADS] [CrossRef] [Google Scholar]
 Rozitis, B., & Green, S. F. 2012, MNRAS, 423, 367 [NASA ADS] [CrossRef] [Google Scholar]
 Rozitis, B., & Green, S. F. 2013a, MNRAS, 433, 603 [NASA ADS] [CrossRef] [Google Scholar]
 Rozitis, B., & Green, S. F. 2013b, MNRAS, 430, 1376 [NASA ADS] [CrossRef] [Google Scholar]
 Rozitis, B., Duddy, S. R., Green, S. F., & Lowry, S. C. 2013, A&A, 555, A20 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Ryabova, G. O. 2002a, Sol. Syst. Res., 36, 168 [NASA ADS] [CrossRef] [Google Scholar]
 Ryabova, G. O. 2002b, Sol. Syst. Res., 36, 254 [NASA ADS] [CrossRef] [Google Scholar]
 Scheeres, D. J., & Gaskell, R. W. 2008, Icarus, 198, 125 [NASA ADS] [CrossRef] [Google Scholar]
 Solem, J. C., & Hills, J. G. 1996, AJ, 111, 1382 [NASA ADS] [CrossRef] [Google Scholar]
 Statler, T. S. 2009, Icarus, 202, 502 [NASA ADS] [CrossRef] [Google Scholar]
 Taylor, P. A., Margot, J.L., Vokrouhlický, D., et al. 2007, Science, 316, 274 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
 Tedesco, E. F., Noah, P. V., Noah, M., & Price, S. D. 2004, IRAS Minor Planet Survey IRASAFPA3RDRIMPSV6.0, NASA Planetary Data System [Google Scholar]
 Veeder, G. J., Hanner, M. S., Matson, D. L., et al. 1989, AJ, 97, 1211 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
 Vokrouhlický, D., Chesley, S. R., & Matson, R.D. 2008, AJ, 135, 2336 [NASA ADS] [CrossRef] [Google Scholar]
 Vorder Bruegge, R. W., & Shoemaker, E. M. 1993, in AAS/Division for Planetary Sciences Meeting Abstracts, 25, #23.17 [Google Scholar]
 Wolters, S. D., Rozitis, B., Duddy, S. R., et al. 2011, MNRAS, 418, 1246 [NASA ADS] [CrossRef] [Google Scholar]
All Tables
Assumed and previously measured thermophysical modelling parameters for thermalinfrared flux fitting and Yarkovsky and YORP effect modelling.
ATPM derived thermophysical properties of (1620) Geographos using the thermalinfrared observations obtained in 1983 at the 3σ confidence level.
Mass and spin change properties of (1620) Geographos derived by ATPM at the 3σ confidence level.
All Figures
Fig. 1 Radar (left column; Hudson & Ostro 1999), flattenedradar (middle column), and lightcurve (right column; Ďurech et al. 2008b) shape models of (1620) Geographos. The shape models have been scaled so that they have the same maximum equatorial diameter of 5.04 km, and they have been illuminated by the Sun along their yaxes. 

Open with DEXTER  
In the text 
Fig. 2 Example ATPM fits to the (1620) Geographos thermalinfared observations (data points; Green 1985; Veeder et al. 1989; Tedesco et al. 2004) using the radar (dashed lines), flattenedradar (dotted lines), and lightcurve (solid lines) shape models. The thermalinfrared spectrum of the flattenedradar shape model in the lower right panel is almost identical to that of the lightcurve shape model. 

Open with DEXTER  
In the text 
Fig. 3 Summary of ATPM chisquare fitting results to the thermalinfrared observations of (1620) Geographos obtained in 1983 using a fixed maximum equatorial diameter of 5.04 ± 0.07 km. The possible thermal inertia distribution (left) and the covariance with thermal inertia of the average roughness fraction (right) derived at the 3σ confidence level for the three different shape models (legend). 

Open with DEXTER  
In the text 
Fig. 4 Sampling distribution of the Yarkovsky semimajor axis drift (solid line) used in the analysis of (1620) Geographos compared against the three measured values and their uncertainties (circle – Chesley et al. 2008; square – Nugent et al. 2012; triangle – Farnocchia et al. 2013). 

Open with DEXTER  
In the text 
Fig. 5 Summary of ATPM Yarkovsky effect modelling results for (1620) Geographos using its three different shape models (legend) and a fixed maximum equatorial diameter of 5.04 ± 0.07 km. a) Average Yarkovsky semimajor axis drift as a function of thermal inertia at a fixed bulk density of 2000 kg m^{3}. b) Average bulk density as a function of thermal inertia derived by comparing the model orbital drift against that measured. c) The distribution of possible bulk densities derived at the 3σ confidence level. 

Open with DEXTER  
In the text 
Fig. 6 Summary of ATPM YORP effect modelling results for (1620) Geographos using its three different shape models (legend) and a fixed maximum equatorial diameter of 5.04 ± 0.07 km. a) Average YORP rotational acceleration as a function of thermal inertia. b) The distribution of possible YORP rotational acceleration values derived at the 3σ confidence level. The grey vertical lines represent the lower and upper bounds of the YORP rotational acceleration acting on (1620) Geographos as measured by Ďurech et al. (2008b). c) Average rate of YORP obliquity shift as a function of thermal inertia. d) The distribution of possible rates of YORP obliquity shift derived at the 3σ confidence level. 

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.