Astrometric asteroid masses: a simultaneous determination

Goffin, E.

doi:10.1051/0004-6361/201322766

Cited by 21 publications

(8 citation statements)

References 45 publications

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“…Although our ML masses have improved, partly due to additional data, our uncertainties tend to be wider than in Siltala and (2011); Goffin (2014); Kochetova and Chernetenko (2014); Baer and Chesley (2017); this work (Table 4)). The green error bars represent 1σ limits while the red error bars represent 3σ limits.…”

Section: Real Datamentioning

confidence: 47%

Asteroid mass estimation with the robust adaptive Metropolis algorithm

Siltala

Granvik

2020

A&A

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Context. The bulk density of an asteroid informs us about its interior structure and composition. To constrain the bulk density one needs an estimate for the mass of the asteroid. The mass is estimated by analyzing an asteroid's gravitational interaction with another object, such as another asteroid during a close encounter. An estimate for the mass has typically been obtained with linearized leastsquares methods despite the fact that this family of methods is not able to properly describe non-Gaussian parameter distributions. In addition, the uncertainties reported for asteroid masses in the literature are sometimes inconsistent with each other and suspected to be unrealistically low. Aims. We present a Markov-chain Monte Carlo (MCMC) algorithm for the asteroid mass estimation problem based on asteroidasteroid close encounters. We verify that our algorithm works correctly by applying it to synthetic data sets. We then use astrometry available through the Minor Planet Center to estimate masses for a select few example cases and compare our results to results reported in the literature. Methods. Our mass estimation method is based on the robust adaptive Metropolis algorithm that has been implemented into the OpenOrb asteroid orbit computation software. Our method has the built-in capability to analyze multiple perturbing asteroids and test asteroids simultaneously. Results. We find that our mass estimates for the synthetic data sets are fully consistent with the ground truth. The nominal masses for real example cases typically agree with the literature but tend to have greater uncertainties than what is reported in recent literature. Possible reasons for this include different astrometric datasets and/or weights, different test asteroids, different force models and/or different algorithms. For (16) Psyche, the target of NASA's Psyche mission, our maximum likelihood mass is approximately 55% of what is reported in the literature. Such a low mass would imply that the bulk density is significantly lower than previously expected and hence disagrees with the theory of (16) Psyche being the metallic core of a protoplanet. We do, however, note that masses reported in recent literature remain within our 3-sigma limits. Conclusions. The new MCMC mass-estimation algorithm performs as expected, but a rigorous comparison with results from a leastsquares algorithm with the exact same dataset remains to be done. The matters of uncertainties in comparison with other algorithms and correlations of observations also warrant further investigation.

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Section: Real Datamentioning

confidence: 47%

Asteroid mass estimation with the robust adaptive Metropolis algorithm

Siltala

Granvik

2020

A&A

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“…Similarly, there are 23 mass estimates for (3) Juno, obtained by studying the orbital deflection of other minor planets (see Fig. A.2 and Chernetenko & Kochetova 2002;Kochetova 2004;Pitjeva 2004Pitjeva , 2005Pitjeva , 2010Pitjeva , 2013Konopliv et al 2006Konopliv et al , 2011Baer et al 2008Baer et al , 2011Folkner et al 2009;Fienga et al 2010Fienga et al , 2011Fienga et al , 2013Fienga et al , 2014Somenzi et al 2010;Zielenbach 2011;Kuchynka & Folkner 2013;Goffin 2014;Kochetova & Chernetenko 2014). Because there is no known satellite of Juno, mass determination relies on these long-range interactions.…”

Section: Discussionmentioning

confidence: 91%

VLT/SPHERE- and ALMA-based shape reconstruction of asteroid (3) Juno

Viikinkoski

Kaasalainen

Ďurech

et al. 2015

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We use the recently released Atacama Large Millimeter Array (ALMA) and VLT/SPHERE science verification data, together with earlier adaptive-optics images, stellar occultation, and lightcurve data to model the 3D shape and spin of the large asteroid (3) Juno with the all-data asteroid modelling (ADAM) procedure. These data set limits on the plausible range of shape models, yielding reconstructions suggesting that, despite its large size, Juno has sizable unrounded features moulded by non-gravitational processes such as impacts.

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“…The vast majority of asteroids, however, have never been visited by spacecraft nor possess satellites [only a handful of large asteroids possess satellites, albeit the fraction is higher at smaller size; about 15% of near-Earth asteroids have satellites (Margot et al, 2002)]. It is nevertheless possible to determine their mass from the gravitational pull they exert on other planetary objects: asteroids, planets and their satellites, and interplanetary spacecrafts (see, e.g., Hilton, 2002;Fienga et al, 2008;Kuchynka and Folkner, 2013;Goffin, 2014).…”

Section: Densitymentioning

confidence: 99%