A&A, accepted August 1, 2022

Determining the dust environment of an unknown comet for a spacecraft fly-by: The case of ESA’s Comet Interceptor mission.

Raphael Marschall, Vladimir Zakharov, Cecilia Tubiana, Michael S. P. Kelley, Carlos Corral van Damme, Colin Snodgrass, Geraint H. Jones, Stavro L. Ivanovski, Frank Postberg, Vincenzo Della Corte, Jean-Baptiste Vincent, Olga Muñoz, Fiorangela La Forgia, Anny-Chantal Levasseur-Regourd, and the Comet Interceptor Team


This page contains interactive plots of the above-mentioned paper. Each interactive plot can be customised by changing the axis rages via the leavers at the top and right. By selecting different options in the legend the displayed data can be changed. Via the menu at the top left of each chart, the data can be downloaded and the current view saved as an image.

For all details on the figures please consult the paper. Here you can download the paper . The abstract can be found below.

There are 5 interactive charts on this page comprising the following figures in the paper:

Figure 3: Dust coma phase curves of 1P/Halley and 67P/Churyumov-Gerasimenko.

Figure 5: Total number of particles during Giotto encounter as a function of mass.

Figure 6: Number of dust particles along the spacecraft trajectory of spacecraft A.

Figure 7: The total number of dust particles encountered along the spacecraft trajectory of spacecraft A of Comet Interceptor.

Figure 10: The dust production rate as a function of Afrho and the power law exponent, b, of the dust size distribution.

The full dataset from this paper is available here: https://doi.org/10.5281/zenodo.6906814

If you use the data or charts presented here please reference the paper: Marschall & Zakharov et al., 2022, A&A

Please contact me if you have any questions about this work and/or the data.


Abstract

Context: Assessment of the dust environment of a comet is needed for data analysis and planning of spacecraft missions, such as ESA’s Comet Interceptor (CI) mission. The distinctive feature of CI is that the target object will be defined shortly before (or even after) launch therefore the properties of the nucleus and dust environment are poorly constrained and therefore make the assessment of the dust environment challenging.

Aims: The main goal of the work is to provide realistic estimations of a dust environment based on very general parameters of possible target objects.

Methods: Contemporary numerical models of dusty-gas coma are used to obtain spatial distribution of dust for a given set of parameters. By varying parameters within a range of possible values we obtain an ensemble of possible dust distributions. Then, this ensemble is statistically evaluated in order to define the most probable cases and hence reduce the dispersion. This ensemble can be used to estimate not only the likely dust abundance along e.g. a fly-by trajectory of a spacecraft but also quantify the associated uncertainty.

Results: We present a methodology of the dust environment assessment for the case when the target comet is not known beforehand (or when its parameters are known with large uncertainty). We provide an assessment of dust environment for the CI mission. We find that the lack of knowledge of any particular comet results in very large uncertainties (~3 orders of magnitude) for the dust densities within the coma. The most sensitive parameters affecting the dust densities are the dust size distribution, the dust production rate and coma brightness, often quantified by Af\rho. Further, the conversion of a coma’s brightness (Af\rho) to a dust production rate is poorly constrained. The dust production rate can only be estimated down to an uncertainty of ~0.5 orders of magnitude if the dust size distribution is known in addition to the Af\rho. All results are publicly available under https://www.doi.org/10.5281/zenodo.6906815.

Conclusions: To accurately predict the dust environment of a poorly known comet, a statistical approach needs to be taken to properly reflect the uncertainties. This can be done by calculating an ensemble of comae covering all possible combinations within parameter space as shown in this work.


Figure 3

The dust coma phase curves of 1P/Halley [Schleicher] and 67P/Churyumov-Gerasimenko [Bertini et al. 2017] are shown.


Figure 5

Total number of particles per 1.5 decades in mass during Giotto encounter as a function of mass for the EDCM Halley case and the data by McDonnell et al. (1997).

The full dataset can be downloaded here: https://doi.org/10.5281/zenodo.6906814.


Figure 6

The number of dust particles per cubic meter according to the EDCM along the spacecraft trajectory of spacecraft A as a function of distance to the comet. The shaded areas show different percentile ranges within which cases fall. The closest approach for S/C A is assumed at 1000 km.
Only the inbound trajectory is shown depicted by negative distances to the comet.
The data has been thinned with respect to the paper for online performance reasons.

The full dataset can be downloaded here: https://doi.org/10.5281/zenodo.6906814.


Figure 7

The total number of dust particles per 1.5 decades in mass encountered according to the EDCM along the spacecraft trajectory of spacecraft A of Comet Interceptor as a function of dust mass. The shaded areas show different percentile ranges within which cases fall. Additionally, the orange curve shows the predicted median densities for a Halley-type comet.

The full dataset can be downloaded here: https://doi.org/10.5281/zenodo.6906814.


Figure 10

The dust production rate as a function of Af\rho and the power law exponent, b, of the dust size distribution. The values of Af\rho are slightly offset depending on the power law exponent to show the overlap resulting from b. Af\rho by itself is a poor predictor of the dust production rate of a comet.
The data has been thinned with respect to the paper for online performance reasons.