4.6 Article

On the trail of a comet's tail: A particle tracking algorithm for comet 67P/Churyumov-Gerasimenko

期刊

ASTRONOMY & ASTROPHYSICS
卷 659, 期 -, 页码 -

出版社

EDP SCIENCES S A
DOI: 10.1051/0004-6361/202141953

关键词

comets; general; comets; individual; 67P/Churyumov-Gerasimenko; zodiacal dust

资金

  1. Germany (DLR)
  2. France (CNES)
  3. Italy (ASI)
  4. Spain (MEC)
  5. Sweden (SNSB)
  6. ESA Technical Directorate
  7. ERC [757390]
  8. Volkswagen Foundation
  9. European Research Council (ERC) [757390] Funding Source: European Research Council (ERC)

向作者/读者索取更多资源

This study used data from the European Space Agency's Rosetta mission to track the motion of particles in image sequences and derive their velocities and accelerations. An algorithm was developed to locate the particles and reconstruct their tracks using the image sequences' pair-nature. The study found significant information about potential genuine particle tracks and obtained preliminary results on velocity, acceleration, and radius distributions.
Context. During the post-perihelion phase of the European Space Agency's Rosetta mission to comet 67P, the Optical, Spectroscopic, and Infrared Remote Imaging System on board the spacecraft took numerous image sequences of the near-nucleus coma, with many showing the motion of individual pieces of debris ejected from active surface areas into space. Aims. We aim to track the motion of individual particles in these image sequences and derive their projected velocities and accelerations. This should help us to constrain their point of origin on the surface, understand the forces that influence their dynamics in the inner coma, and predict whether they will fall back to the surface or escape to interplanetary space. Methods. We have developed an algorithm that tracks the motion of particles appearing as point sources in image sequences. Our algorithm employs a point source detection software to locate the particles and then exploits the image sequences' pair-nature to reconstruct the particle tracks and derive the projected velocities and accelerations. We also constrained the particle size from their brightness. Results. Our algorithm identified 2268 tracks in a sample image sequence. Manual inspection not only found that 1187 (similar to 52%) of them are likely genuine, but in combination with runs on simulated data it also revealed a simple criterion related to the completeness of a track to single out a large subset of the genuine tracks without the need for manual intervention. A tentative analysis of a small (n = 89) group of particles exemplifies how our data can be used, and provides first results on the particles' velocity, acceleration, and radius distributions, which agree with previous work.

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