Bayesian meteor reconstruction using the PAIP-V data

Мұқаба

Дәйексөз келтіру

Толық мәтін

Ашық рұқсат Ашық рұқсат
Рұқсат жабық Рұқсат берілді
Рұқсат жабық Рұқсат ақылы немесе тек жазылушылар үшін

Аннотация

The paper considers the reconstruction problem of events registered by orbital and ground-based detectors with low angular but high temporal resolution. It is shown that in such a situation it is still possible to obtain highly accurate spatiotemporal reconstruction if one combines information on both the geometry and kinematics of the motion and its dynamics (luminescence curve) within a single algorithm. This is especially important in the presence of multiple structural gaps between photodetector channels when only a portion of the event is recorded. In this paper, a Bayesian method implemented by means of the PyMC library is proposed for the reconstruction of track events (tracks of meteors, satellites, etc.): the parametric model takes into account both the features of the event itself and the process of its registration, and the posterior distribution of the parameters is constructed using MCMC sampling. The method is tested on the example of a small sample of meteors of the Geminid-2022 meteor shower recorded by the PAIP-V ground-based detector installed in the Murmansk region.

Авторлар туралы

S. Sharakin

Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University

Хат алмасуға жауапты Автор.
Email: saraev.re17@physics.msu.ru
Moscow, Russia

R. Saraev

Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University; Lomonosov Moscow State University, Faculty of Physics

Email: saraev.re17@physics.msu.ru
Moscow, Russia

Әдебиет тізімі

  1. Adams J.H., Ahmad S., Albert J. et al. An evaluation of the exposure in nadir observation of the JEM-EUSO mission // Astropart. Phys. 2013. V. 44. P. 76-90.
  2. Casolino M., Klimov P., Piotrowski L. Observation of ultra high energy cosmic rays from space: Status and perspectives // Progress of Theoretical and Experimental Physics. 2017. V. 2017. Iss. 12.
  3. Bertaina M., Biktemerova S., Bittermann K. et al. Performance and air-shower reconstruction techniques for the JEM-EUSO mission // Advances in Space Research. 2014. V. 53. Iss. 10. P. 1515-1535.
  4. Barghini D., Bertaina M., Cellino A. et al. UV telescope TUS on board Lomonosov satellite: Selected results of the mission // Advances in Space Research. 2022. V. 70. Iss. 9. P. 2734-2749.
  5. Adams J.H., Ahmad S., Albert J.N. et al. Science of atmospheric phenomena with JEM-EUSO // ExP. Astron. 2015. V. 40. Iss. 1. P. 239-251.
  6. Bacholle S., Barrillon P., Battisti M. et al. Mini-EUSO mission to study earth UV emissions on board the ISS // Astrophysical J. Supplement Series. American Astronomical Society. 2021. V. 253. Iss. 2. P. 36.
  7. Abdellaoui G., Abe S., Adams J. H. et al. EUSO-TA - first results from a ground-based EUSO telescope // Astroparticle Physics. 2018. V. 102. P. 98-111.
  8. Adams J.H., Ahmad S., Allard D. et al. A Review of the EUSO-Balloon Pathfinder for the JEM-EUSO Program // Space Sci.Rev. 2022. V. 218. Iss. 1. Art. ID. 3.
  9. Abdellaoui G., Abe S., Adams J.H. et al. EUSO-SPB1 mission and science // Astroparticle Physics. 2024. V. 154. Art.ID. 102891.
  10. Klimov P., Battisti M., Belov A. et al. Status of the k-EUSO orbital detector of ultra-high energy cosmic rays // Universe. 2022. V. 8. Iss. 2.
  11. POEMMA collaboration, Olinto A.V., Krizmanic J., Adams J.H. et al. The POEMMA (probe of extreme multi-messenger astrophysics) observatory // J. Cosmology and Astroparticle Physics. V. 2021. Iss. 06. Art.ID. 007.
  12. Casolino M., Barghini D., Battisti M. et al. Observation of night-time emissions of the earth in the near UV range from the international space station with the mini-EUSO detector // Remote Sensing of Environment. 2023. V. 284. Art.ID. 113336.
  13. Khrenov B.A., Garipov G.K., Kaznacheeva M.A. et al. An extensive-air-shower-like event registered with the TUS orbital detector // J. Cosmology and Astroparticle Physics. 2020. V. 2020. Iss. 03. Art.ID. 033.
  14. Sharakin S., Hernandez O.I.R. Kinematics reconstruction of the EAS-like events registered by the TUS detector // J. Instrumentation. IOP Publishing, 2021. V. 16, Iss. 07. Art.ID. T07013.
  15. Barghini D., Battisti M., Belov A. et al. Observation of meteors from space with the mini-EUSO detector on board the international space station // Astronomy and Astrophysics. 2024. V. 49236.
  16. Ruiz-Hernandez O.I., Sharakin S., Klimov P. et al. Meteors observations by the orbital telescope TUS // Planetary and Space Science. 2022. V. 218. Art. ID. 105507.
  17. Ceplecha Z. Geometric, Dynamic, Orbital and Photometric Data on Meteoroids from Photographic Fireball Networks // Bulletin of the Astronomical Institutes of Czechoslovakia. 1987. V. 38. Art.ID. 222.
  18. Borovicka J. The Comparison of Two Methods of Determining Meteor Trajectories from Photographs // Bulletin of the Astronomical Institutes of Czechoslovakia. 1990. V. 41. Art.ID. 391.
  19. Gural P.S. A new method of meteor trajectory determination applied to multiple unsynchronized video cameras // Meteoritics & Planetary Science. 2012. V. 47. Iss. 9. P. 1405-1418.
  20. Vida D., Gural P.S., Brown P.G. et al. Estimating trajectories of meteors: an observational Monte Carlo approach - I. Theory // Monthly Notices of the Royal Astronomical Society. 2019. V. 491. Iss. 2. P. 2688-2705.
  21. Sansom E.K., Rutten M.G., Bland P.A. Analyzing meteoroid flights using particle filters // Astronomical J. The American Astronomical Society, 2017. V. 153. Iss. 2. Art.ID. 87.
  22. Jaynes E.T. Probability theory: The logic of science. Cambridge University Press; Annotated edition (June 9, 2003), 2003.
  23. Sivia D., Skilling J. Data analysis: A bayesian tutorial. OUP Oxford, 2006.
  24. Dyk D.A. van, Kang H. Highly Structured Models for Spectral Analysis in High-Energy Astrophysics // Statistical Science. Institute of Mathematical Statistics. 2004. V. 19. Iss. 2. P. 275-293.
  25. Connors A., Esch D. N., Freeman P. et al. Deconvolution in high-energy astrophysics: science, instrumentation, and methods // Bayesian Analysis. International Society for Bayesian Analysis. 2006. V. 1. Iss. 2. P. 189-235.
  26. Gregory P.C., Loredo T.J. A New Method for the Detection of a Periodic Signal of Unknown Shape and Period // Astrophysical J. 1992. V. 398. Art.ID. 146.
  27. Loredo T.J., Berger J.O., Chernoff D.F. et al. Bayesian methods for analysis and adaptive scheduling of exoplanet observations // Statistical Methodology. 2012. V. 9. Iss. 1. P. 101-114.
  28. Loredo T.J., Hendry M.A. Multilevel and hierarchical bayesian modeling of cosmic populations // arXiv: Instrumentation and Methods for Astrophysics. 2019.
  29. Klimov P., Sharakin S., Belov A. et al. System of imaging photometers for upper atmospheric phenomena study in the arctic region // Atmosphere. MDPI AG. 2022. V. 13. Iss. 10. Art.ID. 1572.
  30. Berat C., S. Bottai, D. De Marco et al. Full simulation of space-based extensive air showers detectors with ESAF // Astroparticle Physics. 2010. V. 33. P. 221-247.
  31. Biktemerova S., Guzman A., Mernik T. Performances of JEM-EUSO: angular reconstruction // Exper. Astron. 2015. V. 40. Iss. 1. P. 153-177.
  32. Abe S., Adams J.R. Jr., Allard D. et al. Developments and results in the context of the JEM-EUSO program obtained with the ESAF simulation and analysis framework // Eur. Phys. J. C. 2023. V. 83. Iss. 11. Art. ID. 1028.
  33. Sharakin S., Barghini D., Battisti M. ELVES measurements in the "UV atmosphere" (mini-EUSO) experiment onboard the ISS and their reconstruction // Cosmic Research. 2024. V. 62. Iss. 10. P. 330-338.
  34. Ceplecha Z., Revelle D.O. Fragmentation model of meteoroid motion, mass loss, and radiation in the atmosphere // Meteoritics & Planetary Science. 2005. V. 40. Iss. 1. P. 35-54.
  35. Loredo T.J., Wolpert R.L. Bayesian inference: more than Bayes's theorem //Frontiers in Astronomy and Space Sciences. 2024. V. 11. Art.ID. 1326926.
  36. Anderson J., King I.R. Toward high‐precision astrometry with WFPC2. I. Deriving an accurate point‐spread function // Publications of the Astronomical Society of the Pacific. The University of Chicago Press, 2000. V. 112. Iss. 776. Art.ID. 1360.
  37. Martin O. Bayesian analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ. 2nd edition. Packt Publishing, 2018.
  38. Tran D., Wang H., Torresani L. et al. A closer look at spatiotemporal convolutions for action recognition // CoRR. 2017. V. abs/1711.11248.
  39. Hajduková Jr. M., Koten P., Kornoš L. et al. Meteoroid orbits from video meteors. The case of the Geminids stream // Planetary and Space Science. 2017. V. 143. P. 89-98.
  40. Neslušan L. A summary of the research of Geminid meteoroid stream // Contributions of the Astronomical Observatory Skalnate Pleso. 2015. V. 45. Iss. 1. P. 60-82.
  41. Koten P., Borovicka J., Spurny P. et al. Atmospheric trajectories and light curves of shower meteors // Astronomy and Astrophysics. 12AD. V. 428. P. 683-690.
  42. Jenniskens P., Nenon Q., Albers J. et al. The established meteor showers as observed by CAMS // Icarus. 2016. V. 266. P. 331-354.
  43. Sharakin S.A., Saraev R.E. Probabilistic programming methods for reconstruction of multichannel imaging detector events: ELVES and TRACK // Moscow University Physics Bulletin. 2024. V. 79. P. S772-S780.
  44. Pecina P., Koten P. On the theory of light curves of video-meteors // Astronomy and Astrophysics. 2009. V. 499. Iss. 1. P. 313-320.
  45. Jenniskens P., Gural P.S., Dynneson L. et al. CAMS: Cameras for allsky meteor surveillance to establish minor meteor showers // Icarus. 2011. V. 216. Iss. 1. P. 40-61.
  46. Chen H., Rambaux N., Vaubaillon J. Accuracy of meteor positioning from space- and ground-based observations // Astronomy & Astrophysics. 2020. V. 642. Art.ID. L11.
  47. Arulampalam M.S., Maskell S., Gordon N. et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking // IEEE Transactions on Signal Processing. 2002. V. 50. Iss. 2. P. 174-188.
  48. Sansom E.K., Jansen-Sturgeon T., Rutten M.G. et al. 3D meteoroid trajectories // Icarus. 2019. V. 321. P. 388-406.
  49. Cranmer K., Brehmer J., Louppe G. The frontier of simulation-based inference // Proc. Natl. Acad. Sci. U.S.A. 2020. V. 117(48). P. 30055-30062.
  50. Vida D., Brown P.G., Campbell-Brown M. Modelling the measurement accuracy of pre-atmosphere velocities of meteoroids // Monthly Notices of the Royal Astronomical Society. 2018. V. 479. Iss. 4. P. 4307-4319.

Қосымша файлдар

Қосымша файлдар
Әрекет
1. JATS XML
Жүктеу

© Russian Academy of Sciences, 2025