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Simulator for the observation of atmospheric entries from orbit

Simulator for the observation of atmospheric entries from orbit. D. Baratoux (IRAP) J. Vaubaillon (IMCCE) D. Mimoun (ISAE) M. Gritsevich (Univ. of Helsinki) O. Mousis (UTINAM, Univ. Franche-Comté). A. Bouquet (Student, IRAP). IPPW 10, June 20 th 2013.

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Simulator for the observation of atmospheric entries from orbit

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  1. Simulator for the observation of atmospheric entries from orbit D. Baratoux (IRAP) J. Vaubaillon (IMCCE) D. Mimoun (ISAE) M. Gritsevich (Univ. of Helsinki) O. Mousis (UTINAM, Univ. Franche-Comté) A. Bouquet (Student, IRAP) IPPW 10, June 20th 2013

  2. Simulator for the observation of atmospheric entries from orbit • Context • Simulator • Hypotheses for simulations, analysis of a large sample of meteors • Current results Introduction Conclusions and way forward

  3. Introduction Why do we monitor meteors? • Quantification of the flux of matterentering the atmosphereand enrichingplanetaryatmospheres • Deduction on meteoroidsproperties (composition) • Indirect probing of atmospheres (throughatmosphericlines), process of entry athigh speed • Trajectory reconstruction: • Link to parent body • Meteoriterecovery Credit: Max Planck Institute

  4. 1.Usefuldefinitions • (International MeteorOrganization) • Meteoroid: a solidobjectmoving in interplanetaryspace, considerablysmallerthan a asteroid (10m) and considerablylargerthana molecule • Meteor: A light phenomenonwhichresultsfrom the entry into the Earth'satmosphere of a solidparticlefromspace. • Meteorite: a naturalobject of extraterrestrialorigin (meteoroid) that survives passage through the atmosphere and hits the ground.

  5. 1. Context: the project • Project SPACE-METEOR: How many meteors can we detect from orbit? • Depending on assumptions on meteor flux • Depending on detector and mission configuration (optimal orbit?) • Pros of monitoring from orbit • No weather constraints • No atmospheric extinction • Wide coverage • Access to UV domain Goal of this study Simulator to assess the expected number of detections

  6. 2.Simulator: From meteoroid to meteor detection Credit: ESA Panchromatic τ Luminous Energy Mass 0.5mV2 Detector Measured luminous energy Kinetic energy Velocity • Main difficulties: • Mass evaluation (indirectly if no meteorite!) • τvaries for eachmeteor

  7. 2.Architecture of the simulator(Python language) Distributions Set of events with their properties Masses Speeds Determination of τ Luminous energy Density Number of detections Position in the field of view of the monitoring device Characteristics, position, orientation of the detector

  8. 3.Required data: Masses • Masses distribution: Halliday et al (96) Mass index s: Here s=1.48 at low mass (slope -0.48) Number of events N with mass > MI (per year and million square kilometers) Observations of Canadian Network

  9. 3.Required data(2): Velocities • Velocities distribution: Radar Survey Hunt et al (2004) Maximum at 15-20 km/s Peak width: 10 km/s

  10. 3.Required data (3): Densities • Density distribution: No simple answer Deductions from meteorites are biased Conservative assumption: Uniform distribution (1 to 4)

  11. 3.Luminous efficiency law: analysis of a meteor sample from the Canadian Network • Network of cameras in operation from 1974 to 1985 (12 stations, 60 cameras) • Data: Velocity, height, absolute magnitude for each timestep • Mass evaluation: so-called “photometric” method (Luminous efficiency calibrated on a set of meteors for which kinetic energy came from other means)

  12. 3. Analysis of Canadian Network meteors: Reconstruction of main parameters (Python algorithm) • Method proposed by M. Gritsevich et al • Link between drag and mass loss equation Air density Cross-section area Drag coefficient Drag equation Mass loss equation Massic enthalpy of destruction Heat exchange coefficient

  13. 3. Analysis of Canadian Network meteors: Reconstruction of main parameters (2) It can be demonstrated (M. Gritsevich) that one can write a differential equation linking trajectory to two parameters α and β Determination of luminous efficiency Empirical parameters α and β Assumption on shape and density ρ • Deduction of ρ(Ceplecha-Revelle 2001) α: “ballistic parameter” β: “Mass loss parameter” Ablation coefficient

  14. 3.Condition of detection Analysis of the meteors of the Canadian Network: Luminous efficiency law Total luminous energy of each meteor To be compared to the minimum luminous energy for detection Taking into account shape of the light curve (shape: Canadian Network meteors)

  15. 3.Detectors Use cases: 1-The SPOSH camera: Dedicated to transient events observation Specification: detection at m=6 at 5°/s Field of view: 120°x120° Spectral domain: 430-850 nm Used in ground campaigns (e.g., Draconids 2011) 2-The JEM-EUSO experiment Experiment in high energy astrophysics proposed for the ISS Field of view 60°x60° Spectral domain: near UV (290-430nm)

  16. 4.Results (1) With the SPOSH camera (120°x120°) Evolution of coverage “Horizon to Horizon” above 900km

  17. 4.Results (2) With the SPOSH camera (120°x120°) Hourly rate of detection Maximum of12 detections/hour at 3000km

  18. 4.Results (3) With the SPOSH camera (120°x120°) Underlines the importance of coverage

  19. 4.Results (4) With the JEM-EUSO experiment (60°x60°, onboard ISS) Evolution of coverage with tilt angle

  20. 4.Results (5) With the JEM-EUSO experiment (60°x60°, onboard ISS) Maximum of 1.4 detections/hour

  21. 4.Results (6) Impact of mass index: if s>2 Population shifted towards low masses: low orbits become more interesting Need to refine hypothesis on flux

  22. Conclusions and way forward • Detection rate: 1 to 7 per hour is realistic • Need to refine assumptions (on meteor flux, on luminous efficiency) • Simulator: may be used to confront assumptions with observations once the mission becomes operational • Requirements for trajectory reconstruction? • Detection and spectroscopy in UV domain? (composition)

  23. Thank you for your attention

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