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

2. Simulator for the observation of atmospheric entries from orbit 1.Context 2.Simulator 3.Hypotheses for simulations, analysis of a large sample of meteors 4.Current results Introduction Conclusions and way forward

3. Why do we monitor meteors? Quantification of the flux of matter entering the atmosphere and enriching planetary atmospheres Deduction on meteoroids properties (composition) Indirect probing of atmospheres (through atmospheric lines), process of entry at high speed Trajectory reconstruction:  Link to parent body  Meteorite recovery Introduction Credit: Max Planck Institute

4. 1. Useful definitions (International Meteor Organization) Meteoroid: a solid object moving in interplanetary space, considerably smaller than a asteroid (10m) and considerably larger than a molecule Meteor: A light phenomenon which results from the entry into the Earth's atmosphere of a solid particle from space. Meteorite: a natural object of extraterrestrial origin (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 Mass Velocity Kinetic energy 0.5mV 2 Luminous Energy Measured luminous energy Panchromatic τ Detector Main difficulties: Mass evaluation (indirectly if no meteorite!) τ varies for each meteor Credit: ESA

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

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

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 Drag equation Mass loss equation Drag coefficient Air density Cross-section area Massic enthalpy of destruction Heat exchange coefficient

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

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) Maximum of 12 detections/hour at 3000km With the SPOSH camera (120°x120°) Hourly rate of detection

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