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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 20th 2013
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
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
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.
1. Context: the projectProject 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 studySimulator to assess the expected number of detections
2.Simulator: From meteoroid to meteor detection
Mass
VelocityKinetic energy
0.5mV2
Luminous Energy
Measured luminous energy
Panchromatic τ
Detector
Main difficulties:• Mass evaluation (indirectly if no meteorite!)• τ varies for each meteor
Credit: ESA
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)
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)
3.Required data(2): Velocities• Velocities distribution: Radar Survey Hunt et
al (2004)
Maximum at 15-20 km/sPeak width: 10 km/s
3.Required data (3): Densities
• Density distribution: No simple answer
Deductions from meteorites are biasedConservative assumption: Uniform distribution (1 to 4)
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)
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 coefficientAir density
Cross-section area
Massic enthalpy of destruction
Heat exchange coefficient
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
3.Condition of detectionAnalysis 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)
3.DetectorsUse cases:
1-The SPOSH camera:Dedicated to transient events observationSpecification: detection at m=6 at 5°/sField of view: 120°x120°Spectral domain: 430-850 nmUsed in ground campaigns (e.g., Draconids 2011)2-The JEM-EUSO experimentExperiment in high energy astrophysics proposed for the ISS
Field of view 60°x60°Spectral domain: near UV (290-430nm)
4.Results (1)With the SPOSH camera (120°x120°)Evolution of coverage
“Horizon to Horizon” above 900km
4.Results (2)
Maximum of 12 detections/hour at 3000km
With the SPOSH camera (120°x120°)Hourly rate of detection
4.Results (3)
With the SPOSH camera (120°x120°)Underlines the importance of coverage
4.Results (4)With the JEM-EUSO experiment (60°x60°, onboard ISS)Evolution of coverage with tilt angle
4.Results (5)With the JEM-EUSO experiment (60°x60°, onboard ISS)Maximum of 1.4 detections/hour
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
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)
Thank you for your attention