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