Tarano, Ana Maria

English

NASA Technical Reports Server

Machine Learning Applications for 
Inferring Properties of Asteroids 
Impacting Earth 
Ana Maria Tarano 
December 2nd, 2019
Space Environment and Satellite Systems
2
https://ntrs.nasa.gov/search.jsp?R=20190033925 2020-03-11T13:40:36+00:00Z
Overview
As a body enters the atmosphere, its mechanical energy transforms into heat, light, and ionization. 
However, these energy signatures do not provide direct measurements of the meteoroid’s properties, which are 
critical to establish reliable asteroid impact risk assessments. 
This presentation describes methods to infer meteoroid parameters based on the observed light curve from a 
meteoroid’s ﬂight across the atmosphere. 
4
Table of Contents
Overview
Introduction
Objectives
Methodology
Results: Angle
Results: Velocity
Results: Strength
Results: Diameter
Future Work
5
Results: Density
Introduction
01
Objects greater than 1 km are assumed to lead to global 
damage, including potential human extinction. 
Focus on search of potentially hazardous objects are on > 
140 m diameter.
The smaller, more frequent asteroids are not as frequently 
discovered but are still dangerous (e.g. Chelyabinsk ~20 m).
6
Source: SDT NASA, 2017
Introduction
02
Every day, 80 to 100 tons of material falls upon Earth from 
space in the form of dust and small meteorites.
Asteroid missions are expensive and meteorites are rare.
Earth’s atmosphere is a naturally occurring laboratory that 
should be used to better understand asteroids, especially to 
better understand those that might pose a threat.
7
Source: NASA
Introduction
03
Studying meteoroids entering the atmosphere is becoming more readily 
available through the presence of space assets, such as the Geostationary 
Lightning Mapper, and growing ground-based camera networks.
Other types of observational methods, such as seismic, infrasound or 
spectroscopy, don’t map the ﬂight of the bolides.
Pre-entry parameters, such as diameter, density, angle, velocity, and bulk 
strength,  are critical for asteroid threat assessment. 
The pre-entry parameters of impacting asteroids are not directly measured 
from energy deposition curves derived from optical sensors.
Physics-based models and uncertain mean values are used to infer unknown 
quantities from energy deposition curves when velocity and entry angle are 
known.
10
Introduction
04
Light curve data from asteroids entering the atmosphere is abundant but the 
asteroid’s properties are not commonly directly observed so there is ample 
but incomplete and unlabeled data. 
The meteor and asteroid communities rely heavily on modeling to infer 
properties from the data by reproducing the energy signatures that were 
observed.
We have previously used a genetic algorithm to reproduce the manual labor 
of curve matching to solve for model inputs using a semi-analytical 
fragment-cloud model (FCM).
We leverage an extended version of FCM to generate labeled data to train 
regression models in order to infer model inputs from observed cases. 
Study objective: Can our synthetically trained regression models be 
generalized to infer parameters from real ﬁreballs?
12
Objectives
1
Infer pre-entry meteoroid parameters from 
measured meteor light curves.
2
Automate inference using supervised 
learning methods. 
3
Identify the best performing tuning 
parameters for machine learning 
(hyperparameters).
4
Validate approach against 
well-documented but different test cases: 
Chelyabinsk, Lost City, Kosice, Tagish Lake, 
and Benešov meteors.
14
Review GA 
Methodology 
Genetic algorithms (GAs) are iterative stochastic 
search-based optimizers based on evolution and genetics.
16
Review GA Methodology
Develop GA with Synthetic Curves
Understand your search space without the 
limitations from the energy deposition model.
Test with Real Curves 15x 
Understand strengths and challenges  of 
reduced model to match observations.
Compare Results with Literature
Understand if inference parallels other 
modelers for cases that have different features 
and meteorites.
18
GA Fragment-Cloud Model (FCM)
19
FCM Curve Matching
20
Modeling Assumptions
- σ is constant and the same for clouds and 
fragments.
- Body and fragments are spherical in shape and 
monolithic.
- Initial velocity and entry angle are known and 
accurate. 21
GA Review Chelyabinsk (single main ﬂare)
22
Chelyabinsk’s solutions from GA consistently 
matched the main ﬂare early in the evolution 
process.
The height and magnitude of second peak also 
consistently solved for. Modeling assumptions and 
objective function led to narrowness.
The top ﬂare could not be modeled manually, 
requires non-monolithic body.
GA Review Chelyabinsk (single main ﬂare)
23
Chelyabinsk’s solutions from GA consistently 
matched the main ﬂare early in the evolution 
process.
The height and magnitude of second peak also 
consistently solved for. Modeling assumptions and 
objective function led to narrowness.
The top ﬂare could not be modeled manually, 
requires non-monolithic body.
GA FCM Compared to FCM-RP Capabilities
24
Chelyabinsk’s solutions from GA consistently 
matched the main ﬂare early in the evolution 
process.
The height and magnitude of second peak also 
consistently solved for. Modeling assumptions and 
objective function led to narrowness.
The top ﬂare could not be modeled manually, 
requires non-monolithic body.
Regression Models Methodology Overview
25
Deep Neural Network Model
26
Convolutional Neural Network
27
Random Forest Regression
28
Training Data
n observations, m 
predictors 
Sample 1
Train decision tree
Sample k
Train decision tree
Prediction 1 Prediction kAverage of single 
trees
Results
33
34
Results: DNN Best at Angle and Strength
35
Results: CNN Best at Velocity
36
Results: RF Best at Diameter and Density
37
Discussion
- Range of parameters used in training set is limited but representative of 
asteroid population.
- All the parameters were inferred within modeled/published values.
- R2 scores imply that some parameters are not well constrained.
- Training on synthetic curves leads to good generalizability in the models, if 
all data points are present.
Diameter
2 - 40%
Error from actual 
model input value
Density
1-41%
Error from actual 
model input value
Velocity
0-7%
Error from actual 
model input value
38
Angle wrt Horizontal
2-34%
Error from actual 
model input value
Discussion
- Range of parameters used in training set is limited but representative of 
asteroid population.
- All the parameters were inferred within modeled/published values.
- R2 scores imply that some parameters are not well constrained.
- Training on synthetic curves leads to good generalizability in the models, if 
all data points are present.
Diameter
18.6%
Mean Absolute 
Percentage Error
Density
13.0%
Mean Absolute 
Percentage Error
Velocity
2.73%
Mean Absolute 
Percentage Error
39
Angle wrt Horizontal
19.6%
Mean Absolute 
Percentage Error
Conclusions & Future Work
Approaches using regression models are much more faster and reliable than GA.
Approaches using regression models are much more usable than GA because it does not require 
previous knowledge of velocity or entry angle.
Transforming feature space was most important pre-processing approach toward good results.
01    |    Apply models to 10 potential other curves.
02    |    Use sensor fusion to develop more robust systems.
03    |    Develop methodology for incomplete data.
40
04    |    Train models on synthetically generated light curves directly.


Machine Learning Applications for Inferring Properties of Asteroids Impacting Earth

Abstract

Similar works

Full text

Available Versions

NASA Technical Reports Server