The document discusses using machine learning tools to characterize meteor showers and search for long-period comets. The objectives are to automate meteor identification, search for new meteor shower streams and outbursts, and find rare outbursts that could indicate dangerous long-period comets. The methods include classifying meteors vs non-meteors using CNNs, LSTMs, and random forests. Unsupervised clustering is used to detect new showers and outbursts from orbital data. The goals are to expand sky coverage, monitor for outbursts in real-time, and publish search areas for comets inferred from meteor data.

1. SPACE WEATHER MISSION 02
Machine learning tools for
meteor shower characterization
in search of long-period comets
Andres Plata Stapper
Antonio Ordoñez
Jack Collison
Marcelo De Cicco
Susana Zoghbi
PLANETARY DEFENSE: MISSION 01

2. PLANETARY DEFENSE: LONG-PERIOD COMETS
Meet the team

3. PLANETARY DEFENSE: LONG-PERIOD COMETS
Mission Statement
Provide more warning time for long period comet
impacts by applying machine learning to meteor
shower observations, whose trajectories enable
dedicated searches along predicted orbits.

4. PLANETARY DEFENSE: LONG-PERIOD COMETS
Objectives
● Improve and automate the identification of meteors on images detected in
meteor shower surveys using machine learning and deep learning
● Search for meteor shower streams and outbursts from the ever growing
meteor database to estimate parent body types and their associated
orbital parameters
● Find rare meteor outbursts from dust trail encounters that trace dangerous
long-period comets

5. PLANETARY DEFENSE: LONG-PERIOD COMETS
Background

6. PLANETARY DEFENSE: LONG-PERIOD COMETS
Why meteors?
Perihelion Outbound 1 Rev Inbound
Meteor
outburst
Comet

7. PLANETARY DEFENSE: LONG-PERIOD COMETS
Why meteors?
https://www.meteorshowers.org

8. PLANETARY DEFENSE: LONG-PERIOD COMETS
Meteor shower surveys

9. PLANETARY DEFENSE: LONG-PERIOD COMETS
Meteor shower surveys

10. PLANETARY DEFENSE: LONG-PERIOD COMETS
Mapping meteors in the sky

11. PLANETARY DEFENSE: LONG-PERIOD COMETS
Meteor Classification

12. PLANETARY DEFENSE: LONG-PERIOD COMETS
Meteor time-series
X
Y
Time
Intensity

13. PLANETARY DEFENSE: LONG-PERIOD COMETS
Non-meteor time-series
X
Y
TimeIntensity

14. PLANETARY DEFENSE: LONG-PERIOD COMETS
Describing the data
● Extract descriptive features, such as straightness
of trajectory, brightness of light curve, shape of
light curve, etc.
● Trained a random forest to classify meteor vs
non-meteor in dataset of ~200,000 objects from
CAMS
● Result: meteor classification precision = 90% and
recall = 81%
X
Y
Time
Intensity

15. PLANETARY DEFENSE: LONG-PERIOD COMETS
LSTM for time-series
● Long Short Term Memory (LSTM) networks can
efficiently characterize time-series
● Inputs: XY position, time, and intensity
● Result: meteor classification precision = 90%
and recall = 89%
Adapted from
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
X1
Y1
T1
I1
X2
Y2
T2
I2
X3
Y3
T3
I3
H H H
σ

16. PLANETARY DEFENSE: LONG-PERIOD COMETS
Meteors vs. Non Meteors
Non Meteors Meteors
Clouds Planes Birds Small Bright Behind
Clouds

17. PLANETARY DEFENSE: LONG-PERIOD COMETS
Convolutional Neural Network (CNN)
Standard AlexNet architecture adapted to this dataset
Results: Precision: 88.6% Recall: 90.3%

18. PLANETARY DEFENSE: LONG-PERIOD COMETS
Meteor identification results
Method Input Precision Recall F1
CNN Images 88.3 90.3 89.5
RF Tracklets 90.0 80.6 84.9
LSTM Tracklets 90.0 89.1 89.6

19. PLANETARY DEFENSE: LONG-PERIOD COMETS
Stream and Outburst Characterization

20. Partition
historical
meteor orbital
data
(992226
meteors)
Clustering
Validation
Showers and
Outbursts
From single meteor level orbital parameters to shower and outburst characterization
Dimension
reduction
PLANETARY DEFENSE: LONG-PERIOD COMETS
Meteor shower detection

21. PLANETARY DEFENSE: LONG-PERIOD COMETS
Period Solar Longitude 90-135

22. Dim1 (38.45%)Dim1 (38.45%)
Dim2(17.62%)
PLANETARY DEFENSE: LONG-PERIOD COMETS
Meteor data mining - PCA
122225 meteor orbits ( 9 orbital parameters)
Meteor

23. PLANETARY DEFENSE: LONG-PERIOD COMETS
Multidimensional scaling via t-SNE
Dimension reduction
of meteor orbital
data via t-distributed
stochastic neighbor
embedding (t-SNE)
Meteor

24. PLANETARY DEFENSE: LONG-PERIOD COMETS
Directional parameters
Latitude
Direction of the
Radiant

25. PLANETARY DEFENSE: LONG-PERIOD COMETS
Established meteor showers
IAU meteor
shower
classification
correctly
rescued
by
t-SNE from
meteor orbital
parameters
Unlabeled
Meteors

26. PLANETARY DEFENSE: LONG-PERIOD COMETS
Unsupervised machine learning via DBscan
DBScan
Identifies the
established
showers in
addition to new
previously
undescribed
showers

27. Previously
uncharacterized
meteor
showers
PLANETARY DEFENSE: LONG-PERIOD COMETS
Potential new meteor showers
Needs validation!

28. Space time areas of
high density of
meteors
-
Outbursts
Allows for dedicated
searches for Long
Period Comets along
predicted orbits
Needs validation!
PLANETARY DEFENSE: LONG-PERIOD COMETS
Potential new outbursts

29. PLANETARY DEFENSE: LONG-PERIOD COMETS
Conclusions and breakthroughs
● Improved and automated the identification of meteors above human level
performance on images detected in meteor shower surveys using machine
learning and deep learning
● Recovered known meteor shower streams and characterized previously
unknown meteor showers from meteor orbital data
● We are able to find rare meteor outbursts from dust trail encounters that
could trace the orbits of dangerous long-period comets

30. PLANETARY DEFENSE: LONG-PERIOD COMETS
Acknowledgements
Frontier Development Lab
Peter Jenniskens
Pete Gural
Sara Jennings
James Parr
Siddha Ganju
JL Galache
Yarin Gal
FDL participants
NASA
Darlene Weidemann
Victoria Friedensen
SETI
Bill Diamond
IBM
Troy Hernandez
Graham Mackintosh
NVIDIA
Alison Lowndes

31. PLANETARY DEFENSE: LONG-PERIOD COMETS
Questions

32. PLANETARY DEFENSE: LONG-PERIOD COMETS
Future directions
● Expand sky surveillance coverage
● Real-time monitoring of the meteor orbital data looking
for outbursts
● Publish search areas for the putative comets inferred
from the meteors
● Application that visually maps the comet orbit onto the
sky and provides search areas

33. PLANETARY DEFENSE: LONG-PERIOD COMETS
Directional parameters
Solar Longitude
-
Time of the year

34. Partition
historical
meteor orbital
data
Unsupervised
Machine
Learning
Validation
Showers and
Outbursts
From single meteor level orbital parameters to meteor showers and outburst characterization
Dimension
reduction
Total Number of meteor orbits 992226
Data partitioned in steps of 45° Solar Longitude
Data shown here corresponds to orbits 90-135° Solar Longitude
Total number of 122295
PLANETARY DEFENSE: LONG-PERIOD COMETS
Meteor shower detection

35. PLANETARY DEFENSE: LONG-PERIOD COMETS
True Positives
Network predicts a meteor and it’s actually a meteor

36. PLANETARY DEFENSE: LONG-PERIOD COMETS
False Positives
Network predicts a meteor but it’s not a meteor

37. PLANETARY DEFENSE: LONG-PERIOD COMETS
True Negatives

38. PLANETARY DEFENSE: LONG-PERIOD COMETS
False Negatives
Network predicts non meteor but it is a meteor

39. Training Set:
Meteors: ~3,600
Non Meteors: ~23,800
Validation Set:
Meteors: 455
Non Meteors: ~2,900
Test Set:
Meteors: 455
Non Meteors: ~2,900
PLANETARY DEFENSE: LONG-PERIOD COMETS
Image Data: Meteor vs. Non Meteor
Data Augmentation:
(Standard protocol)
Rotation
Vertical Flip
Horizontal Flip

40. PLANETARY DEFENSE: LONG-PERIOD COMETS
Meteor shower surveys