Application des réseaux de neurones aux environnements

planétaires

P. Garnier, M. Bouayad, Q. Lenouvel, T. Noël, N. Vachon, V. Génot, J. Lasue, S. Toledo, J. Inglada, B. Renard

SupernovaGalaxy

Gravitationnal waves Exoplanets

Space weather : solar activity

impact Moon craters

Polar auroras

Astronomy/Astrophysics Planetology/Aeronomy

Applications of deep learning in astrophysics

A recent and growing approach in planetology / aeronomy

IRAP PEPS major scientific objectives :• Formation and evolution of planetary environments• Which influence of the Sun on the planets and the

atmospheric escape ?

Which data / constraints / objectives for us ?

Type / amount of data ?

- Images/scalars/multi-dimensional- Temporal / spatial data- Few/Many data

Specific constraints

- Strong temporal dynamics- Uncertainty even among experts- Physics is still present !!!!!

Main objectives

- Classification/Regression/Detection- Provide real-time catalogs and do science- Provide added-value to the community

that uses shared database

3 example studies involving deep learning

Comet’s dustEarth magnetic reconnection

Martian shocks

Deep learning for comets dust

Comets are among the most pristine objects of solar system

Studying these icy dust balls brings constraints on solar system formation

Credit : Johannes-Kepler-Observatory, April 1997

Dust tail

Ion tail

• Rosetta : first mission to follow a comet’s nucleus (67P/Churyumov-Gerasimenko), with a lander and an orbiter

• Dust in situ analysis with several instruments :

– COmetary Secondary Ion Mass Analyzer (COSIMA) [50µm to > 500µm] (Kisselet al., 2007)

– Micro-Imaging Dust Analysis System (MIDAS) [< 20µm] (Riedler et al., 2007)

Deep learning for comets dust

Structure of the dust Constraints on comet formation

COSIMA target 2CF (Langevin et al., 2016)

Classification of dust

aggregates proposed by

COSIMA team :

(a) Compact particle

(b) Shattered cluster

(c) Glued cluster

(d) Rubble pile

Deep learning for comets dust

Our objective : automatically classify all collected dust aggregates based on a Convolutional Neural Network (CNN)

BUT few labeled agregates to train : 176 !!

Deep learning for comets dust

Log and ReLU

Polar mapping

72 imagesfor each

aggregate

Data augmentation

……

Preprocessing of the dust aggregates images

Increasing the contrast (Log and ReLU) Data augmentation (rotation + left/right images) Polar mapping due to radial

symmetry assumed Adding a « no aggregate » class

11232 images for

training the CNN

Deep learning for comets dust

A CNN architecture inspired by SAR techniques

Conv196 channels3x3 kernel

Conv296 channels3x3 kernel

MaxPool12x2 kernel

Conv3256

channels3x3 kernel

MaxPool22x2 kernel

80x80 pixels

The classifier (MLP)

CompactShatteredGluedRubble-pileNone

400 neurons5 neurons

Deep learning for comets dust

Very difficult to recognize the classes (intrinsic confusion between classes)

Performance of CNN classification on test set…

Deep learning for comets dust

Very difficult to recognize the classes (intrinsic confusion between classes)

Manual “blind” classification by J. Lasue and myself :<60% global accuracy vs original paper !!!! (and 60% among us 2)

Performance of CNN classification on test set…

… before we check ourselves the classification by COSIMA team« Experts vs experts »

Detailed analysis reveals more a continuum of dust structures than distinct classes

Deep learning for comets dust

BUT the « no aggregate class » works perfectly !

Change of objective : automatic detection

Segmentation to select aggregates Now : statistical analysis of the

size/volume distribution

Earth reconnection

Earth

Earth reconnection

Earth

Reconnection process

Core of reconnection process : Electron Diffusion Region (EDR)

Earth reconnection

Magnetospheric Multi Scale Mission (NASA)

Launch 2015, 4 spacecraft

Main objective : study reconnectionprocess, in particular the EDR region

Excellent time resolution (order of ms)

32 EDR events published to date : complex to identify, no thresholdapproach possible

Automatic detection of EDR regions based on neural network to provide catalogs and better understand reconnection process

Earth reconnection

Detailed and debated analysis of variables (features selection) to be included in the neural network

Box plots for feature selection

Definition of new features for the EDR detection to reveal agyrotropy of electrons

Supervised classification by a MLP deep neural network of temporal data

Definition of 5 classes to classify the whole Earth plasma environment, but imbalanced datasets with few EDRs : weighted classes

Velocity distribution function of electrons (agyrotropic left, gyrotropic right)

Earth reconnection

Promising results since good recall for EDRs (rare event detection)

Earth reconnection

Promising results since good recall for EDRs (rare event detection)

Real Classes

Predicted classes

Test on independant continous event

EDR correctly recognized !NEXT :

- refinement of model- propose real time

detection of possible EDRs to MMS team

Martian shocks and plasma regions

MARS

Full zoology of « boundaries »shock, Magnetic Pile-Up Boundary or Induced Magnetospheric Boundary….

Strong dynamics of the plasma regions and boundaries

Study this dynamics to betterunderstand the martian escape history

MagnetosheathSolar wind

Shock detection• Not a rare event (twice

every orbit) : 11000 for Mars Express mission

• Classic approach based on threshold detection

• Aim : use similar supervised deep neural network for several space missions and planets

Martian shocks and plasma regions

Electrons

Ions velocity

Ions temperature

Magnetic field

Distance to planet

Specific problems

Martian shocks and plasma regions

• Published catalogs for supervised learning provide a single time for shock crossings, we want to keep fine structure

• From an author to another, several minutes of difference depending on their method

• For any expert, uncertainty of serveral minutes ; the shock is not a boundary but a transition region

Need to add pre/post processing steps

Time difference for shock detection between 2 published datasets

Feature selection and creation to enhance time variations

Rupture algorithm for change point detection

+ k-fold+ HyperOpt bayesian

optimization

Layers : 8:40:38:3

Martian shocks and plasma regions

Metrics with k-foldMetrics including time tolerance

Good results for shock detection, assuming some temporal tolerance(better than threshold method by Hall et al. 2016)

Same algorithm being applied to Mars Express / MAVEN / Venus Express

Martian shocks and plasma regions

What about physics ?

Analysis of metrics coherent withexperts knowledge : shockdetection difficult away from the nose due to plasma turbulence

Algorithm keeps traces of fine structure with e.g. oscillatingshocks that are interesting for us

Solar Wind

Shock

Planetaryenvironment

Automatic data labeling for the community in the CDPP/AMDA planetary database : prototype of user added value

Discrete class labelling : planetary environment (0), shock (1), Solar Wind (2)

Martian shocks and plasma regions

Mars Express electrons fluxes

Or with continuous class value given for finer interpretation :

MAVEN electrons fluxes

THANK YOU !!

Building the dataset

Our dataset is derived from a subset of the catalog identified by Hall (~1500 bow shock crossings out of ~11800)

→ measurements in a ±15 min window around shock crossings

→ ~1 800 000 data vectors

→ labeled in 3 classes (Solar wind, Bow shock, Close environment)

→ the class of interest represents 6.2% of the dataset

Detailed algorithm layout

- Classification with the MLP classifier (param. : architecture, loss, opt.)- Reduction of the number of vectors by extracting the class variations - Post-processing : correction of the variations (param. : Dt_corr)- Extracting the bow shock crossings from the variations- Post-processing : merging the crossings that are separated by less than

Dt (param. : Dt_cross)- Returns the list of timed crossings

v1

v2

c0

MLP classification (no post-processing)

- Temporality ignored (the algorithm is trained vector by vector)- Loss function : jaccard distance- Optimizer : adam

Results example :

Evaluation of identified crossings

Now that we identified the bow shock crossings, we have to evaluate the accuracy and recall with respect to the crossings training list.

We then define a tolerance window of ±Dt around the true crossings, and evaluate the performance of the algorithm for a fixed Dt.

Ruptures library- Change point detection for non-stationary signals:ex:

Without

With

Shock position estimationfor sample labeling:

Change point Change point Change point Change point

beginningend

Grusbeck catalog

Fangcatalog

catalogsunion

Rupture relocation

AMDAdata

To dataset

samplelabeling

Rupture relocation and sample labeling

Raw data based on catalog

Rupture prediction relocates shock position

TALK ABOUT SCALAR PRODUCT PREDICTION ?

0 1 2

Different zones are labeled

To dataset

Discussion: Detection delta timeDistribution of shock date estimations for each catalog compared with ruptures’ estimations.

Grusbeck:mean = -13sstdev = 135s

Fang:mean = 132sstdev = 376s

Postprocessing

Simple moving average on probabilities for each class.

True classRaw predictionPost processed predictionAbove

In

Below

Optimization: hyperopt library

Results:Every point in the same 2%

No significant improvements even if batch size is included in the optimization space

ANN structure

Feedforward multilayer perceptron

Input layer: 8 neurons [relu]Hidden layer 1: 40 neurons [relu]Hidden layer 2: 38 neurons [tanh]Output layer: 3 neurons [softmax]

Extrapolation to Venus Express

Similar measurements on Venus Express

Similar metrics with a MAVEN trained algorithm on VEX data