ASTRONOMICAL TIME SERIES

ANALYSIS USING INFORMATION

THEORETIC LEARNING

Pablo A. Estévez, DIE, Universidad de Chile

Workshop on CI Challenges, September 2012

Joint work with

Pavlos Protopapas, University of Harvard

Pablo Zegers, Universidad de los Andes, Chile

Pablo Huijse, PhD Student, Universidad de Chile

Jose C. Principe, University of Florida, Gainesville

Astronomical Time Series: Light Curves

Light Curve: Stellar brightness (magnitude or flux)

versus time.

Variable stars: stars whose luminosity varies over

time (3% of the stars in the universe are variables,

and 1% are periodic variable stars)

Light Curve Analysis: Useful for period detection,

event detection, stellar classification, extra solar

planet discovery, measure distance to earth, etc.

An Example of a Light Curve

Challenges

Light curves are unevenly spaced or irregularly

sampled, with gaps of different sizes. This is due to:

Time constraints on the observation time

Day-night cycle, weather conditions

Equipment operability

Light curves are noisy due to photometric errors,

atmospheric and sky background

Astronomical surveys generate tens of millions of

light curves. Light curve generation rate will continue

growing during the next years.

Variable stars

Eclipsing binary stars Pulsating star

Problem Statement

Discriminate periodic versus non-periodic light

curves in astronomical survey databases

Estimate the underlying period of periodic light

curves.

Goal: To develop an automated method for

periodic detection and estimation based on

information theoretic learning.

Information theoretic learning (ITL)

Apply concepts of information theory such as entropy

and mutual information to machine learning

Renyi’s quadratic entropy, with Gaussian kernel

Renyi´s entropy is a generalization of Shannon’s entropy

IP: Information potential is the argument of the logarithm

CORRENTROPY (Generalized Correlation): It measures

similarity between feature vectors separated by a

certain time delay

Proposed discrimination metric

It combines correntropy (generalized correlation)

with a periodic kernel

The periodic kernel measures similarity among

samples separated by a given period

The new metric provides a periodogram, whose

peaks are associated with the fundamental

frequencies present in the data

It is computed directly from the available samples

Correntropy Kernelized Periodogram (CKP)

Correntropy Kernelized Periodogram

Synthetic data example: sin(2 pi t /P) + noise in time and

magnitude

Noise in time simulates uneven sampling

True period 2.456 days

The CKP reaches a global maximum at the corresponding

true period (left figure).

Statistical Test Using CKP

99%

90%

Degree of

confidence

Receiver Operator Characteristic

ROC curves for CKP, and alternative methods: LS-periodogram and AoV-

periodogram. Dataset: 750 periodic light curves and 1500 aperiodic light

curves from the MACHO survey. Due to the natural classs imbalance, very low

false positive rates are required (0.1%).

EROS Survey

Survey of the Magellanic Clouds and the Galactic bulge

Data taken from ESO Observatory, in La Silla, Chile

EROS main goal: search for the dark matter of the

Galactic halo

EROS survey is a goldmine for stellar variability studies:

Cepheids, RR-Lyrae, Eclipsing Binaries, and Supernovas.

Each EROS field has ~17,300 light curves.

There are 88x32 fields of the Large Magellanic Cloud

(LMC), i.e.

48.744.522 light curves =>48.7M light curves

Computational Time Requirements

for EROS Survey

Computational time measured using NVIDIA Tesla C2070

GPU (448 cores)

Sweeping 20000 trial periods with CKP, the total time

per light curve (~650 samples): 1.5 [s]

For 48.7M light curves: ~845 days!

Evaluating 600 precomputed trial periods (by using

correntropy and other methods) and optimizing the code:

0.2 [s] per light curve

For 48.7M million light curves: ~113 days!

NCSA Dell/NVIDIA Cluster: FORGE

National Center for Supercomputing Applications (NCSA)

at the University of Illinois at Urbana-Champaign

We are using a queue with 12 machines each having 8

cores with Tesla C2070 GPUs => 96 GPUs

Computing eight EROS fields using a machine with 8 cores

takes 1 hour

So far we have processed 1.2M light curves in 40 mins

At this rate for computing 48.7M light curves: ~30 hours!

FORGE has 44 machines with 288 GPUs in total. Using

the whole cluster we might process 1 BILLION light curves

in 10 days

Conclusions & Future Work

A framework for light curve analysis based on ITL

and kernel methods has been introduced.

CKP allows discriminating between periodic and

non-periodic light curves with high accuracy and

low number of false positives.

Required: Efficient computation of ITL based

methods

Challenge: Applying our methods to large

untested astronomical databases.

ALMA Site in Northern Chile

THE END

P.Huijse, P. Estevez, P. Zegers, P. Protopapas, J.

Principe, “Period Estimation in Astronomical Time

Series using Slotted Correntropy”, IEEE Signal

Processing Letters, Vol. 18, n°6, pp. 371-374,

2011.

P.Huijse, P. Estevez, P. Protopapas, P. Zegers, J.

Principe, “An Information Theoretic Algorithm for

Finding Periodicities in Stellar Light Curves”, IEEE

Transactions on Signal Processing, Vol. 60, n°10, pp.

5135-5145, 2012.

Computational Intelligence Applied to Time Series Analysis

Pablo A. Estévez

Department of Electrical Engineering

University of Chile

University of Cyprus, Cyprus

September 14, 2012

Outline

First Topic

Introduction to Self-Organizing Maps (SOM)

SOMs for temporal sequence processing

Short-term Gamma Memories

Experimental Results

Conclusions

Second Topic

Analysis of Astronomical Time Series

Information Theoretic Learning Approach

Kohonen’s Map

Self-Organizing Feature Map (SOM)

Unsupervised Neural Networks

Vector Quantization of Feature Space

Topological Ordered Mapping

Main Applications:

Dimensionality reduction

Visualization of high-dimensional data in 2D or 3D maps

Clustering

Knowledge discovery in Databases

Topological Ordered Map

SOM defines a fixed grid in output space

Each node in the output grid is associated with a prototype (codebook) vector in input space

Neighborhood is measured in the output space

This neighborhood is used for updating codebook vectors in input space

Example: Kohonen’s Map in 2D

It uses a 2D output grid for visualization of high-dimensional data

Example of Neural Gas

Connections are created between the best matching unit and the second closest Connections are allowed aging and are removed eventually if not refreshed

SOMs for data temporal processing

Several recent extensions of SOM for processing data sequences that are temporally or spatially connected

For example: words, DNA sequences, time series

Models differ on the notion of context, i.e. the way they store sequences

Each neuron is represented by a weight (codebook) vector and a context (several) vector(s)

diw

dc i

Gamma Memories

The Gamma filter is defined in the time domain as

where is the input signal, is the filter output, and are the filter parameters

Parameter controls the tradeoff between depth and resolution of the filter

1

1( 1) (1 ) 1

K

k k

k

k k k

y n c n

c n c n c n

0( ) ( )c n x n ( )y n

,k k

Cascade of K-stages

A recursive rule for context descriptor of order-K can be constructed

The K context descriptors are described as

1 1

1 1

1

0

1

( ) 1 ,

: previous winner

n n

n n

I I

k k k

I I

n

c n c c k

c w

I

Gamma SOM Map

Delay Coordinate Embedding

Takens´embedding theorem allows us to reconstruct the dynamics of an n-dimensional space state starting by a one-dimensional time series, e.g. strange attractor.

To embed a time series, the following delay coordinate vector is constructed:

Embedding parameters (t,m) are found by

using ad-hoc methods

First minimum of the average mutual information (t)

False nearest neighbor algorithm (m)

( ) ( ), ( ), , ( ( 1) )i i is t x t x t x t mt t

Gamma Filtering Embedding

Gamma SOM construct a Gamma filtered embedding, as follows:

Where is the weight vector and are the contexts

Embedding parameters are determined by sweeping an array of (, K) values

Find the top 10 combinations of parameters with lower temporal quantization errors

Project to the principal direction by using PCA

Search for the 1D-PCA projection (allowing for shift delays) having maximal mutual information with the original time series

1( ) ( ), ( ), , ( )i i i i

Ku t w t c t c t

iw c i

tu

Experiments

Chaotic Lorenz System: state variable

NH3-Far Infrared Laser:

Data set A in the Santa Fe Time Series Competition

( )x t

Phase Portrait for Lorenz original dataset

Bicup 2006 challenge time series

Phase Portrait for noisy Lorenz dataset

1D projection of Gamma SOM for noisy Lorenz dataset

2D projection of Gamma SOM for Laser Time Series

Conclusions

Gamma SOM models can reconstruct the state space by using Gamma filtering embedding

Useful tools for non-linear time series analysis

Advantage of noise reduction

Future work: Time series prediction

References

Estevez, P.A., Hernandez, R.: Gamma SOM for Temporal Sequence Processing. In: Advances in Self-Organizing Maps, WSOM 2009, LNCS 5629, St. Augustine, FL, pp. 63-71 (2009)

Estevez, P.A., Hernandez, R., Perez, C.A., Held, C.M.: Gamma-filter Self-organizing Neural Networks for Unsupervised Sequence Processing. Electronics Letters (2011)-

Estevez, P.A., Hernandez, R.: Gamma –filter Self-Organizing Neural Networks for Time Series Analysis. In: Advances in Self-Organizing Maps, WSOM 2011, LNCS 5629, Espoo, Finland, pp. 63-71 (2011)

Estevez, P.A. and Vergara, J.: Nonlinear Time Series Analysis by Using Gamma Growing Neural Gas, WSOM 2012, Santiago, Chile (in press)

ALMA Site in Northern Chile