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D.Fustes, D.Ordóñez, C.Dafonte, M.Manteiga and B. Arcay
Introduction
 GGG (Galician Group for Gaia): Part of CU8 in DPAC.
Involved in classification and parameterization tasks
using AI techniques
 Work with simulated data of the RVS instrument:
 Estimation of physical parameters:
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

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Effective temperatures
Superficial gravities
Metallicities
Abundancies of alpha elements
Gaia RVS simulated data
 Library compiled by A.
Recio, P. de Laverny and
B. Plez
 971 points per spectra.
 Different SNR levels:
5,10,50, 200, ..
 70% data to train the
Network and 30% to test
the model
 Use of ANN networks to
perform the
parameterization
Discrete Wavelet Transform
 Redundant filtering
process:
 High-pass filters to
generate Details
 Low-pass filters to
generate
Approximations
 Use of level 3 DWT:
A3+D3+D2+D1, 997
points
Feature selection
 Reduce the spectra to fewer dimensionality
 Reduce the complexity of the models
 Reduce the computational needs
 Variability-based methods: Reduce the dimensionality of a
set capturing most of its variability (PCA)
 They can not be specialized to capture the features relevant to
the estimation of each parameter
 Genetic Algorithm to select relevant areas for each
parameter
Genetic algorithm
 Based on the Evolution’s Theory
 Best individuals reproduce and pass to the next
generation
 Fitness function: Train the ANN, test it and inverse the
mean error. Computationally expensive!!!
Distributed computation
 Huge computation needs lead to scalable solutions
 Multicomputers are cheaper than supercomputers
 Ways to distribute the algorithm
 Low level: Distribute the ANN computation:

It should be performed in hardware
 Medium level: Distribute the ANN learning
 Possible with batch learning
 Online learning perform better in this case
 High level: Distribute the fitness computation
 It was implemented in C++ with MPI and OpenMp
Results(1)
 SNR 200
 Original spectra
PARAMETER
ERROR
STD. DEVIATION
PIXELS
Teff
111
173
289
Logg
0,17
0,24
284
Alpha
0,06
0,08
285
Metalicity
0,11
0,18
285
Results(2)
 SNR 200
 Wavelet domain
PARAMETER
ERROR
STD. DEVIATION
PIXELS
Teff
106
177
291
Logg
0,16
0,23
297
Alpha
0,06
0,08
301
Metalicity
0,12
0,19
286
Thank You for your attention!!!
Any question?