An adaptive robust regression method: Application to galaxy spectrum baseline estimation

Raphael Bacher (1, 2) , Florent Chatelain (3) , Olivier J.J. Michel (1)

1 GIPSA-CICS - GIPSA - Communication Information and Complex Systems
2 CRAL - Centre de Recherche Astrophysique de Lyon
3 GIPSA-SAIGA - GIPSA - Signal et Automatique pour la surveillance, le diagnostic et la biomécanique

In this paper, a new robust regression method based on the Least Trimmed Squares (LTS) is proposed. The novelty of this approach consists in a simple adaptive estimation of the number of outliers. This method can be applied to baseline estimation, for example to improve the detection of gas spectral signature in astronomical hy-perspectral data such as those produced by the new Multi Unit Spec-troscopic Explorer (MUSE) instrument. To do so a method following the general idea of the LOWESS algorithm, a classical robust smoothing method, is developed. It consists in a windowed local linear regression, the local regression being done here by the new adap-tive LTS approach. The developed method is compared with state-of-the art baseline estimated algorithms on simulated data closed to the real data produced by the MUSE instrument.

Mots clés: Baseline Estimation; Robust Regression; Spectroscopy; Least Trimmed Squares; Astronomy

Domaines: Méthodologie [stat.ME]

Raphael Bacher, Florent Chatelain, Olivier J.J. Michel. An adaptive robust regression method: Application to galaxy spectrum baseline estimation. ICASSP 2016 - 41st IEEE International Conference on Acoustics, Speech and Signal Processing, Mar 2016, Shanghai, China. pp.4423 - 4427, ⟨10.1109/ICASSP.2016.7472513⟩. ⟨hal-01462974⟩