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Finding Hidden Events in Astrophysical Data using PCA and Mixture of Gaussians Clustering

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Abstract:

The Principal Component Analysis (PCA) is applied to a set of astronomic data to obtain a separation between variations of luminosity and noisy fluctuations. A clustering with the Mixture of Gaussians method, performed in the principal subspace, allows us to classify the data according to the features of interest. Our results are compared with those obtained by the AGAPE (Andromeda Galaxy and Amplified Pixels Experiment) collaboration.

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Authors and Affiliations

  1. Dipartimento di Fisica ‘E. R. Caianiello’, University of Salerno, Baronissi (SA), Italy, IT

    Maria Funaro, Maria Marinaro & Silvia Scarpetta

  2. INFM – University of Salerno, Baronissi (SA), Italy, IT

    Alfredo Petrosino

Authors
  1. Maria Funaro
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  2. Maria Marinaro
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  3. Alfredo Petrosino
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  4. Silvia Scarpetta
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Additional information

Received: 22 December 2000, Received in revised form: 26 March 2001, Accepted: 20 April 2001

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Funaro, M., Marinaro, M., Petrosino, A. et al. Finding Hidden Events in Astrophysical Data using PCA and Mixture of Gaussians Clustering. Pattern Anal Appl 5, 15–22 (2002). https://doi.org/10.1007/s100440200002

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  • DOI: https://doi.org/10.1007/s100440200002

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