Journal Article

An improved quasar detection method in EROS-2 and MACHO LMC data sets

K. Pichara, P. Protopapas, D.-W. Kim, J.-B. Marquette and P. Tisserand

in Monthly Notices of the Royal Astronomical Society

Published on behalf of The Royal Astronomical Society

Volume 427, issue 2, pages 1284-1297
Published in print December 2012 | ISSN: 0035-8711
Published online December 2012 | e-ISSN: 1365-2966 | DOI:
An improved quasar detection method in EROS-2 and MACHO LMC data sets

More Like This

Show all results sharing this subject:

  • Astronomy and Astrophysics


Show Summary Details


We present a new classification method for quasar identification in the EROS-2 and MACHO data sets based on a boosted version of a random forest classifier. We use a set of variability features including parameters of a continuous autoregressive model. We prove that continuous autoregressive parameters are very important discriminators in the classification process. We create two training sets (one for EROS-2 and one for MACHO data sets) using known quasars found in the Large Magellanic Cloud (LMC). Our model's accuracy in both EROS-2 and MACHO training sets is about 90 per cent precision and 86 per cent recall, improving the state-of-the-art models, accuracy in quasar detection. We apply the model on the complete, including 28 million objects, EROS-2 and MACHO LMC data sets, finding 1160 and 2551 candidates, respectively. To further validate our list of candidates, we cross-matched our list with 663 previously known strong candidates, getting 74 per cent of matches for MACHO and 40 per cent in EROS.

The main difference on matching level is because EROS-2 is a slightly shallower survey which translates to significantly lower signal-to-noise ratio light curves.

Keywords: methods: data analysis; Magellanic Clouds; quasars: general

Journal Article.  5793 words.  Illustrated.

Subjects: Astronomy and Astrophysics

Full text: subscription required

How to subscribe Recommend to my Librarian

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.