# An improved quasar detection method in EROS-2 and MACHO LMC datasets

arXiv: https://arxiv.org/abs/1304.0401 · HTML: https://mlsanity.com/paper/1304.0401/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2013.

## Built on by (most cited first, 3 of 3)

- [Informative Bayesian model selection for RR Lyrae star classifiers](https://mlsanity.com/paper/2105.11531/) (2021)
- [The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurves](https://mlsanity.com/paper/2106.03736/) (2021)
- [Informative regularization for a multi-layer perceptron RR Lyrae classifier under data shift](https://mlsanity.com/paper/2303.06544/) (2023)

## Abstract (arXiv)

We present a new classification method for quasar identification in the EROS-2 and MACHO datasets based on a boosted version of Random Forest classifier. We use a set of variability features including parameters of a continuous auto regressive model. We prove that continuous auto regressive parameters are very important discriminators in the classification process. We create two training sets (one for EROS-2 and one for MACHO datasets) using known quasars found in the LMC. Our model's accuracy in both EROS-2 and MACHO training sets is about 90% precision and 86% 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 datasets, finding 1160 and 2551 candidates respectively. To further validate our list of candidates, we crossmatched our list with a previous 663 known strong candidates, getting 74% of matches for MACHO and 40% in EROS-2. The main difference on matching level is because EROS-2 is a slightly shallower survey which translates to significantly lower signal-to-noise ratio lightcurves.