Browsing by Author "Protopapas, P."

Now showing 1 - 5 of 5
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    An improved quasar detection method in EROS-2 and MACHO LMC data sets
    (2012) Pichara Baksai, Karim Elías; Protopapas, P.; Kim, D.-W.; Marquette, J.-B.; Tisserand, P.
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    An Information Theory Approach on Deciding Spectroscopic Follow-ups
    (2020) Astudillo, J.; Protopapas, P.; Pichara Baksai, Karim Elías; Huijse, P.
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    Meta-classification for variable stars
    (2016) Pichara Baksai, Karim Elías; Protopapas, P.; Leon, D.
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    Scalable end-to-end recurrent neural network for variable star classification
    (OUP, 2020) Becker Troncoso, Ignacio; Pichara Baksai, Karim Elías; Catelan, Márcio; Protopapas, P.; Aguirre Orellana, Carlos Alfonso; Nikzat, Fatemeh
    During the last decade, considerable effort has been made to perform automatic classification of variable stars using machine-learning techniques. Traditionally, light curves are represented as a vector of descriptors or features used as input for many algorithms. Some features are computationally expensive, cannot be updated quickly and hence for large data sets such as the LSST cannot be applied. Previous work has been done to develop alternative unsupervised feature extraction algorithms for light curves, but the cost of doing so still remains high. In this work, we propose an end-to-end algorithm that automatically learns the representation of light curves that allows an accurate automatic classification. We study a series of deep learning architectures based on recurrent neural networks and test them in automated classification scenarios. Our method uses minimal data pre-processing, can be updated with a low computational cost for new observations and light curves, and can scale up to massive data sets. We transform each light curve into an input matrix representation whose elements are the differences in time and magnitude, and the outputs are classification probabilities. We test our method in three surveys: OGLE-III, Gaia, and WISE. We obtain accuracies of about 95 per cent in the main classes and 75 per cent in the majority of subclasses. We compare our results with the Random Forest classifier and obtain competitive accuracies while being faster and scalable. The analysis shows that the computational complexity of our approach grows up linearly with the light-curve size, while the traditional approach cost grows as Nlog (N).
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    Streaming classification of variable stars
    (OUP, 2019) Zorich, L; Pichara Baksai, Karim Elías; Protopapas, P.
    In the last years, automatic classification of variable stars has received substantial attention. Using machine learning techniques for this task has proven to be quite useful. Typically, machine learning classifiers used for this task require to have a fixed training set, and the training process is performed offline. Upcoming surveys such as the Large Synoptic Survey Telescope will generate new observations daily, where an automatic classification system able to create alerts online will be mandatory. A system with those characteristics must be able to update itself incrementally. Unfortunately, after training, most machine learning classifiers do not support the inclusion of new observations in light curves, they need to re-train from scratch. Naively re-training from scratch is not an option in streaming settings, mainly because of the expensive pre-processing routines required to obtain a vector representation of light curves (features) each time we include new observations. In this work, we propose a streaming probabilistic classification model; it uses a set of newly designed features that work incrementally. With this model, we can have a machine learning classifier that updates itself in real time with new observations. To test our approach, we simulate a streaming scenario with light curves from Convention, Rotation and planetary Transits (CoRoT), Orbital Gravitational Lensing Experiment (OGLE), and Massive Compact Halo Object (MACHO) catalogues. Results show that our model achieves high classification performance, staying an order of magnitude faster than traditional classification approaches.