Chilean virtual observatory

Solar, Mauricio; Araya, Mauricio; Arévalo, Luis Martínez; Parada, Víctor; Contreras, Ricardo; Mardones, Diego

doi:10.1109/clei.2015.7359465

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…The following results were obtained at the Chilean Virtual Observatory (ChiVO) datacenter [61] on an Intel Xeon CPU E5-2680 2.50 GHz with 12 cores and 64 GB of RAM.…”

Section: Resultsmentioning

confidence: 99%

On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders

Mena

Olivares

Bugueño

et al. 2021

Signals

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Light curve analysis usually involves extracting manually designed features associated with physical parameters and visual inspection. The large amount of data collected nowadays in astronomy by different surveys represents a major challenge of characterizing these signals. Therefore, finding good informative representation for them is a key non-trivial task. Some studies have tried unsupervised machine learning approaches to generate this representation without much effectiveness. In this article, we show that variational auto-encoders can learn these representations by taking the difference between successive timestamps as an additional input. We present two versions of such auto-encoders: Variational Recurrent Auto-Encoder plus time (VRAEt) and re-Scaling Variational Recurrent Auto Encoder plus time (S-VRAEt). The objective is to achieve the most likely low-dimensional representation of the time series that matched latent variables and, in order to reconstruct it, should compactly contain the pattern information. In addition, the S-VRAEt embeds the re-scaling preprocessing of the time series into the model in order to use the Flux standard deviation in the learning of the light curves structure. To assess our approach, we used the largest transit light curve dataset obtained during the 4 years of the Kepler mission and compared to similar techniques in signal processing and light curves. The results show that the proposed methods obtain improvements in terms of the quality of the deep representation of phase-folded transit light curves with respect to their deterministic counterparts. Specifically, they present a good balance between the reconstruction task and the smoothness of the curve, validated with the root mean squared error, mean absolute error, and auto-correlation metrics. Furthermore, there was a good disentanglement in the representation, as validated by the Pearson correlation and mutual information metrics. Finally, a useful representation to distinguish categories was validated with the F1 score in the task of classifying exoplanets. Moreover, the S-VRAEt model increases all the advantages of VRAEt, achieving a classification performance quite close to its maximum model capacity and generating light curves that are visually comparable to a Mandel–Agol fit. Thus, the proposed methods present a new way of analyzing and characterizing light curves.

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“…The following results were obtained at the Chilean Virtual Observatory (ChiVO) datacenter [61] on an Intel Xeon CPU E5-2680 2.50 GHz with 12 cores and 64 GB of RAM.…”

Section: Resultsmentioning

confidence: 99%

On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders

Mena

Olivares

Bugueño

et al. 2021

Signals

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“…Due to the large amount of data that was processed, it was necessary to use a cluster provided by ChiVO [2] (Chilean Virtual Observatory) in which 6257 labeled data, corresponding to 121 GB, and 1797 not labeled data (Candidates), corresponding to another 33 GB, were downloaded. Thus, following the standard set separation for machine learning, we used approximately 4000, 1000 and 1000 registers grouped as training, validation and testing sets respectively (64/18/18%).…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Several approaches have been proposed by astronomers for detecting extrasolar planets, being the finegrained analysis of periodicities in star light-curves the most successful so far. However, the large volume of data that is being generated by modern observatories [2], including large surveys of astronomical objects, requires the use of automatized methods that can reproduce the analysis performed by astronomers to decide if the data supports the existence of an exoplanet or not. Fortunately, the advances in numerical methods, machine learning and data science in general allow us to apply algorithms and computational techniques that learn and predict from complex patterns in a reasonable frame of time.…”

Section: Introductionmentioning

confidence: 99%

Classical Machine Learning Techniques in the Search of Extrasolar Planets

Mena

Bugueño

Araya

2019

CLEIej

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The field of astronomical data analysis has experienced an important paradigm shift in the recent years. The automation of certain analysis procedures is no longer a desirable feature for reducing the human effort, but a must have asset for coping with the extremely large datasets that new instrumentation technologies are producing. In particular, the detection of transit planets --- bodies that move across the face of another body --- is an ideal setup for intelligent automation. Knowing if the variation within a light curve is evidence of a planet, requires applying advanced pattern recognition methods to a very large number of candidate stars. Here we present a supervised learning approach to refine the results produced by a case-by-case analysis of light-curves, harnessing the generalization power of machine learning techniques to predict the currently unclassified light-curves. The method uses feature engineering to find a suitable representation for classification, and different performance criteria to evaluate them and decide. Our results show that this automatic technique can help to speed up the very time-consuming manual process that is currently done by expert scientists.

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