All papers
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1
Robust period estimation using mutual information for multiband light curves in the synoptic survey era
The Large Synoptic Survey Telescope (LSST) will produce an unprecedented amount of light curves using six optical bands. Robust and efficient methods that can aggregate data from multidimensional sparsely sampled time-series are needed. In this paper we present a new method for light curve period estimation based on quadratic mutual information (QMI). The proposed method does not assume a particular model for the light curve nor its underlying probability density and it is robust to non-Gaussian noise and outliers. By combining the QMI from several bands the true period can be estimated even when no single-band QMI yields the period. Period recovery performance as a function of average magnitude and sample size is measured using 30,000 synthetic multiband light curves of RR Lyrae and Cepheid variables generated by the LSST Operations and Catalog simulators. The results show that aggregating information from several bands is highly beneficial in LSST sparsely sampled time-series, obtaining an absolute increase in period recovery rate up to 50%. We also show that the QMI is more robust to noise and light curve length (sample size) than the multiband generalizations of the Lomb–Scargle and AoV periodograms, recovering the true period in 10%–30% more cases than its competitors. A python package containing efficient Cython implementations of the QMI and other methods is provided.
The Astrophysical Journal Supplement Series, 2018, 10.3847/1538-4365/aab77c
2
MPCC: Matching Priors and Conditionals for Clustering
Clustering is a fundamental task in unsupervised learning that depends heavily on the data representation that is used. Deep generative models have appeared as a promising tool to learn informative low-dimensional data representations. We propose Matching Priors and Conditionals for Clustering (MPCC), a GAN-based model with an encoder to infer latent variables and cluster categories from data, and a flexible decoder to generate samples from a conditional latent space. With MPCC we demonstrate that a deep generative model can be competitive/superior against discriminative methods in clustering tasks surpassing the state of the art over a diverse set of benchmark datasets. Our experiments show that adding a learnable prior and augmenting the number of encoder updates improve the quality of the generated samples, obtaining an inception score of 9.49±0.15 and improving the Fréchet inception distance over the state of the art by a 46.9% in CIFAR10.
Computer Vision – ECCV 2020, 2020, 10.1007/978-3-030-58592-1_39
3
ATAT: Astronomical Transformer for time series And Tabular data
We present ATAT, the Astronomical Transformer for time series And Tabular data, a classification model that receives as input both light-curves and tabular data from astronomical sources. ATAT consists of a light-curve Transformer with a new time modulation that encodes the time of each observation, and a feature Transformer that uses a Quantile Feature Tokenizer. This model was conceived by the ALeRCE alert broker in the context of the recent Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC). ATAT outperforms previous decision tree-based ensemble approaches in terms of classification when trained over the ELAsTiCC dataset. Importantly, some of its variants do not require human-engineered features, with significantly reduced inference computational times (400x faster). The use of Transformer multimodal architectures, combining light-curve and tabular data, opens new possibilities for classifying alerts from a new generation of large etendue telescopes, such as the Vera C. Rubin Observatory, in real-world brokering scenarios.