Browsing by Author "Pichara, Karim"

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    A Reinforcement Learning-Based Follow-up Framework
    (2023) Astudillo, Javiera; Protopapas, Pavlos; Pichara, Karim; Becker, Ignacio
    Classification and characterization of variable and transient phenomena are critical for astrophysics and cosmology. Given the volume of nightly data produced by ongoing and future surveys such as LSST, it is critical to develop automatic tools that assist in observation decision-making, maximizing scientific output without resource wastage. We propose a reinforcement learning-based recommendation system for real-time astronomical observation of sources. We assess whether it is worth making further observations and recommend the best instrument from a preexisting candidate set of instruments. Current possible choices include single-band, multiband, and spectroscopic observations, although it is generalizable to any other kind of instrumentation. We rely on a reward metric to make recommendations, which incorporates the gain in a classification sense and the cost incurred for the queried observations. This metric is flexible and easily adaptable to different application scenarios. We run 24 simulations in an offline setting with preexisting observations from Gaia DR2 and SDSS DR14. We propose four comparison strategies, including the baseline strategy, which recommends based on the most similar past cases to the current case. Our strategy surpasses all other strategies in regard to reward. We reach an accuracy of 0.932, comparable to using the accuracy reached using all possible resources (0.948) but with half the number of photometric observations and 1000 times fewer spectroscopic resources. The baseline strategy lacks the complexity to achieve competitive results with our proposed strategy. Our framework is meant to aid continuous online observation decision-making and can be extended to incorporate multiple environmental and observation conditions.
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    Distinguishing a planetary transit from false positives: a Transformer-based classification for planetary transit signals
    (2023) Salinas, Helem; Pichara, Karim; Brahm, Rafael; Perez-Galarce, Francisco; Mery, Domingo
    Current space-based missions, such as the Transiting Exoplanet Survey Satellite (TESS), provide a large database of light curves that must be analysed efficiently and systematically. In recent years, deep learning (DL) methods, particularly convolutional neural networks (CNN), have been used to classify transit signals of candidate exoplanets automatically. However, CNNs have some drawbacks; for example, they require many layers to capture dependencies on sequential data, such as light curves, making the network so large that it eventually becomes impractical. The self-attention mechanism is a DL technique that attempts to mimic the action of selectively focusing on some relevant things while ignoring others. Models, such as the Transformer architecture, were recently proposed for sequential data with successful results. Based on these successful models, we present a new architecture for the automatic classification of transit signals. Our proposed architecture is designed to capture the most significant features of a transit signal and stellar parameters through the self-attention mechanism. In addition to model prediction, we take advantage of attention map inspection, obtaining a more interpretable DL approach. Thus, we can identify the relevance of each element to differentiate a transit signal from false positives, simplifying the manual examination of candidates. We show that our architecture achieves competitive results concerning the CNNs applied for recognizing exoplanetary transit signals in data from the TESS telescope. Based on these results, we demonstrate that applying this state-of-the-art DL model to light curves can be a powerful technique for transit signal detection while offering a level of interpretability.
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    Informative regularization for a multi-layer perceptron RR Lyrae classifier under data shift
    (2023) Perez-Galarce, Francisco; Pichara, Karim; Huijse, Pablo; Catelan, Marcio; Mery Quiroz, Domingo Arturo