Browsing by Author "Becker, Ignacio"

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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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    New variable stars from the photographic archive : semi-automated discoveries, attempts at automatic classification and the new field 104 Her
    (2018) Antipin, Sergei V.; Becker, Ignacio; Belinski, Alexander A.; Kolesnikova, Darya M.; Pichara Baksai, Karim Elías; Sokolovsky, K. V.; Zharova, A. V.; Zubareva, A. M.
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    StelNet: Hierarchical Neural Network for Automatic Inference in Stellar Characterization
    (2021) Garraffo, Cecilia; Protopapas, Pavlos; Drake, Jeremy J.; Becker, Ignacio; Cargile, Phillip
    Characterizing the fundamental parameters of stars from observations is crucial for studying the stars themselves, their planets, and the galaxy as a whole. Stellar evolution theory predicting the properties of stars as a function of stellar age and mass enables translating observables into physical stellar parameters by fitting the observed data to synthetic isochrones. However, the complexity of overlapping evolutionary tracks often makes this task numerically challenging, and with a precision that can be highly variable, depending on the area of the parameter space the observation lies in. This work presents StelNet, a Deep Neural Network trained on stellar evolutionary tracks that quickly and accurately predicts mass and age from absolute luminosity and effective temperature for stars with close-to-solar metallicity. The underlying model makes no assumption on the evolutionary stage and includes the pre-main-sequence phase. We use bootstrapping and train many models to quantify the uncertainty of the model. To break the model's intrinsic degeneracy resulting from overlapping evolutionary paths, we also built a hierarchical model that retrieves realistic posterior probability distributions of the stellar mass and age. We further test and train StelNet using a sample of stars with well-determined masses and ages from the literature.