Panopticon: a novel deep learning model to detect single transit events with no prior data filtering in PLATO light curves

• URL: http://arxiv.org/abs/2409.03466v1
• Date: Thu, 5 Sep 2024 12:21:51 GMT
• Title: Panopticon: a novel deep learning model to detect single transit events with no prior data filtering in PLATO light curves
• Authors: H. G. Vivien, M. Deleuil, N. Jannsen, J. De Ridder, D. Seynaeve, M. -A. Carpine, Y. Zerah,
• Abstract summary: We develop a deep learning model, Panopticon, to detect transits in high precision photometric light curves. We trained the model on a set of simulated PLATO light curves in which we injected, at pixel level, either planetary, eclipsing binary, or background eclipsing binary signals. The approach is able to recover 90% of our test population, including more than 25% of the Earth-analogs, even in the unfiltered light curves.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To prepare for the analyses of the future PLATO light curves, we develop a deep learning model, Panopticon, to detect transits in high precision photometric light curves. Since PLATO's main objective is the detection of temperate Earth-size planets around solar-type stars, the code is designed to detect individual transit events. The filtering step, required by conventional detection methods, can affect the transit, which could be an issue for long and shallow transits. To protect transit shape and depth, the code is also designed to work on unfiltered light curves. We trained the model on a set of simulated PLATO light curves in which we injected, at pixel level, either planetary, eclipsing binary, or background eclipsing binary signals. We also include a variety of noises in our data, such as granulation, stellar spots or cosmic rays. The approach is able to recover 90% of our test population, including more than 25% of the Earth-analogs, even in the unfiltered light curves. The model also recovers the transits irrespective of the orbital period, and is able to retrieve transits on a unique event basis. These figures are obtained when accepting a false alarm rate of 1%. When keeping the false alarm rate low (<0.01%), it is still able to recover more than 85% of the transit signals. Any transit deeper than 180ppm is essentially guaranteed to be recovered. This method is able to recover transits on a unique event basis, and does so with a low false alarm rate. Thanks to light curves being one-dimensional, model training is fast, on the order of a few hours per model. This speed in training and inference, coupled to the recovery effectiveness and precision of the model make it an ideal tool to complement, or be used ahead of, classical approaches.

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