Panopticon: a deep learning model to detect individual transits in unfiltered light curves

In the context of large scale photometric surveys, monitoring hundreds of thousands of stars in the search for exoplanets, one of the main bottlenecks remains reliable and rapid identification of exoplanet candidates. As it stands, the detection of exoplanets in light curves remains a complicated process, which can be thrown off by stellar activity, or instrument systematics. The task becomes increasingly harder for long period planets, taking away the ability to search for periodic signals within the high precision light curves. In an effort to find Earth-analogs, which are by definition long period planets, often with shallow transits, our ability to avoid periodicity in the detection process is key. Additionally, since current filtering methods are not well suited to filter unique, shallow, transits, they risk erasing the presence of these signals altogether before the detection step can be run. Such cases not only lead to missed planets, but they also induce a bias in the final distribution, by removing key planets in our sample.

To this end, we develop the Panopticon deep learning model, trained to identify transits individually in unfiltered light curves. First trained on simulated PLATO data [1], we report the model’s ability to correctly identify >99% of the light curves containing transits with a SNR>3 (Fig.1), while keeping a false alarm rate of less than 0.01% [2]. When applied on a new, independent, dataset in a blind search scenario, we are able to confidently recover the transiting planets in >98% of the cases. In a second time, a dedicated version of the model was trained on TESS data to measure the impact of real world data on the model. As for previously, we find the model to be highly effective at recovering transits, correctly reporting >93% of the light curves containing transits, while achieving a false alarm rate of <0.5%.

In both instances, we find the only limiting factor of the model is the individual transit signal to noise ratio, while the periodicity has no impact on performances. We also report very fast training time for these performances, of the order of a few hours. Training, or retraining, of a model to suit a new set of light curves only requires a limited, easy to prepare, sample to produce satisfactory results. The resulting models, used for the detection, have an almost instantaneous inference time, and that on light curves that do not need to undergo any form of filtering. This makes it a strong contender to be used as a preliminary analysis, in parallel to any detection pipeline, or exploring archival data for missed transits.

Fig. 1: Planet recovery depending the individual physical parameters of the system. The purple bins show the complete sample, with the recovered sample shown in green. Overlaid in orange is the recovered fraction of planets per bin.

 

[1] Jannsen, N., “PlatoSim: an end-to-end PLATO camera simulator for modelling high-precision space-based photometry”, Astronomy and Astrophysics, vol. 681, Art. no. A18, 2024. doi:10.1051/0004-6361/202346701.

[2] Vivien, H. G., “PANOPTICON: A novel deep learning model to detect single transit events with no prior data filtering in PLATO light curves”, Astronomy and Astrophysics, vol. 694, Art. no. A293, EDP, 2025. doi:10.1051/0004-6361/202452124.