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Abstract:

Abstract Doppler Imaging (DI) is a well-established technique to map a physical field at a stellar surface from a time series of high-resolution spectra. In this proof-of-concept study, we aim to show that traditional DI algorithms, originally designed for rapidly-rotating stars, have also the ability to model the activity of Sun-like stars, when observed with new-generation highly-stable spectrographs, and search for low-mass planets around them. We used DI to retrieve the relative brightness distribution at the surface of the Sun from radial velocity (RV) observations collected by HARPS-N between 2022 and 2024. The brightness maps obtained with DI have a typical angular resolution of ~36○ and are a good match to low-resolution disc-resolved Dopplergrams of the Sun at epochs when the absolute, disc-integrated RV exceeds ~2 m s−1. The RV residuals after DI correction exhibit a dispersion of about 0.6 m s−1, comparable with existing state-of-the-art activity correction techniques. Using planet injection-recovery tests, we also show that DI can be a powerful tool for blind planet searches, so long as the orbital period is larger than ~100 days (i.e. 3 to 4 stellar rotation periods), and that it yields planetary mass estimates with an accuracy comparable to, for example, multi-dimensional Gaussian process regression. Finally, we highlight some limitations of traditional DI algorithms, which should be addressed to make DI a reliable alternative to state-of-the-art RV-based planet search techniques.

Abstract:

In recent years, supergranulation has emerged as one of the biggest challenges for the detection of Earth-twins in radial velocity planet searches. We used eight years of Sun-as-a-star radial velocity observations from HARPS-N to measure the quiet-Sun’s granulation and supergranulation properties of most of its 11-yr activity cycle, after correcting for the effects of magnetically active regions using two independent methods. In both cases, we observe a clear, order of magnitude variation in the time-scale of the supergranulation component, which is largest at activity minimum and is strongly anticorrelated with the relative Sunspot number. We also explored a range of observational strategies which could be employed to characterize supergranulation in stars other than the Sun, showing that a comparatively long observing campaign of at least 23 nights is required, but that up to 10 stars can be monitored simultaneously in the process. We conclude by discussing plausible explanations for the ‘supergranulation’ cycle.

Abstract:

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