Modelling stellar variability in archival HARPS data:I -- Rotation and activity properties with multi-dimensional Gaussian Processes

Although instruments for measuring the radial velocities (RVs) of stars now routinely reach sub-meter per second accuracy, the detection of low-mass planets is still very challenging. The rotational modulation and evolution of spots and/or faculae can induce variations in the RVs at the level of a few m/s in Sun-like stars. To overcome this, a multi-dimensional Gaussian Process framework has been developed to model the stellar activity signal using spectroscopic activity indicators together with the RVs. A recently published computationally efficient implementation of this framework, S+LEAF 2, enables the rapid analysis of large samples of targets with sizeable data sets. In this work, we apply this framework to HARPS observations of 268 well-observed targets with precisely determined stellar parameters. Our long-term goal is to quantify the effectiveness of this framework to model and mitigate activity signals for stars of different spectral types and activity levels. In this first paper in the series, we initially focus on the activity indicators (S-index and Bisector Inverse Slope), and use them to a) measure rotation periods for 49 slow rotators in our sample, b) explore the impact of these results on the spin-down of middle-aged late F, G & K stars, and c) explore indirectly how the spot to facular ratio varies across our sample. Our results should provide valuable clues for planning future RV planet surveys such as the Terra Hunting Experiment or the PLATO ground-based follow-up observations program, and help fine-tune current stellar structure and evolution models

Revisiting K2-233 spectroscopic time-series with multidimensional Gaussian Processes

Detecting planetary signatures in radial velocity time-series of young stars is challenging due to their inherently strong stellar activity. However, it is possible to learn information about the properties of the stellar signal by using activity indicators measured from the same stellar spectra used to extract radial velocities. In this manuscript, we present a reanalysis of spectroscopic HARPS data of the young star K2-233, which hosts three transiting planets. We perform a multidimensional Gaussian Process regression on the radial velocity and the activity indicators to characterise the planetary Doppler signals. We demonstrate, for the first time on a real dataset, that the use of a multidimensional Gaussian Process can boost the precision with which we measure the planetary signals compared to a one-dimensional Gaussian Process applied to the radial velocities alone. We measure the semi-amplitudes of K2-233 b, c, and d as 1.31(-0.74)(+0.81), 1.81(-0.67)(+0.71), and 2.72(-0.70)(+0.66) m/s, which translates into planetary masses of 2.4(-1.3)(+1.5), 4.6(-1.7)(+1.8), and 10.3(-2.6)(+2.4), respectively. These new mass measurements make K2-233 d a valuable target for transmission spectroscopy observations with JWST. K2-233 is the only young system with two detected inner planets below the radius valley and a third outer planet above it. This makes it an excellent target to perform comparative studies, to inform our theories of planet evolution, formation, migration, and atmospheric evolution.Comment: Accepted for publication in MNRA

TOI-837 b is a young Saturn-sized exoplanet with a massive 70 M_⊕ core

We present an exhaustive photometric and spectroscopic analysis of TOI-837, a F9/G0 35 Myr young star, hosting a transiting exoplanet, TOI-837 b, with an orbital period of ∼8.32 d. Utilizing data from the Transiting Exoplanet Survey Satellite and groundbased observations, we determine a planetary radius of 0.818+0.034−0.024 RJ for TOI-837 b. Through detailed High Accuracy Radial Velocity Planet Searcherspectroscopic time series analysis, we derive a Dopplersemi-amplitude of 34.7+5.3−5.6 m s−1, corresponding to a planetary mass of 0.379+0.058−0.061 MJ. The derived planetary properties suggest a substantial core of approximately 70 M⊕, constituting about 60 per cent of the planet’s total mass. This finding poses a significant challenge to existing theoretical models of core formation. We propose that future atmospheric observations with JWST could provide insights into resolving ambiguities of TOI-837 b, offering new perspectives on its composition, formation, and evolution

TOI-837 b is a young Saturn-sized exoplanet with a massive 70 M_⊕ core

We present an exhaustive photometric and spectroscopic analysis of TOI-837, a F9/G0 35 Myr young star, hosting a transiting exoplanet, TOI-837 b, with an orbital period of ∼8.32 d. Utilizing data from the Transiting Exoplanet Survey Satellite and groundbased observations, we determine a planetary radius of 0.818+0.034−0.024 RJ for TOI-837 b. Through detailed High Accuracy Radial Velocity Planet Searcherspectroscopic time series analysis, we derive a Dopplersemi-amplitude of 34.7+5.3−5.6 m s−1, corresponding to a planetary mass of 0.379+0.058−0.061 MJ. The derived planetary properties suggest a substantial core of approximately 70 M⊕, constituting about 60 per cent of the planet’s total mass. This finding poses a significant challenge to existing theoretical models of core formation. We propose that future atmospheric observations with JWST could provide insights into resolving ambiguities of TOI-837 b, offering new perspectives on its composition, formation, and evolution

Investigating stellar activity through eight years of Sun-as-a-star observations

Stellar magnetic activity induces both distortions and Doppler-shiftsin the absorption line profiles of Sun-like stars. Those effects produce apparent radial velocity (RV) signals which greatly hamper the search for potentially habitable, Earth-like planets. In this work, we investigate these distortions in the Sun using cross-correlation functions (CCFs), derived from intensive monitoring with the high-precision spectrograph HARPS-N. We show that the RV signal arising from line-shape variations on time-scales associated with the Sun’s rotation and activity cycle can be robustly extracted from the data, reducing the RV dispersion by half. Once these have been corrected, activity-induced Doppler-shifts remain, that are modulated at the solar rotation period, and that are most effectively modelled in the time domain, using Gaussian processes (GPs). Planet signatures are still best retrieved with multidimensonal GPs, when activity is jointly modelled from the raw RVs and indicators of the line width or of the Ca II H & K emission. After GP modelling, the residual RVs exhibit a dispersion of 0.6–0.8 m s−1, likely to be dominated by signals induced by supergranulation. Finally, we find that the statistical properties of the RVs evolve significantly over time, and that this evolution is primarily driven by sunspots, which control the smoothness of the signal. Such evolution, which reduces the sensitivity to long-period planet signatures, is no longer seen in the activity-induced Doppler-shifts, which is promising for long term RV monitoring surveys such as the Terra Hunting Experiment or the PLATO follow-up campaign

Investigating stellar activity through eight years of Sun-as-a-star observations

Stellar magnetic activity induces both distortions and Doppler-shiftsin the absorption line profiles of Sun-like stars. Those effects produce apparent radial velocity (RV) signals which greatly hamper the search for potentially habitable, Earth-like planets. In this work, we investigate these distortions in the Sun using cross-correlation functions (CCFs), derived from intensive monitoring with the high-precision spectrograph HARPS-N. We show that the RV signal arising from line-shape variations on time-scales associated with the Sun’s rotation and activity cycle can be robustly extracted from the data, reducing the RV dispersion by half. Once these have been corrected, activity-induced Doppler-shifts remain, that are modulated at the solar rotation period, and that are most effectively modelled in the time domain, using Gaussian processes (GPs). Planet signatures are still best retrieved with multidimensonal GPs, when activity is jointly modelled from the raw RVs and indicators of the line width or of the Ca II H & K emission. After GP modelling, the residual RVs exhibit a dispersion of 0.6–0.8 m s−1, likely to be dominated by signals induced by supergranulation. Finally, we find that the statistical properties of the RVs evolve significantly over time, and that this evolution is primarily driven by sunspots, which control the smoothness of the signal. Such evolution, which reduces the sensitivity to long-period planet signatures, is no longer seen in the activity-induced Doppler-shifts, which is promising for long term RV monitoring surveys such as the Terra Hunting Experiment or the PLATO follow-up campaign

Assessing the economic effects of Hainan’s free trade port policy using the HCW method

Hainan’s Free Trade Port (FTP) policy is a major initiative to promote the opening up and development of China’s southernmost province. The policy aims to create a world-class business environment and a high-level market system by 2035. However, the economic effects of this policy are not well understood. This paper applies the HCW method, a panel data approach for program evaluation, to assess the impact of Hainan’s FTP policy on four key indicators: trade, investment, talent and tax burden. The paper constructs optimal control groups for each indicator using the HCW and compares the actual outcomes of Hainan with the counterfactual outcomes that would have occurred in the absence of the policy intervention. The paper finds that Hainan’s FTP policy has increased its foreign trade and foreign direct investment significantly, but has not achieved noticeable improvements in attracting high-quality talent. The paper also conducts placebo tests to check the robustness and significance of the results and discusses some policy implications and future research directions based on the findings

Federated learning based proactive content caching in edge computing

Content caching is a promising approach in edge computing to cope with the explosive growth of mobile data on 5G networks, where contents are typically placed on local caches for fast and repetitive data access. Due to the capacity limit of caches, it is essential to predict the popularity of files and cache those popular ones. However, the fluctuated popularity of files makes the prediction a highly challenging task. To tackle this challenge, many recent works propose learning based approaches which gather the users' data centrally for training, but they bring a significant issue: users may not trust the central server and thus hesitate to upload their private data. In order to address this issue, we propose a Federated learning based Proactive Content Caching (FPCC) scheme, which does not require to gather users' data centrally for training. The FPCC is based on a hierarchical architecture in which the server aggregates the users' updates using federated averaging, and each user performs training on its local data using hybrid filtering on stacked autoencoders. The experimental results demonstrate that, without gathering user's private data, our scheme still outperforms other learning-based caching algorithms such as m-epsilon-greedy and Thompson sampling in terms of cache efficiency