scholarly journals Decoding the radial velocity variations of HD 41248 with ESPRESSO

2020 ◽  
Vol 635 ◽  
pp. A13 ◽  
Author(s):  
J. P. Faria ◽  
V. Adibekyan ◽  
E. M. Amazo-Gómez ◽  
S. C. C. Barros ◽  
J. D. Camacho ◽  
...  

Context. Twenty-four years after the discoveries of the first exoplanets, the radial-velocity (RV) method is still one of the most productive techniques to detect and confirm exoplanets. But stellar magnetic activity can induce RV variations large enough to make it difficult to disentangle planet signals from the stellar noise. In this context, HD 41248 is an interesting planet-host candidate, with RV observations plagued by activity-induced signals. Aims. We report on ESPRESSO observations of HD 41248 and analyse them together with previous observations from HARPS with the goal of evaluating the presence of orbiting planets. Methods. Using different noise models within a general Bayesian framework designed for planet detection in RV data, we test the significance of the various signals present in the HD 41248 dataset. We use Gaussian processes as well as a first-order moving average component to try to correct for activity-induced signals. At the same time, we analyse photometry from the TESS mission, searching for transits and rotational modulation in the light curve. Results. The number of significantly detected Keplerian signals depends on the noise model employed, which can range from 0 with the Gaussian process model to 3 with a white noise model. We find that the Gaussian process alone can explain the RV data while allowing for the stellar rotation period and active region evolution timescale to be constrained. The rotation period estimated from the RVs agrees with the value determined from the TESS light curve. Conclusions. Based on the data that is currently available, we conclude that the RV variations of HD 41248 can be explained by stellar activity (using the Gaussian process model) in line with the evidence from activity indicators and the TESS photometry.

2019 ◽  
Vol 488 (3) ◽  
pp. 3067-3075 ◽  
Author(s):  
Coel Hellier ◽  
D R Anderson ◽  
A H M J Triaud ◽  
F Bouchy ◽  
A Burdanov ◽  
...  

Abstract We report the discovery of WASP-166b, a super-Neptune planet with a mass of 0.1 MJup (1.9 MNep) and a bloated radius of 0.63 RJup. It transits a V = 9.36, F9V star in a 5.44-d orbit that is aligned with the stellar rotation axis (sky-projected obliquity angle λ = 3 ± 5 deg). Variations in the radial-velocity measurements are likely the result of magnetic activity over a 12-d stellar rotation period. WASP-166b appears to be a rare object within the ‘Neptune desert’.


2021 ◽  
Vol 9 ◽  
Author(s):  
Jie Liang ◽  
Zhengyi Shi ◽  
Feifei Zhu ◽  
Wenxin Chen ◽  
Xin Chen ◽  
...  

There is uncertainty in the neuromusculoskeletal system, and deterministic models cannot describe this significant presence of uncertainty, affecting the accuracy of model predictions. In this paper, a knee joint angle prediction model based on surface electromyography (sEMG) signals is proposed. To address the instability of EMG signals and the uncertainty of the neuromusculoskeletal system, a non-parametric probabilistic model is developed using a Gaussian process model combined with the physiological properties of muscle activation. Since the neuromusculoskeletal system is a dynamic system, the Gaussian process model is further combined with a non-linear autoregressive with eXogenous inputs (NARX) model to create a Gaussian process autoregression model. In this paper, the normalized root mean square error (NRMSE) and the correlation coefficient (CC) are compared between the joint angle prediction results of the Gaussian process autoregressive model prediction and the actual joint angle under three test scenarios: speed-dependent, multi-speed and speed-independent. The mean of NRMSE and the mean of CC for all test scenarios in the healthy subjects dataset and the hemiplegic patients dataset outperform the results of the Gaussian process model, with significant differences (p < 0.05 and p < 0.05, p < 0.05 and p < 0.05). From the perspective of uncertainty, a non-parametric probabilistic model for joint angle prediction is established by using Gaussian process autoregressive model to achieve accurate prediction of human movement.


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