Revisiting the Warm Sub-Saturn TOI-1710b
Astronomy & Astrophysics(2024)
Abstract
The Transiting Exoplanet Survey Satellite (TESS) provides a continuous suiteof new planet candidates that need confirmation and precise mass determinationfrom ground-based observatories. This is the case for the G-type star TOI-1710,which is known to host a transiting sub-Saturn planet(M_p=28.3±4.7M_⊕) in a long-period orbit(P=24.28 d). Here we combine archival SOPHIE and new and archival HARPS-Nradial velocity data with newly available TESS data to refine the planetaryparameters of the system and derive a new mass measurement for the transitingplanet, taking into account the impact of the stellar activity on the massmeasurement. We report for TOI-1710b a radius ofR_p=5.15±0.12R_⊕, a mass ofM_p=18.4±4.5M_⊕, and a mean bulk density ofρ_ p=0.73±0.18g cm^-3, which are consistent at1.2σ, 1.5σ, and 0.7σ, respectively, with previousmeasurements. Although there is not a significant difference in the final massmeasurement, we needed to add a Gaussian process component to successfully fitthe radial velocity dataset. This work illustrates that adding moremeasurements does not necessarily imply a better mass determination in terms ofprecision, even though they contribute to increasing our full understanding ofthe system. Furthermore, TOI-1710b joins an intriguing class of planets withradii in the range 4-8 R_⊕ that have no counterparts in theSolar System. A large gaseous envelope and a bright host star make TOI-1710b avery suitable candidate for follow-up atmospheric characterization.
MoreTranslated text
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2018
被引用106 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话