Chrome Extension
WeChat Mini Program
Use on ChatGLM

Temperate Exoplanets Observable with Ariel : an Update with New Targets from TESS

crossref(2023)

Cited 0|Views5
Abstract
In 2018 and 2022, we have published an analysis about the observability of temperate planets (with an equilibrium temperature of about 350-500 K) with Ariel. This presentation is an update of this analysis which aims at using new targets identified in particular from the TESS database and analysing their observability with Ariel. Using the parameters of these new targets, we give an estimate of the number of transits needed for these objects to be observed in the Tier 2 mode of the space mission, and we define the information which could be derived about their atmospheric composition. We have identified about 15 targets which could be observable with ARIEL in the Tier 2 mode, which allows an identification of the main atmospheric absorbers. This list includes a gas giant, a few big Neptunes and several super-Earths/small Neptunes. This study is a follow-up of Encrenaz et al., Exp. Astr. 46, 31 (2018) and Exp. Astr. 53, 375 (2022).
More
Translated text
Key words
Extraterrestrial Organic Matter
PDF
Bibtex
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
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

要点】:本文更新了2018年和2022年的分析,使用TESS数据库中新发现的目标,评估了这些温度适中行星(约350-500K平衡温度)通过ARIEL任务的观测性,并估计了观测这些对象所需的星际穿越次数以及可能获得的大气成分信息。

方法】:通过分析TESS数据库中的新目标参数,计算了在ARIEL任务的Tier 2模式下观测这些行星所需的星际穿越次数,并定义了可能得出的大气成分信息。

实验】:研究了约15个可能通过ARIEL的Tier 2模式观测的目标,包括一个气态巨行星、几个大型的海王星以及几个超级地球/小型海王星,具体数据集未在摘要中提及。