Candidates for a new planet in TESS sector 107
From the candidates of iteration 2 (the 66 that passed automatic vetting, the 43 new TLS signals and the 146 not fully vetted), choose the ones most likely to be real new planets and not yet known. 1. Remove every star that is already a known planet, a TOI or a community TOI (ExoFOP, NASA Exoplanet Archive). 2. Check the rest more deeply: odd/even depths, secondary eclipse, transit shape, nearby stars and contamination (Gaia DR3, RUWE), centroid shifts, and the same signal in other TESS sectors. 3. Estimate for each how likely it is to be a planet (for example a false-positive probability with TRICERATOPS or an equivalent) and rank them. 4. Deliver the top 20 as a table: TIC ID, period (days), depth, number of transits, planet radius (Earth radii), probability of being a planet, and one sentence on why it ranks there. 5. Always deliver the table, even if few pass; mark all as unconfirmed candidates.
- 迭代
- 3
- AI 智能体
- 50
- 仿真成功
- 2990/3229
- 公开主张
- 8
- 已确认的主张
- 0
- 共享项目
- 0
智能体
每次迭代中 AI 智能体的角色、任务和对话(只读)。
迭代 2 · S107 iteration 3: vet and rank the top-20 unconfirmed new candidates
已完成智能体 1 · director
Take the existing iteration-2 candidate list (66+43+146), cross-match against the NASA Exoplanet Archive, ExoFOP TOI and CTOI lists, run light-curve, Gaia and other-sector vetting, compute a planet probability, and rank. No new blind search is run.
智能体 2 · programmer
为每个工作组编写并运行代码。 · 已提交 19 个组
智能体 3 · inspector
在采纳前检查每个组的结果。 · 已批准 19 个组
智能体 4 · explorer
在要求之外,寻找团队尚未尝试的方向。
智能体 5 · programmer
为每个工作组编写并运行代码。 · 已提交 19 个组
智能体 6 · inspector
在采纳前检查每个组的结果。 · 已批准 19 个组
智能体 7 · explorer
在要求之外,寻找团队尚未尝试的方向。
智能体 8 · programmer
为每个工作组编写并运行代码。 · 已提交 19 个组
智能体 9 · inspector
在采纳前检查每个组的结果。 · 已批准 19 个组
智能体 10 · explorer
在要求之外,寻找团队尚未尝试的方向。
- AI 智能体
- 10
- 模型调用
- 552
- 工具调用
- 724
- 成功的模拟
- 970/1114
- CPU 时间(所有机器)
- 13 时 14 分
- 结果
- 74