BTSbot-nearby 发现 SN 2024jlf:快速自主跟进探测 18.5 Mpc 处 II 型 IIP 超新星中的相互作用
The BTSbot-nearby discovery of SN 2024jlf: rapid, autonomous follow-up probes interaction in an 18.5 Mpc Type IIP supernova
AI总结:
利用 BTSbot-nearby 自主跟进,对 18.5 Mpc 处 IIP 型超新星 SN 2024jlf 进行快速光谱观测,揭示其快速增亮及前身星增强质量损失,并限制质量损失率。
摘要:
我们展示了 IIP 型超新星(SN)2024jlf 的观测结果,包括在首次发光后仅 0.7 天(约 17 小时)就开始的光谱观测。快速跟进由新的 BTSbot-nearby 程序实现,该程序涉及自主触发对 Zwicky Transient Facility 数据中与邻近(D<60 Mpc)星系重合且由 BTSbot 机器学习模型识别的新暂现源的目标机会请求。早期测光和首次发光前不久的非探测显示,SN 2024jlf 最初增亮速度超过 4 星等/天,快于约 90% 的 II 型超新星。早期光谱揭示了微弱的闪光电离特征:Hα、He II 和 C IV 的窄且短寿命(1.3 < τ [天] < 1.8)发射线。假设风速 v_w=50 km/s,这些特性表明红超巨星前身星在爆炸前最后一年表现出增强的质量损失。我们通过将观测与两个独立辐射流体动力学代码的模型网格进行匹配,将质量损失率限制在 10^-4 < Mdot [太阳质量/年] < 10^-3。BTSbot-nearby 自动化最小化了光谱跟进的延迟,从而能够观测暂现源表现出的短暂早期现象。
英文摘要:
We present observations of the Type IIP supernova (SN) 2024jlf, including spectroscopy beginning just 0.7 days ($\sim$17 hours) after first light. Rapid follow-up was enabled by the new $\texttt{BTSbot-nearby}$ program, which involves autonomously triggering target-of-opportunity requests for new transients in Zwicky Transient Facility data that are coincident with nearby ($D<60$ Mpc) galaxies and identified by the $\texttt{BTSbot}$ machine learning model. Early photometry and non-detections shortly prior to first light show that SN 2024jlf initially brightened by $>$4 mag/day, quicker than $\sim$90% of Type II SNe. Early spectra reveal weak flash ionization features: narrow, short-lived ($1.3 < τ~\mathrm{[d]} < 1.8$) emission lines of H$α$, He II, and C IV. Assuming a wind velocity of $v_w=50$ km s$^{-1}$, these properties indicate that the red supergiant progenitor exhibited enhanced mass-loss in the last year before explosion. We constrain the mass-loss rate to $10^{-4} < \dot{M}~\mathrm{[M_\odot~yr^{-1}]} < 10^{-3}$ by matching observations to model grids from two independent radiative hydrodynamics codes. $\texttt{BTSbot-nearby}$ automation minimizes spectroscopic follow-up latency, enabling the observation of ephemeral early-time phenomena exhibited by transients.
机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。