剥离包络超新星的辐射转移建模 II：光变曲线的神经网络模拟
Radiative Transfer Modeling of Stripped-envelope Supernovae II: Neural Network Emulation of Light Curves
AI总结：
本研究提出首个剥离包络超新星光变曲线的神经网络模拟器，可从多波段光变曲线独立约束关键参数，其参数推断准确性优于经典Arnett模型，并对3个已研究的超新星完成光变曲线拟合。
AI中文摘要：
我们提出首个剥离包络超新星（SESN）光变曲线的神经网络模拟器，其基于用辐射转移（RT）代码sedona模拟的4499条光变曲线网格训练。利用该模拟器，我们表明可从多波段光变曲线推断镍质量（m_ni）、抛射物质量（m_ejecta）、抛射物速度轮廓及镍-56混合程度。我们发现，与传统半解析模型相比，该模拟器中抛射物质量与抛射物速度的简并性显著更弱；模拟器可独立约束抛射物质量和抛射物速度对光变曲线的影响，而非如传统半解析模型那样刻意使二者简并。我们还表明，对于模拟的ZTF类和LSST类光变曲线，该推断的准确性显著优于经典Arnett模型。最后，我们给出3个已深入研究的SESN（SN~1994I、SN~2007gr和iPTF13bvn）的光变曲线拟合结果，约束了它们的m_ni、m_ejecta及镍-56混合情况。
英文摘要：
We present the first neural-network emulator of stripped-envelope supernova (SESN) lightcurves, trained on a grid of 4499 light curves simulated with the radiative transfer (RT) code sedona. Using this emulator, we show that m_ni, m_ejecta, the ejecta velocity profile, and the degree of Ni-56 mixing can all be inferred from multiband lightcurves. We find that the degeneracy between ejecta mass and ejecta velocity is substantially weaker with this emulator than in traditional semianalytical models. The emulator is able to independently constrain the influence of ejecta mass and of ejecta velocity on the resulting lightcurve, rather than making them degenerate by design as traditional semianalytical models do. We additionally show that this inference is significantly more accurate than that done by the classical Arnett model for both simulated ZTF-like and LSST-like lightcurves. Finally, we present lightcurves fits to three well-studied SESNe: SN~1994I, SN~2007gr, and iPTF13bvn, constraining their m_ni, m_ejecta, and Ni-56 mixing.
