论文标题
Hypergal：超新星的高光谱场景建模与整体场光谱仪Sedmachine
HyperGal: hyperspectral scene modeling for supernova typing with the Integral Field Spectrograph SEDmachine
论文作者
论文摘要
时域天文学的最新发展，例如Zwicky Transient设施，使每天都可以扫描整个可见天空，从而每晚发现数百个新瞬变。其中有10至15个是超新星（SNE），必须在宇宙学之前对其进行分类。频谱能量分配机（SEDM）是一种低分辨率积分光谱仪，已设计，构建和操作，以通过ZTF主相机检测到的光谱分类。当瞬态太近其宿主星系芯时，当前的PYSEDM管道受污染的限制。这可能导致不正确的键入，并最终偏向宇宙学分析，并在局部环境特性方面影响SN样品均匀性。我们提出了一个新的场景建模者，以从其结构化背景中提取瞬态频谱，以提高SEDM的打字效率。 Hypergal是一种完全色彩的场景建模者，它使用频率前光度图像生成宿主星系的高光谱模型。它基于用作物理动机的光谱插值器的Cigale Sed Fitter。带有点源和扩散背景成分的星系模型投影到SEDM光谱空间观测空间上，并调整为观察。完整的过程将在5000个模拟立方体上进行验证。我们将对比度引入了SN位置的瞬时通量比。从实际SEDM观测值的估计对比度分布中，我们表明，超级级正确分类了约95％的SNE IA。与标准提取方法相比，HyperGal正确分类了10％的SNE IA。误报率小于2％，一半是标准提取方法。假设对核心折叠SNE的对比度分布相似，那么Hypergal分类为14％（11％）额外的SNE II（IBC）。
Recent developments in time domain astronomy, like the Zwicky Transient Facility, have made possible a daily scan of the entire visible sky, leading to the discovery of hundreds of new transients every night. Among them, 10 to 15 are supernovae (SNe), which have to be classified prior to cosmological use. The Spectral Energy Distribution machine (SEDm), a low resolution Integral Field Spectrograph, has been designed, built, and operated to spectroscopically classify targets detected by the ZTF main camera. The current Pysedm pipeline is limited by contamination when the transient is too close to its host galaxy core; this can lead to an incorrect typing and ultimately bias the cosmological analyses, and affect the SN sample homogeneity in terms of local environment properties. We present a new scene modeler to extract the transient spectrum from its structured background, aiming at improving the typing efficiency of the SEDm. HyperGal is a fully chromatic scene modeler, which uses pre-transient photometric images to generate a hyperspectral model of the host galaxy; it is based on the CIGALE SED fitter used as a physically-motivated spectral interpolator. The galaxy model, complemented by a point source and a diffuse background component, is projected onto the SEDm spectro-spatial observation space and adjusted to observations. The full procedure is validated on 5000 simulated cubes. We introduce the contrast as the transient-to-total flux ratio at SN location. From estimated contrast distribution of real SEDm observations, we show that HyperGal correctly classifies ~95% of SNe Ia. Compared to the standard extraction method, HyperGal correctly classifies 10% more SNe Ia. The false positive rate is less than 2%, half as much as the standard extraction method. Assuming a similar contrast distribution for core-collapse SNe, HyperGal classifies 14% (11%) additional SNe II (Ibc).
