论文标题
了解瞬时光曲线近似神经网络方法的特性
Understanding of the properties of neural network approaches for transient light curve approximations
论文作者
论文摘要
现代时间域的光度测验收集了许多天文学对象的观察结果，即将到来的大规模调查时代将提供有关其特性的更多信息。光谱随访对于诸如超新星等瞬态尤其至关重要，这些对象中的大多数都没有受到此类研究的约束。 } {通量时间序列被积极用作光度分类和表征的负担得起的替代方案，例如峰值识别和光度下降估计。但是，收集的时间序列是多维的，并且不规则地采样，同时还包含异常值，没有任何定义明确的系统不确定性。 This paper presents a search for the best-performing methods to approximate the observed light curves over time and wavelength for the purpose of generating time series with regular time steps in each passband.}{We examined several light curve approximation methods based on neural networks such as multilayer perceptrons, Bayesian neural networks, and normalizing flows to approximate observations of a single light curve.测试数据集包括模拟的Parperc和真实的Zwicky瞬态设施明亮的瞬态瞬态瞬态光曲线。} {与最先进的模型相比，测试表明，即使只有一些观察到足以适应网络并提高近似值的质量。这项工作中描述的方法的计算复杂性低，并且比高斯过程快得多。此外，我们从进一步的峰识别和瞬态分类的角度分析了近似技术的性能。该研究结果已在Github上可用于科学界的开放式且用户友好的Fulu Python库中发布。
Modern-day time-domain photometric surveys collect a lot of observations of various astronomical objects and the coming era of large-scale surveys will provide even more information on their properties. Spectroscopic follow-ups are especially crucial for transients such as supernovae and most of these objects have not been subject to such studies. }{Flux time series are actively used as an affordable alternative for photometric classification and characterization, for instance, peak identifications and luminosity decline estimations. However, the collected time series are multidimensional and irregularly sampled, while also containing outliers and without any well-defined systematic uncertainties. This paper presents a search for the best-performing methods to approximate the observed light curves over time and wavelength for the purpose of generating time series with regular time steps in each passband.}{We examined several light curve approximation methods based on neural networks such as multilayer perceptrons, Bayesian neural networks, and normalizing flows to approximate observations of a single light curve. Test datasets include simulated PLAsTiCC and real Zwicky Transient Facility Bright Transient Survey light curves of transients.}{The tests demonstrate that even just a few observations are enough to fit the networks and improve the quality of approximation, compared to state-of-the-art models. The methods described in this work have a low computational complexity and are significantly faster than Gaussian processes. Additionally, we analyzed the performance of the approximation techniques from the perspective of further peak identification and transients classification. The study results have been released in an open and user-friendly Fulu Python library available on GitHub for the scientific community.
