.png){kind=icon}
返回
An Interpretable Machine-learning Framework for Modeling High-resolution Spectroscopic Data*
.png){kind=icon}
OA
AI
DOI：10.3847/1538-4357/aca0a2.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Comparison of echelle spectra to synthetic models has become a computational statistics challenge, with over 10,000 individual spectral lines affecting a typical cool star echelle spectrum. Telluric artifacts, imperfect line lists, inexact continuum placement, and inflexible models frustrate the scientific promise of these information-rich data sets. Here we debut an interpretable machine-learning framework blase that addresses these and other challenges. The semiempirical approach can be viewed as transfer learning-first pretraining models on noise-free precomputed synthetic spectral models, then learning the corrections to line depths and widths from whole-spectrum fitting to an observed spectrum. The auto-differentiable model employs back-propagation, the fundamental algorithm empowering modern deep learning and neural networks. Here, however, the 40,000+ parameters symbolize physically interpretable line profile properties such as amplitude, width, location, and shape, plus radial velocity and rotational broadening. This hybrid data-/model-driven framework allows joint modeling of stellar and telluric lines simultaneously, a potentially transformative step forward for mitigating the deleterious telluric contamination in the near-infrared. The blase approach acts as both a deconvolution tool and semiempirical model. The general-purpose scaffolding may be extensible to many scientific applications, including precision radial velocities, Doppler imaging, chemical abundances for Galactic archeology, line veiling, magnetic fields, and remote sensing. Its sparse-matrix architecture and GPU acceleration make blase fast. The open-source PyTorch-based code blase includes tutorials, Application Programming Interface documentation, and more. We show how the tool fits into the existing Python spectroscopy ecosystem, demonstrate a range of astrophysical applications, and discuss limitations and future extensions.
Keyword:
ORBITAL MOTION
LINE
TEMPERATURE
ABSORPTION
ATMOSPHERE
EXOPLANET
ASTROPY
PROJECT
STARS
WINDS
期刊
IF:
5.4
论文数:
8.3W
被引数:
32.0W
机构
引用论文
分享收藏The EXPRES Stellar Signals Project II. State of the Field in Disentangling Photospheric Velocities.png)
ASTRONOMICAL JOURNALOAAI
ASTRONOMICAL JOURNALIF5.1
OAAI分享收藏Bioreductive deposition of palladium (0) nanoparticles onShewanella oneidensiswith catalytic activity towards reductive dechlorination of polychlorinated biphenyls钯 (0) 纳米颗粒在 Shewanella oneidensis 上的生物还原沉积，对多氯联苯的还原脱氯具有催化活性
PREAI
分享收藏
查看更多内容