本文選題：恒星 + 基本參數�。� 參考：《天文學報》2016年04期

【摘要】：深度學習是當前機器學習、模式識別和人工智能領域中的一項熱點研究技術,非常適用于處理復雜的大規(guī)模數據.基于深度學習理論構建了一個5層的棧式自編碼深度神經網絡,對恒星大氣物理參數進行自動估計,網絡各層的節(jié)點數分別為3821-500-100-50-1.使用美國大型巡天項目Sloan發(fā)布的Sloan Digital Sky Survey(SDSS)實測光譜以及由Kurucz的New Opacity Distribution Function(NEWODF)模型得到的理論光譜進行了實驗驗證,對有效溫度(Teff)、表面重力加速度(lg g)和金屬豐度([Fe/H])3個物理參數進行了自動估計.結果表明,棧式自編碼深度神經網絡的估計精度較好,其中在SDSS數據上的平均絕對誤差分別為:79.95(Teff/K),0.0058(lg(Teff/K)),0.1706(lg(g/(cm·s~(-2)))),0.1294 dex([Fe/H]);在理論數據上的平均絕對誤差分別是:15.34(Teff/K),0.0011(lg(Teff/K)),0.0214(lg(g/(cm·s~(-2)))),0.0121 dex([Fe/H]).
[Abstract]:Deep learning is a hot research technology in the field of machine learning, pattern recognition and artificial intelligence, which is very suitable for dealing with complex large-scale data. Based on the depth learning theory, a five-layer self-coding depth neural network is constructed. The parameters of stellar atmosphere are estimated automatically. The number of nodes in each layer of the network is 3821-500-100-50-1. The measured spectra of Sloan Digital Sky Survey (SDSS) published by Sloan and the theoretical spectra obtained from Kurucz's New Opacity Distribution function ODF model are verified experimentally. Three physical parameters, I. e., effective temperature, surface gravity acceleration (LG) and metal abundance ([Fe / H]), are estimated automatically. 緇撴灉琛ㄦ槑,鏍堝紡鑷紪鐮佹繁搴︾緇忕綉緇滅殑浼拌綺懼害杈冨ソ,鍏朵腑鍦⊿DSS鏁版嵁涓婄殑騫沖潎緇濆璇樊鍒嗗埆涓，

本文編號：2037532