9-13 September 2019
University of Geneva A100 Sciences II
Europe/Zurich timezone

Searching the Metal-poor Star with Deep Learning Method

Not scheduled
5m
A100 Sciences II, 30 quai Ernest Ansermet, 1205 Geneva (University of Geneva A100 Sciences II)

A100 Sciences II, 30 quai Ernest Ansermet, 1205 Geneva

University of Geneva A100 Sciences II

30, quai Ernest Ansermet 1205 Geneva
posters

Speaker

Dr Jiannan Zhang (National Astronomical Observatories, CAS)

Description

Searching for the rare metal-poor stars requires fast and effective analysis methods on the vast spectral survey data. Here, we develop a deep learning network to search for metal-poor and carbon-enhanced metal-poor (CEMP) stars in low-resolution spectra. We train a deep convolutional neural network (CNN) on a synthesized stellar spectra grid with Teff ranging from 5000K to 7500K, log g ranging from 0 dex to 5 dex, and [Fe/H] ranging from -5 dex to 0.5 dex. The deep CNN also employs the spectral absorption-line indices and intrinsic colors for all the spectra to measure the three fundamental parameters and to identify the metal-poor stellar spectra. The tests on synthesized spectra at different noise levels show that the deep CNN have the efficiency of the metal-poor recognition and good accuracy of parameterization.

Primary authors

Dr Jiannan Zhang (National Astronomical Observatories, CAS) Dr Bing Du (National Astronomical Observatories, CAS)

Presentation Materials

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