Searching for Galactic H ii Regions from the LAMOST Spectroscopic Database

In this paper, we pioneer a new machine-learning method to search for H ii regions in spectra from The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). H ii regions are emission nebulae created when young and massive stars ionize nearby gas clouds with high-energy ultraviolet radiation. Having more H ii region samples will help us understand the formation and evolution of stars. Machine-learning methods are often applied to search for special celestial bodies such as H ii regions. LAMOST has conducted spectral surveys and provided a wealth of valuable spectra for the research of special and rare celestial bodies. To overcome the problem of sparse positive samples and diversification of negative samples, a novel method called the self-calibrated convolution network is introduced and implemented for spectral processing. A deep network classifier with a structure called a self-calibrated block provides a high precision rate, and the recall rate is improved by adding the strategy of positive-unlabeled bagging. Experimental results show that this method can achieve better performance than other current methods. Eighty-nine spectra are identified as Galactic H ii regions after cross-matching with the WISE Catalog of Galactic H ii Regions, confirming the effectiveness of the method proposed in this paper.

Análise computacional do hidrocarboneto aromático policíclico antraceno e sua aplicação na astroquímica

Este trabalho objetiva analisar o espectro de emissão na região entre 5 e 15 μm do antraceno, uma espécie química de relevância astroquímica, obtido por modelagem computacional. E também comparar resultados obtidos com conjuntos de bases diferentes. A classe molecular escolhida possui características que permitem sua presença abundante em ambientes hostis do espaço, despertando interesse acerca de suas propriedades físico-químicas. A estrutura molecular foi desenhada na plataforma Gabedit e os cálculos da abordagem quântica realizados pelo software ORCA. Foram utilizadas duas funções de base, 6-31G* e 6-31G**, para comparação entre resultados. Para a análise dos métodos e inferências de semelhanças e diferenças, foi utilizado o NASA Ames PAH IR Spectroscopic Database (PAHdb), como banco de dados de referência. Os resultados obtidos apresentam um avermelhamento nas bandas vibracionais, mesmo utilizando a função de descrita pelos autores do PAHdb, expondo a direta relação entre complexidade e eficácia dos métodos comparados. Ainda assim, os resultados obtidos foram satisfatórios. Uma vez que as bandas de emissão desta molécula, em comparação com dados espectroscópicos, permitem inferir a presença da mesma em galáxias ativas, como Seyfert 1, Seyfert 2 e Starburst.

Automated spectroscopic modelling with optimised convolutional neural networks

Convolutional neural networks (CNN) for spectroscopic modelling are currently tuned manually, and the effects of their hyperparameters are not analysed. These can result in sub-optimal models. Here, we propose an approach to tune one-dimensional CNN (1D-CNNs) automatically. It consists of a parametric representation of 1D-CNNs and an optimisation of hyperparameters to maximise a model’s performance. We used a large European soil spectroscopic database to demonstrate our approach for estimating soil organic carbon (SOC) contents. To assess the optimisation, we compared it to random search, and to understand the effects of the hyperparameters, we calculated their importance using functional Analysis of Variance. Compared to random search, the optimisation produced better final results and showed faster convergence. The optimal model produced the most accurate estimates of SOC with $$\hbox {RMSE} = 9.67 \pm 0.51$$ RMSE = 9.67 ± 0.51 (s.d.) and $${R}^2 = 0.89 \pm 0.013$$ R 2 = 0.89 ± 0.013 (s.d.). The hyperparameters associated with model training and architecture critically affected the model’s performance, while those related to the spectral preprocessing had little effect. The optimisation searched through a complex hyperparameter space and returned an optimal 1D-CNN. Our approach simplified the development of 1D-CNNs for spectroscopic modelling by automatically selecting hyperparameters and preprocessing methods. Hyperparameter importance analysis shed light on the tuning process and increased the model’s reliability.