Abstract
The Hα emission line in rest wavelength frame of optical spectra is valuable characteristics for nebulae detection. Searching and recognizing the spectra with Hα emission line from massive data are necessary for the further study, while the most of methods existed currently do not adapt to such spectral data, especially for the spectra with weak Hα emission line. To address this issue, a new algorithm (named WEDA) for detection of spectra with Hα emission line is provided in this paper. Firstly, the difference factor μ between the line characteristics of the specific data is defined as its weight in recognizing of the whole lines table. Secondly, a tuning functionf(τ, δ) based on the momentum formula is defined to update the weights during the process. In this step, the spectra with Hα emission line are analysed and classified as 3 different situations. The amount of spectra with Hα emission line is different in 3 different situations, so the speed of weight of update is different in 3 different situations. The weight of update helps us detect the data containing weak Hα emission line in the 3 situations. Based on this, a new integrated algorithm especially for the detection of the spectra with Hα is provided. In the end, by using several spectral datasets from the DR5 of LAMOST survey, experiments results indicate that the WEDA shows higher accuracy basically unaffected by the dataset size and the signal to noise ratio(SNR) than the other similar algorithms.
Highlights
With the development of technology, increasing amounts of spectral data have been obtained by astronomical telescopes
The work seen as classification tasks can be finsihed automatically via computers; for example, the detection of the Hα emission line can be seen as a binary classification task that attempts to classify spectral data as {1, −1}, where 1 represents that this spectral data contains the Hα emission line, and −1 represents that this spectral data does not contain the Hα emission line
The entire dataset is divided into three parts based on the SNR, the ranges of which are 0-10, 10-50 and above 50, because different SNRs will lead to different amounts of data with Hα emission line and different qualities of the performance of WEDA
Summary
With the development of technology, increasing amounts of spectral data have been obtained by astronomical telescopes. The challenge we face today is how to find the spectral data we need from massive amounts of spectral data. There is a lot of work to analyze the spectral data [1], even facilities [2] Due to the large amount of work, a lot of the work cannot be finished manually, and some of the work can be considered to be classification tasks in machine learning. The results of many classification methods do not meet the requirements of. Data that meet our needs are often rare, which makes model training harder. To adapt to a complex data environment, we will weight the data to distinguish it. The ranking algorithm and weight algorithm are combined to highlight the data we need. WEDA is used to find data with the Hα emission line on the LAMOST survey dataset
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