Skip to main content
SpringerLink
Log in

DSRL: A low-resolution stellar spectral of LAMOST automatic classification method based on discrete wavelet transform and deep learning methods

  • Research
  • Published:
Experimental Astronomy Aims and scope Submit manuscript

Abstract

Automatic classification of stellar spectra contributes to the study of the structure and evolution of the Milky Way and star formation. Currently available methods exhibit unsatisfactory spectral classification accuracy. This study investigates a method called DSRL, which is primarily used for automated and accurate classification of LAMOST stellar spectra based on MK classification criteria. The method utilizes discrete wavelet transform to decompose the spectra into high-frequency and low-frequency information, and combines residual networks and long short-term memory networks to extract both high-frequency and low-frequency features. By introducing self-distillation (DSRL-1, DSRL-2, and DSRL-3), the classification accuracy is improved. DSRL-3 demonstrates superior performance across multiple metrics compared to existing methods. In both three-class(F ,G ,K) and ten-class(A0, A5, F0, F5, G0, G5, K0, K5, M0, M5) experiments, DSRL-3 achieves impressive accuracy, precision, recall, and F1-Score results. Specifically, the accuracy performance reaches 94.50% and 97.25%, precision performance reaches 94.52% and 97.29%, recall performance reaches 94.52% and 97.22%, and F1-Score performance reaches 94.52% and 97.23%. The results indicate the significant practical value of DSRL in the classification of LAMOST stellar spectra. To validate the model, we visualize it using randomly selected stellar spectral data. The results demonstrate its excellent application potential in stellar spectral classification.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data Availability

All dataset used in this work are publicly available. Details on how to access the data can be found On the websites: http://www.lamost.org/dr8/v2.0/.

Notes

  1. http://www.lamost.org/lmusers/cms/article/view?id=1

  2. http://www.lamost.org/dr8/v2.0/

  3. http://www.lamost.org/lmusers/cms/article/view?id=1

  4. http://www.lamost.org/dr8/v2.0/doc/faq

  5. http://www.lamost.org/dr8/v2.0/search#classification

References

  1. Han, Z.: Lamost’s view on the gaia-sausage-enceladus merger event. Sci. China Phys. Mech. Astron. 64, 1–1 (2021)

    Article  Google Scholar 

  2. Struve, O.: On the origin of bright lines in spectra of stars of class b. AJ. 73, 94 73, 94 (1931)

  3. Smith, J., Draine, B., Dale, D., Moustakas, J., Kennicutt, R., Jr., Helou, G., Armus, L., Roussel, H., Sheth, K., Bendo, G., et al.: The mid-infrared spectrum of star-forming galaxies: global properties of polycyclic aromatic hydrocarbon emission. A. J. 656(2), 770 (2007)

    Article  Google Scholar 

  4. Johnson, H.L., Morgan, W.: Fundamental stellar photometry for standards of spectral type on the revised system of the yerkes spectral atlas. AJ. 117, 313 117, 313 (1953)

  5. Wang, M., Wu, J., Jiang, B., Zhang, Y.: Searching for galactic h ii regions from the lamost database based on the multihead wdcnn model. AJSS 267(2), 39 (2023)

    Article  Google Scholar 

  6. York, D.G., Adelman, J., Anderson, J.E., Jr., Anderson, S.F., Annis, J., Bahcall, N.A., Bakken, J., Barkhouser, R., Bastian, S., Berman, E., et al.: The sloan digital sky survey: Technical summary. A. J. 120(3), 1579 (2000)

    Article  ADS  Google Scholar 

  7. Eisenstein, D., Weinberg, D.H., Agol, E., Aihari, H., Aubourg, E., Bailey, S., Kollmeier, J., Shetrone, M.D., Schlesinger, K., Smith, V.V., Yanny, B., Malanushenko, E., Malanushenko, V., Oravetz, D., Reid, I.N., Rix, H.-W.: Sdss-iii: Massive spectroscopic surveys of the distant universe, the milky way, and extra-solar planetary systems. A. J. 142, 72 (2011)

    Article  ADS  Google Scholar 

  8. Blanton, M.R., Roweis, S.: K-corrections and filter transformations in the ultraviolet, optical, and near-infrared. A. J. 133(2), 734 (2007)

    Article  ADS  Google Scholar 

  9. Stoughton, C., Lupton, R.H., Bernardi, M., Blanton, M.R., Burles, S., Castander, F.J., Connolly, A., Eisenstein, D.J., Frieman, J.A., Hennessy, G., et al.: Sloan digital sky survey: early data release. A. J. 123(1), 485 (2002)

    Article  ADS  Google Scholar 

  10. Kimura, M., Maihara, T., Iwamuro, F., Akiyama, M., Tamura, N., Dalton, G.B., Takato, N., Tait, P., Ohta, K., Eto, S., et al.: Fibre multi-object spectrograph (fmos) for the subaru telescope. P. A. S. J. 62(5), 1135–1147 (2010)

    ADS  Google Scholar 

  11. Liu, W., Zhu, M., Dai, C., He, D., Yao, J., Tian, H., Wang, B., Wu, K., Zhan, Y., Chen, B., et al.: Classification of large-scale stellar spectra based on deep convolutional neural network. M. N. R. A. S. 483(4), 4774–4783 (2019)

    Article  ADS  Google Scholar 

  12. Perryman, M., Boer, K.S., Gilmore, G., Høg, E., Lattanzi, M., Lindegren, L., Luri, X., Mignard, F., Pace, O., De Zeeuw, P.: Gaia: Composition, formation and evolution of the galaxy. A & A 369(1), 339–363 (2001)

    Article  ADS  Google Scholar 

  13. Munari, U.: GAIA Spectroscopy: Science and Technology vol. 298, (2003)

  14. Zhao, G., Zhao, Y.-H., Chu, Y.-Q., Jing, Y.-P., Deng, L.-C.: Lamost spectral survey-an overview. R. A. A. 12(7), 723 (2012)

    Google Scholar 

  15. Cui, X.-Q., Zhao, Y.-H., Chu, Y.-Q., Li, G.-P., Li, Q., Zhang, L.-P., Su, H.-J., Yao, Z.-Q., Wang, Y.-N., Xing, X.-Z., et al.: The large sky area multi-object fiber spectroscopic telescope (lamost). R. A. A. 12(9), 1197 (2012)

    Google Scholar 

  16. Luo, A.-L., Zhao, Y.-H., Zhao, G., Deng, L.-C., Liu, X.-W., Jing, Y.-P., Wang, G., Zhang, H.-T., Shi, J.-R., Cui, X.-Q., et al.: The first data release (dr1) of the lamost regular survey. R. A. A. 15(8), 1095 (2015)

    Google Scholar 

  17. Li, H.-N., Zhao, G., Christlieb, N., Wang, L., Wang, W., Zhang, Y., Hou, Y., Yuan, H.: Spectroscopic analysis of metal-poor stars from lamost: early results. A. J. 798(2), 110 (2015)

    Article  Google Scholar 

  18. Corbally, C.J., Gray, R.O., Garrison, R.F.: The mk process at 50 years. a powerful tool for astrophysical insight. The MK Process at 50 Years: A Powerful Tool for Astrophysical Insight 60 (1994)

  19. Liu, C., Cui, W.-Y., Zhang, B., Wan, J.-C., Deng, L.-C., Hou, Y.-H., Wang, Y.-F., Yang, M., Zhang, Y.: Spectral classification of stars based on lamost spectra. R. A. A. 15(8), 1137 (2015)

    Google Scholar 

  20. Vilavicencio-Arcadia, E., Navarro, S.G., Corral, L.J., Martínez, C.A., Nigoche, A., Kemp, S.N., Ramos-Larios, G.: Application of artificial neural networks for the automatic spectral classification. Math. Probl. Eng. 2020, 1–15 (2020)

    Article  Google Scholar 

  21. Wang, K., Guo, P., Luo, A.-L.: A new automated spectral feature extraction method and its application in spectral classification and defective spectra recovery. M. N. R. A. S. 465(4), 4311–4324 (2017)

    Article  ADS  Google Scholar 

  22. Li, X.-R., Lin, Y.-T., Qiu, K.-B.: Stellar spectral classification and feature evaluation based on a random forest. R. A. A. 19(8), 111 (2019)

    Google Scholar 

  23. Kuntzer, T., Tewes, M., Courbin, F.: Stellar classification from single-band imaging using machine learning. A & A 591, 54 (2016)

    Article  ADS  Google Scholar 

  24. Dafonte, B.: A blended artificial intelligence approach for spectral classification of stars in massive astronomical surveys. Entropy 22(5) (2020)

  25. Sharma, K., Singh, H.P., Gupta, R., Kembhavi, A., Vaghmare, K., Shi, J., Zhao, Y., Zhang, J., Wu, Y.: Stellar spectral interpolation using machine learning. M. N. R. A. S. 496(4), 5002–5016 (2020)

    Article  ADS  Google Scholar 

  26. Sharma, K., Kembhavi, A., Kembhavi, A., Sivarani, T., Abraham, S., Vaghmare, K.: Application of convolutional neural networks for stellar spectral classification. M. N. R. A. S. 491(2), 2280–2300 (2020)

    Article  ADS  Google Scholar 

  27. Zhang, L., Bao, C., Ma, K.: Self-distillation: Towards efficient and compact neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 44(8), 4388–4403 (2021)

    Google Scholar 

  28. Lei, Z., He, R., Németh, P., Vos, J., Zou, X., Hu, K., Xiao, H., Yan, H., Zhao, J.: Hot subdwarf stars identified in lamost dr8 with single-lined and composite spectra. A. J. 942(2), 109 (2023)

    Article  Google Scholar 

  29. ...Xiang, M.S., Liu, X.W., Yuan, H.B., Huang, Y., Huo, Z.Y., Zhang, H.W., Chen, B.Q., Zhang, H.H., Sun, N.C., Wang, C., Zhao, Y.H., Shi, J.R., Luo, A.L., Li, G.P., Wu, Y., Bai, Z.R., Zhang, Y., Hou, Y.H., Yuan, H.L., Li, G.W., Wei, Z.: The LAMOST stellar parameter pipeline at Peking University - lsp3. M. N. R. A. S. 448(1), 822–854 (2015)

    Article  ADS  Google Scholar 

  30. Liu, C., Cui, W.-Y., Zhang, B., Wan, J.-C., Deng, L.-C., Hou, Y.-H., Wang, Y.-F., Yang, M., Zhang, Y.: Spectral classification of stars based on lamost spectra. Res. Astron. Astrophys. 15(8), 1137 (2015)

    Article  ADS  Google Scholar 

  31. Jing-Min, Z., Chen-Ye, M., Lu, W., Li-Ting, D., Ting-Ting, X., Lin-Pin, A., Wei-Hong, Z.: A new stellar spectral feature extraction method based on two-dimensional fourier spectrum image and its application in the stellar spectral classification based on deep network. Chin. Astron. Astrophys. 44(3), 334–344 (2020)

    Article  ADS  Google Scholar 

  32. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

  33. Daubechies, I.: The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. Inf. Theory 36(5), 961–1005 (1990). https://doi.org/10.1109/18.57199

    Article  ADS  MathSciNet  Google Scholar 

  34. Too, J., Abdullah, A.R., Saad, N.M., Ali, N.M., Zawawi, T.N.S.T.: Exploring the relation between emg pattern recognition and sampling rate using spectrogram. J. Electr. Eng. Technol. 14, 947–953 (2019)

    Article  Google Scholar 

  35. Canal, M.R.: Comparison of wavelet and short time fourier transform methods in the analysis of emg signals. J. Med. Syst. 34, 91–94 (2010)

    Article  Google Scholar 

  36. Saravanan, N., Ramachandran, K.: Incipient gear box fault diagnosis using discrete wavelet transform (dwt) for feature extraction and classification using artificial neural network (ann). Expert Syst. Appl. 37(6), 4168–4181 (2010)

    Article  Google Scholar 

  37. Shemi, P., Shareena, E.: Analysis of ecg signal denoising using discrete wavelet transform. In: 2016 IEEE International Conference on Engineering and Technology (ICETECH), pp. 713–718 (2016). IEEE

  38. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

  39. Chernodub, A., Nowicki, D.: Sampling-based gradient regularization for capturing long-term dependencies in recurrent neural networks. In: Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part II 23, pp. 90–97 (2016). Springer

  40. Vorontsov, E., Trabelsi, C., Kadoury, S., Pal, C.: On orthogonality and learning recurrent networks with long term dependencies. In: International Conference on Machine Learning, pp. 3570–3578 (2017). PMLR

  41. Dong, X., Bao, J., Zheng, Y., Zhang, T., Chen, D., Yang, H., Zeng, M., Zhang, W., Yuan, L., Chen, D., et al: Maskclip: Masked self-distillation advances contrastive language-image pretraining. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10995–11005 (2023)

  42. Xing, H., Xiao, Z., Zhan, D., Luo, S., Dai, P., Li, K.: Selfmatch: Robust semisupervised time-series classification with self-distillation. Int. J. Intell. Syst. 37(11), 8583–8610 (2022)

    Article  Google Scholar 

  43. Prechelt, L.: Automatic early stopping using cross validation: quantifying the criteria. Neural Netw. 11(4), 761–767 (1998)

    Article  Google Scholar 

Download references

Acknowledgements

We thank the anonymous referee for valuable helpful comments and suggestions. This work is supported by the National Key Research and Development Program (2022YFF0711500), the National Natural Science Foundation of China (12273077), the National Natural Science Foundation of China (11803022), and Innovation Fund of Engineering Research Center for Integration and Application of E-Learning Technology, Ministry of Education(1221004).

Author information

Authors and Affiliations

  1. College of Artificial Intelligence, Tianjin University of Science and Technology, No.29,13th Street, Binhai New Area, 300457, Tianjin, China

    Hao Li, Qing Zhao, Chengkui Zhang, Yuan Wang & Yarui Chen

  2. National Astronomical Observatories, Chinese Academy of Sciences, No.20 Datun Road, Chaoyang District, 100101, Beijing, China

    Chenzhou Cui & Dongwei Fan

  3. Ministry of Education, Engineering Research Center for Integration and Application of E-Learning Technology, No.75 Fuxing Road, Haidian District, 100039, Beijing, China

    Yarui Chen

Authors
  1. Hao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qing Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chengkui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chenzhou Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dongwei Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yarui Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

H.L. wrote the main manuscript text, and Q.Z. and C.Z. draw the figures, C.Z., D.F., Y.W. and Y.C. gave suggestions for revision. All authors reviewed the manuscript.

Corresponding author

Correspondence to Qing Zhao.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, H., Zhao, Q., Zhang, C. et al. DSRL: A low-resolution stellar spectral of LAMOST automatic classification method based on discrete wavelet transform and deep learning methods. Exp Astron 57, 20 (2024). https://doi.org/10.1007/s10686-024-09940-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10686-024-09940-0

Keywords

Search

Navigation