Consistent and accurate estimation of stellar parameters from HARPS-N Spectroscopy using Deep Learning




Deep Learning, Spectrography, Stellar parameters, Attention


Consistent and accurate estimation of stellar parameters is of great importance for information retrieval in astrophysical research. The parameters span a wide range from effective temperature to rotational velocity. We propose to estimate the stellar parameters directly from spectral signals coming from the HARPS-N spectrograph pipeline before any spectrum-processing steps are applied to extract the 1D spectrum. We propose an attention-based model to estimate the stellar parameters, which estimate both mean and uncertainty of the stellar parameters through estimation of the parameters of a Gaussian distribution. The estimated distributions create a basis to generate data-driven Gaussian confidence intervals for the estimated stellar parameters. We show that residual networks and attention-based models can estimate the stellar parameters with high accuracy for low Signal-to-noise ratio (SNR) compared to previous methods. With an observation of the Sun from the HARPS-N spectrograph, we show that the models can estimate stellar parameters from real observational data.


C. A. L. Bailer-Jones. Stellar parameters from very low resolution spectra and medium band filters: Teff, logg and (m/h) using neural networks. 2000.

C. A. L. Bailer-Jones, M. Irwin, G. Gilmore, and T. von Hippel. Physical parametrization of stellar spectra: the neural network approach. Monthly Notices of the Royal Astronomical Society, 292(1):157{166, 11 1997.

A. Casey, D. W. Hogg, M. K. Ness, H.-W. Rix, A. Y. Q. Ho, and G. F. Gilmore. The cannon 2: A data-driven model of stellar spectra for detailed chemical abundance analyses. 2016.

F. Castelli and R. L. Kurucz. New grids of ATLAS9 model atmospheres. arXiv preprint astro- ph/0405087, 2004.

X. Dumusque, A. Glenday, D. F. Phillips, N. Buchschacher, A. C. Cameron, M. Cecconi, D. Charbonneau, R. Cosentino, A. Ghedina, D. W. Latham, and et al. Harps-n observes the sun as a star. The Astrophysical Journal, 814(2):L21, Nov 2015.

S. Fabbro, K. Venn, T. O'Briain, S. Bialek, C. Kielty, F. Jahandar, and S. Monty. An application of deep neural networks in the analysis of stellar spectra, 2017.

D. F. Gray. The observation and analysis of stellar photospheres. Cambridge University Press, 2005.

K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition, 2015.

S. Jetley, N. A. Lord, N. Lee, and P. H. S. Torr. Learn to pay attention. CoRR, abs/1804.02391, 2018.

A. Kendall and Y. Gal. What uncertainties do we need in bayesian deep learning for computer vision? CoRR, abs/1703.04977, 2017.

D. P. Kingma and J. Ba. Adam: A method for stochastic optimization, 2014.

R. Kurucz and B. Bell. Kurucz cd-rom 13. ATLAS9 stellar atmosphere programs and, 2:1, 1993.

R. L. Kurucz. Atlas: A computer program for calculating model stellar atmospheres. SAO Special report, 309, 1970.

Y. Lee, T. Beers, J. Carlin, H. Newberg, Y. Hou, G. Li, A.-L. Luo, Y. Wu, M. Yang, H. Zhang, W. Zhang, and Y. Z. Niaot. Application of the segue stellar parameter pipeline to lamost stellar spectra. The Astronomical Journal, 150:187, 12 2015.

Y. Lee, T. Beers, T. Sivarani, C. Prieto, L. Koesterke, R. Wilhelm, P. Re Fiorentin, C. Bailer-Jones, J. Norris, C. Rockosi, B. Yanny, H. Newberg, K. Covey, H. Zhang, and A.-L. Luo. The segue stellar parameter pipeline. I. Description and comparison of individual methods. The Astronomical Journal, 136:2022, 10 2008.

I. Loshchilov and F. Hutter. Fixing weight decay regularization in adam. CoRR, abs/1711.05101, 2017.

M. Mayor, F. Pepe, D. Queloz, F. Bouchy, G. Rupprecht, G. Lo Curto, G. Avila, W. Benz, J. L. Bertaux, X. Bon ls, T. Dall, H. Dekker, B. Delabre, W. Eckert, M. Fleury, A. Gilliotte, D. Gojak, J. C. Guzman, D. Kohler, J. L. Lizon, A. Longinotti, C. Lovis, D. Megevand, L. Pasquini, J. Reyes, J. P. Sivan, D. Sosnowska, R. Soto, S. Udry, A. van Kesteren, L. Weber, and U. Weilenmann. Setting New Standards with HARPS. The Mes- senger, 114:20-24, Dec. 2003.

M. Ness, D. W. Hogg, H.-W. Rix, A. Y. Q. Ho, and G. Zasowski. The cannon: A data-driven approach to stellar label determination. The Astrophysical Journal, 808(1):16, Jul 2015.

D. A. Nix and A. S. Weigend. Estimating the mean and variance of the target probability distribution. In Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), volume 1, pages 55-60 vol.1, 1994.

O. Oktay, J. Schlemper, L. L. Folgoc, M. C. H. Lee, M. P. Heinrich, K. Misawa, K. Mori, S. G. McDonagh, N. Y. Hammerla, B. Kainz, B. Glocker, and D. Rueckert. Attention u-net: Learning where to look for the pancreas. CoRR, abs/1804.03999, 2018.

J. Schlemper, O. Oktay, L. Chen, J. Matthew, C. L. Knight, B. Kainz, B. Glocker, and D. Rueckert. Attention-gated networks for improving ultrasound scan plane detection. CoRR, abs/1804.05338, 2018.

R. Smiljanic, A. J. Korn, M. Bergemann, A. Frasca, L. Magrini, T. Masseron, E. Pancino, G. Ruchti, I. San Roman, L. Sbordone, and et al. The Gaia-ESO survey: The analysis of high-resolution UVES spectra of FGK-type stars. Astronomy & Astrophysics, 570:A122, Oct 2014.

P. Vincent, H. Larochelle, Y. Bengio, and P.- A. Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine Learning, ICML '08, page 1096-1103, New York, NY, USA, 2008. Association for Computing Machinery.