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Xie, Zhuoting

University of British Columbia

Knowledge of galaxy distance is important for cosmological studies. Recent deep learning-based approaches may not leverage the full potential of the neural network. We propose a generative model to reconstruct 1D electromagnetic spectra with application to estimate astronomical redshift. The generative model is an auto-decoding neural field network. We represent each spectrum as a high-dimensional embedding which is converted to spectra reconstruction by the following decoder. We optimize the decoder in restframe simultaneously with the embedding by maximizing the structural similarity between the reconstructed and the observed spectra. We then train a classifier based on the reconstructed spectra for redshift classification. During inference, we fit the auto-decoder to the test spectra and then use the classifier to estimate redshift. Compared with a regressor, our classification model features a simplified optimization surface. We combine spectroscopic data from the zCOSMOS [?], the DEIMOS [18], and the VIPERS [38] surveys as our dataset. We split the data into training and testing data and outperform the baseline by 0.6% on test redshift accuracy.

Text

2024-08-08

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/88909

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/