%0 Journal Article %1 liu2020learning %A Liu, Bin %A Bordoloi, Rongmon %D 2020 %K deep_learning quasar spectrum %T A Deep Learning Approach to Quasar Continuum Prediction %U http://arxiv.org/abs/2006.04814 %X We present a novel deep learning model, intelligent quasar continuum neural network (iQNet), which predicts the intrinsic continuum of any quasar in the rest-frame wavelength range 1020 Angstroms $łeq$ 1600 Angstroms. We train this network using quasar spectra at low redshift ($z 0.2$) from the Hubble Spectroscopic Legacy Archive, and apply it to predict quasar continua from different astronomical surveys. We introduce a standardization process to the data, reducing the absolute fractional flux error (AFFE) of the predicted continua approximately by half. We use principal component analysis and Gaussian mixture model to classify the HSLA quasar spectra into four classes and use them to synthesize mock quasar spectra create a training data set for iQNet. iQNet achieves a median AFFE of 1.31% on the training quasar spectra, approximately ten times better than traditional PCA-based prediction methods, and 4.17% on the testing quasar spectra. We apply iQNet to predict the continua of $\sim$ 3200 quasar spectra at higher redshift ($2< z 5$) and measure the redshift evolution of mean transmitted flux ($< F >$) in the Ly-$\alpha$ forest region. We measure a gradual evolution of $< F >$ with redshift, which we characterize as a power-law evolution. These estimates are broadly consistent with literature, but provide a more accurate measurement as we measure the quasar continuum with minimum contamination from the Ly-$\alpha$ forest. This work proves that the iQNet model can predict the quasar continuum with high accuracy and shows the viability of such methods for quasar continuum prediction.

@article{liu2020learning, abstract = {We present a novel deep learning model, intelligent quasar continuum neural network (iQNet), which predicts the intrinsic continuum of any quasar in the rest-frame wavelength range 1020 Angstroms $\leq \lambda \leq$ 1600 Angstroms. We train this network using quasar spectra at low redshift ($z \sim 0.2$) from the Hubble Spectroscopic Legacy Archive, and apply it to predict quasar continua from different astronomical surveys. We introduce a standardization process to the data, reducing the absolute fractional flux error (AFFE) of the predicted continua approximately by half. We use principal component analysis and Gaussian mixture model to classify the HSLA quasar spectra into four classes and use them to synthesize mock quasar spectra create a training data set for iQNet. iQNet achieves a median AFFE of 1.31% on the training quasar spectra, approximately ten times better than traditional PCA-based prediction methods, and 4.17% on the testing quasar spectra. We apply iQNet to predict the continua of $\sim$ 3200 quasar spectra at higher redshift ($2< z \leq 5$) and measure the redshift evolution of mean transmitted flux ($< F >$) in the Ly-$\alpha$ forest region. We measure a gradual evolution of $< F >$ with redshift, which we characterize as a power-law evolution. These estimates are broadly consistent with literature, but provide a more accurate measurement as we measure the quasar continuum with minimum contamination from the Ly-$\alpha$ forest. This work proves that the iQNet model can predict the quasar continuum with high accuracy and shows the viability of such methods for quasar continuum prediction.}, added-at = {2020-06-11T09:37:55.000+0200}, author = {Liu, Bin and Bordoloi, Rongmon}, biburl = {https://www.bibsonomy.org/bibtex/2e74ec0e77741cd865d282760b5241172/kiwasawa}, description = {A Deep Learning Approach to Quasar Continuum Prediction}, interhash = {a5ca2cb6baf042e750de573bd6d1bf4b}, intrahash = {e74ec0e77741cd865d282760b5241172}, keywords = {deep_learning quasar spectrum}, note = {cite arxiv:2006.04814Comment: Submitted to MNRAS, 19 pages, 14 figures}, timestamp = {2020-06-11T09:37:55.000+0200}, title = {A Deep Learning Approach to Quasar Continuum Prediction}, url = {http://arxiv.org/abs/2006.04814}, year = 2020 }