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Unveiling Hidden Lyman Alpha Emitters in the DESI DR1 Data

Fuente: arXiv

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Main Authors:	Chan, Jui-Kuan, Lan, Ting-Wen, Prochaska, J. Xavier, Saito, Shun, Aguilar, J., Ahlen, S., Bianchi, D., Brooks, D., Cuceu, A., de la Macorra, A., Dey, Biprateep, Doel, P., Font-Ribera, A., Forero-Romero, J. E., Gaztañaga, E., Gontcho, Satya Gontcho A, Gutierrez, G., Hahn, C., Jimenez, J., Joyce, R., Juneau, S., Kirkby, D., Kremin, A., Landriau, M., Manera, M., Meisner, A., Miquel, R., Moustakas, J., Nadathur, S., Percival, W. J., Poppett, C., Prada, F., Pérez-Ràfols, I., Rossi, G., Sanchez, E., Schlegel, D., Schubnell, M., Silber, J., Sprayberry, D., Tarlé, G., Weaver, B. A., Zou, H.
Format:	Preprint
Published:	2026
Subjects:	Astrophysics of Galaxies
Online Access:	Acceder al recurso
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_version_	1866914559875350528
author	Chan, Jui-Kuan Lan, Ting-Wen Prochaska, J. Xavier Saito, Shun Aguilar, J. Ahlen, S. Bianchi, D. Brooks, D. Cuceu, A. de la Macorra, A. Dey, Biprateep Doel, P. Font-Ribera, A. Forero-Romero, J. E. Gaztañaga, E. Gontcho, Satya Gontcho A Gutierrez, G. Hahn, C. Jimenez, J. Joyce, R. Juneau, S. Kirkby, D. Kremin, A. Landriau, M. Manera, M. Meisner, A. Miquel, R. Moustakas, J. Nadathur, S. Percival, W. J. Poppett, C. Prada, F. Pérez-Ràfols, I. Rossi, G. Sanchez, E. Schlegel, D. Schubnell, M. Silber, J. Sprayberry, D. Tarlé, G. Weaver, B. A. Zou, H.
author_facet	Chan, Jui-Kuan Lan, Ting-Wen Prochaska, J. Xavier Saito, Shun Aguilar, J. Ahlen, S. Bianchi, D. Brooks, D. Cuceu, A. de la Macorra, A. Dey, Biprateep Doel, P. Font-Ribera, A. Forero-Romero, J. E. Gaztañaga, E. Gontcho, Satya Gontcho A Gutierrez, G. Hahn, C. Jimenez, J. Joyce, R. Juneau, S. Kirkby, D. Kremin, A. Landriau, M. Manera, M. Meisner, A. Miquel, R. Moustakas, J. Nadathur, S. Percival, W. J. Poppett, C. Prada, F. Pérez-Ràfols, I. Rossi, G. Sanchez, E. Schlegel, D. Schubnell, M. Silber, J. Sprayberry, D. Tarlé, G. Weaver, B. A. Zou, H.
contents	We present an automatic method based on machine-learning convolutional neural network (CNN) architecture to detect Lyman alpha emitters (LAE) hidden in the Data Release 1 spectroscopic dataset of the Dark Energy Spectroscopic Instrument (DESI). Those LAEs mostly have incorrect redshift estimations because the current DESI pipeline is not designed to detect and measure the redshifts of galaxies at $z>2$. To uncover those sources, we first visually inspect thousands of DESI spectra and construct a sample, consisting of both LAEs and non-LAEs, for training and testing the CNN-based model to (1) detect LAEs in DESI spectra and (2) determine their Ly$α$ redshifts. The final model yields $95.2\%$ purity and $95.9\%$ completeness for detecting LAEs. We apply this model to approximately $2\times10^{6}$ spectra of sources targeted as emission-line galaxies and detect 19,685 LAEs from $z\sim2$ to $3.5$ within 12 minutes with a single GPU, illustrating the high efficiency of this model for identifying LAEs. The detected LAEs are mostly at the bright end of the luminosity function with Ly$α$ luminosity $L_{\rm Lyα} \gtrsim 10^{43}$ erg/s. The high signal-to-noise composite spectrum of the detected LAEs further shows various spectral features, including P-Cygni profiles of metal lines and MgII emission lines, possible indicators of Lyman continuum escape fraction, revealing the rich astrophysical information in this LAE sample. Finally, this sample can be used to train and validate the pipelines for redshift determination of LAEs for the preparation of the DESI-II survey.
format	Preprint
id	arxiv_https___arxiv_org_abs_2605_12503
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	Unveiling Hidden Lyman Alpha Emitters in the DESI DR1 Data Chan, Jui-Kuan Lan, Ting-Wen Prochaska, J. Xavier Saito, Shun Aguilar, J. Ahlen, S. Bianchi, D. Brooks, D. Cuceu, A. de la Macorra, A. Dey, Biprateep Doel, P. Font-Ribera, A. Forero-Romero, J. E. Gaztañaga, E. Gontcho, Satya Gontcho A Gutierrez, G. Hahn, C. Jimenez, J. Joyce, R. Juneau, S. Kirkby, D. Kremin, A. Landriau, M. Manera, M. Meisner, A. Miquel, R. Moustakas, J. Nadathur, S. Percival, W. J. Poppett, C. Prada, F. Pérez-Ràfols, I. Rossi, G. Sanchez, E. Schlegel, D. Schubnell, M. Silber, J. Sprayberry, D. Tarlé, G. Weaver, B. A. Zou, H. Astrophysics of Galaxies We present an automatic method based on machine-learning convolutional neural network (CNN) architecture to detect Lyman alpha emitters (LAE) hidden in the Data Release 1 spectroscopic dataset of the Dark Energy Spectroscopic Instrument (DESI). Those LAEs mostly have incorrect redshift estimations because the current DESI pipeline is not designed to detect and measure the redshifts of galaxies at $z>2$. To uncover those sources, we first visually inspect thousands of DESI spectra and construct a sample, consisting of both LAEs and non-LAEs, for training and testing the CNN-based model to (1) detect LAEs in DESI spectra and (2) determine their Ly$α$ redshifts. The final model yields $95.2\%$ purity and $95.9\%$ completeness for detecting LAEs. We apply this model to approximately $2\times10^{6}$ spectra of sources targeted as emission-line galaxies and detect 19,685 LAEs from $z\sim2$ to $3.5$ within 12 minutes with a single GPU, illustrating the high efficiency of this model for identifying LAEs. The detected LAEs are mostly at the bright end of the luminosity function with Ly$α$ luminosity $L_{\rm Lyα} \gtrsim 10^{43}$ erg/s. The high signal-to-noise composite spectrum of the detected LAEs further shows various spectral features, including P-Cygni profiles of metal lines and MgII emission lines, possible indicators of Lyman continuum escape fraction, revealing the rich astrophysical information in this LAE sample. Finally, this sample can be used to train and validate the pipelines for redshift determination of LAEs for the preparation of the DESI-II survey.
title	Unveiling Hidden Lyman Alpha Emitters in the DESI DR1 Data
topic	Astrophysics of Galaxies
url	https://arxiv.org/abs/2605.12503