Unveiling Hidden Lyman Alpha Emitters in the DESI DR1 Data

@misc{chan2026unveiling,
  title = {{Unveiling Hidden Lyman Alpha Emitters in the DESI DR1 Data}},
  author = {Jui-Kuan Chan and Ting-Wen Lan and J. Xavier Prochaska and Shun Saito and J. Aguilar and S. Ahlen and D. Bianchi and D. Brooks and A. Cuceu and A. de la Macorra and Biprateep Dey and P. Doel and A. Font-Ribera and J. E. Forero-Romero and E. Gaztañaga and Satya Gontcho A Gontcho and G. Gutierrez and C. Hahn and J. Jimenez and R. Joyce and S. Juneau and D. Kirkby and A. Kremin and M. Landriau and M. Manera and A. Meisner and R. Miquel and J. Moustakas and S. Nadathur and W. J. Percival and C. Poppett and F. Prada and I. Pérez-Ràfols and G. Rossi and E. Sanchez and D. Schlegel and M. Schubnell and J. Silber and D. Sprayberry and G. Tarlé and B. A. Weaver and H. Zou},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2605.12503},
  eprint = {2605.12503},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2605.12503},
  note = {CC BY 4.0},
}

TY  - UNPB
TI  - Unveiling Hidden Lyman Alpha Emitters in the DESI DR1 Data
AU  - Jui-Kuan Chan
AU  - Ting-Wen Lan
AU  - J. Xavier Prochaska
AU  - Shun Saito
AU  - J. Aguilar
AU  - S. Ahlen
AU  - D. Bianchi
AU  - D. Brooks
AU  - A. Cuceu
AU  - A. de la Macorra
AU  - Biprateep Dey
AU  - P. Doel
AU  - A. Font-Ribera
AU  - J. E. Forero-Romero
AU  - E. Gaztañaga
AU  - Satya Gontcho A Gontcho
AU  - G. Gutierrez
AU  - C. Hahn
AU  - J. Jimenez
AU  - R. Joyce
AU  - S. Juneau
AU  - D. Kirkby
AU  - A. Kremin
AU  - M. Landriau
AU  - M. Manera
AU  - A. Meisner
AU  - R. Miquel
AU  - J. Moustakas
AU  - S. Nadathur
AU  - W. J. Percival
AU  - C. Poppett
AU  - F. Prada
AU  - I. Pérez-Ràfols
AU  - G. Rossi
AU  - E. Sanchez
AU  - D. Schlegel
AU  - M. Schubnell
AU  - J. Silber
AU  - D. Sprayberry
AU  - G. Tarlé
AU  - B. A. Weaver
AU  - H. Zou
PY  - 2026
DA  - 2026/10/18
PB  - arXiv
DO  - 10.48550/arxiv.2605.12503
UR  - https://arxiv.org/abs/2605.12503
AB  - 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 Lyα redshifts. The final model yields 95.2% purity and 95.9% completeness for detecting LAEs. We apply this model to approximately 2×10^6 spectra of sources targeted as emission-line galaxies and detect 19,685 LAEs from z∼2 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 Lyα luminosity L_Lyα ≳ 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.
ER  -