Paper
Highly Efficient Selection of High-Redshift Emission-Line Galaxies for future DESI-like surveys with Deep Multi-band Imaging
Authors
Yoquelbin Salcedo Hernandez, Jeffrey A. Newman, Brett. H. Andrews, Biprateep Dey, Rongpu. Zhou, Noah Sailer, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, R. Canning, F. J. Castander, E. Chaussidon, T. Claybaugh, A. Cuceu, A. de la Macorra, Arjun Dey, P. Doel, S. Ferraro, A. Font-Ribera, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, H. K. Herrera-Alcantar, R. Joyce, S. Juneau, R. Kehoe, D. Kirkby, T. Kisner, A. Kremin, O. Lahav, C. Lamman, M. Landriau, M. E. Levi, M. Manera, A. Meisner, R. Miquel, J. Moustakas, S. Nadathur, N. Palanque-Delabrouille, W. J. Percival, F. Prada, I. Pérez-Ràfols, A. Raichoor, G. Rossi, E. Sanchez, D. Schlegel, M. Schubnell, H. Seo, J. Silber, D. Sprayberry, G. Tarlé, B. A. Weaver, C. Yèche, H. Zou
Abstract
Emission-line galaxies (ELGs) are an important tracer of baryon acoustic oscillations (BAO) and large-scale structure (LSS) at $z > 1$. In this work, we investigate the feasibility of using deep wide-area multi-band imaging (e.g., from the Rubin Observatory) to efficiently select high redshift ELGs. Using Hyper Supreme-Cam $grizy$ photometry and COSMOS2020 many-band photometric redshifts, we designed simple color cuts guided by a probabilistic random forest classifier to select galaxies at $z = 1.1$--$1.6$. We then empirically tested and refined these color cuts using two samples of galaxies with deep spectroscopy and broad color coverage obtained with the Dark Energy Spectroscopic Instrument (DESI). Compared to DESI ELGs at $z = 1.1$--$1.6$, we achieve a higher redshift measurement success rate (89% versus 69%), a much higher correct redshift range success rate (84% versus 34%), and a far higher net surface density yield (1372 $\mathrm{deg^{-2}}$ versus 660 $\mathrm{deg^{-2}}$). Combining our sample with current DESI ELGs would increase the net ELG number density by a factor of $\sim3$, moving it out of the shot-noise limited regime and reducing the uncertainties on the BAO scale parameter at $z = 1.1$--$1.6$ by a factor of $\sim 2$.
Metadata
Related papers
Fractal universe and quantum gravity made simple
Fabio Briscese, Gianluca Calcagni • 2026-03-25
POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan
Marta Moscati, Muhammad Saad Saeed, Marina Zanoni, Mubashir Noman, Rohan Kuma... • 2026-03-25
LensWalk: Agentic Video Understanding by Planning How You See in Videos
Keliang Li, Yansong Li, Hongze Shen, Mengdi Liu, Hong Chang, Shiguang Shan • 2026-03-25
Orientation Reconstruction of Proteins using Coulomb Explosions
Tomas André, Alfredo Bellisario, Nicusor Timneanu, Carl Caleman • 2026-03-25
The role of spatial context and multitask learning in the detection of organic and conventional farming systems based on Sentinel-2 time series
Jan Hemmerling, Marcel Schwieder, Philippe Rufin, Leon-Friedrich Thomas, Mire... • 2026-03-25
Raw Data (Debug)
{
"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.20405v1</id>\n <title>Highly Efficient Selection of High-Redshift Emission-Line Galaxies for future DESI-like surveys with Deep Multi-band Imaging</title>\n <updated>2026-02-23T23:02:03Z</updated>\n <link href='https://arxiv.org/abs/2602.20405v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.20405v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Emission-line galaxies (ELGs) are an important tracer of baryon acoustic oscillations (BAO) and large-scale structure (LSS) at $z > 1$. In this work, we investigate the feasibility of using deep wide-area multi-band imaging (e.g., from the Rubin Observatory) to efficiently select high redshift ELGs. Using Hyper Supreme-Cam $grizy$ photometry and COSMOS2020 many-band photometric redshifts, we designed simple color cuts guided by a probabilistic random forest classifier to select galaxies at $z = 1.1$--$1.6$. We then empirically tested and refined these color cuts using two samples of galaxies with deep spectroscopy and broad color coverage obtained with the Dark Energy Spectroscopic Instrument (DESI). Compared to DESI ELGs at $z = 1.1$--$1.6$, we achieve a higher redshift measurement success rate (89% versus 69%), a much higher correct redshift range success rate (84% versus 34%), and a far higher net surface density yield (1372 $\\mathrm{deg^{-2}}$ versus 660 $\\mathrm{deg^{-2}}$). Combining our sample with current DESI ELGs would increase the net ELG number density by a factor of $\\sim3$, moving it out of the shot-noise limited regime and reducing the uncertainties on the BAO scale parameter at $z = 1.1$--$1.6$ by a factor of $\\sim 2$.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='astro-ph.GA'/>\n <category scheme='http://arxiv.org/schemas/atom' term='astro-ph.CO'/>\n <published>2026-02-23T23:02:03Z</published>\n <arxiv:primary_category term='astro-ph.GA'/>\n <author>\n <name>Yoquelbin Salcedo Hernandez</name>\n </author>\n <author>\n <name>Jeffrey A. Newman</name>\n </author>\n <author>\n <name>Brett. H. Andrews</name>\n </author>\n <author>\n <name>Biprateep Dey</name>\n </author>\n <author>\n <name>Rongpu. Zhou</name>\n </author>\n <author>\n <name>Noah Sailer</name>\n </author>\n <author>\n <name>J. Aguilar</name>\n </author>\n <author>\n <name>S. Ahlen</name>\n </author>\n <author>\n <name>D. Bianchi</name>\n </author>\n <author>\n <name>D. Brooks</name>\n </author>\n <author>\n <name>R. Canning</name>\n </author>\n <author>\n <name>F. J. Castander</name>\n </author>\n <author>\n <name>E. Chaussidon</name>\n </author>\n <author>\n <name>T. Claybaugh</name>\n </author>\n <author>\n <name>A. Cuceu</name>\n </author>\n <author>\n <name>A. de la Macorra</name>\n </author>\n <author>\n <name>Arjun Dey</name>\n </author>\n <author>\n <name>P. Doel</name>\n </author>\n <author>\n <name>S. Ferraro</name>\n </author>\n <author>\n <name>A. Font-Ribera</name>\n </author>\n <author>\n <name>J. E. Forero-Romero</name>\n </author>\n <author>\n <name>E. Gaztañaga</name>\n </author>\n <author>\n <name>S. Gontcho A Gontcho</name>\n </author>\n <author>\n <name>G. Gutierrez</name>\n </author>\n <author>\n <name>H. K. Herrera-Alcantar</name>\n </author>\n <author>\n <name>R. Joyce</name>\n </author>\n <author>\n <name>S. Juneau</name>\n </author>\n <author>\n <name>R. Kehoe</name>\n </author>\n <author>\n <name>D. Kirkby</name>\n </author>\n <author>\n <name>T. Kisner</name>\n </author>\n <author>\n <name>A. Kremin</name>\n </author>\n <author>\n <name>O. Lahav</name>\n </author>\n <author>\n <name>C. Lamman</name>\n </author>\n <author>\n <name>M. Landriau</name>\n </author>\n <author>\n <name>M. E. Levi</name>\n </author>\n <author>\n <name>M. Manera</name>\n </author>\n <author>\n <name>A. Meisner</name>\n </author>\n <author>\n <name>R. Miquel</name>\n </author>\n <author>\n <name>J. Moustakas</name>\n </author>\n <author>\n <name>S. Nadathur</name>\n </author>\n <author>\n <name>N. Palanque-Delabrouille</name>\n </author>\n <author>\n <name>W. J. Percival</name>\n </author>\n <author>\n <name>F. Prada</name>\n </author>\n <author>\n <name>I. Pérez-Ràfols</name>\n </author>\n <author>\n <name>A. Raichoor</name>\n </author>\n <author>\n <name>G. Rossi</name>\n </author>\n <author>\n <name>E. Sanchez</name>\n </author>\n <author>\n <name>D. Schlegel</name>\n </author>\n <author>\n <name>M. Schubnell</name>\n </author>\n <author>\n <name>H. Seo</name>\n </author>\n <author>\n <name>J. Silber</name>\n </author>\n <author>\n <name>D. Sprayberry</name>\n </author>\n <author>\n <name>G. Tarlé</name>\n </author>\n <author>\n <name>B. A. Weaver</name>\n </author>\n <author>\n <name>C. Yèche</name>\n </author>\n <author>\n <name>H. Zou</name>\n </author>\n </entry>"
}