Bayesian Component Separation for DESI LAE Automated Spectroscopic Redshifts and Photometric Targeting
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2025
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| author | Uzsoy, Ana Sofía M. Saydjari, Andrew K. Dey, Arjun Raichoor, Anand Finkbeiner, Douglas P. Gawiser, Eric Lee, Kyoung-Soo Ahlen, Steven Bianchi, Davide Brooks, David Claybaugh, Todd Cuceu, Andrei de la Macorra, Axel Doel, Peter Font-Ribera, Andreu Forero-Romero, Jaime E. Gaztañaga, Enrique Gontcho, Satya Gontcho A Gutierrez, Gaston Ishak, Mustapha Kehoe, Robert Kirkby, David Kremin, Anthony Landriau, Martin Guillou, Laurent Le Meisner, Aaron Miquel, Ramon Moustakas, John Palanque-Delabrouille, Nathalie Prada, Francisco Pérez-Ràfols, Ignasi Rossi, Graziano Sanchez, Eusebio Schlegel, David Schubnell, Michael Seo, Hee-Jong Sprayberry, David Tarlé, Gregory Weaver, Benjamin Alan Zou, Hu |
| author_facet | Uzsoy, Ana Sofía M. Saydjari, Andrew K. Dey, Arjun Raichoor, Anand Finkbeiner, Douglas P. Gawiser, Eric Lee, Kyoung-Soo Ahlen, Steven Bianchi, Davide Brooks, David Claybaugh, Todd Cuceu, Andrei de la Macorra, Axel Doel, Peter Font-Ribera, Andreu Forero-Romero, Jaime E. Gaztañaga, Enrique Gontcho, Satya Gontcho A Gutierrez, Gaston Ishak, Mustapha Kehoe, Robert Kirkby, David Kremin, Anthony Landriau, Martin Guillou, Laurent Le Meisner, Aaron Miquel, Ramon Moustakas, John Palanque-Delabrouille, Nathalie Prada, Francisco Pérez-Ràfols, Ignasi Rossi, Graziano Sanchez, Eusebio Schlegel, David Schubnell, Michael Seo, Hee-Jong Sprayberry, David Tarlé, Gregory Weaver, Benjamin Alan Zou, Hu |
| contents | Lyman Alpha Emitters (LAEs) are valuable high-redshift cosmological probes traditionally identified using specialized narrow-band photometric surveys. In ground-based spectroscopy, it can be difficult to distinguish the sharp LAE peak from residual sky emission lines using automated methods, leading to misclassified redshifts. We present a Bayesian spectral component separation technique to automatically determine spectroscopic redshifts for LAEs while marginalizing over sky residuals. We use visually inspected spectra of LAEs obtained using the Dark Energy Spectroscopic Instrument (DESI) to create a data-driven prior and can determine redshift by jointly inferring sky residual, LAE, and residual components for each individual spectrum. We demonstrate this method on 881 spectroscopically observed $z = 2-4$ DESI LAE candidate spectra and determine their redshifts with $>$90% accuracy when validated against visually inspected redshifts. Using the $Δχ^2$ value from our pipeline as a proxy for detection confidence, we then explore potential survey design choices and implications for targeting LAEs with medium-band photometry. This method allows for scalability and accuracy in determining redshifts from DESI spectra, and the results provide recommendations for LAE targeting in anticipation of future high-redshift spectroscopic surveys. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06870 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian Component Separation for DESI LAE Automated Spectroscopic Redshifts and Photometric Targeting Uzsoy, Ana Sofía M. Saydjari, Andrew K. Dey, Arjun Raichoor, Anand Finkbeiner, Douglas P. Gawiser, Eric Lee, Kyoung-Soo Ahlen, Steven Bianchi, Davide Brooks, David Claybaugh, Todd Cuceu, Andrei de la Macorra, Axel Doel, Peter Font-Ribera, Andreu Forero-Romero, Jaime E. Gaztañaga, Enrique Gontcho, Satya Gontcho A Gutierrez, Gaston Ishak, Mustapha Kehoe, Robert Kirkby, David Kremin, Anthony Landriau, Martin Guillou, Laurent Le Meisner, Aaron Miquel, Ramon Moustakas, John Palanque-Delabrouille, Nathalie Prada, Francisco Pérez-Ràfols, Ignasi Rossi, Graziano Sanchez, Eusebio Schlegel, David Schubnell, Michael Seo, Hee-Jong Sprayberry, David Tarlé, Gregory Weaver, Benjamin Alan Zou, Hu Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Applications Lyman Alpha Emitters (LAEs) are valuable high-redshift cosmological probes traditionally identified using specialized narrow-band photometric surveys. In ground-based spectroscopy, it can be difficult to distinguish the sharp LAE peak from residual sky emission lines using automated methods, leading to misclassified redshifts. We present a Bayesian spectral component separation technique to automatically determine spectroscopic redshifts for LAEs while marginalizing over sky residuals. We use visually inspected spectra of LAEs obtained using the Dark Energy Spectroscopic Instrument (DESI) to create a data-driven prior and can determine redshift by jointly inferring sky residual, LAE, and residual components for each individual spectrum. We demonstrate this method on 881 spectroscopically observed $z = 2-4$ DESI LAE candidate spectra and determine their redshifts with $>$90% accuracy when validated against visually inspected redshifts. Using the $Δχ^2$ value from our pipeline as a proxy for detection confidence, we then explore potential survey design choices and implications for targeting LAEs with medium-band photometry. This method allows for scalability and accuracy in determining redshifts from DESI spectra, and the results provide recommendations for LAE targeting in anticipation of future high-redshift spectroscopic surveys. |
| title | Bayesian Component Separation for DESI LAE Automated Spectroscopic Redshifts and Photometric Targeting |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Applications |
| url | https://arxiv.org/abs/2504.06870 |