Validation of the DESI 2024 Lya forest BAO analysis using synthetic datasets
The first year of data from the Dark Energy Spectroscopic Instrument (DESI) contains the largest set of Lyman-a (Lya) forest spectra ever observed. This data, collected in the DESI Data Release 1 (DR1) sample, has been used to measure the Baryon Acoustic Oscillation (BAO) feature at redshift z = 2.3...
| Autores: | , , , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/446309 |
| Acceso en línea: | https://hdl.handle.net/2117/446309 https://dx.doi.org/10.1088/1475-7516/2025/01/148 |
| Access Level: | acceso abierto |
| Palabra clave: | Baryon acoustic oscillations Lyman alpha forest Dark energy experiments Àrees temàtiques de la UPC::Física::Astronomia i astrofísica |
| Sumario: | The first year of data from the Dark Energy Spectroscopic Instrument (DESI) contains the largest set of Lyman-a (Lya) forest spectra ever observed. This data, collected in the DESI Data Release 1 (DR1) sample, has been used to measure the Baryon Acoustic Oscillation (BAO) feature at redshift z = 2.33. In this work, we use a set of 150 synthetic realizations of DESI DR1 to validate the DESI 2024 Lya forest BAO measurement presented in [1]. The synthetic data sets are based on Gaussian random fields using the log-normal approximation. We produce realistic synthetic DESI spectra that include all major contaminants affecting the Lya forest. The synthetic data sets span a redshift range 1.8 < z < 3.8, and are analysed using the same framework and pipeline used for the DESI 2024 Lya forest BAO measurement. To measure BAO, we use both the Lya auto-correlation and its cross-correlation with quasar positions. We use the mean of correlation functions from the set of DESI DR1 realizations to show that our model is able to recover unbiased measurements of the BAO position. We also fit each mock individually and study the population of BAO fits in order to validate BAO uncertainties and test our method for estimating the covariance matrix of the Lya forest correlation functions. Finally, we discuss the implications of our results and identify the needs for the next generation of Lya forest synthetic data sets, with the top priority being to simulate the effect of BAO broadening due to non-linear evolution. |
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