Dark Energy Survey Year 3 results: Measurement of the baryon acoustic oscillations with three-dimensional clustering
Kwan Chuen Chan, S. Àvila, A. Carnero Rosell, I. Ferrero, J. Elvin-Poole, E. Sánchez, H. Camacho, A. Porredon, M. Crocce, T. M. C. Abbott, M. Aguena, S. Allam, F. Andrade-Oliveira, E. Bertin, S. Bocquet, D. Brooks, D. L. Burke, M. Carrasco Kind, J. Carretero, F. J. Castander, R. Cawthon, Christopher J. Conselice, M. Costanzi, M. E. S. Pereira, J. De Vicente, S. Desai, H. T. Diehl, P. Doel, S. Everett, B. Flaugher, P. Fosalba, J. García-Bellido, E. Gaztañaga, D. W. Gerdes, T. Giannantonio, D. Gruen, R. A. Gruendl, G. Gutiérrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, Dragan Huterer, D. J. James, K. Kuehn, O. Lahav, C. Lidman, M. Lima, J. L. Marshall, J. Mena-Fernández, F. Menanteau, R. Miquel, A. Palmese, F. Paz-Chinchón, A. Pieres, A. A. Plazas Malagón, Marco Raveri, M. Rodríguez-Monroy, A. Roodman, Ashley J. Ross, V. Scarpine, I. Sevilla-Noarbe, M. Smith, E. Suchyta, M. E. C. Swanson, G. Tarlé, D. Thomas, D. L. Tucker, M. Vincenzi, N. Weaverdyck
Abstract
The three-dimensional correlation function offers an effective way to summarize the correlation of the large-scale structure even for imaging galaxy surveys. We have applied the projected three-dimensional correlation function, ${\ensuremath{\xi}}_{\mathrm{p}}$ to measure the baryonic acoustic oscillations (BAO) scale on the first-three years Dark Energy Survey data. The sample consists of about 7 million galaxies in the redshift range $0.6<{z}_{\mathrm{p}}<1.1$ over a footprint of $4108\text{ }\text{ }{\mathrm{deg}}^{2}$. Our theory modeling includes the impact of realistic true redshift distributions beyond Gaussian photo-$z$ approximation. ${\ensuremath{\xi}}_{\mathrm{p}}$ is obtained by projecting the three-dimensional correlation to the transverse direction. To increase the signal-to-noise of the measurements, we have considered a Gaussian stacking window function in place of the commonly used top-hat. ${\ensuremath{\xi}}_{\mathrm{p}}$ is sensitive to ${D}_{\mathrm{M}}({z}_{\mathrm{eff}})/{r}_{\mathrm{s}}$, the ratio between the comoving angular diameter distance and the sound horizon. Using the full sample, ${D}_{\mathrm{M}}({z}_{\mathrm{eff}})/{r}_{\mathrm{s}}$ is constrained to be $19.00\ifmmode\pm\else\textpm\fi{}0.67$ (top-hat) and $19.15\ifmmode\pm\else\textpm\fi{}0.58$ (Gaussian) at ${z}_{\mathrm{eff}}=0.835$. The constraint is weaker than the angular correlation $w$ constraint ($18.84\ifmmode\pm\else\textpm\fi{}0.50$), and we trace this to the fact that the BAO signals are heterogeneous across redshift. While ${\ensuremath{\xi}}_{\mathrm{p}}$ responds to the heterogeneous signals by enlarging the error bar, $w$ can still give a tight bound on ${D}_{\mathrm{M}}/{r}_{\mathrm{s}}$ in this case. When a homogeneous BAO-signal subsample in the range $0.7<{z}_{\mathrm{p}}<1.0$ (${z}_{\mathrm{eff}}=0.845$) is considered, ${\ensuremath{\xi}}_{\mathrm{p}}$ yields $19.80\ifmmode\pm\else\textpm\fi{}0.67$ (top-hat) and $19.84\ifmmode\pm\else\textpm\fi{}0.53$ (Gaussian). The latter is mildly stronger than the $w$ constraint ($19.86\ifmmode\pm\else\textpm\fi{}0.55$). We find that the ${\ensuremath{\xi}}_{\mathrm{p}}$ results are more sensitive to photo-$z$ errors than $w$ because ${\ensuremath{\xi}}_{\mathrm{p}}$ keeps the three-dimensional clustering information causing it to be more prone to photo-$z$ noise. The Gaussian window gives more robust results than the top-hat as the former is designed to suppress the low signal modes. ${\ensuremath{\xi}}_{\mathrm{p}}$ and the angular statistics such as $w$ have their own pros and cons, and they serve an important crosscheck with each other.