Measuring the 2D Baryon Acoustic Oscillation signal of galaxies in WiggleZ: Cosmological constraints
arXiv:1611.08040 · doi:10.1093/mnras/stw2725
Abstract
We present results from the 2D anisotropic Baryon Acoustic Oscillation (BAO) signal present in the final dataset from the WiggleZ Dark Energy Survey. We analyse the WiggleZ data in two ways: firstly using the full shape of the 2D correlation function and secondly focussing only on the position of the BAO peak in the reconstructed data set. When fitting for the full shape of the 2D correlation function we use a multipole expansion to compare with theory. When we use the reconstructed data we marginalise over the shape and just measure the position of the BAO peak, analysing the data in wedges separating the signal along the line of sight from that parallel to the line of sight. We verify our method with mock data and find the results to be free of bias or systematic offsets. We also redo the pre-reconstruction angle averaged (1D) WiggleZ BAO analysis with an improved covariance and present an updated result. The final results are presented in the form of , , and for three redshift bins with effective redshifts , , and . Within these bins and methodologies, we recover constraints between 5% and 22% error. Our cosmological constraints are consistent with Flat CDM cosmology and agree with results from the Baryon Oscillation Spectroscopic Survey (BOSS).
18 pages, 18 figures. Published version of results that appear in Hinton's PhD thesis, arXiv:1604.01830
References in corpus (10)
- Wilkinson Microwave Anisotropy Probe (WMAP) Three Year Results: Implications for Cosmology
- Five-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Cosmological Interpretation
- Improved Cosmological Constraints from New, Old and Combined Supernova Datasets
- Measuring the Baryon Acoustic Oscillation scale using the SDSS and 2dFGRS
- Improving Cosmological Distance Measurements by Reconstruction of the Baryon Acoustic Peak
- Nonlinear Evolution of Baryon Acoustic Oscillations
- Galaxy And Mass Assembly (GAMA): autoz spectral redshift measurements, confidence and errors
- Constraining Anisotropic Baryon Oscillations
- The WiggleZ Dark Energy Survey: measuring the cosmic growth rate with the two-point galaxy correlation function
- What is the best way to measure baryonic acoustic oscillations?
Cited by in corpus (20)
- Overview of the Instrumentation for the Dark Energy Spectroscopic Instrument
- Dark Energy Survey Year 3 Results: A 2.7% measurement of Baryon Acoustic Oscillation distance scale at redshift 0.835
- The WiggleZ Dark Energy Survey: Final Data Release and the Metallicity of UV-Luminous Galaxies
- Baryon Acoustic Oscillation Theory and Modelling Systematics for the DESI 2024 results
- Strengthening the bound on the mass of the lightest neutrino with terrestrial and cosmological experiments
- Dark Energy Survey: A 2.1% measurement of the angular Baryonic Acoustic Oscillation scale at redshift =0.85 from the final dataset
- HI intensity mapping with MeerKAT: 1/f noise analysis
- Baryon acoustic oscillations reconstruction using convolutional neural networks
- A comparison between Shapefit compression and Full-Modelling method with PyBird for DESI 2024 and beyond
- Barry and the BAO Model Comparison
- Compressed baryon acoustic oscillation analysis is robust to modified-gravity models
- The miniJPAS survey: clusters and galaxy groups detection with AMICO
- Biased tracer reconstruction with halo mass information
- The BINGO project VIII: On the recoverability of the BAO signal on HI intensity mapping simulations
- Interpretable and physics-informed emulator for the linear matter power spectrum from machine learning
- Dark Energy Survey: DESI-Independent Angular BAO Measurement
- Ricci cosmology in light of astronomical data
- Baryon Acoustic Oscillations in tomographic Angular Density and Redshift Fluctuations
- Streaming Velocity Effects on the Post-reionization 21 cm Baryon Acoustic Oscillation Signal
- Searching optimal scales for reconstructing cosmological initial conditions using convolutional neural networks