Parameter Estimation from Improved Measurements of the CMB from QUaD
QUaD collaboration: S. Gupta (1), P. Ade (1), J. Bock (2,3), M. Bowden, (1,4), M. L. Brown (5), G. Cahill (6), P. G. Castro (7,8), S. Church (4), T., Culverhouse (9), R. B. Friedman (9), K. Ganga (10), W. K. Gear (1), J., Hinderks (5,11), J. Kovac (3), A. E. Lange (4)

TL;DR
This paper demonstrates that high-quality CMB polarization data from the QUaD experiment significantly improves constraints on cosmological parameters, confirming consistency with satellite data and providing the best limits from polarization measurements.
Contribution
It presents new constraints on cosmological parameters using QUaD polarization data, showing improved precision over previous satellite data and validating the consistency of multi-experiment datasets.
Findings
Polarization data reduces confidence regions for all parameters.
QUaD polarization data provides the best limits on certain parameters.
Multi-experiment datasets are consistent and improve parameter constraints.
Abstract
We evaluate the contribution of cosmic microwave background (CMB) polarization spectra to cosmological parameter constraints. We produce cosmological parameters using high-quality CMB polarization data from the ground-based QUaD experiment and demonstrate for the majority of parameters that there is significant improvement on the constraints obtained from satellite CMB polarization data. We split a multi-experiment CMB dataset into temperature and polarization subsets and show that the best-fit confidence regions for the LCDM 6-parameter cosmological model are consistent with each other, and that polarization data reduces the confidence regions on all parameters. We provide the best limits on parameters from QUaD EE/BB polarization data and we find best-fit parameters from the multi-experiment CMB dataset using the optimal pivot scale of k_p=0.013 Mpc-1 to be {omch2, ombh2, H_0, A_s,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
