All Outputs (3)

Constraints on primordial gravitational waves from the cosmic microwave background (2020)
Journal Article
Clarke, T. J., Copeland, E. J., & Moss, A. (2020). Constraints on primordial gravitational waves from the cosmic microwave background. Journal of Cosmology and Astroparticle Physics, 2020(10), Article 002. https://doi.org/10.1088/1475-7516/2020/10/002

Searches for primordial gravitational waves have resulted in constraints in a large frequency range from a variety of sources. The standard Cosmic Microwave Background (CMB) technique is to parameterise the tensor power spectrum in terms of the tenso... Read More about Constraints on primordial gravitational waves from the cosmic microwave background.

Planck 2018 results: VI. Cosmological parameters (2020)
Journal Article
Aghanim, N., Akrami, Y., Ashdown, M., Aumont, J., Baccigalupi, C., Ballardini, M., …Zonca, A. (2020). Planck 2018 results: VI. Cosmological parameters. Astronomy and Astrophysics, 641, Article A6. https://doi.org/10.1051/0004-6361/201833910

We present cosmological parameter results from the final full-mission Planck measurements of the cosmic microwave background (CMB) anisotropies, combining information from the temperature and polarization maps and the lensing reconstruction. Compared... Read More about Planck 2018 results: VI. Cosmological parameters.

Accelerated Bayesian inference using deep learning (2020)
Journal Article
Moss, A. (2020). Accelerated Bayesian inference using deep learning. Monthly Notices of the Royal Astronomical Society, 496(1), 328-338. https://doi.org/10.1093/mnras/staa1469

We present a novel Bayesian inference tool that uses a neural network (NN) to parametrize efficient Markov Chain Monte Carlo (MCMC) proposals. The target distribution is first transformed into a diagonal, unit variance Gaussian by a series of non-lin... Read More about Accelerated Bayesian inference using deep learning.