Outputs (35)

Using machine learning to optimise chameleon fifth force experiments (2024)
Journal Article
Briddon, C., Burrage, C., Moss, A., & Tamosiunas, A. (2024). Using machine learning to optimise chameleon fifth force experiments. Journal of Cosmology and Astroparticle Physics, 2024(2), Article 11. https://doi.org/10.1088/1475-7516/2024/02/011

The chameleon is a theorised scalar field that couples to matter and possess a screening mechanism, which weakens observational constraints from experiments performed in regions of higher matter density. One consequence of this screening mechanism is... Read More about Using machine learning to optimise chameleon fifth force experiments.

Chameleon screening in cosmic voids (2022)
Journal Article
Tamosiunas, A., Briddon, C., Burrage, C., Cutforth, A., Moss, A., & Vincent, T. (2022). Chameleon screening in cosmic voids. Journal of Cosmology and Astroparticle Physics, 2022(11), Article 056. https://doi.org/10.1088/1475-7516/2022/11/056

A key goal in cosmology in the upcoming decade will be to form a better understanding of the accelerated expansion of the Universe. Upcoming surveys, such as the Vera C. Rubin Observatory's 10-year Legacy Survey of Space and Time (LSST), Euclid and t... Read More about Chameleon screening in cosmic voids.

CMB constraints on monodromy inflation at strong coupling (2022)
Journal Article
Copeland, E. J., Cunillera, F., Moss, A., & Padilla, A. (2022). CMB constraints on monodromy inflation at strong coupling. Journal of Cosmology and Astroparticle Physics, 2022(09), Article 080. https://doi.org/10.1088/1475-7516/2022/09/080

We carry out a thorough numerical examination of field theory monodromy inflation at strong coupling. We perform an MCMC analysis using a Gaussian likelihood, fitting multiparameter models using CMB constraints on the spectral index and the tensor to... Read More about CMB constraints on monodromy inflation at strong coupling.

Chameleon screening depends on the shape and structure of NFW halos (2022)
Journal Article
Tamosiunas, A., Briddon, C., Burrage, C., Cui, W., & Moss, A. (2022). Chameleon screening depends on the shape and structure of NFW halos. Journal of Cosmology and Astroparticle Physics, 2022(4), Article 047. https://doi.org/10.1088/1475-7516/2022/04/047

Chameleon gravity is an example of a model that gives rise to interesting phenomenology on cosmological scales while simultaneously possessing a screening mechanism, allowing it to avoid solar system constraints. Such models result in non-linear fiel... Read More about Chameleon screening depends on the shape and structure of NFW halos.

SELCIE: a tool for investigating the chameleon field of arbitrary sources (2021)
Journal Article
Briddon, C., Burrage, C., Moss, A., & Tamosiunas, A. (2021). SELCIE: a tool for investigating the chameleon field of arbitrary sources. Journal of Cosmology and Astroparticle Physics, 2021(12), 1-24. https://doi.org/10.1088/1475-7516/2021/12/043

The chameleon model is a modified gravity theory that introduces an additional scalar field that couples to matter through a conformal coupling. This `chameleon field' possesses a screening mechanism through a nonlinear self-interaction term which al... Read More about SELCIE: a tool for investigating the chameleon field of arbitrary sources.

A High-Resolution Investigation of the Multi-Phase ISM in a Galaxy during the First Two Billion Years (2021)
Journal Article
Dye, S., Eales, S. A., Gomez, H. L., Jones, G. C., Smith, M. W. L., Borsato, E., …Vlahakis, C. (2022). A High-Resolution Investigation of the Multi-Phase ISM in a Galaxy during the First Two Billion Years. Monthly Notices of the Royal Astronomical Society, 510(3), 3734–3757. https://doi.org/10.1093/mnras/stab3569

We have carried out the first spatially-resolved investigation of the multi-phase interstellar medium (ISM) at high redshift, using the z = 4.24 strongly-lensed sub-millimetre galaxy H-ATLASJ142413.9+022303 (ID141). We present high-resolution (down t... Read More about A High-Resolution Investigation of the Multi-Phase ISM in a Galaxy during the First Two Billion Years.

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.

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.

The shape dependence of chameleon screening (2018)
Journal Article
Burrage, C., Copeland, E. J., Moss, A., & Stevenson, J. A. (2018). The shape dependence of chameleon screening. Journal of Cosmology and Astroparticle Physics, 2018(1), https://doi.org/10.1088/1475-7516/2018/01/056

Chameleon scalar fields can screen their associated fifth forces from detection by changing their mass with the local density. These models are an archetypal example of a screening mechanism, and have become an important target for both cosmological... Read More about The shape dependence of chameleon screening.

Deep recurrent neural networks for supernovae classification (2017)
Journal Article
Charnock, T., & Moss, A. (2017). Deep recurrent neural networks for supernovae classification. Astrophysical Journal, 837(2), https://doi.org/10.3847/2041-8213/aa603d

We apply deep recurrent neural networks, which are capable of learning complex sequential information, to classify supernovae (code available at https://github.com/adammoss/supernovae). The observational time and filter fluxes are used as inputs to t... Read More about Deep recurrent neural networks for supernovae classification.