169 citations · 353 across the 12 of their papers we have counts for
12 papers
KiDS+VIKING-450 cosmology with Bayesian hierarchical model redshift distributions
George T. Kyriacou, Arrykrishna Mootoovaloo, Alan F. Heavens +3
Tomographic redshift distributions from photometric data are crucial ingredients in cosmic shear analysis, since they are required for the theoretical calculation of the signal bas…
: Normalising Flows for Joint Cosmological Analysis
Arrykrishna Mootoovaloo, Carlos García-García, David Alonso +1
Given the growth in the variety and precision of astronomical datasets of interest for cosmology, the best cosmological constraints are invariably obtained by combining data from d…
Assessment of Gradient-Based Samplers in Standard Cosmological Likelihoods
Arrykrishna Mootoovaloo, Jaime Ruiz-Zapatero, Carlos García-García +1
We assess the usefulness of gradient-based samplers, such as the No-U-Turn Sampler (NUTS), by comparison with traditional Metropolis-Hastings algorithms, in tomographic $3 \times 2…
Extreme data compression for Bayesian model comparison
Alan F. Heavens, Arrykrishna Mootoovaloo, Roberto Trotta +1
We develop extreme data compression for use in Bayesian model comparison via the MOPED algorithm, as well as more general score compression. We find that Bayes factors from data co…
Analytical marginalisation over photometric redshift uncertainties in cosmic shear analyses
Jaime Ruiz-Zapatero, Boryana Hadzhiyska, David Alonso +3
As the statistical power of imaging surveys grows, it is crucial to account for all systematic uncertainties. This is normally done by constructing a model of these uncertainties a…
Kernel-Based Emulator for the 3D Matter Power Spectrum from CLASS
Arrykrishna Mootoovaloo, Andrew H. Jaffe, Alan F. Heavens +1
The 3D matter power spectrum, is a fundamental quantity in the analysis of cosmological data such as large-scale structure, 21cm observations, and weak lensing. Existing…
Parameter Inference for Weak Lensing using Gaussian Processes and MOPED
Arrykrishna Mootoovaloo, Alan F. Heavens, Andrew H. Jaffe +1
In this paper, we propose a Gaussian Process (GP) emulator for the calculation of a) tomographic weak lensing band-power spectra, and b) coefficients of summary data massively comp…
Imbalance Learning for Variable Star Classification
Zafiirah Hosenie, Robert Lyon, Benjamin Stappers +2
The accurate automated classification of variable stars into their respective sub-types is difficult. Machine learning based solutions often fall foul of the imbalanced learning pr…
Comparing Multi-class, Binary and Hierarchical Machine Learning Classification schemes for variable stars
Zafiirah Hosenie, Robert Lyon, Benjamin Stappers +1
Upcoming synoptic surveys are set to generate an unprecedented amount of data. This requires an automatic framework that can quickly and efficiently provide classification labels f…
Marginal Likelihoods from Monte Carlo Markov Chains
Alan Heavens, Yabebal Fantaye, Arrykrishna Mootoovaloo +4
In this paper, we present a method for computing the marginal likelihood, also known as the model likelihood or Bayesian evidence, from Markov Chain Monte Carlo (MCMC), or other sa…
No evidence for extensions to the standard cosmological model
Alan Heavens, Yabebal Fantaye, Elena Sellentin +4
We compute the Bayesian Evidence for models considered in the main analysis of Planck cosmic microwave background data. By utilising carefully-defined nearest-neighbour distances i…
Bayes Factors via Savage-Dickey Supermodels
A. Mootoovaloo, Bruce A. Bassett, M. Kunz
We outline a new method to compute the Bayes Factor for model selection which bypasses the Bayesian Evidence. Our method combines multiple models into a single, nested, Supermodel…