340 citations · 616 across the 10 of their papers we have counts for
10 papers
Inferring the Hubble Constant Using Simulated Strongly Lensed Supernovae and Neural Network Ensembles
Gonçalo Gonçalves, Nikki Arendse, Doogesh Kodi Ramanah +1
Strongly lensed supernovae are a promising new probe to obtain independent measurements of the Hubble constant (). In this work, we employ simulated gravitationally lensed T…
AI-driven spatio-temporal engine for finding gravitationally lensed type Ia supernovae
Doogesh Kodi Ramanah, Nikki Arendse, Radosław Wojtak
We present a spatio-temporal AI framework that concurrently exploits both the spatial and time-variable features of gravitationally lensed supernovae in optical images to ultimatel…
Optimal machine-driven acquisition of future cosmological data
Andrija Kostić, Jens Jasche, Doogesh Kodi Ramanah +1
We present maps classifying regions of the sky according to their information gain potential as quantified by the Fisher information. These maps can guide the optimal retrieval of…
The Young Supernova Experiment: Survey Goals, Overview, and Operations
D. O. Jones, R. J. Foley, G. Narayan +68
Time domain science has undergone a revolution over the past decade, with tens of thousands of new supernovae (SNe) discovered each year. However, several observational domains, in…
The Quijote simulations
Francisco Villaescusa-Navarro, ChangHoon Hahn, Elena Massara +26
The Quijote simulations are a set of 44,100 full N-body simulations spanning more than 7,000 cosmological models in the hyperplane.…
Painting halos from cosmic density fields of dark matter with physically motivated neural networks
Doogesh Kodi Ramanah, Tom Charnock, Guilhem Lavaux
We present a novel halo painting network that learns to map approximate 3D dark matter fields to realistic halo distributions. This map is provided via a physically motivated netwo…
Explicit Bayesian treatment of unknown foreground contaminations in galaxy surveys
Natalia Porqueres, Doogesh Kodi Ramanah, Jens Jasche +1
The treatment of unknown foreground contaminations will be one of the major challenges for galaxy clustering analyses of coming decadal surveys. These data contaminations introduce…
Cosmological inference from Bayesian forward modelling of deep galaxy redshift surveys
Doogesh Kodi Ramanah, Guilhem Lavaux, Jens Jasche +1
We present a large-scale Bayesian inference framework to constrain cosmological parameters using galaxy redshift surveys, via an application of the Alcock-Paczyński (AP) test. Our…
Optimal and fast E/B separation with a dual messenger field
Doogesh Kodi Ramanah, Guilhem Lavaux, Benjamin D. Wandelt
We adapt our recently proposed dual messenger algorithm for spin field reconstruction and showcase its efficiency and effectiveness in Wiener filtering polarized cosmic microwave b…
Wiener filter reloaded: fast signal reconstruction without preconditioning
Doogesh Kodi Ramanah, Guilhem Lavaux, Benjamin D. Wandelt
We present a high performance solution to the Wiener filtering problem via a formulation that is dual to the recently developed messenger technique. This new dual messenger algorit…