Time Delay Cosmography with a Neural Ratio Estimator
Jul 28, 2023
Speakers
Ève Campeau-Poirier
Speaker · 0 followers
Laurence Perreault-Levasseur
Speaker · 0 followers
Adam Coogan
Speaker · 0 followers
Yashar Hezaveh
Speaker · 0 followers
About
We explore the use of a Neural Ratio Estimator (NRE) to determine the Hubble constant ($H_0$) in the context of time delay cosmography. Assuming a Singular Isothermal Ellipsoid (SIE) mass profile for the deflector, we simulate time delay measurements, image position measurements, and modeled lensing parameters. We train the NRE to output the posterior distribution of $H_0$ given the time delay measurements, the relative Fermat potentials (calculated from the modeled parameters and the measured image positions), the deflector redshift, and the source redshift. We compare the accuracy and precision of the NRE with traditional explicit likelihood methods in the limit where the latter is tractable and reliable, using Gaussian noise to emulate measurement uncertainties in the input parameters. The NRE posteriors track closely the ones from the conventional method and, while they show a slight tendency to overestimate uncertainties for the quads lensing configuration, they can be combined in a population inference without bias.We explore the use of a Neural Ratio Estimator (NRE) to determine the Hubble constant ($H_0$) in the context of time delay cosmography. Assuming a Singular Isothermal Ellipsoid (SIE) mass profile for the deflector, we simulate time delay measurements, image position measurements, and modeled lensing parameters. We train the NRE to output the posterior distribution of $H_0$ given the time delay measurements, the relative Fermat potentials (calculated from the modeled parameters and the measured i…
Like the format? Trust SlidesLive to capture your next event!
Professional recording and live streaming, delivered globally.
Sharing
Recommended Videos
Presentations on similar topic, category or speaker
MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations
Watch later
Anqi Li, …
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
Watch later
Memory-Based Meta-Learning on Non-Stationary Distributions
Watch later
Tim Genewein, …
Approximately Optimal Core Shapes for Tensor Decompositions
Watch later
Panel
Watch later
