Assessment of Gradient-Based Samplers in Standard Cosmological Likelihoods

Understand

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 $3 \times 2$ point analyses.

• Specifically, we use the DES Year 1 data and a simulated future LSST-like survey as representative examples of these studies, containing a significant number of nuisance parameters (20 and 32, respectively) that affect the performance of rejection-based samplers.
• To do so, we implement a differentiable forward model using JAX-COSMO (Campagne et al.
• 2023), and we use it to derive parameter constraints from both datasets using the NUTS algorithm as implemented in {\S}4, and the Metropolis-Hastings algorithm as implemented in Cobaya (Lewis 2013).