Rubin-LSST

Abstract:

The Jovian atmosphere contains a wide diversity of vortices, which have a large range of sizes, colors, and forms in different dynamical regimes. The formation processes for these vortices are poorly understood, and aside from a few known, long-lived ovals, such as the Great Red Spot and Oval BA, vortex stability and their temporal evolution are currently largely unknown. In this study, we use JunoCam data and a citizen science project on Zooniverse to derive a catalog of vortices, some with repeated observations, from 2018 May to 2021 September, and we analyze their associated properties, such as size, location, and color. We find that different-colored vortices (binned as white, red, brown, and dark) follow vastly different distributions in terms of their sizes and where they are found on the planet. We employ a simplified stability criterion using these vortices as a proxy, to derive a minimum Rossby deformation length for the planet of ∼1800 km. We find that this value of L d is largely constant throughout the atmosphere and does not have an appreciable meridional gradient.

Abstract:

We assess the usefulness of gradient-based samplers, such as the no-U-turn sampler (NUTS), by comparison with traditional Metropolis–Hastings (MH) algorithms, in tomographic 3 × 2 point analyses. Specifically, we use the Dark Energy Survey (DES) Year 1 data and a simulated dataset for the Large Synoptic Survey Telescope (LSST) 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, and we use it to derive parameter constraints from both data sets using the NUTS algorithm implemented in NUMPYRO, and the Metropolis–Hastings algorithm as implemented in COBAYA. When quantified in terms of the number of effective number of samples taken per likelihood evaluation, we find a relative efficiency gain of O(10) in favour of NUTS. However, this efficiency is reduced to a factor ∼ 2 when quantified in terms of computational time, since we find the cost of the gradient computation (needed by NUTS) relative to the likelihood to be ∼ 4.5 times larger for both experiments. We validate these results making use of analytical multivariate distributions (a multivariate Gaussian and a Rosenbrock distribution) with increasing dimensionality. Based on these results, we conclude that gradient-based samplers such as NUTS can be leveraged to sample high-dimensional parameter spaces in Cosmology, although the efficiency improvement is relatively mild for moderate (O(50)) dimension numbers, typical of tomographic large-scale structure analyses.