2023.05.11 - Peter Scicluna
Channel: Coloquio IRyA
Date: 2023-05-14

Speaker info: Dr. Peter Scicluna, Postdoctoral Fellow, ESO/Space Science Institute

Title: Accelerating our knowledge of interstellar and circumstellar dust with machine learning using Ampere
Abstract:
The ubiquity of astrophysical dust makes understanding its physics and chemistry essential, even if only so its effects can be removed from observations to study other physics. Dust is in almost every observation, scattering and absorbing light from stars and galaxies at short wavelengths, and emitting at long wavelengths such that roughly half of all photons have been processed by dust at some point. The optical properties of dust are influenced by the size, shape and mineralogy of the grains, which all impact the shape and strength of the absorption and scattering in both the continuum and in features. The impacts of these different physical parameters are often overlapping, such that there are strong degeneracies between different models - for example, changing the size, shape and composition of grains all alter the shape of the 10 micron silicate feature, and similarly can alter the long-wavelength slope of the dust emissivity. To probe the underlying physics, therefore we must both quantify these degeneracies by understanding the parameter distributions, and include prior knowledge in our inference. Therefore it behoves us to adopt Bayesian approaches. However, the physical models required to interpret observations of dust (e.g. Mie theory, the discrete-dipole approximation or radiative transfer) are notoriously expensive in terms of computation time and may not even have a well-defined likelihood function, making many traditional approaches to Bayesian computation infeasible; for example, to interpret extinction we have to model both the dust (Mie theory) and the star’s emission simultaneously. On the other hand, approximate Bayesian approaches tend to produce poor estimates of parameter distributions. Recent advances combining simulation-based inference (SBI) with machine-learning tools such as Normalising Flows have produced a family of asymptotically-exact approaches with the speed of approximate Bayesian computing, known as Neural Inference, which can be many orders of magnitude faster than MCMC. I will give a brief overview of these approaches, before demonstrating how they can be applied to improve our understanding of dust, using the python package Ampere. They are particularly well-suited to problems involving complex, high-dimensional datasets (such as mid-infrared spectra and images) and models with long computing times. I will present the results of applying neural inference to understand the composition of dust produced by evolved stars and dust in the ISM, before finally discussing how it can be applied to a wider range of observables and environments.