Conveners
4.2 Simulation-based inference
- Maurizio Pierini (CERN)
The next generation of observatories such as the Vera C. Rubin Observatory and Euclid are posing a massive data challenge. An obstacle we need to overcome is the inference of accurate redshifts from photometric observations that can be limited to a handful of bands. We addressed this challenge with a forward modeling framework, pop-COSMOS, calibrated by fitting a population model to...
Machine learning can be a powerful tool to discover new signal types in astronomical data. In our recent study, we have applied it for the first time to search for long-duration transient gravitational waves triggered by pulsar glitches, which could yield physical insight into the mostly unknown depths of the pulsar. Other methods previously applied to search for such signals rely on matched...
Modern simulation-based inference techniques leverage neural networks to solve inverse problems efficiently. One notable strategy is neural posterior estimation (NPE), wherein a neural network parameterizes a distribution to approximate the posterior. This approach is particularly advantageous for tackling low-latency or high-volume inverse problems. However, the accuracy of NPE varies...
We present a Machine Learning approach to perform fully Bayesian
inference of the neutron star equation of state given results from
parameter estimation from gravitational wave signals of binary neutron
star (BNS) mergers. The detection of gravitational waves from BNS merger
GW170817 during the second observing run of the ground based
gravitational wave detector network provided a new...
Strong gravitational lenses are a singular probe of the Universe's small-scale structure --- they are sensitive to the gravitational effects of low-mass ($<10^{10} M_\odot$) halos even without a luminous counterpart. Recent strong-lensing analyses of dark matter structure rely on simulation-based inference (SBI). Modern SBI methods, which leverage neural networks as density estimators, have...
Modern machine learning will allow for simulation-based inference from reionization-era 21cm observations at the Square Kilometre Array. Our framework combines a convolutional summary network and a conditional invertible network through a physics-inspired latent representation. It allows for an optimal and extremely fast determination of the posteriors of astrophysical and cosmological...
Cosmic Dawn (CD) and Epoch of Reionization (EoR) are epochs of the Universe which host invaluable information about the cosmology and astrophysics of X-ray heating and hydrogen reionization. Radio interferometric observations of the 21-cm line at high redshifts have the potential to revolutionize our understanding of the Universe during this time. However, modelling the evolution of these...
Recently, conditional normalizing flows have shown promise to directly approximate the posterior distribution via amortized stochastic variational inference from raw simulation data without resorting to likelihood modelling.
In this contribution, I will discuss an open-source GitHub package, "jammy_flows", a pytorch-based project which comes with many state of the art normalizing flows out of...
The Dark Matter Particle Explorer (DAMPE), a satellite-borne experiment capable of detecting gamma rays from few GeV to 10 TeV, studies the galactic and extragalactic gamma-ray sky and is at the forefront of the search for dark-matter spectral lines in the gamma-ray spectrum. In this contribution we detail the development of a convolutional neural network (CNN) model for the trajectory...
