Conveners
1.3 Simulation-based inference
- Tommaso Dorigo (INFN Sezione di Padova)
Markov chain Monte Carlo (MCMC) simulations is a very powerful approach to tackle a large variety of problems in all computational science. The recent advances in machine learning techniques have provided new ideas in the domain of Monte Carlo simulations. The ability of artificial neural networks to model a very wide class of probability distributions through the Variational Autoregressive...
KM3NeT is a research infrastructure housing two underwater Cherenkov telescopes located in the Mediterranean Sea. It consists of two configurations which are currently under construction: ARCA with 230 detection units corresponding to 1 cubic kilometre of instrumented water volume and ORCA with 115 detection units corresponding to a mass of 7 Mton. The ARCA (Astroparticle Research with Cosmics...
Particle physics detectors introduce distortions in the observed data due to their finite resolution and other experimental factors, the task of correcting for these effects is known as unfolding. While traditional unfolding methods are restricted to binned distributions of a single observable, recently proposed ML-based methods enable unbinned, high-dimensional unfolding over the entire phase...
Statistical anomaly detection empowered by AI is a subject of growing interest in high-energy physics and astrophysics. AI provides a multidimensional and highly automatized solution to enable signal-agnostic data validation, and new physics searches.
The unsupervised nature of the anomaly detection task combined with the highly complex nature of the LHC and astrophysical data give rise to a...
Projects such as the imminent Vera C. Rubin Observatory are critical tools for understanding cosmological questions like the nature of dark energy. By observing huge numbers of galaxies, they enable us to map the large scale structure of the Universe. To do this, however, we need reliable ways of estimating galaxy redshifts from only photometry. I will present an overview of our pop-cosmos...
Quantifying tension between different experimental efforts aiming to constrain the same physical models is essential for validating our understanding of the Universe. A commonly used metric of tension is the ratio, R, of the joint Bayesian evidence to the product of individual evidences for two experimental datasets under some common model. R can be interpreted as a measure of our relative...
In this talk we propose a Physics based AI framework for precise radiometer calibration in global 21cm cosmology. These experiments aim to study formation of the first stars and galaxies by detecting the faint 21-cm radio emission from neutral hydrogen. The global or sky-averaged signal is predicted to be five orders of magnitude dimmer than the foregrounds. Therefore detection of the signal...
PolySwyft is an implementation of a sequential simulation-based nested sampler by merging two algorithms that are commonly used for Bayesian inference: PolyChord and swyft. PolySwyft uses the NRE functionality of swyft and generates a new joint training dataset with PolyChord to iteratively estimate more accurate posterior distributions. PolySwyft can be terminated using pre-defined rounds...
The Galactic centre serves as a laboratory for fundamental physics, particularly in the context of indirect dark matter searches. This study explores the potential of the James Webb Space Telescope to shed light on self-annihilating, sub-GeV dark matter candidates by examining their influence on exoplanet overheating and providing sensitivity estimates via probabilistic programming languages.
