Conveners
4.4 Explainable AI
- Ik Siong Heng (University of Glasgow)
A particularly interesting application of autoencoders (AE) for High Energy Physics is their use as anomaly detection (AD) algorithms to perform a signal-agnostic search for new physics. This is achieved by training AEs on standard model physics and tagging potential new physics events as anomalies. The use of an AE as an AD algorithm relies on the assumption that the network better...
Catalogs of sources have many sources with unknown physical nature. In particular, Fermi-LAT catalogs of gamma-ray sources have about one third of sources with unknown multi-wavelength counterparts. Some of the gamma-ray sources may be visible only in gamma rays, such as distant pulsars with radio jets not pointing at the observer. Machine learning algorithms provide a tool to perform a...
In high-energy physics (HEP), neural-network (NN) based algorithms have found many applications, such as quark-flavor identification of jets in experiments like the Compact Muon Solenoid (CMS) at the Large Hadron Collider (LHC) at CERN. Unfortunately, complete training pipelines often encounter application-specific obstacles like the processing of many and large files of HEP data format such...
Semivisible jets are a novel signature arising in Hidden Valley (HV) extensions of the SM with a confining interaction [1]. Originating from a double shower and hadronization process and containing undetectable dark bound states, semivisible jets are expected to have a substantially different radiation pattern compared to SM jets.
Unsupervised...
I will present an explainable deep learning framework for extracting new knowledge about the underlying physics of cosmological structure formation. I will focus on an application to dark matter halos, which form the building blocks of the cosmic large-scale structure and wherein galaxy formation takes place. The goal is to use an interpretable neural network to generate a compressed, “latent”...
Weakly supervised machine learning has emerged as a powerful tool in particle physics, enabling the classification of data without relying on extensive labeled examples. This approach holds immense potential for the identification of exotic objects in the gamma-ray sky, particularly those arising from dark matter annihilation. In this contribution, we present our methodology for exploring this...
The Bayesian evidence can be used to compare and select models based on observed data. However, calculating the evidence can be computationally expensive and sometimes analytically intractable. I present a novel method for rapid computation of the Bayesian evidence based on normalizing flows that rely only on the existence of a set of independent and identically distributed samples extracted...
Machine learning, in its conventional form, has often been criticised for being a black box, providing outputs without a clear rationale. To obtain more interpretable results we can make use of symbolic regression (SR) which, as opposed to traditional regression techniques, goes beyond curve-fitting and attempts to determine the underlying mathematical equations that best describe the data. In...
We present a pipeline to infer the equation of state of neutron stars from observations based on deep neural networks. In particular, using the standard (deterministic), as well as Bayesian (probabilistic) deep networks, we explore how one can infer the interior speed of sound of the star given a set of mock observations of total stellar mass, stellar radius and tidal deformability. We...
Whilst gravitational waves from compact binary signals are well modelled, other transient signals do not not necessarily have a clearly defined waveform. Searches for these kinds of signals are often un-modelled so do not say much about the system that produced the gravitational wave. Having a method that can extract some information on the structure and dynamics of the system could be crucial...
