Conveners
3.2 Physics-informed AI & Integration of physics and ML
- Tilman Plehn (Universität Heidelberg, ITP)
Analyses in HEP experiments often rely on large MC simulated datasets. These datasets are usually produced with full-simulation approaches based on Geant4, or exploiting parametric “fast” simulations introducing approximations and reducing the computational cost.
In the present work, we discuss a prototype of a fast simulation framework that we call “FlashSim” targeting analysis level data...
Presented is a novel method for analyzing particle identification (PID) by incorporating machine learning techniques, applied to a physics case within the fixed-target program at the LHCb experiment at CERN. Typically, a PID classifier is constructed by integrating responses from specialized subdetectors, utilizing diverse techniques to ensure redundancy and broad kinematic coverage. The...
Within the Compact Muon Solenoid (CMS) Collaboration, various Deep Neural Networks (DNNs) and Machine Learning (MLs) approaches have been employed to investigate the production of a new massive particle that undergoes decay into Higgs Boson pairs (HH) which further decay into a pair of b-quarks and a pair of tau leptons and discriminate the HH signal from the backgrounds.
However, these...
The intracluster medium (ICM) holds signatures of the dynamical history of
the galaxy cluster, including the dark matter density profile, mergers with
other clusters, and energetic activity (from supernovae and supermassive
black holes) in its member galaxies. For all but the most relaxed galaxy
clusters observed at high spatial resolution by instruments such as the
Chandra and...
Blazars are among the most powerful extragalactic sources, emitting across the entire electromagnetic spectrum, from radio to very high energy gamma-ray bands. As powerful sources of non-thermal radiation, blazars are frequently monitored using various telescopes, leading to the accumulation of substantial multi-wavelength data over different time periods. Also, over the years, the complexity...
A major task in particle physics is the measurement of rare signal processes. These measurements are highly dependent on the classification accuracy of these events in relation to the huge background of other Standard Model processes. Reducing the background by a few tens of percent with the same signal efficiency can already increase the sensitivity considerably.
This study demonstrates...
The Alpha Magnetic Spectrometer-02 (AMS-02) experiment is a magnetic spectrometer on the International Space Station (ISS) that can measure the flux of particles from cosmic sources in a rigidity window ranging from GVs to a few TVs and up to at least Nickel (charge Z=28). High-precision measurements of fluxes of rare nuclei, such as Sc, Ti, and Mn, provide unique constraints to models of...
We report progress in using transformer models to generate particle theory Lagrangians. By treating Lagrangians as complex, rule-based constructs similar to linguistic expressions, we employ transformer architectures —proven in language processing tasks— to model and predict Lagrangians. A dedicated dataset, which includes the Standard Model and a variety of its extensions featuring various...
This study investigates the adaptation of leading classifiers, such as Transformers and Convolutional Graph Neural Networks, as anomaly detectors using different training techniques. The focus lies in their utilization with proton-proton collisions simulated by the DarkMachines collaboration, where some exotic signatures are aimed to be detected as anomalies.
Adaptations of these...
