5 papers
Differentiable Parametric Simulation and Reconstruction Models in Parnassus
Abdelrahman Elabd, Eilam Gross, Dmitrii Kobylianskii +2
Parnassus is a framework for fast detector simulation and reconstruction, directly mapping truth-level particles onto reconstructed objects. Such models can be built from deep gene…
Towards anomaly detection searches for new physics signatures including Higgs bosons with weakly supervised machine learning
Chi Lung Cheng, Julia Gonski, Runze Li +5
The Higgs boson, with its universal coupling to mass, provides a broadly applicable portal to sectors beyond the Standard Model and is therefore a natural anchor for anomaly detect…
Vision Transformers and Graph Neural Networks for Charged Particle Tracking in the ATLAS Muon Spectrometer
Jonathan Renusch
The identification and reconstruction of charged particles, such as muons, is a main challenge for the physics program of the ATLAS experiment at the Large Hadron Collider. This ta…
Weakly Supervised Anomaly Detection in Events with a Higgs Boson and Exotic Physics
Chi Lung Cheng, Sarah Demers, Sascha Diefenbacher +3
We present a machine learning-based anomaly detection strategy designed to identify anomalous physics in events containing resonant Standard Model physics and demonstrate this meth…
Generator Based Inference (GBI)
Chi Lung Cheng, Ranit Das, Runze Li +5
Statistical inference in physics is often based on samples from a generator (sometimes referred to as a ``forward model") that emulate experimental data and depend on parameters of…