34 citations · 41 across the 5 of their papers we have counts for
5 papers
ParticleTransformer is all you need for reconstructing hadronic tau leptons
Nalong-Norman Seeba, Laurits Tani, Torben Lange +1
The large number of events expected during the TeraZ program at FCC-ee will allow for precision measurements and searches for physics beyond the Standard Model,…
Reconstructing hadronically decaying tau leptons with a jet foundation model
Laurits Tani, Joosep Pata, Joschka Birk
The limited availability and accuracy of simulated data has motivated the use of foundation models in high energy physics, with the idea to first train a task-agnostic model on lar…
A unified machine learning approach for reconstructing hadronically decaying tau leptons
Laurits Tani, Nalong-Norman Seeba, Hardi Vanaveski +2
Tau leptons serve as an important tool for studying the production of Higgs and electroweak bosons, both within and beyond the Standard Model of particle physics. Accurate reconstr…
Tau lepton identification and reconstruction: a new frontier for jet-tagging ML algorithms
Torben Lange, Saswati Nandan, Joosep Pata +2
Identifying and reconstructing hadronic decays () is an important task at current and future high-energy physics experiments, as represent an i…
Comparison of Bayesian and particle swarm algorithms for hyperparameter optimisation in machine learning applications in high energy physics
Laurits Tani, Christian Veelken
When using machine learning (ML) techniques, users typically need to choose a plethora of algorithm-specific parameters, referred to as hyperparameters. In this paper, we compare t…