99 citations · 256 across the 9 of their papers we have counts for
9 papers
Accurate and robust methods for direct background estimation in resonant anomaly detection
Ranit Das, Thorben Finke, Marie Hein +4
Resonant anomaly detection methods have great potential for enhancing the sensitivity of traditional bump hunt searches. A key component of these methods is a high quality backgrou…
Back To The Roots: Tree-Based Algorithms for Weakly Supervised Anomaly Detection
Thorben Finke, Marie Hein, Gregor Kasieczka +6
Weakly supervised methods have emerged as a powerful tool for model-agnostic anomaly detection at the Large Hadron Collider (LHC). While these methods have shown remarkable perform…
Learning the language of QCD jets with transformers
Thorben Finke, Michael Krämer, Alexander Mück +1
Transformers have become the primary architecture for natural language processing. In this study, we explore their use for auto-regressive density estimation in high-energy jet phy…
Boosting mono-jet searches with model-agnostic machine learning
Thorben Finke, Michael Krämer, Maximilian Lipp +1
We show how weakly supervised machine learning can improve the sensitivity of LHC mono-jet searches to new physics models with anomalous jet dynamics. The Classification Without La…
What's Anomalous in LHC Jets?
Thorsten Buss, Barry M. Dillon, Thorben Finke +5
Searches for anomalies are a significant motivation for the LHC and help define key analysis steps, including triggers. We discuss specific examples how LHC anomalies can be define…
Classification of Fermi-LAT blazars with Bayesian neural networks
Anja Butter, Thorben Finke, Felicitas Keil +2
The use of Bayesian neural networks is a novel approach for the classification of gamma-ray sources. We focus on the classification of Fermi-LAT blazar candidates, which can be div…
Autoencoders for unsupervised anomaly detection in high energy physics
Thorben Finke, Michael Krämer, Alessandro Morandini +2
Autoencoders are widely used in machine learning applications, in particular for anomaly detection. Hence, they have been introduced in high energy physics as a promising tool for…
Classification of Fermi-LAT sources with deep learning using energy and time spectra
Thorben Finke, Michael Krämer, Silvia Manconi
Despite the growing number of gamma-ray sources detected by Fermi-LAT, about one third of the sources in each survey remains of uncertain type. We present a new deep neural network…
Casting a graph net to catch dark showers
Elias Bernreuther, Thorben Finke, Felix Kahlhoefer +2
Strongly interacting dark sectors predict novel LHC signatures such as semi-visible jets resulting from dark showers that contain both stable and unstable dark mesons. Distinguishi…