FastJet user manual

Abstract: FastJet is a C++ package that provides a broad range of jet finding and analysis tools. It includes efficient native implementations of all widely used 2-to-1 sequential recombination jet algorithms for pp and e+e- collisions, as well as access to 3rd party jet algorithms through a plugin mechanism, including all currently used cone algorithms. FastJet also provides means to facilitate the manipulation of jet substructure, including some common boosted heavy-object taggers, as well as tools for estimation of pileup and underlying-event noise levels, determination of jet areas and subtraction or suppression of noise in jets.

• The paper introduces the FastJet package with efficient jet clustering algorithms like anti-kt and Cambridge/Aachen for diverse collision types.
• It details robust methodologies for background estimation using active, passive, and Voronoi jet areas to correct jet kinematics in noisy environments.
• The manual showcases advanced jet substructure tools and a flexible plugin system, empowering customizable analysis in high-energy physics experiments.

Overview of the FastJet User Manual

The paper "FastJet User Manual" provides a comprehensive guide to using the FastJet C++ package, version 3.0.1, which is a pivotal tool in particle physics for jet finding and analysis. It aims to deliver efficient implementations of jet algorithms for $pp$ and $e^+e^-$ collisions, support various third-party algorithms through plugins, and offer advanced tools for jet substructure analysis and background estimation.

Core Components and Algorithms

FastJet stands out for its ability to manage both native and plugin jet algorithms. Native implementations include sequential recombination algorithms like the $k_t$, anti-$k_t$, and Cambridge/Aachen algorithms, each characterized by specific distance measures and clustering strategies. The anti-$k_t$ algorithm, in particular, emulates a perfect cone algorithm with its circular jets, a feature appealing for its theoretical and practical robustness.

Additionally, FastJet's architecture supports accessing and integrating third-party implementations through a plugin facility, expanding its applicability to a broader range of jet algorithms, including historically significant ones like SISCone and various cone algorithms used at different collider experiments.

Background Estimation and Jet Areas

A key feature addressed in the manual is the use of jet areas for background estimation. The concept of active, passive, and Voronoi jet areas allows physicists to quantify a jet's susceptibility to underlying event noise, enabling more accurate event interpretations. The methodology for differential background subtraction using jet areas is critical for analyses in noisier environments, such as proton-proton and heavy-ion collisions.

The manual details methods like the median and rescaling techniques for $\rho$ and $\sigma$ estimation, which are essential for correcting jet kinematics in environments with significant noise overlap.

Jet Substructure Tools

In response to the growing need for detailed jet analysis, FastJet provides tools such as the mass-drop tagger and the JetMedianBackgroundEstimator to dissect jet substructure, critical for identifying boosted particles (e.g., Higgs or top quarks) amidst complex backgrounds. The framework also supports user-extendable transformers that allow additional, context-specific jet grooming strategies.

Practical Implications and Usability

The FastJet package is built for high efficiency, leveraging computational geometry for optimal performance across varying scales of jet events. It is designed to accommodate the large multiplicities typical in high-energy physics with $N\ln N$ strategies for certain algorithms, ensuring scalability.

The modular and extensible nature of the package, along with its robust plugin system, offers a flexible environment for physicists to incorporate custom jet algorithms and analysis techniques. This adaptability makes FastJet a staple tool in experimental high-energy physics.

Future Prospects

Continued development could see FastJet enhancing its support for machine learning-based models that automatically adjust jet grooming and noise suppression in response to empirical data, thereby improving the precision of jet measurements. Additionally, extensions aimed at integrating real-time data processing capabilities could prove invaluable for time-sensitive experimental settings.

In conclusion, the FastJet User Manual serves as an essential reference for researchers conducting jet-based analyses, offering detailed algorithms, practical tools for managing jet substructures, and facilities to integrate custom solutions tailored to specific research needs. The package's ability to handle the complexity and scale of modern particle physics experiments highlights its pivotal role in advancing the field.