Systematic Analysis of High Energy Collider Data

TL;DR

This paper reviews a systematic approach to analyzing high energy collider data from various experiments, introducing algorithms for data understanding, model-independent searches, hypothesis testing, fast simulation, and physics inference.

Contribution

It presents a suite of algorithms and a non-conventional framework for comprehensive, systematic analysis of collider data across multiple experiments.

Findings

Algorithms effectively identify data features and anomalies.

Systematic, model-independent searches enhance discovery potential.

Fast simulation tools improve analysis efficiency.

Abstract

These proceedings outline steps toward a systematic analysis of frontier energy collider data: specifically, those data collected at Tevatron Runs I and II, LEP Run II, HERA Runs I and II, and the future LHC. Algorithms designed to understand the gross features of the data (Vista), to systematically and model-independently search for new physics at the electroweak scale (Sleuth), to automate tests of specific hypotheses against those data (Quaero), to turn an existing full detector simulation into a fast simulation (TurboSim), and to infer the physics underlying any hint observed in the data (Bard) are reviewed. A somewhat non-conventional viewpoint is adopted throughout.