Bayesian Analysis

After making some general remarks, I consider two examples that illustrate the use of Bayesian Probability Theory. The first is a simple one, the physicist's favorite "toy," that provides a forum for a discussion of the key conceptual issue of Bayesian analysis: the assignment of prior probabilities. The other example illustrates the use of Bayesian ideas in the real world of experimental physics.Comment: 14 pages, 5 figures, Workshop on Confidence Limits, CERN, 17-18 January, 200

Strategy for discovering a low-mass Higgs boson at the Fermilab Tevatron

We have studied the potential of the CDF and DZero experiments to discover a low-mass Standard Model Higgs boson, during Run II, via the processes $p\bar{p}$ -> WH -> $\ell\nu b\bar{b}$, $p\bar{p}$ -> ZH -> $\ell^{+}\ell^{-}b\bar{b}$ and $p\bar{p}$ -> ZH ->$\nu \bar{\nu} b\bar{b}$. We show that a multivariate analysis using neural networks, that exploits all the information contained within a set of event variables, leads to a significant reduction, with respect to {\em any} equivalent conventional analysis, in the integrated luminosity required to find a Standard Model Higgs boson in the mass range 90 GeV/c**2 < M_H < 130 GeV/c**2. The luminosity reduction is sufficient to bring the discovery of the Higgs boson within reach of the Tevatron experiments, given the anticipated integrated luminosities of Run II, whose scope has recently been expanded.Comment: 26 pages, 8 figures, 7 tables, to appear in Physical Review D, Minor fixes and revision

Simulation-Based Frequentist Inference with Tractable and Intractable Likelihoods

High-fidelity simulators that connect theoretical models with observations are indispensable tools in many sciences. When coupled with machine learning, a simulator makes it possible to infer the parameters of a theoretical model directly from real and simulated observations without explicit use of the likelihood function. This is of particular interest when the latter is intractable. We introduce a simple modification of the recently proposed likelihood-free frequentist inference (LF2I) approach that has some computational advantages. The utility of our algorithm is illustrated by applying it to three pedagogically interesting examples: the first is from cosmology, the second from high-energy physics and astronomy, both with tractable likelihoods, while the third, with an intractable likelihood, is from epidemiology

Analysis Description Languages for the LHC

An analysis description language is a domain specific language capable of describing the contents of an LHC analysis in a standard and unambiguous way, independent of any computing framework. It is designed for use by anyone with an interest in, and knowledge of, LHC physics, i.e., experimentalists, phenomenologists and other enthusiasts. Adopting analysis description languages would bring numerous benefits for the LHC experimental and phenomenological communities ranging from analysis preservation beyond the lifetimes of experiments or analysis software to facilitating the abstraction, design, visualization, validation, combination, reproduction, interpretation and overall communication of the analysis contents. Here, we introduce the analysis description language concept and summarize the current efforts ongoing to develop such languages and tools to use them in LHC analyses.Comment: Accepted contribution to the proceedings of The 8th Annual Conference on Large Hadron Collider Physics, LHCP2020, 25-30 May, 2020, onlin