Dr. Lorenz Gärtner

Dr. Lorenz Gärtner

Physicist working on high energy physics, machine learning, emerging tech. Experience in energy and policy.

Constructing model-agnostic likelihoods


The general method behind a line of work on reinterpreting particle-physics measurements without redoing the full experimental analysis for every new-physics model: a likelihood built directly from a measurement's signal-region yields, that lets anyone test their own model against the data by supplying only its predicted event distribution. Introduced and validated on simulated data.

Published as: Constructing model-agnostic likelihoods, a method for the reinterpretation of particle physics results, Eur. Phys. J. C 84, 693 (2024).

Posterior samples on a set of effective Wilson coefficients from combining the method with a projected future dataset (50 ab\(^{-1}\)): constraints sharpen substantially with more data, resolving degeneracies present in a smaller, 362 fb\(^{-1}\) sample.

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