On the Utility Function of Experiments in Fundamental Science

TL;DR

This paper discusses formulating a utility function for multipurpose experiments in fundamental science to optimize design choices and facilitate AI-assisted exploration of experimental configurations.

Contribution

It introduces the concept of a utility function for multipurpose experiments, enabling AI tools to optimize experimental design and balance multiple scientific goals.

Findings

Proposes a framework for defining utility functions in complex experiments

Highlights the potential of AI to optimize experimental design

Addresses the challenge of balancing multiple scientific objectives

Abstract

The majority of experiments in fundamental science today are designed to be multi-purpose: their aim is not simply to measure a single physical quantity or process, but rather to enable increased precision in the measurement of a number of different observable quantities of a natural system, to extend the search for new phenomena, or to exclude a larger phase space of candidate theories. Most of the time, a combination of the above goals is pursued; this breadth of scope adds a layer of complexity to the already demanding task of designing the measurement apparatus in an optimal way, by defining suitable geometries and choosing the most advantageous materials and appropriate detection technologies. The precise definition of a global optimality criterion may then require experimentalists to find a consensus on the relative scientific worth of those goals. In this work, we discuss the problem of formulating a utility function for multipurpose experiments, as an enabling step to employ artificial intelligence tools to explore the design space and assist humans in finding solutions at the Pareto front.