Koberinski, Adam (2026) Searching high and low: precision measurement, machine learning, and experimental discovery in particle physics. [Preprint]

Text SyntheseSubmissionFinal.pdf Download (410kB)

Abstract

Discoveries in particle physics have traditionally occurred through direct detection, usually by producing new particles in particle accelerators. However, the lack of new discoveries from the Large Hadron Collider at CERN has undermined confidence that direct discovery is likely at technologically accessible energies. In this paper, I provide an epistemic analysis of more and less direct search strategies in current-day particle physics. I make explicit the epistemic features of these search strategies in what is has traditionally been called the process of scientific discovery. I describe two promising methods of indirect detection of new physics, possible to conduct at comparatively lower energies and at significantly reduced cost: precision measurement and anomaly detection via unsupervised machine learning. I argue that indirect detections can reliably lead to discovery, and that direct versus indirect searches have complementary epistemic and practical advantages. I provide an account of indirect discovery that proceeds from discrepancies, to anomalies, and finally to (theory-mediated) discovery. I further argue that these methods are capable of leading to new discoveries when mediated through the generalized effective field theory framework. Indirect searches should not replace direct detection efforts; instead, they provide complementary epistemic advantages and jointly make for a more exhaustive search for physics beyond the Standard Model.

---

Export/Citation:

EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL

Social Networking:

Share |

---

Item Type:	Preprint
Creators:	Creators Email ORCID Koberinski, Adam akoberin@uwo.ca 0000-0001-7605-4214
Additional Information:	Forthcoming in Synthese Topical Collection: The Philosophy of Experiments
Keywords:	Particle physics; experiment; measurement; Machine Learning; Scientific Discovery
Subjects:	General Issues > Experimentation Specific Sciences > Physics > Quantum Field Theory General Issues > Theory Change
Depositing User:	Dr. Adam Koberinski
Date Deposited:	09 Jan 2026 16:08
Last Modified:	09 Jan 2026 16:08
Item ID:	27809
Subjects:	General Issues > Experimentation Specific Sciences > Physics > Quantum Field Theory General Issues > Theory Change
Date:	2026
URI:	https://philsci-archive.pitt.edu/id/eprint/27809

Monthly Views for the past 3 years

Monthly Downloads for the past 3 years

Plum Analytics

Actions (login required)

View Item