Paper short abstract:

In data-intensive sciences such as particle physics images become essential sites for evidential exploration and debate through procedures of black-boxing, synthesis, and contrasting. This paper addresses the challenges of data analysis using as an example the Higgs search at the LHC (CERN).

Paper long abstract:

Contemporary experimental particle physics is amongst the most data-intensive sciences and thus provides an interesting test case for critical data studies. Approximately 30 petabytes of data produced at CERN's Large Hadron Collider (LHC) annually need to be controlled and processed in multiple ways before physicists are ready to claim novel results: data are filtered, stored, distributed, analyzed, reconstructed, synthesized, etc. involving collaborations of 3000 scientists and heavily distributed work. Adopting a science-as-practice approach, this paper focuses on the associated challenges of data analysis using as an example the recent Higgs search at the LHC, based on a long-term qualitative study. In particle physics, data analysis relies on statistical reasoning. Physicists thus use a variety of standard and advanced statistical tools and procedures. I will emphasize that, and show how, the computational practice of data analysis is inextricably tied to the production and use of specific visual representations. These "statistical images" constitute "the Higgs" (or its absence) in the sense of making it "observable" and intelligible. The paper puts forward two main theses: (1) that images are constitutive of the prime analysis results due to the direct visual grasp of the data that they afford within large-scale collaborations and (2) that data analysis decisively relies on the computational and pictorial juxtaposition of "real" and "simulated data", based on multiple models of different kind. In data-intensive sciences such as particle physics images thus become essential sites for evidential exploration and debate through procedures of black-boxing, synthesis, and contrasting.