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The knowledge of the material budget with a high precision is fundamental for measurements of direct photon production using the photon conversion method due to its direct impact on the total systematic uncertainty. Moreover, it influences many aspects of the charged-particle reconstruction performance. In this article, two procedures to determine data-driven corrections to the material-budget description in ALICE simulation software are developed. One is based on the precise knowledge of the gas composition in the Time Projection Chamber. The other is based on the robustness of the ratio between the produced number of photons and charged particles, to a large extent due to the approximate isospin symmetry in the number of produced neutral and charged pions. Both methods are applied to ALICE data allowing for a reduction of the overall material budget systematic uncertainty from 4.5% down to 2.5%. Using these methods, a locally correct material budget is also achieved. The two proposed methods are generic and can be applied to any experiment in a similar fashion.
The knowledge of the material budget with a high precision is fundamental for measurements of direct photon production using the photon conversion method due to its direct impact on the total systematic uncertainty. Moreover, it influences many aspects of the charged-particle reconstruction performance. In this article, two procedures to determine data-driven corrections to the material-budget description in ALICE simulation software are developed. One is based on the precise knowledge of the gas composition in the Time Projection Chamber. The other is based on the robustness of the ratio between the produced number of photons and charged particles, to a large extent due to the approximate isospin symmetry in the number of produced neutral and charged pions. Both methods are applied to ALICE data allowing for a reduction of the overall material budget systematic uncertainty from 4.5% down to 2.5%. Using these methods, a locally correct material budget is also achieved. The two proposed methods are generic and can be applied to any experiment in a similar fashion.
In this article, we profile an empirically grounded, cognitive approach to immersion in digital fiction by combining text-driven stylistic analysis with insights from theories of cognition and reader-response research. We offer a new analytical method for immersive features in digital fiction by developing deictic shift theory for the affordances of digital media. We also provide empirically substantiated insights to show how immersion is experienced cognitively by using Andy Campbell and Judi Alston’s (2015) digital fiction piece WALLPAPER as a case study. We add ‘interactional deixis’ and ‘audible deixis’ to Stockwell’s (2002) model to account for the multimodal nature of immersion in digital fiction. We also show how extra-textual features can contribute to immersion and thus propose that they should be accounted for when analysing immersion across media. We conclude that the analytical framework and reader response protocol that we develop here can be adapted for application to texts across media.