Revolutionizing High Energy Physics: Iot And Ai-Powered Systems For Real-Time Monitoring And Data Processing
Main Article Content
Abstract
Background: In the field of HEP, IoT, and AI offer promising chances to improve HEP experiments because of the following: real-time monitoring, data collection, and analysis. Nevertheless, the integration of these technologies in the field has been disoriented, and the number of studies on their operational consequences remains limited.
Objective: The quantitative data collected during this study will provide insight into present-day IoT and AI in HEP experiments: the extent of participants’ familiarity with them, perceived changes resulting from their application in data processing, as well as the difficulties that arise with the use of these technologies. The current research aims to identify the patterns of the use of these technologies and their performance in improving the efficiency of experiments.
Methods: An online cross-sectional survey was conducted on 250 professionals working in HEPs; Information on the important domains including, but not limited to, familiarity with IoTs, the influence of AI in data handling, and the necessity of real-time monitoring. This study adopted descriptive and inferential methods of statistical analysis: normality tests, linear regression tests, and reliability analysis using Cronbach Alpha was conducted on the data that was collected.
Results: The study also revealed the inconsistency of the respondents’ familiarity with IoT concluding through the Shapiro-Wilk test that the findings’ distribution was not normal (p< 0.001). Analyzing the results of the regression, we observed the following figures, based on which the conclusion can be made on the weak correlation between the surveyed topics: IoT familiarity, the impact of AI, and time savings; R-square was equal to 0.06. The reliability analysis of the Likert scale questions (Cronbach’s Alpha = 0.31) suggested low internal homogeneity in the responses and a possibility of roadblocks in the assessment of coherent attitudes within the sample. While a significant amount of the respondents interviewed reported that they have witnessed a reduction in time due to AI there was not enough evidence to support that a disproportional amount of understanding of IoT leads to better processing.
Conclusions: The current research indicates that though IoT and AI paradigms are gradually making an entrance into the HEP experiments they are not equally adopted and efficient. This nontrivial piece results from the technical conflict that has not abated from continuing to hinder the routine integration. That said, future endeavors should dedicate their efforts to removing these challenges through improving technical education, physical facilities, and details of IoTs & AI in HEP experiments. More research needs to be done to hone the assessment instruments and understand additional antecedent variables that impact the effectiveness of the dissemination of those technologies.