The experiments at the Large Hadron Collider at CERN produce a total of about 90 petabytes of data per year. A relatively high-end desktop computer today may have a disk that can store about 1 terabyte of data, which means it takes the disk space of about 100,000 desktop computers to store the annual data collected by the CERN LHC experiments. The physics researchers who work at CERN use powerful computers to analyze this data. The goal of our analysis is always to learn more about the fundamental structure of the universe. We are particularly trying to probe aspects of nature that we do not yet fully understand and discover something that we do not yet know.
In very general terms, the analysis of our data can be divided into two very broad categories. The first category could be labeled "measurements" where we try to more precisely measure the value of some property of nature that we have already observed and for which we have a mathematical model that predicts what the measurement should reveal. If the measurements differ from expectations, then we have indirectly discovered something about nature previously unknown. The second category could be labeled "searches" where we actually search directly for a new undiscovered particle or a new undiscovered phenomena. Often, we are searching for something predicted by some proposed model of physics developed by a theoretical physicist.
When looking for new phenomena it is vital that we do not introduce presuppositional ideas or bias into our experiments. It is well documented that human bias can subconsciously skew the experimental analysis. In my last blog post I mentioned that, "we go to great lengths to minimize any bias toward one conclusion or another, particularly when looking for some unknown phenomena or particle." A reader of my blog, Keith, asked a number of questions about how we minimize bias in our experiments. The lengths we go to and the methods we use are quite informative and can have applications to other arenas in life where we want to come to some objective conclusions.
