A particle physicists can do a large variety of things, I’ll list some here:
(1) Theory – these brilliant minds refine and come up with new ideas to explain things that we don’t currently understand, such as dark matter. These theories involve a lot of original thinking and math!
(2) Detector physics – in order to detect particles, a lot of work also goes into building and upgrading the detectors. I’d say a large chunk of particle physicists are involved in this part! The detector needs designing, all parts need testing, and assembling!
(3) Experimental data analysis – I belong to this group, where we plough through the massive amounts of data we get from the experiments, and try to make sense of it. This is the part where we give measurements, and discover new particles if they exist 😀
Joanna gave a very nice summary of what we do. But, as is true of many jobs, there are lots of variations. For example, like Joanna, I do data analysis. But, I tend to straddle the boundary between data analysis and and advanced analysis methods. I develop ideas and tools for probabilistic reasoning and machine learning. For example, I started thinking about the application of something called graph networks to experimental physics. I was inspired, a couple of weeks ago when I was attending a meeting near Washington DC, by an interesting talk by a student of a colleague at Caltech. I entered an intense few days of “paper reading” to try to understand better what I had heard. Then, since I am often too busy these days to pursue every idea I have, as I often do, I gave a talk (this week at CERN) to an audience of young scientists (for the most part) in which I threw out some ideas that I thought they may wish to pursue. One idea I suggested was to apply graph networks to model how our experimental apparatus changes with time and see if we can build an AI system that can spot (or even predict) failures in the various interacting devices.
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Harrison commented on :
Joanna gave a very nice summary of what we do. But, as is true of many jobs, there are lots of variations. For example, like Joanna, I do data analysis. But, I tend to straddle the boundary between data analysis and and advanced analysis methods. I develop ideas and tools for probabilistic reasoning and machine learning. For example, I started thinking about the application of something called graph networks to experimental physics. I was inspired, a couple of weeks ago when I was attending a meeting near Washington DC, by an interesting talk by a student of a colleague at Caltech. I entered an intense few days of “paper reading” to try to understand better what I had heard. Then, since I am often too busy these days to pursue every idea I have, as I often do, I gave a talk (this week at CERN) to an audience of young scientists (for the most part) in which I threw out some ideas that I thought they may wish to pursue. One idea I suggested was to apply graph networks to model how our experimental apparatus changes with time and see if we can build an AI system that can spot (or even predict) failures in the various interacting devices.