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Machine learning proliferates in particle physics (symmetrymagazine.org)
44 points by qubitcoder 35 days ago | hide | past | web | favorite | 7 comments



"proliferates" ? skepticism? Afaik, people in CERN have been using neural nets to identify trajectories at least since the 80s.


Exactly! They basically invented modern big data/data science. ROOT[1] is one of the oldest statistical frameworks after all. And they have machine learning in it too[2][3].

[1] https://root.cern.ch/

[2] https://root.cern.ch/tmva

[3] http://iopscience.iop.org/article/10.1088/1742-6596/898/7/07...


Note also, they are working heavily on ROOT 7[1], which will do some refactor, not sure probably even better ML/DL support. Not sure why did they target c++14 instead of c++17 though.

[1] https://root.cern.ch/root-7


The comments of buboard and xvilka are on point.

One thing I'd add though, is that physicists still have a healthy amount of skepticism on ML/NN. They don't like the "black box" approach, where the algorithm is regarded to "provide" the truth, but we're required to fully understand the math behind any implementation, as well as to be able to answer deeper questions on the results we're acquiring.

Background : I'm completing my master in Particle Physics, and have had the opportunity to work along these kinds of people. They have wrestled their entire careers with enormous amounts of data, create new solutions to problems the industry had not even formulated, and keep themselves on the forefront of Statistics and ML.

I feel that the things I've learned by trotting along grey-haired old-school Unix wizards could not be replicated by any courses, bootcamps or manuals.


I wish there were concrete examples of how machine learning is being used.


Pretty broad but, from one section in the article, sounds like they're using convolutional layers in their networks?


Check out the podcast Linear Digressions, one of the hosts Katie went from physics to data science/ML work.




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