Neural Network Zeros in on Quarks

Having successfully dodged high school physics I would not normally be sucked in by an article on quarks, but this one involved neural network computing, which I do understand pretty well.

 
It seems that  a couple of weeks ago physicists at Fermilab, near Chicago, made the most precise measurement yet of a top quark.  A quark is an elementary particle, the most fundamental building block of matter.  Quarks come in six flavors (I’m not making this up!), four of which can be produced only by high-energy collisions.  Think updated cyclotron.  The top quark is one of these four, and first observed in 1995, it is the most recently discovered quark.  The physicists–unsatisfied, of course, with having simply identified the particle–wanted to measure it.
 
It turns out that the way to measure a quark is to observe its decay and work backward from non-quark to quark.  This involves heavy-duty statistical analysis of many,  many observations. The scientists at Fermilab collected a large set of sample data on quark decay, and then in order to zero in on bona fide quarks, they trained a neural network to identify which particle events were not related to top quark decay.  When the neural net had sorted out the quark imitators, the physicists could size up the real quarks more accurately.
 
The top quark is relatively large for an elementary particle.  Until last month it was believed to be about the size of an atom of gold.  What is the current estimate? Too daunting a calculation to quote.  But if you go to the information the Fermilab has on display, you–or some of you, anyway–will begin to get the picture. 

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