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Highlights From MODE And EUCAIF

After a month of intense travel, which among other things included attendance to the MODE Workshop...

Win A MSCA Post-Doctoral Fellowship!

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The Anomaly That Wasn't: An Example Of Shifting Consensus In Science

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An Innovative Proposal

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Tommaso DorigoRSS Feed of this column.

Tommaso Dorigo is an experimental particle physicist, who works for the INFN at the University of Padova, and collaborates with the CMS and the SWGO experiments. He is the president of the Read More »

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Physicists from the CMS experiment at CERN's Large Hadron Collider have used the total data sample of 13 TeV proton-proton collisions collected in the past few years to search for resonant decays of heavy hadrons into pairs of J/Psi mesons, and they found three of them. 
One of the three new resonances is likely to be the same as a particle already identified for the first time by the competitor LHCb experiment, while the other two are new finds. LHCb is also a LHC detector, but it is one optimized for heavy hadron spectroscopy; while CMS is a "general purpose" detector built with the primary goal of finding the Higgs boson (a 2012 success) and searching for new phenomena at the highest-energy frontier. 
Now and then I find the time to write music for piano. It is a compelling, satisfying activity that however demands my full immersion for several hours at a time - if I want anything to come out from it. It happened again last Sunday, when I spent the whole day at the keyboard of the beautiful Yamaha C3 artistic edition I bought last year (and am still paying). But in truth, the work is only initially at the keyboard of the piano: after having taken note of a few themes and ideas, the activity switches to a software called Finale, which enables one to write sheet music and check it through a synthesizer that lets you hear what you wrote sounds like without having to go back and forth to the piano. 
Yes, I know - I have touched on this topic already a couple of times in this blog, so you have the right to be bored and surf away. I am bound to talk about this now and then anyway, though, because this is the focus of my research these days. 
Recently I was in the Elba island (a wonderful place) for a conference on advanced detectors for fundamental physics, and I presented a poster there on the topic of artificial-intelligence-assistend design of instruments for fundamental physics. Below is the poster (I hope it's readable in this compressed version - if you really want a better pic just ask).



Neural networks are everywhere today. They are used to drive vehicles, classify images, translate texts, determine your shopping preferences, or finding your quickest route to the supermarket. Their power at making sense of small or large datasets alike is enabling great progress in a number of areas of human knowledge, too. Yet there is nothing magical about them, and in fact what makes them powerful is something that has been around for century: differential calculus.
When I explain to the public (in this blog, or at public conferences or schools) how the Large Hadron Collider operates, I have to gloss over a lot of detail that is unnecessary to grasp the important concepts, which enable other discussions on interesting subnuclear physics. This is good practice, and it also saves me from having to study details I have forgotten along the way - they say that what you are left with when you forget everything is culture, and I tend to agree. I have a good culture in particle physics and that's all I need to do some science popularization ;-)
As the twentythree regular readers of this blog know [(c) A.Manzoni], in recent times I have moved the main focus of my research to advanced applications of deep learning to fundamental science. That does not mean that I am not continuing to participate in the CMS experiment at the CERN Large Hadron Collider - that remains the main focus of my research; but it does mean that what remains of my brain functionalities is mostly invested in thinking about future applications of today's and tomorrow's computer science innovations.