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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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Since most of the building blocks of our own body are protons, the above title might disturb sensitive readers. On the other hand, describing a proton as a bag of garbage has several merits, as it is a fruitful analogy that may be carried forward when we wish to examine experiments that studied the structure of matter. I will explain what I mean in a moment below.
The occasional reader of this blog will excuse me if yet again I do not report here of this or that new result by the LHC collaboration, and instead discuss matters of lesser relevance. But to me, education is important. Even if I am not a University professor, but rather an employee of a research institute (and as such, not obliged to spend some of my time teaching), I do teach courses to university students. I do that because I believe I can give students a positive imprinting on the beauty of physics, on the exciting nature of research in fundamental science, and on how interesting all of that is. 
As I am waiting in Prague airport for my flight back home, after a few days spent discussing the options of the SWGO collaboration for the detectors we are going to build, I came across (through compulsive scrolling on twitter) a thread that caught my attention. It was about emails to academics on PhD openings. Since I found the discussion there a bit too forgiving on the academics, I wish to express my position here - possibly in a less toxic environment.
Last week I received in my mailbox a copy of the Princeton University Press book "Machine Learning for Physics and Astronomy" by Viviana Acquaviva. They sent me a copy because I had reviewed its contents for Princeton Press.


I am happy with the book. When I accepted to review it, I was a bit hesitant because I am not a computer scientist. I might pass as an expert in machine learning because after all I have been developing such tools for 20 years now (or maybe I should say over 30, as my first attempt was in 1992, with a bootstrap-powered classification method), but I feel I still lack knowledge in some of the theoretical underpinnings, and there are holes in my knowledge base. 
Having spent the past 12 months coding up an end-to-end model of an astrophysics experiment, with the sole aim of searching for an optimal solution for its design by use of stochastic gradient descent, I am the least qualified person to judge the aesthetic value of the results I am finally getting from it. 
Therefore it makes sense to ask you, dear reader, what you think of the eerily arcane geometries that the system is proposing. I do not think that to be a good judge you need to know the details of how the model is put together, but I will nevertheless make an attempt at briefing you on it, just in case it makes a difference in your judgment.

These days I am in Paris, for a short vacation - for once, I am following my wife in a work trip; she performs at the grand Halle at la Villette (she is a soprano singer), and I exploit the occasion to have some pleasant time in one of the cities I like the most.


This morning I took the metro to go downtown, and found myself standing up in a wagon full of people. When my eyes wandered to the pavement, I saw that the plastic sheet had circular bumps, presumably reducing the chance of slips. And the pattern immediately reminded me of the Monte Carlo method, as it betrayed the effect of physical sampling of the ground by the passengers' feet: