I once was an active chessplayer, but work duties have long taken tournaments off my plate - I simply do not have the time to sit through long hours of chess battles. So I play blitz online on chess.com (my handle is "tommasodorigo", in case you wondered).
Yesterday I gladly attended a symposium in honor of Giorgio Bellettini, who just turned 90. The italian physicist, who had a very big impact in particle physics in his long and illustrious career, is still very active -e.g. he makes all the hard questions at the conferences he attends, as he has always done. The symposium included recollections of Giorgio's career and achievements by colleagues who collaborated with him and/or shared a part of his path. Among them there were talks by Samuel Ting, Paul Grannis, Michelangelo Mangano, Hans Grasmann, Mario Greco.I also was allowed to give a short recollection of a couple of episodes, that underline the exceptional disposition of Giorgio with students. Here is a quick-and-dirty English translation of my speech (it was in Italian).
... if you are a researcher in physics or astrophysics and you are working with machine learning, that is.Between September 23 and 25 - just when summer is over - we will meet in Valencia, Spain, to discuss the latest developments in deep learning applications to optimization of experiments in fundamental science. This is the fourth workshop of the MODE Collaboration, which focuses on a new frontier of application of deep learning: co-design and high-level optimization, and the tools to pull it off.
By and large, particle physicists confronted with the need to awe and enthuse an audience of laypersons will have no hesitation in choosing to speak about the Higgs boson and its mysteries - undoubtedly a fascinating story that requires one to start with the 1960ies and the intuition of a handful of theoretical physicists, and then grows epic in a crescendo of colliders that sought and missed the Higgs boson, and then the LHC which finally found the elusive signal of production and decay of that particle.
Last week I was in Amsterdam, where I attended the first European AI for Fundamental Physics conference (EUCAIF). Unfortunately I could not properly follow the works there, as in the midst of it I got grounded by a very nasty bronchial bug. Then over the weekend I was able to drag myself back home, and today, still struggling with the after-effects, am traveling to Rome for another relevant event.
From April 30 to May 3 more than 300 researchers in fundamental physics will gather in Amsterdam for the first edition of the EUCAIF conference, an initiative supported by the APPEC, NuPecc and ECFA consortia, which is meant to structure future European research activities in fundamental physics with Artificial Intelligence technologies.
In a world where we live hostages of advertisement, where our email addresses and phone numbers are sold and bought by companies eager to intrude in our lives and command our actions, preferences, tastes; in a world where appearance trumps substance 10 to zero, where your knowledge and education are less valued than your looks, a world where truth is worth dimes and myths earn you millions - in this XXI century world, that is, Universities look increasingly out of place.
Peter Higgs passed away yesterday, at the age of 94. The scottish physicist, a winner of the 2013 Nobel Prize in Physics together with Francois Englert, hypothesized in 1964 the existence of the most mysterious elementary particle we know of, the Higgs boson, which was only discovered 48 years later by the ATLAS and CMS collaborations at the CERN Large Hadron Collider.
In the course of Statistics for Data Analysis I give every spring to PhD students in Physics I spend some time discussing the apparently trivial problem of evaluating the significance of an excess of observed events N over expected background B. This is a quite common setup in many searches in Physics and Astrophysics: you have some detection apparatus that records the number of phenomena of a specified kind, and you let it run for some time, whereafter you declare that you have observed N of them. If the occurrence of each phenomenon has equal probability and they do not influence one another, that number N is understood to be sampled from a Poisson distribution of mean B.
In the course of Statistics for Data Analysis I give every spring to PhD students in Physics I spend some time discussing the apparently trivial problem of evaluating the significance of an excess of observed events N over expected background B. This is a quite common setup in many searches in Physics and Astrophysics: you have some detection apparatus that records the number of phenomena of a specified kind, and you let it run for some time, whereafter you declare that you have observed N of them. If the occurrence of each phenomenon has equal probability and they do not influence one another, that number N is understood to be sampled from a Poisson distribution of mean B.
About a month ago I was contacted by a colleague who invited me to write a piece on the topic of science outreach for an electronic journal (Ithaca). I was happy to accept, but when I later pondered on what I would have liked to write, I could not help thinking back at a piece on the power and limits of the use of analogies in the explanation of physics, which I wrote 12 years ago as a proceedings paper for a conference themed on physics outreach in Torino. It dawned on me that although 12 years had gone by, my understanding of what constitutes good techniques for engagement of the public and for effective communication of scientific concepts had not widened very significantly.
At a recent meeting of the board of editors of a journal I am an editor of, it was decided to produce a special issue (to commemorate an important anniversary). As I liked the idea I got carried away a bit, and proposed to write an article for it.
Muon tomography is one of the most important spinoffs of fundamental research with particle detectors -if not the most important.
