A Quantum Diaries Survivor

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. 

I recently held an accelerated course in "Statistical data analysis for fundamental science" for the Instats site. Within only 15 hours of online lectures (albeit these are full 1-hour blocks, unlike the leaky academic-style hours that last 75% of that) I had to cover not just parameter estimation, hypothesis testing, modeling, and goodness of fit, plus several ancillary concepts of high relevance such as ancillarity (yep), conditioning, the likelihood principle, coverage, and frequentist versus bayesian inference, but an introduction to machine learning! How did I do?

A calorimeter in physics is something that measures heat. However, there are mainly two categories of such objects: ones that measure macroscopic amounts of heat, and ones that measure the heat released by subatomic particles when they smash against matter. I am sure you guess which is the class of instruments I am going to discuss in this article.A further distinction among calorimeters for particle physics is the one concerning the kind of particles these devices aim to measure. Electromagnetic calorimeters target electrons and photons, and hadronic calorimeters target particles made of quarks and gluons. Here I will discuss only the latter, which are arguably more complex to design.Smashing protons

In the past two weeks I visited two schools in Veneto to engage students with the topic of Artificial Intelligence, which is something everybody seems to be happy to hear about these days: on the 10th of January I visited a school in Vicenza, and on the 17th a school in Venice. In both cases there were about 50-60 students, but there was a crucial difference: while the school in Venezia (the "Liceo Marco Foscarini", where I have been giving lectures in the past within the project called "Art and Science") was a classical liceum and the high-schoolers who came to listen to my presentation were between 16 and 18 years old, the one in Vicenza was a middle school, and its attending students were between 11 and 13 years old. 

From tomorrow onwards (once or twice a week until February 5), I will be giving an online course on the topic of "Statistical Methods for Fundamental Science" for the INSTATS organization. This is a 5-day, 15-hour set of lectures that I put together to suit the needs of students and researchers who work in any scientific discipline, who wish to improve their understanding and practice of statistical methods for data analysis.

2023 is over and I am looking back at my achievements and failures, to take stock and try to learn something from the matter. This blog looks like a reasonably good place for such an exercise, so I am writing here an inventory of what happened to me in the past 12 months. Sorry if this sounds very boring!

Next month I will be giving three lectures to high-school students on using artificial intelligence for research in fundamental physics, and as usual I am not yet worried by the schedule enough to start thinking at the presentations. Except that in one case the school professor who organizes the event asked me for some preliminary task for the students "to get them in the mood" of the contents of the lecture.