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