A Quantum Diaries Survivor

The title of this post is no news for particle physicists - particle detectors are complex instruments and they work by interpreting the result of stochastic phenomena taking place when radiation interacts with the matter of which detectors are built, and it looks only natural that deep learning algorithms can help improve our measurements in such a complex environment.However, in this post I will give an example of something qualitatively different to providing an improvement of a measurement: one where a deep convolutional network model may extract information that we were simply incapable of making sense of. This means that the algorithm allows us to employ our detector in a new way.

The neutron, discovered in 1932 by Chadwick, is a fascinating particle whose existence allows for the stability of heavy nuclei and a wealth of atoms of different properties. Without neutrons, Hydrogen would be the only stable element: protons cannot be brought together and bound in a stable system, so e.g. Helium-2 (an atom made of two protons with two electrons) is very short-lived, as are atoms with more protons and no neutrons. So our Universe would be a very dull place.

Interference is a fascinating effect, and one which can be observed in a wide variety of physical systems - any system that involves the propagation of waves from different sources. We can observe interference between waves in the sea or in a lake, or even in our bathtub; we can hear the effect of interference between sound waves; or we can observe the fascinating patterns created by interference effects in light propagation. In addition to all that, we observe interference between the amplitudes of quantum phenomena by studying particle physics processes.

A bit over a half into my course of particle physics for Masters students in Statistical Sciences I usually find myself describing the CMS detector in some detail, and that is what happened last week. The courseMy course has a duration of 64 hours, and is structured in four parts. In the first part, which usually takes about 24 hours to complete, I go over the most relevant part of 20th Century physics. We start from the old quantum theory and then we look at special relativity, the fundaments of quantum mechanics, the theory of scattering, the study of hadrons and the symmetries that lead to the quark model, to finish with the Higgs mechanism and the Standard Model. 

Old timers of this blog will recall that I am an avid stone collector. In fact, of all experimental sciences I am fond of (Physics, Astronomy, Geology above others) Geology is the one that fascinated me first, as a six or seven year old child. We are talking about almost fifty years ago, when newspaper stands in Italy used to sell small packets containing pictures of soccer players (they were not even adhesive back then: you had to use your own glue to attach them in the proper place within collection albums which were sold separately) . Kids collected those "figurine", and exchanged them with their peer after school hours (or even during school hours). Other collections offered were ones of minerals, fossils, stickers, etcetera.

Do you remember the DAMA-LIBRA experiment? It is a underground detector made of sodium iodide crystals buried under the rock of the Gran Sasso mountain in central Italy, which took data for over a decade in the search of the elusive signal that slowly-moving, massive particles would produce when they bounced off atoms of the active detector material. 

A long time ago, before starting the studies which would lead to a career as a particle physicist, I studied music. After getting a degree as a master in Antique Instruments, I studied composition for four years. But I was not particularly well versed in that tough discipline, and I did the right thing in dropping out. I was 18, and I decided that Science was going to be my job, not music. But I kept an interest in music and I continued - a bit erratically - to study the piano.

The title of this post is the same of a non-technical presentation I gave today at the 2021 USERN Congress. The USERN (Universal Scientific and Education Research Network) is an organization fostering the diffusion of science, which provides prizes to researchers who distinguish themselves for their scientific advancements, and strives for science across borders. As a member of its advisory board I was invited to give a presentation in the first session of the virtual congress, which deals with human versus artificial intelligence.

In the next few days I have a busy schedule with a few lectures gravitating around the use of deep learning technologies for fundamental physics research. This is of course no news, but I thought that my blog is the proper place to list a few pointers, in case some of you is interested in following one or two of these events. After all, deep learning is all the rage these days, and even if fundamental physics is not your bread and butter, you may hopefully find useful inspiration in the kind of use cases that field provides for cutting edge applications of artificial intelligence.

This past Thursday I held a public lecture, together with my long-time friend Ivan Bianchi, on the topic of Art and Artificial Intelligence. The event was organized by the "Galileo Festival" in Padova, for the Week of Innovation.Ivan is a professor of Contemporary Art at the University of Padova. We have known each other since we were two year olds, as our mothers were friends. We took very different career paths but we both ended up in academic and research jobs in Padova, and we have been able to take part together in several events where art and science are at the focus. Giving a lecture together is twice as fun!

I believe oceans of ink were spent, ever since pens were a thing, writing on the mentor-student relationship, its do's and don'ts, and the consequences of deviations from proper practice. And rightly so, as the balancing act required for a proper, effective teaching action is entirely non trivial. The fact that our didactical systems and academia are in constant evolution, that rules and courses formats change over time, and that as humans we tend to forget what has been learnt in the past (on good practice, I mean), require us to keep thinking about the topic, and continue to keep the discussion alive. 

I very much would like to write about the Nobel prize in physics here today, but I realize I cannot really pay a good service to the three winners, nor to my readers, on that topic. The reason is, quite bluntly, that I am not qualified to do that without harming my self-respect. Also, I never knew about the research of two of the winners. As for the third, I do know Giorgio Parisi's research in qualitative terms, and I happen to know him personally; well, at least we are Facebook friends, as maybe 500 of his contacts can also claim - plus, he once invited me to a symposium at the Accademia dei Lincei, of which he his vice-president. And I did write about his scientific accomplishments in the past here, on two occasions.