A Quantum Diaries Survivor

Time and again, I get surprised by observing how scientific graphs meant to provide summarized, easy-to-access information get misunderstood, misinterpreted, or plainly ignored by otherwise well-read (mis-)users. It really aches me to see how what should be the bridge over the knowledge gap between scientists and the general public becomes yet another hurdle. 

When subnuclear particles traverse matter they give rise to a multitude of physical phenomena. The richness of the different processes is a crucial asset for the construction of sensitive particle detectors, and it is interesting in its own right. Indeed, it has been a very vigorously pursued field of research of its own ever since the end of the nineteenth century, with the discovery of X rays(produced when electrons released their kinetic energy as they reached the cathode of an accelerating tube), and then after Rutherford's team bombarded gold foils with alpha particles (helium nuclei) emitted by a radioactive substance.

The virtual conference "From Quarks to Cosmos with AI", organized by Carnegie-Mellon University and which took place last week, included a set of problems in particle and astroparticle physics that participants were invited to tackle with machine learning tools, during four 2-hour afternoon sessions.I took part to the conference by lecturing about applications of differentiable programming to fundamental physics, as well as by organizing (with my collaborators Giles Strong and Lukas Layer) a data challenge centered on a tough regression problem.

With the delta variant of Covid-19 surging in many countries - e.g., over 100,000 new cases per day foreseen in the UK in the next few days, and many other countries following suit - we may feel depressed at the thought that this pandemic is going to stay with us for a lot longer than some originally foresaw.In truth, if you could sort out your sources well, you would have predicted this a long time ago: epidemiologists had in fact foreseen that there would continue to be waves of contagions, although at some point mitigated by the vaccination campaigns. However, so much misinformation and falsehood on the topic has been since dumped on all media, and in particular on the internet, that it is easy to pick up wrong information.

Following my strong belief that science dissemination, and open borders science, is too important to pursue as a goal to constrain it by fears of being stripped of good ideas and scooped by fast competitors, I am offering here some ideas on a reserch plan I am going to follow in the coming months.The benefits of sharing thoughts early on is evident: you may, by reading about them below, be struck with a good idea which may further improve my plan, and decide to share it with me; you might become a collaborator - which would add to the personpower devoted to the research. You might point out problems, issues to address, or mention that some or all of the research has already been done by somebody else, and published - which would save me a lot of time!

After over one year of forced confinement, due to the still ongoing Covid-19 pandemic, academics around the world seem to have settled down on the idea that after all, we can still do our job via videoconferencing. We had to adapt to the situation as everybody else, of course, and in a general sense we are a privileged minority - other human occupations which are only possible in person suffered way more.

One of the reasons why I love my job as a researcher in experimental physics is that every day brings along a new problem to solve, and through decades of practice I have become quick at solving them, so I typically enjoy doing it. And it does not matter whether the problem at hand is an entirely new, challenging one or a textbook thing that has been solved a million times before. It is your problem, and it deserves your attention and dedication.

In the previous post I mentioned a research project that I was about to conclude, centered on the detection of anomalies in multidimensional data. Here I would like to give some more detail of that research, as the article I wrote on the subject is now publically accessible in the Cornell preprint arXiv (and is being sent to a refereed journal).

Usually, when we talk about our research we discuss things we have recently published, highlighting the importance or novelty of their contribution to the advancement of human understanding or knowledge of the specific field of Science we work on. So it is only normal for me to try and go against that particular cliché here, and talk about things I will publish in the future. Admittedly, it is a bit of a mine field (it is never easy to be an anticonformist), but I will try to avoid stepping on the most obvious triggers (violations of confidentiality, scooping risks, impossible promises).1. A new tool for anomaly detection

The CMS Collaboration submitted for publication last week a nice new result, where proton-proton collisions data collected by the experiment during the past run of the Large Hadron Collider were scanned in search of very peculiar events featuring a weak boson (W or Z) along with two energetic photons. The rate of these rare processes was measured and found in good agreement with predictions of the Standard Model of particle physics.

The title of this post coincides with the one of a scientific report which was submitted for publication in Reviews in Physics last Sunday; and it is also the meaning of the acronym "AMVA4NewPhysics", the name of an European Union-funded Innovative Training Network I directed as scientific coordinator from 2015 to 2019. 

Quantum Mechanics (QM), the theory that describes the behaviour of matter at microscopic distance scales apparently unfathomable by our senses, is very hard to understand and make sense of. And indeed to this day, 100 years after its first formulation, there are thick debates among theorists on the very meaning and interpretation of the wave function, which is the mathematical description of a quantum system. Yet we deeply rely on QM to figure out the organization of matter at molecular, atomic, and subatomic level. It works, despite the open questions. And today we deeply rely on QM for our technology.