One of the advantages of belonging to a European ITN like AMVA4NewPhysics is the opportunity to participate to a range of outreach activities. Such an opportunity was given to me at the beginning of September, by volunteering at a literature festival called Festivaletteratura, taking place in Mantova, Italy. Continue reading “Volunteering at a literature festival”
Casual reader, be warned – the contents of this article, specifically the second part of it, are highly volatile, speculative stuff. But hey, that is the stuff that dreams are made of. And I have one or two good reasons to dream on.
The environment
Machine Learning is ubiquitous today. Self-driving cars; self-shaving robots (just kidding, but I’m sure they can be constructed if the need arises); programs that teach themselves chess and become world-champion-class players overnight; Siri; google search engines; google translate – okay, I am going too far. But you know it: machine learning has become a player in almost Continue reading “Can Neural Networks Design The Detector Of A Future Particle Collider?”
Hi everyone, my name is Giovanni Banelli and I’m the last student joining the AMVA4NewPhysics network. I will be based in Munich (Technical University) and formally I’m the only theorist among the ESRs; hence I will be working on the theory/phenomenology side of the use of advanced statistical tools in searches for New Physics. Continue reading “Science taken wide”
Do you know the works of Tim Blais, the guy behind “A Capella Science”? I sincerely hope you do, but otherwise this post is for you. Tim has a youtube page where he publishes his amazing works.
Tim sings modified lyrics of famous songs, and mixes them with multiple tracks of his own voice imitating each of the instruments of the underlying orchestra, or other choral voices. Until here you could well say there’s nothing new under Continue reading “A Capella Science at CERN”
What is spectroscopy ?
(A) the observation of ghosts by infrared visors or other optical devices
(B) the study of excited states of matter through observation of energy emissions
If you answered (A), you are probably using a lousy internet search engine; and btw, you are rather dumb. Ghosts do not exist.
Otherwise you are welcome to read on. We are, in fact, about to discuss a cutting-edge spectroscopy measurement, performed by the CMS experiment using lots of proton-proton collisions by the CERN Large Hadron Collider (LHC).
Introduction
Well folks, it’s been quite a while since my last post; apologies for that, it’s been a busy few months recently.
Towards the end of last year I wrote a post on optimising the hyper parameters (depth, width, learning rate, et cetera) of neural networks. In this post I described how I was trying to use Bayesian methods to ‘quickly’ find useful sets of parameters. Continue reading “Hyper-parameters revisited”
One of the best things about being a parent is that as an adult you can play with kids’ toys and nobody judges it as strange (men never grow up). As my kids are still very young, so far I’ve had possibilities to play with dolls (not my favourite), car toys (great memories come back), soap bubbles and other simple toys. Continue reading “In a random maze”
A paper by B. Fornal and B. Grinstein published last week in Physical Review Letters is drawing a lot of interest to one of the most well-known pieces of subnuclear physics since the days of Enrico Fermi: beta decay.
The mechanism by means of which a neutron transmutes into a proton has been studied for decades before the neutron was officially discovered (by Chadwick, in 1932). That is because beta decay is at the heart of radioactive processes: nuclei of elements rich with neutrons can turn into others by turning one of their neutrons into a proton, with the emission of an electron and an antineutrino. The details of that reaction were understood thanks to Fermi and a handful of other Continue reading “Is Dark Matter Lurking in Neutron Decays?”
The week before last I was presenting an update of some of my analysis work to the rest of my group. The work involved developing a neural-network to classify particle-collisions at the LHC. Continue reading “Train-time/test-time data augmentation”
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