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Acknowledging Giorgio's Mentoring Superpowers

Yesterday I gladly attended a symposium in honor of Giorgio Bellettini, who just turned 90. The...

A Workshop You Should Not Miss

... if you are a researcher in physics or astrophysics and you are working with machine learning...

A Cool Rare Decay

By and large, particle physicists confronted with the need to awe and enthuse an audience of laypersons...

Move Over - The Talk I Will Not Give

Last week I was in Amsterdam, where I attended the first European AI for Fundamental Physics...

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Tommaso DorigoRSS Feed of this column.

Tommaso Dorigo is an experimental particle physicist, who works for the INFN at the University of Padova, and collaborates with the CMS and the SWGO experiments. He is the president of the Read More »

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Although unconventional, the ideas of Gregory Ryskin on vacuum energy sound interesting to me, so I invited him to share them with you in this guest post. 

Ryskin's physics journey began with fluid dynamics, first in Russia, then in the US, at Caltech. Later, the flow of complex fluids, such as polymer solutions or liquid crystals. Then Brownian motion and Markov processes. In 2000, he became interested in geology and geophysics, particularly in the causes of mass extinctions and the origin of the Earth’s magnetic field. His current research is focused on cosmology. His academic home is Northwestern University, Department of Chemical and Biological Engineering.
The text below is Gregory's.

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If you are a follower of Science20, you probably know that  I have always been very liberal in this column about what deserves to be mentioned as a possible new idea in Physics. I even invited some "non-conventional", independent scientists to write about their own ideas and pet theories here, in many occasions. I do not think this collides with the main purpose of this blog, which is to discuss real science and do some proper outreach and dissemination. In fact, I find it instructive and enlightening on what really Science is.

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.