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A New Free Tool For The Optimization Of Muon Tomography

Muon tomography is one of the most important spinoffs of fundamental research with particle detectors...

On Overfitting In Statistics And In Machine Learning

I recently held an accelerated course in "Statistical data analysis for fundamental science" for...

An Idea For Future Calorimetry

A calorimeter in physics is something that measures heat. However, there are mainly two categories...

Comparing Student Reactions To Lectures In Artificial Intelligence And Physics

In the past two weeks I visited two schools in Veneto to engage students with the topic of Artificial...

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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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Creativity is one of the things that really makes us human - in fact, a number of human activities which we identify as specific of our nature, and which we believe could hardly be mimicked by artificial intelligence, rely on our inventiveness and capability of creating new objects, images, concepts, methods, or finding new purpose in old tools. Art, among all of these activities, is the quintessential result of our willful act of creating beauty - or even ugliness, if that is considered a worthy pursuit by the artist.
 
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.