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On Rating Universities

In a world where we live hostages of advertisement, where our email addresses and phone numbers...

Goodbye Peter Higgs, And Thanks For The Boson

Peter Higgs passed away yesterday, at the age of 94. The scottish physicist, a winner of the 2013...

Significance Of Counting Experiments With Background Uncertainty

In the course of Statistics for Data Analysis I give every spring to PhD students in Physics I...

The Analogy: A Powerful Instrument For Physics Outreach

About a month ago I was contacted by a colleague who invited me to write a piece on the topic of...

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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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The DeepLearn school series, now reaching the seventh edition, offers insight into artificial intelligence and applications in a week-long course, tightly packing a significant number of high-profile instructors. The present edition, currently being held at the Technical University of Lulea, in the north of Sweden, features the following:

- Sean Benson, Netherlands Cancer Institute
- Thomas Breuel, Nvidia
- Hao Chen, Hong Kong University of Science and Technology
- Janlin Chen, University of Missouri
- Nadya Chernyavskaya, CERN
- Efstratios Gavves, University of Amsterdam
- Quanquan Gu, University of California Los Angeles
For some reasons, my personal web page features high in web searches for master thesis offers. I got to learn this by inquiring with a few students who asked me to supervise them remotely on some of the offered topics: where did they get to know about my research activities, and what led them to pick my offers? They all answered that they bumped into my web page section "thesis offers". Well, at least that was no wasted time when I wrote it.
In a recent post in this blog I discussed the idea of exploiting the properties of negative muons for a new kind of imaging technique of unknown volumes of material. The idea is based on the fact that negative muons stopped inside matter have a lifetime that is modified by nuclear interactions, so that a precise detection of their lifetime and point of decay becomes a means of inferring the composition of unknown volumes. Here, I want to offer the results of a quick simulation of the processes, to show that the idea is not so far-fetched.

Different techniques for muon tomography
Are you a recently (<8 years) appointed Ph.D. graduate in fundamental physics, who wants to work in Italy? This post is for you. The INFN is opening 20 positions for foreigners who would like to join a group of research in one of the INFN sections (there are 25 across Italy). The positions are for one year, renewable, and the salary is competitive, given that it is roughly at the level of a starting associate professor in Italy. Note, also Ph.D. students who plan to graduate before November 1st 2023 can apply! 


Also, the 8-years limit can be waived if you spent time in maternity, military service, or illness. The winning candidates are expected to start their contract before November 2023.
In the final day of the ICNFP 2022 conference in Kolympari (Greece), we could listen to an enlightening presentation by Prof. Marek Karliner (Tel Aviv University), who is an absolute authority on the matter of the theory of hadron spectroscopy. 
A reader of this blog left an interesting question in the comments thread of the article I wrote on recent ATLAS results two days ago. As I tried to answer the question exhaustively, I think the material might be of interest of other readers here, so I decided to make an independent post of it, adding some more detail.

John asks whether it is possible that what we see, when we plot the mass of a particle, is the true distribution of values of the particle - i.e. that the particle does not have only one mass, but a distribution of values. The question is not an idle one! So let us discuss it below. I will make a few points to clarify matters.

1. We estimate by proxy