Half day off June 8, 2006
Posted by dorigo in personal, physics, travel.1 comment so far
I am guilty. I feel guilty.
I skipped a whole afternoon of talks at the CDF collaboration meeting, to go on a tour of the Elba island with my family.
We headed towards Lacona, which has a nice long sand beach, but there was too much people there, and so we left. We found a small beach nearby that had clear waters and not nearly as many visitors, and spent there our afternoon…
Later, I learned that Rick Field had given a fantastic review talk on QCD physics from the seventies onwards.
Rick Field is a colleague in CDF, but well before CDF was born he used to publish papers on QCD with none less than Feynman. In fact, the Feynman-Field model is a cornerstone of modern QCD.
I feel sorry for having missed his talk. On the other hand… My kids are three and seven years old, but they are growing fast. I have to grab the chances to spend leisure afternoons with them - there are a finite number of them left!
How to find the simplest theory June 8, 2006
Posted by dorigo in humor, physics, science.3 comments
Commenting on my post on Supersymmetry, Torbjorn Larsson writes:
A modern variant of Ockham’s razor is “Of two equivalent theories or explanations, all other things being equal, the simpler one is to be preferred”. This version isn’t slanted to specifically minimise entities but to generally simplify theory. Doesn’t supersymmetry win out in such a case?
This reminds me of a funny cartoon I once found in the web somewhere… Here it is (I hope I don't get sued for copyright violations):
The talk slides I promised June 8, 2006
Posted by dorigo in computers, mathematics, personal, physics, science.5 comments
Ok rather than burying them in a newer version of yesterday's post about my talk, I will post the slides I promised in a brand new post here.
I did discuss God's Choice algorithm here a few days ago. Let me do it graphically now, and at the end of this post I'll show a practical example of its application.
Imagine you have two kinds of events in your dataset. Let's call "background" the one you want to get rid of, and "signal" the one you'd like to enrich the dataset of.
Further imagine you know the details of the behavior of your background in a set of kinematical variables.
Now let's fish out of your dataset a handful of events - say 50 (5 balls in the graph here). You look at them and ask the question: do these smell of background ?
You test the 50-event sample by comparing its distribution in the kinematic variables to your background model, and get back from the test a single number, which tells you what is the probability that those 50 events are background.
