Yet another blog restart

05 Jul 2024

Code I wouldn't put into production anymore - manual auto vectorization

Let's be clear, this is no work of my own. It is based on what [Florian Lemaitre](http://cds.cern.ch/record/2668250) on...
06 Jan 2016

Heisenberg's uncertainty not so relevant after all?

The idea goes like this: silicon detectors (like modern silicon pixel detectors) can reach a better spatial resolution ...
22 Dec 2015

What I don't like about binomial errors and how significant is the performance difference of two BDTs?

A topic I already discussed in my master thesis is the determination of efficiencies, commonly denoted as ε=k/N. Even assuming absolute certainty on k and N (that means: no fit is involved, it's just counting, the only uncertainty one would assume is the standard √N when refering to the uncertainty on a Poisson mean) the error propagation seems hard and is often done wrong. The naive propagation (relative error is quadratic sum of relative errors of k and N) is clearly wrong because it neglects the correlation of k and N. Once realising this, one uses the so-called binomial error, √(ε * (1-ε)/N), which I also dislike.