Archive for the ‘Particle Physics’ Category

Python at the Large Hadron Collider and CERN

Tuesday, October 20th, 2015

Python at the Large Hadron Collider and CERN hosted by Michael Kennedy.

From the webpage:

The largest machine ever built is the Large Hadron Collider at CERN. It’s primary goal was the discovery of the Higgs Boson: the fundamental particle which gives all objects mass. The LHC team of 1000’s of physicists achieved that goal in 2012 winning the Nobel Prize in physics. Kyle Cranmer is here to share how Python was at the core of this amazing achievement!

You’ll learn about the different experiment including ATLAS and CMS. We talk a bit about the physics involved in the discovery before digging into the software and computer technology used at CERN. The collisions generate a tremendous amount of data and the technology to filter, gather, and understand the data is super interesting.

You’ll also learn about Crayfis, the app that turns your phone into a cosmic ray detector. No joke. Kyle is taking citizen science to a whole new level.

Bio on Kyle Crammer:

Kyle Cranmer is an American physicist and a professor at New York University at the Center for Cosmology and Particle Physics and Affiliated Faculty member at NYU’s Center for Data Science. He is an experimental particle physicist working, primarily, on the Large Hadron Collider, based in Geneva, Switzerland. Cranmer popularized a collaborative statistical modeling approach and developed statistical methodology, which was used extensively for the discovery of the Higgs boson at the LHC in July, 2012.

CRAYFIS – Join the first and only crowd-sourced cosmic ray detector. You might just help discover something big.

Not heavy with technical information but a nice glimpse into the computing side of CERN.

Share with students to encourage them to pick up programming skills as we once did typing.

the HiggsML challenge

Saturday, May 24th, 2014

the HiggsML challenge

The challenge runs from May 12th to September 2014.

From the challenge:

In a nutshell, we provide a data set containing a mixture of simulated signal and background events, built from simulated events provided by the ATLAS collaboration at CERN. Competitors can use or develop any algorithm they want, and the one who achieves the best signal/background separation wins! Besides classical prizes for the winners, a special “HEP meets ML” prize will also be awarded with an invitation to CERN; we are also seeking to organise a NIPS workshop.

For this HEP challenge we deliberately picked one of the most recent and hottest playgrounds: the Higgs decaying into a pair of tau leptons. The first ATLAS results were made public in december 2013 in a CERN seminar, ATLAS sees Higgs boson decay to fermions. The simulated events that participants will have in their hands are the same that physicists used. Participants will be working in realistic conditions although we have simplified quite a bit the original problem so that it became tractable without any background in physics.

HEP physicist, even ATLAS physicists, who have experience with multivariate analysis, neural nets, boosted decision trees and the like are warmly encouraged to compete with machine learning experts.

The Laboratoire de l’Accélerateur Linéaire (LAL) is a French lab located in the vicinity of Paris. It is overseen by both the CNRS (IN2P3) and University Paris-Sud. It counts 330 employees (125 researchers and 205 engineers and technicians) and brings internationally recognized contributions to experimental Particle Physics, Accelerator Physics, Astroparticle Physics, and Cosmology.

Contact : for any question of general interest about the challenge, please consult and use the forum provided on the Kaggle web site. For private comments, we are also reachable at

Now there is a machine learning challenge for the summer!

Not to mention more science being done on the basis of public data sets.

Be sure to forward this to both your local computer science and physics department.

Particle Physics – Stanford

Monday, February 13th, 2012

Leonard Susskind lectures on particle physics. Like astronomy (both optical and radio), particle physics was a leading source of “big data” before there was “big data.”

Particle Physics: Basic Concepts

Particle Physics: Standard Model

Interesting in its own right, another field for testing data mining software.