Big LHC Experiments Go Deep

by Markus Stoye

This week the first Inter-experimental LHC Machine Learning IML workshop took place at CERN. I showed my results on using deep learning for hadronic particle labeling (flavour tagging), a method that offers significant improvements in the labeling of heavy flavour jets for the CMS experiment (which I am member of). Despite deep learning as a topic is all over the media, the big CERN experiments have not used it a lot this far. In fact my application is, to my knowledge, the very first deep-learning application in CMS reconstruction.

The workshop featured several presentations on deep learning using

AMVA4NewPhysics Deliverable 4.1: Report of the Performance of Algorithms for Data-Driven Background Shape Modeling

by AMVA4NewPhysics press office

And here it is, the second – but really synchronous in publication with the first – scientific deliverable of our network. Deliverable 4.1, titled “Report of the Performance of Algorithms for Data-Driven Background Shape Modeling“, is a report of studies performed by network members operating within Work Package 4, also known as “New Statistical Learning Tools for HEP Analysis“.

The research presented in this document aims at constructing a precise representation of background processes to searches for small signals in hadron collider data. Specifically, we focused on the multijet QCD background,

AMVA4NewPhysics Deliverable 1.1: MVA for Higgs Boson Searches at the LHC

by the AMVA4NP press office

It is with a certain satisfaction that I can announce today that the AMVA4NewPhysics network is in complete control of its planned schedule, and has now started to provide real research-grade output, delivering its first two scientific products of relevance. Deliverable 1.1 (from work package 1, which focuses on MVA applications to Higgs boson studies) and Deliverable 4.1 (from work package 4, which focuses on the development of entirely new Machine Learning tools with in mind their application to specific HEP

Six months in

by Giles Strong

Ciao. As the title suggests, it’s been about half a year now since I started my PhD research, and last week I presented a summary of my work so far to the CMS group here in Padova. I thought it would be an interesting exercise to translate my presentation into a more blog-friendly form, but for the more scientifically minded, I’ll link the original at the end. Here goes!

Secondment in Padua

by Cecilia Tosciri

A few days ago I left Padua, where I spent one intensive month, working with other network members and ESR fellows (Giles, Greg and Pablo) at the Statistical Department.

A first observation

When people from different disciplines work together, like in the case of physicists and statisticians, the first stumbling block is the communication. This is mostly because

Hermione had become a bit pink

by Pietro Vischia

Hi there!

So, in a recent blog post I told a bit about me. However, this morning I was lazily re-reading it, when I suddenly remembered that a text is nothing more than a collection of elements, linked one to each other by some rules (grammar) and some standard associations: by standard associations, I mean that the topic you are writing about dictates the words that you are most likely to use. For example, if you are writing about

MVAs @ LIP: it begins!

by Giles Strong

G’day! The past two months have seen the first bit of MVA application in my research. I’ve also had the pleasure of helping to supervise three students from IST, the local university, who’ve been working on summer internships at LIP. The students (António, João, and Ricardo) have expressed interest in detailing their work as guest-posts on here

MoMEMta is out!

by Alessia Saggio

Ladies and gentlemen,

I’m very pleased to announce that… the 1^st release of MoMEMta is out! From now on, you’ll be able to use the Matrix Element Method by yourself in a really easy way!

Before letting you have fun with MoMEMta, I would like to spend just a few words on the reason why we need it and why its development is (and will be!) so important in the Particle Physics world.

The purpose of this framework

The Compound Poisson Strikes Back

by Tommaso Dorigo

Today I learned a lesson the hard way – in a nutshell, the lesson is that you should not forget what you yourself teach!

During my “Statistics for Data Analysis” lectures at the PhD course in Padova (and elsewhere – for instance here, here, here, and here) I usually start the course by drawing the students’ attention to the pitfalls of mistaking one statistical distribution for another in a given problem. And the way I do it is by citing one particular example when two physicists were led to believe they