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AMVA4NewPhysics

A Marie Sklodowska-Curie ITN funded by the Horizon2020 program of the European Commission

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 Continue reading “Big LHC Experiments Go Deep”

Fighting Gender Bias

by Tommaso Dorigo

As the few regulars of this blog know, the AMVA4NewPhysics network has in its genes a strong will to fight for gender neutrality in its areas of operation – research in Particle Physics and Applied Statistics. We started off this endeavour 2.5 years ago by including three women as PI of beneficiary nodes out of a total of eight, which was *almost* good. But their research record was outstanding, too, which helped us getting funded!

So that was easy. What was less easy was to deliver what we promised in our programme – a hiring practice capable of producing a gender-balanced pool Continue reading “Fighting Gender Bias”

Understanding Neural-Networks: Part I

by Giles Strong

Last week, as part of one of my PhD courses, I gave a one hour seminar covering one of the machine learning tools which I have used extensively in my research: neural networks. Preparation of the seminar was very useful for me, since it required me to make sure that I really understood how the networks function, and I (think I) finally got my head around back-propagation – more on that later. In this post, and depending on length, the next (few), I intend to reinterpret my seminar into something which might be of use to you, dear reader. Here goes!

A neural network is a method in the field of machine learning. This field aims to build predictive models to help solve complex tasks by exposing a flexible system to a large amount of data. The system is then allowed to learn by itself how to best form its predictions. Continue reading “Understanding Neural-Networks: Part I”

Five New Resonances Discovered by LHCb – Huh, So What ?

by Tommaso Dorigo

While I was busy reporting the talks at the “Neutrino Telescope”  conference in Venice, LHCb released a startling new result, which I have not much time to describe in much detail this evening (it’s Friday evening here in Italy and I’m going to call the week off), and yet wish to share with you as soon as possible.

The spectroscopy of low- and intermediate-mass hadrons (whatever this means) is a complex topic which either enthuses particle Continue reading “Five New Resonances Discovered by LHCb – Huh, So What ?”

On the origins of “hysteresis”

by Fabricio Jimenez

A few years ago, back when I was a Summer Student at CERN, me and other physics students had some debate on the origins of the word “hysteresis.” We were just coming back from CinéTransat – an open-air cinema at La Perle du Lac park in Geneva, and having a random chat until Sabina jokingly accused Josefa of being hysteric, for a reason I can’t remember right now. From there, they started a discussion on how the word “hysteric” was related to “uterus” (at some point in the past, hysteria was defined as a psychological disorder related to the organ), and to “hysterectomy” (the removal Continue reading “On the origins of “hysteresis””

Tales of a nomadic researcher

by Pablo de Castro

This is a short essay about the perks and quirks of living away and travelling as part of the job description, which is part of the deal when you join a Maria Skłodowska-Curie Innovative Training Network (ITN) as AMVA4NewPhysics and might apply for the most part to other research positions. The points made here are based on my own personal experiences and discussions with people in analogous situations. I am eager to hear your thoughts regarding this matter in the comment section!

Continue reading “Tales of a nomadic researcher”

Female Scientists On Woman’s Day

by AMVA4NewPhysics Press Office

Today is March 8th – the day internationally devoted to women. And we in AMVA4NewPhysics are sensitive to the subject of raising awareness in the themes of relevance to this day across the world. Discrimination of people based on sex is just as bad as it is any other form of discrimination (based on creed, race, and other categorizations). And we know that women in practically all countries of the world suffer from discrimination that causes them to receive lower wages for the same job, to have fewer chances of career advancement, to have fewer job opportunities, to Continue reading “Female Scientists On Woman’s Day”

Do Not Name Him Donald!

by Grzegorz Kotkowski

Recently I’ve encountered an interesting article about the trends of the female names in the US. It shows the impact of the famous Disney Movies on the names that are given to the newborns. As the “Frozen” movie has become very popular a lot of girls born in 2014 got names as Elsa or  Merida.

I want to consider the same dataset in order to perform the analogous analysis but for names of the US presidents. My guess is that it should well represent if a Continue reading “Do Not Name Him Donald!”

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, Continue reading “AMVA4NewPhysics Deliverable 4.1: Report of the Performance of Algorithms for Data-Driven Background Shape Modeling”

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