A few days before I returned from CERN at the beginning of the month, I attended a talk on the upcoming TrackML challenge. This is a competition beginning this month in which members of the public will be invited to try and find a solution to the quite tricky problem of accurate reconstruction of particle trajectories in the collisions at the LHC. The various detectors simply record the hits where particles pass by, however to make use of this data, the hits in surrounding detector layers must be combined into a single flight path, called a track. Continue reading “Higgs Hacking”
I have spent the last three months in the SDG company in Milan for my private sector secondment, which is a part of my contract. During this time I was able to have a closer look at how it is to work in a consulting company and to put my hands on real business problems. Herein, I want to summarise some of my experiences and deliberate on the R language – an inseparable friend of statisticians. Continue reading “Having a taste of business”
I am happy to report that an important new product of the AMVA4NewPhysics ITN is now public. This is generically titled “Report on a Statistical Learning Method for Model-Independent Searches for New Physics“, and labeled D4.2 as per the grant agreement we signed with the European Union. The document is available at the following link:
What is this document about ? It is a description of the studies for the development of a software package aiming at automating the searches for new physics in LHC data, by evidencing anomalous clusterings of events that are hard to explain with known physics processes. I am sure that Fabricio and Grzegorz, the two main developers of the software (Deliverable 4.3, available on github at https://github.com/Grzes91/PenalizedAD) and its documentation (D4.2) will be happy to post in this blog a more complete description of the new package and its possible uses in particle physics research.
A few months ago I wrote about some work I was doing on improving the way a certain kind of particle is detected at CMS, by replacing the existing algorithm with a neural network. I recently resumed this work and have now got to the point where I show significant improvement over the existing method. The design of the neural network, however, was one that I imported from some other work, and what I want to do is to adjust it to better suit my problem. Continue reading “Adjusting hyper-parameters: First step into Bayesian optimisation of DNNs”
Continuing the series of 101 things to do in the cramped confines of a budget airliner:
Last Saturday evening I flew back from the mid-term meeting of my research network. The trip from Brussels to Lisbon takes about three hours, and since my current work requires an internet connection, I’d planned to relax (as best I could). Idle thoughts, however, during a pre-flight Duvel had got me thinking about autoencoders. Continue reading “Classification with autoencoders: idle thought to working prototype in 2 hours”
So, it’s been a while since my last post, apologies for that, but the summer has been both busy and eventful, so let me summarise what’s been happening. Continue reading “Summer activities at LIP-Lisbon”
One of the best parts of being a physics PhD student is having the chance to broaden your knowledge by attending seminars and schools especially designed for helping you to be more efficient in your research. I was fortunate to have such an opportunity by attending the first CMS Physics Object School (POS) which took place from September 4th to 8th in Bari, Italy. Continue reading “First CMS Physics Object School in Bari”
On the 19th of May I was very glad to take part in the RooStats tutorial organised by the AMVA4NewPhysics Network as a part of a workshop in Oviedo. RooStats is a ROOT library that uses the “RooFit” package, and provides classes to perform statistical analysis. The tutorial was attended by all the ESR from our Network, among which I was the only non-physicist. I am a statistician who does not use ROOT at all. For this reason, my attendance at the tutorial could seem Continue reading “My impressions on the RooStats Tutorial”
Have you ever wondered how Facebook suggests the tags for the picture you post on your wall, or how the photo library on your computer manages to automatically create albums containing pictures of particular people? Well, they use facial recognition software based on Convolutional Neural Network (CNN).
CNN is the most popular and effective method for object recognition, and it is a specialized kind of neural network for processing data that has a known grid-like topology. The network employs a mathematical operation Continue reading “Convolutional Neural Networks and neutrinos”