Search

AMVA4NewPhysics

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

Author

GilesStrong

Particle physicist working at LIP-Lisbon as part of the AMVA4NewPhysics ITN and the CMS collaboration. PhD student at IST, Lisbon. Research interests include di-Higgs production, multivariate analysis and machine learning techniques, and heavy-flavour modelling in Monte Carlo generation.

Higgs Hacking

by Giles Strong

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”

Staying at CERN

by Giles Strong

Bonjour! As I write, I’m three weeks into my month long secondment at CERN, near Geneva. CERN, home to the Large Hadron Collider, is the world’s largest particle-physics research centre. It is also the location of the CMS experiment, which I work on. Continue reading “Staying at CERN”

Efficiency revisited

by Giles Strong

Cover photo unrelated – it’s just some rad fractal broccoli.

Just over one and a half years ago I wrote a post on some of the tips and tricks I’d found useful in trying to organise myself and improve my efficiency. Searching for a post topic, it was suggested that I revisit this to compare how my workload and approaches have changed, so here goes! Continue reading “Efficiency revisited”

Adjusting hyper-parameters: First step into Bayesian optimisation of DNNs

by Giles Strong

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”

On acquiring skills – shu, ha, ri

by Giles Strong

For the past six or so years, I’ve practised a martial art called Shorinji Kempo. Like many other arts, it incorporates several philosophies and concepts. One of these, The Three Teachings of Ken, concerns one’s progression in learning the various techniques. Simply put, it describes three stages of mastery: shu – learn & copy, ha – adjust & adapt, ri – master & break free. Continue reading “On acquiring skills – shu, ha, ri”

Classification with autoencoders: idle thought to working prototype in 2 hours

by Giles Strong

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”

Summer activities at LIP-Lisbon

by Giles Strong

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”

CT-PPS Detector Alignment

by Giles Strong

Continuing on from my last post, in which I described part of the service work I am doing in the CMS experiment, I’ll now give an overview of the second project I work on, which takes place in the context of the CT-PPS sub-detector of the CMS experiment.

CT-PPS, located on both sides of the main bulk of CMS some 200 metres from the interaction point, stands for CMS-TOTEM Precision Proton Spectrometer. The experiment is a joint project Continue reading “CT-PPS Detector Alignment”

Tau Identification At CMS With Neural Networks

by Giles Strong

Both the CMS and ATLAS collaborations are pretty vast, with around 5000 qualified scientist between them, and even more members working towards qualification. Everyone listed as ‘qualified’ will be listed as an author on any publication the collaboration produces, regardless of who actually did the major work for the analysis. Continue reading “Tau Identification At CMS With Neural Networks”

Blog at WordPress.com.

Up ↑