Machine Learning for particle physics

Student Brendan Martin

Brendan Martin is currently in year 4 of the MPhys Mathematical Physics degree.  He completed a Career Development Summer Project in machine learning.


I worked in the area of machine learning for particle physics. Machine Learning can be an extremely useful tool for analysing data from experiments – in classifying particles or identifying interesting event topologies, for example. Designing accurate, computationally cheap algorithms is therefore hugely important. Under the supervision of Prof Luigi Del Debbio, I investigated the relationship between the bias, variance and noise of a given data set using a deep neural network as an estimator. I gained insight into the fascinating, quickly developing field of machine learning whilst simultaneously improving my programming skills.

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Discovering what makes you tick

CubeSat

Chris Acheson reflects on the Career Development Summer Project he completed, as well as the employment he has undertaken since graduating with an MPhys in Physics in June 2018.


By the end of Senior Honours year on my MPhys degree I was feeling pretty lost. I didn’t know what I wanted to do after graduating, and was generally feeling pretty demotivated on the course. On a whim, I applied for a summer placement in industry with an interesting sounding CubeSat software company, Bright Ascension. After a friendly telephone interview, I was informed that my application had been successful. What followed was a fascinating summer.

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