Model independent searches for new physics with supervised and unsupervised learning

New methods and tools involving neural networks and machine learning algorithms are to be used and optimized, using real data as well as simulated data based on various theoretical implementations, in order to correctly interpret any signal of new physics and distinguish it from regular Standard Model electroweak and strong processes. 

1. Make use of unsupervised ML on real data to detect possible anomalies. Test the whole procedure by injecting several new physics theory models (in terms of Monte Carlo simulated data) and iterate until the algorithm  / network is “ trained” to be able to discover new physics in the actual real data. If new physics is not found in the current very large dataset (140 /fb) already available, the resulting ML will be ready to run on the new coming data to be taken by the LHC from 2022, possibly just in time to be ready by the end of the Master program (at least a decent data sample is expected in 2022). Some References: 1) Internal ATLAS presentation: ATLAS exotics workshop Sept 2021.pdf; 2) Dark Machines for a challenge focused on outlier detection https://arxiv.org/abs/2105.14027 ; 3) LHC Olympics 2020 for a challenge that includes both outlier and group anomaly detection approaches https://arxiv.org/abs/2101.08320 4) Hunting anomalies with an AI trigger https://cerncourier.com/a/hunting-anomalies-with-an-ai-trigger/ 
2. The various scenarios of new physics theories discussed in the projects document may show up in a given process at the LHC. Model independent searches for new physics are proposed in final states with leptons recorded with the ATLAS detector. New methods and tools involving neural networks are to be used and optimized, using real data as well as simulated data based on various theoretical implementations, in order to correctly interpret any signal of new physics and distinguish it from regular SM electroweak and strong processes. Let us, for example, concentrate on final states with two leptons and missing transverse energy. The main SM background comes from pp→W+W-+X→ l+l-+ννbar (MET)+X and pp→Z(→l+l-) Z(→ννbar)+X→ l+l-+MET+X, as well as pp→ Z (→ l+l-) H(→ invisible)+X. New physics, on the other hand, may come from several of the processes discussed detailed in the document document: production of pairs of direct sleptons or lightest charginos (1a, 1b), mono-Z and mono-Z’ (3a and 3b), and more. The goal is to study various variables, such as M(ll), MET, MT2, decay angles and more, and feed them into a neural network in order to characterise any signal beyond the SM and possibly identify the new physics at work. Reference example: Searching for exotic particles in high energy collisions with deep learning 

Finally the two proposed projects will perform some comparisons of the performance, I.e. compare the unsupervised versus the supervised learning on some given new physics models. There will be a chance to look at the first data from LHC run-3 at 13.6 TeV.