A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.
The production of dark matter particles from confining dark sectors may lead to many novel experimental signatures. Depending on the details of the theory, dark quark production in proton-proton collisions could result in semivisible jets of particles: collimated sprays of dark hadrons of which only some are detectable by particle collider experiments. The experimental signature is characterised by the presence of reconstructed missing momentum collinear with the visible components of the jets. This complex topology is sensitive to detector inefficiencies and mis-reconstruction that generate artificial missing momentum. With this work, we propose a signal-agnostic strategy to reject ordinary jets and identify semivisible jets via anomaly detection techniques. A deep neural autoencoder network with jet substructure variables as input proves highly useful for analyzing anomalous jets. The study focuses on the semivisible jet signature; however, the technique can apply to any new physics model that predicts signatures with anomalous jets from non-SM particles.
A 280 ml liquid hydrogen target has been constructed and tested for the MUSE experiment at PSI to investigate the proton charge radius via simultaneous measurement of elastic muon-proton and elastic electron-proton scattering. To control systematic uncertainties at a sub-percent level, strong constraints were put on the amount of material surrounding the target and on its temperature stability. The target cell wall is made of 120 µm-thick Kapton ® , while the beam entrance and exit windows are made of 125 µm-thick aluminized Kapton ® . The side exit windows are made of Mylar ® laminated on aramid fabric with an areal density of 368 g/m 2 . The target system was successfully operated during a commissioning run at PSI at the end of 2018. The target temperature was stable at the 0.01 K level. This suggests a density stability at the 0.02% level, which is about a factor of ten better than required.
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