Fabrication and First Full Characterisation of Timing Properties of 3D Diamond Detectors

Tracking detectors at future high luminosity hadron colliders are expected to be able to stand unprecedented levels of radiation as well as to efficiently reconstruct a huge number of tracks and primary vertices. To face the challenges posed by the radiation damage, new extremely radiation hard materials and sensor designs will be needed, while the track and vertex reconstruction problem can be significantly mitigated by the introduction of detectors with excellent timing capabilities. Indeed, the time coordinate provides extremely powerful information to disentangle overlapping tracks and hits in the harsh hadronic collision environment. Diamond 3D pixel sensors optimised for timing applications provide an appealing solution to the above problems as the 3D geometry enhances the already outstanding radiation hardness and allows to exploit the excellent timing properties of diamond. We report here the first full timing characterisation of 3D diamond sensors fabricated by electrode laser graphitisation in Florence. Results from a 270MeV pion beam test of a first prototype and from tests with a β source on a recently fabricated 55×55μm2 pitch sensor are discussed. First results on sensor simulation are also presented.

Secondary vertex finding in jets with neural networks

Jet classification is an important ingredient in measurements and searches for new physics at particle colliders, and secondary vertex reconstruction is a key intermediate step in building powerful jet classifiers. We use a neural network to perform vertex finding inside jets in order to improve the classification performance, with a focus on separation of bottom vs. charm flavor tagging. We implement a novel, universal set-to-graph model, which takes into account information from all tracks in a jet to determine if pairs of tracks originated from a common vertex. We explore different performance metrics and find our method to outperform traditional approaches in accurate secondary vertex reconstruction. We also find that improved vertex finding leads to a significant improvement in jet classification performance.

On the challenges of searching for GeV-scale long-lived particles at the LHC

Many models of dark matter predict long-lived particles (LLPs) that can give rise to striking signatures at the LHC. Existing searches for displaced vertices are however tailored towards heavy LLPs. In this work we show that this bias severely affects their sensitivity to LLPs with masses at the GeV scale. To illustrate this point we consider two dark sector models with light LLPs that decay hadronically: a strongly-interacting dark sector with long-lived exotic mesons, and a Higgsed dark sector with a long-lived dark Higgs boson. We study the sensitivity of an existing ATLAS search for displaced vertices and missing energy in these two models and find that current track and vertex cuts result in very low efficiency for light LLPs. To close this gap in the current search programme we suggest two possible modifications of the vertex reconstruction and the analysis cuts. We calculate projected exclusion limits for these modifications and show that they greatly enhance the sensitivity to LLPs with low mass or short decay lengths.

Event vertex reconstruction with deep neural networks for the DarkSide-20k experiment

While deep learning techniques are becoming increasingly more popular in high-energy and, since recently, neutrino experiments, they are less confidently used in direct dark matter searches based on dual-phase noble gas TPCs optimized for low-energy signals from particle interactions. In the present study, the application of modern deep learning methods for event vertex reconstruction is demonstrated with an example of the 50-tonne liquid argon DarkSide-20k TPC with 8200 photosensors. The developed methods successfully reconstruct event positions within sub-cm precision and apply to any dual-phase argon or xenon TPC of arbitrary size with any sensor shape and array pattern.

LHC Experiments

The chapter gives an overview of the track and vertex reconstruction methods of the LHC experiments that were used in production during Run 2 of the LHC, which ended in autumn of 2018.