Abstract: A dedicated reconstruction algorithm to find decay vertices in the ATLAS muon spectrometer is presented. The algorithm searches the region just upstream of or inside the muon spectrometer volume for multi-particle vertices that originate from the decay of particles with long decay paths. The performance of the algorithm is evaluated using both a sample of simulated Higgs boson events, in which the Higgs boson decays to long-lived neutral particles that in turn decay to b (b) over bar final states, and pp collision data at root s = 7 TeV collected with the ATLAS detector at the LHC during 2011.

Abstract: A high-resolution PET system, which incorporates a silicon detector probe into a conventional PET scanner, has been proposed to obtain increased image quality in a limited region of interest. Detailed simulation studies have previously shown that the additional probe information improves the spatial resolution of the reconstructed image and increases lesion detectability, with no cost to other image quality measures. The current study expands on the previous work by using a laboratory prototype of the silicon PET-probe system to examine the resolution improvement in an experimental setting. Two different versions of the probe prototype were assessed, both consisting of a back-to-back pair of 1-mm thick silicon pad detectors, one arranged in 32 x 16 arrays of 1.4mm x 1.4mm pixels and the other in 40 x 26 arrays of 1.0mm x 1.0mm pixels. Each detector was read out by a set of VATAGP7 ASICs and a custom-designed data acquisition board which allowed trigger and data interfacing with the PET scanner, itself consisting of BGO block detectors segmented into 8 x 6 arrays of 6mm x 12mm x 30mm crystals. Limited-angle probe data was acquired from a group of Na-22 point-like sources in order to observe the maximum resolution achievable using the probe system. Data from a Derenzo-like resolution phantom was acquired, then scaled to obtain similar statistical quality as that of previous simulation studies. In this case, images were reconstructed using measurements of the PET ring alone and with the inclusion of the probe data. Images of the Na-22 source demonstrated a resolution of 1.5mm FWHM in the probe data, the PET ring resolution being approximately 6 mm. Profiles taken through the image of the Derenzo-like phantom showed a clear increase in spatial resolution. Improvements in peak-to-valley ratios of 50% and 38%, in the 4.8mm and 4.0mm phantom features respectively, were observed, while previously unresolvable 3.2mm features were brought to light by the addition of the probe. These results support the possibility of improving the image resolution of a clinical PET scanner using the silicon PET-probe.

Abstract: A multi-TeV muon collider offers a spectacular opportunity in the direct exploration of the energy frontier. Offering a combination of unprecedented energy collisions in a comparatively clean leptonic environment, a high energy muon collider has the unique potential to provide both precision measurements and the highest energy reach in one machine that cannot be paralleled by any currently available technology. The topic generated a lot of excitement in Snowmass meetings and continues to attract a large number of supporters, including many from the early career community. In light of this very strong interest within the US particle physics community, Snowmass Energy, Theory and Accelerator Frontiers created a cross-frontier Muon Collider Forum in November of 2020. The Forum has been meeting on a monthly basis and organized several topical workshops dedicated to physics, accelerator technology, and detector R&D. Findings of the Forum are summarized in this report.

Abstract: A new algorithm for the determination of the initial flavour of B-s(0) mesons is presented. The algorithm is based on two neural networks and exploits the b hadron production mechanism at a hadron collider. The first network is trained to select charged kaons produced in association with the B-s(0) meson. The second network combines the kaon charges to assign the B-s(0) flavour and estimates the probability of a wrong assignment. The algorithm is calibrated using data corresponding to an integrated luminosity of 3 fb(-1) collected by the LHCb experiment in proton-proton collisions at 7 and 8 TeV centre-of-mass energies. The calibration is performed in two ways: by resolving the B-s(0)-B-s(0) flavour oscillations in B-s(0) -> D-s(-)pi(+) decays, and by analysing flavour-specific B-s2*(5840)(0) -> B+K- decays. The tagging power measured in B-s(0) -> D-s(-)pi(+) decays is found to be (1.80 +/- 0.19 ( stat) +/- 0.18 (syst))%, which is an improvement of about 50% compared to a similar algorithm previously used in the LHCb experiment.

Abstract: A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.