Search for light long-lived particles in pp collisions at √s = 13 TeV using displaced vertices in the ATLAS inner detector

A search for long-lived particles (LLPs) using 140 fb−1 of pp collision data with √s = 13 TeV recorded by the ATLAS experiment at the LHC is presented. The search targets LLPs with masses between 5 and 55 GeV that decay hadronically in the ATLAS inner detector. Benchmark models with LLP pair production from exotic decays of the Higgs boson and models featuring long-lived axionlike particles (ALPs) are considered. No significant excess above the expected background is observed. Upper limits are placed on the branching ratio of the Higgs boson to pairs of LLPs, the cross section for ALPs produced in association with a vector boson, and, for the first time, on the branching ratio of the top quark to an ALP and a u/c quark

Accuracy versus precision in boosted top tagging with the ATLAS detector

Abstract The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at √ s = 13 TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available.</jats:p

Longitudinal growth and morphology of the hippocampus through childhood: iImpact of prematurity and implications for memory and learning

The effects of prematurity on hippocampal development through early childhood are largely unknown. The aims of this study were to (1) compare the shape of the very preterm (VPT) hippocampus to that of full-term (FT) children at 7 years of age, and determine if hippocampal shape is associated with memory and learning impairment in VPT children, (2) compare change in shape and volume of the hippocampi from term-equivalent to 7 years of age between VPT and FT children, and determine if development of the hippocampi over time predicts memory and learning impairment in VPT children. T1 and T2 magnetic resonance images were acquired at both term equivalent and 7 years of age in 125 VPT and 25 FT children. Hippocampi were manually segmented and shape was characterized by boundary point distribution models at both time-points. Memory and learning outcomes were measured at 7 years of age. The VPT group demonstrated less hippocampal infolding than the FT group at 7 years. Hippocampal growth between infancy and 7 years was less in the VPT compared with the FT group, but the change in shape was similar between groups. There was little evidence that the measures of hippocampal development were related to memory and learning impairments in the VPT group. This study suggests that the developmental trajectory of the human hippocampus is altered in VPT children, but this does not predict memory and learning impairment. Further research is required to elucidate the mechanisms for memory and learning difficulties in VPT children.Deanne K. Thompson, Cristina Omizzolo, Christopher Adamson, Katherine J. Lee, Robyn Stargatt, Gary F. Egan ... et al