CP Violation, an experimental perspective

I present a review of current and near-future experimental investigations of CP violation. In this review, I cover limits on particle electric dipole moments (EDMs) and CP violation studies in the K and B systems. The wealth of results from the new B factories provide impressive constraints on the CKM quark mixing matrix elements. Current and future measurements are focusing on processes dominated by loop diagrams, which probe physics at high mass scales in low-energy experiments.Comment: Invited plenary talk, DPF meeting, August 200

Designing Observables for Measurements with Deep Learning

Many analyses in particle and nuclear physics use simulations to infer fundamental, effective, or phenomenological parameters of the underlying physics models. When the inference is performed with unfolded cross sections, the observables are designed using physics intuition and heuristics. We propose to design optimal observables with machine learning. Unfolded, differential cross sections in a neural network output contain the most information about parameters of interest and can be well-measured by construction. We demonstrate this idea using two physics models for inclusive measurements in deep inelastic scattering.Comment: Submitted to EPJ

Designing observables for measurements with deep learning

Abstract: Many analyses in particle and nuclear physics use simulations to infer fundamental, effective, or phenomenological parameters of the underlying physics models. When the inference is performed with unfolded cross sections, the observables are designed using physics intuition and heuristics. We propose to design targeted observables with machine learning. Unfolded, differential cross sections in a neural network output contain the most information about parameters of interest and can be well-measured by construction. The networks are trained using a custom loss function that rewards outputs that are sensitive to the parameter(s) of interest while simultaneously penalizing outputs that are different between particle-level and detector-level (to minimize detector distortions). We demonstrate this idea in simulation using two physics models for inclusive measurements in deep inelastic scattering. We find that the new approach is more sensitive than classical observables at distinguishing the two models and also has a reduced unfolding uncertainty due to the reduced detector distortions

Improved Wind and Rain Estimation Over the Ocean Using QuikSCAT

The QuikSCAT scatterometer has proved to be a valuable tool in measuring the near-surface wind vector over the ocean. In raining conditions the instrument effectiveness is diminished by rain contamination of the radar return. To compensate for rain effects, two alternative estimation techniques have been proposed, simultaneous wind-rain retrieval and rainonly retrieval, which are appropriate under certain conditions. This paper proposes and outlines a Bayes estimator selection technique whereby a best estimate is selected from the simultaneous wind-rain, the rain-only and the conventional wind-only estimates. In this paper the Bayes estimator selection technique is introduced with a quick overview of the application to QuikSCAT wind and rain estimation. Results are demonstrated at both conventional and high resolutions for a case study which indicate that wind and rain estimates after Bayes estimator selection are more consistent with measured rain and have reduced noise levels over those produced by any of the individual estimators

Prevalence of traumatic brain injury amongst children admitted to hospital in one health district : a population-based study

There is a dearth of information regarding the prevalence of brain injury, serious enough to require hospital admission, amongst children in the United Kingdom. In North Staffordshire a register of all children admitted with traumatic brain injury (TBI) has been maintained since 1992 presenting an opportunity to investigate the incidence of TBI within the region in terms of age, cause of injury, injury severity and social deprivation. The register contains details of 1553 children with TBI, two thirds of whom are male. This population-based study shows that TBI is most prevalent amongst children from families living in more deprived areas, however, social deprivation was not related to the cause of injury. Each year, 280 per 100,000 children are admitted for ≥24 hours with a TBI, of these 232 will have a mild brain injury, 25 moderate, 17 severe, and 2 will die. The incidence of moderate and severe injuries is higher than previous estimates. Children under 2 years old account for 18.5% of all TBIs, usually due to falls, being dropped or non-accidental injuries. Falls account for 60% of TBIs in the under 5s. In the 10-15 age group road traffic accidents were the most common cause (185, 36.7%). These findings will help to plan health services and target accident prevention initiatives more accurately

Automated classification of three-dimensional reconstructions of coral reefs using convolutional neural networks

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hopkinson, B. M., King, A. C., Owen, D. P., Johnson-Roberson, M., Long, M. H., & Bhandarkar, S. M. Automated classification of three-dimensional reconstructions of coral reefs using convolutional neural networks. PLoS One, 15(3), (2020): e0230671, doi: 10.1371/journal.pone.0230671.Coral reefs are biologically diverse and structurally complex ecosystems, which have been severally affected by human actions. Consequently, there is a need for rapid ecological assessment of coral reefs, but current approaches require time consuming manual analysis, either during a dive survey or on images collected during a survey. Reef structural complexity is essential for ecological function but is challenging to measure and often relegated to simple metrics such as rugosity. Recent advances in computer vision and machine learning offer the potential to alleviate some of these limitations. We developed an approach to automatically classify 3D reconstructions of reef sections and assessed the accuracy of this approach. 3D reconstructions of reef sections were generated using commercial Structure-from-Motion software with images extracted from video surveys. To generate a 3D classified map, locations on the 3D reconstruction were mapped back into the original images to extract multiple views of the location. Several approaches were tested to merge information from multiple views of a point into a single classification, all of which used convolutional neural networks to classify or extract features from the images, but differ in the strategy employed for merging information. Approaches to merging information entailed voting, probability averaging, and a learned neural-network layer. All approaches performed similarly achieving overall classification accuracies of ~96% and >90% accuracy on most classes. With this high classification accuracy, these approaches are suitable for many ecological applications.This study was funded by grants from the Alfred P. Sloan Foundation (BMH, BR2014-049; https://sloan.org), and the National Science Foundation (MHL, OCE-1657727; https://www.nsf.gov). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript

Optimizing Observables with Machine Learning for Better Unfolding

Most measurements in particle and nuclear physics use matrix-based unfolding algorithms to correct for detector effects. In nearly all cases, the observable is defined analogously at the particle and detector level. We point out that while the particle-level observable needs to be physically motivated to link with theory, the detector-level need not be and can be optimized. We show that using deep learning to define detector-level observables has the capability to improve the measurement when combined with standard unfolding methods