Interdisciplinary Machine Learning Methods for Particle Physics

Following the discovery of a Higgs boson-like particle in the summer of 2012 at the Large Hadron Collider (LHC) at CERN, the high-energy particle physics community has prioritized its thorough study. As part of a comprehensive plan to investigate the many combinations of production and decay of the Standard Model Higgs boson, this thesis describes a continued search for this particle produced in association with a leptonically-decaying vector boson (i.e. a W or Z boson) and decaying into a pair of tau leptons. In Run 1 at the LHC, ATLAS researchers were able to set an upper constraint on the signal strength of this process at μ = σ/σ_SM \u3c 5.6 with 95% confidence using 20.3 fb^-1 of collision data collected at a center-of-mass energy of √s = 8 TeV. My thesis work, which builds upon and extends the Run 1 analysis structure, takes advantage of an increased center-of-mass energy in Run 2 of the LHC of √s = 13 TeV as well as 139 fb^-1 of data, approximately seven times the amount used for the Run 1 analysis. While the higher center-of-mass energy in Run 2 yields a higher expected cross-section for this process, the analysis faces the additional challenges of two newly-considered final states, a higher number of simultaneous interactions per event, and a novel neural network-based background estimation technique. I also describe advanced machine learning techniques I have developed to support tau identification in the ATLAS High-Level Trigger as well as predicting and analyzing the dynamics of many-body systems such as 3D motion capture data of choreography

A Hydrology Temperature Model for a Small Mountain Watershed

A small mountain watershed located in the Wasatch Mountains of North Central Utah is calibrated to a lumped, deterministic simulation model which is capable of predicting daily streamflow and stream temperature. The input information required is daily precipitation and maximum and minimum air temperatures. In this study, the area of watershed modeling is reviewed in general and as it specifically applies to the study watershed. The degree of correlation between observed data and predicted output is only mediocre. The model remains unverified for streamflow prediction and is poorly verified for stream temperature

The Role of Physical Activity on Cardiovascular Disease Risk Factors in Postmenopausal Women

Cardiovascular disease (CVD) is the leading cause of death among women in the US. CVD is still thought to be a "man's disease" and remains underappreciated by the public and under treated by health-care providers. Yet, unlike men, a large proportion of deaths attributable to CVD occur in asymptomatic women, making early detection and diagnosis difficult. Therefore, both the development of primary CVD prevention strategies to decrease the risk of CVD and screening tools that will aid in the early detection of women who are at increased risk for CVD has major public health implications. Hormone therapy (HT) has been shown to beneficially affect adverse changes to CVD risk factors that occur during menopause; however, HT is no longer indicated for general CVD prevention. Increased physical activity (PA) levels, either separately or as part of a lifestyle intervention, may decrease CVD risk in women; however, previous reports have not adequately accounted for concurrent changes in HT status.Strategies for primary CVD prevention and early detection in postmenopausal women were examined using 508 women from the Woman on the Move through Activity and Nutrition (WOMAN) study. At baseline, PA was found to be related to more favorable lipid and lipoprotein subclass levels; however, some of these associations were influenced by current HT use. Results at 18 months suggested that a lifestyle intervention was effective for general CVD risk factor reduction regardless of HT continuation or discontinuation. Additionally, lifestyle appeared to attenuate increases in lipid levels that resulted from discontinuing HT. Finally, a simple walking endurance test may provide supplemental information when ascertaining CVD risk in women. In the post-WHI era, concern and confusion about the risks associated with HT has left women and health-care providers searching for alternative means to decrease risk of CVD. Findings from the current report suggest that a non-pharmacological approach for CVD risk factor reduction is both safe and effective for primary CVD prevention in postmenopausal women. In light of the current controversies surrounding the use of HT, the promotion of healthy lifestyle behaviors for CVD risk factor reduction has important public health implications

A Nation in Concert: The Role of the National Song Festivals in the Estonian Independence Movement, 1987-1991

This paper examines the role of the national song festivals in the Estonian independence movement during the years 1987-1991. Drawing from theory on social movements, collective action, nationalism, identity formation, collective memory, musicology, and festival studies, I argue that the song festivals created the atmosphere of mass confidence, euphoria, safety, and solidarity that propelled the Estonian people to demand independence from the Soviet Union. I take a cognitive, individual-based perspective of the political events to emphasize the significance of micro-level explanation of political protest and participation. This research was informed by qualitative interviews with native Estonians and contemporary publications of The Current Digest of the Soviet Press. Analysis of the song festivals in the context of theory on nation-building and collective action, supplemented by qualitative materials, suggests that the festivals played a much larger role in the movement than most of the literature acknowledges. My findings reinforce the need for cognitive, individual-level research to explain processes of political mobilization

Fast Point Cloud Generation with Diffusion Models in High Energy Physics

Many particle physics datasets like those generated at colliders are described by continuous coordinates (in contrast to grid points like in an image), respect a number of symmetries (like permutation invariance), and have a stochastic dimensionality. For this reason, standard deep generative models that produce images or at least a fixed set of features are limiting. We introduce a new neural network simulation based on a diffusion model that addresses these limitations named Fast Point Cloud Diffusion (FPCD). We show that our approach can reproduce the complex properties of hadronic jets from proton-proton collisions with competitive precision to other recently proposed models. Additionally, we use a procedure called progressive distillation to accelerate the generation time of our method, which is typically a significant challenge for diffusion models despite their state-of-the-art precision.Comment: 11 pages, 8 figure

Experiments in road surfacing, Bulletin, no. 77

The Bulletin is a publication of the New Hampshire Agricultural Experiment Station, College of Life Sciences and Agriculture, University of New Hampshire, Durham, New Hampshire

The Prairie Post Quarterly Newsletter of the High Plains Regional Climate Center- October 2017

Inside this issue: Message from the interim director........................................1 Staff spotlight...........................1 Tribal engagement.................2 Product highlights..................3 Update on regional climate conditions..................................4 ENSO tool...................................4 Wind River project..................5 Recent and upcoming travel and activities.............................

Enhancing ACIS Maps: Increasing Usability through a GIS Portal

The High Plains Regional Climate Center (HPRCC) is one of six NOAA Regional Climate Centers (RCCs) in the United States that aims to provide timely climate data and information to the public for cost-effective decision-making. As part of a three-tiered approach to climate services, the RCCs address needs on the national, regional, state, and local scales for a variety of sectors including agriculture, energy, natural resource management, research, transportation, and water resources. Working together, the RCCs develop and disseminate a wide range of valueadded climate products and services. One of the HPRCC’s most popular products is the Applied Climate Information System (ACIS) Climate Summary Maps, which have been in production since 2003 (Fig. 1; https://hprcc.unl.edu/maps .php?map=ACISClimateMaps). The maps are utilized by a variety of sectors and are often used in print and online publications. Over time, the maps have become a staple for climate and drought monitoring as they are updated on a daily basis using near-real-time temperature and precipitation data. To provide quick and efficient access for users, all maps are pre-generated utilizing the Grid Analysis and Display System (GRADS) and its Cressman interpolation scheme, with static images appearing instantly when requested. The pregeneration process takes about 7 hours, using over 10 million data values, which are aggregated to create 18,576 maps on a daily basis. Each of these maps is created for a predefined list of time periods (last 7 days, last 14 days, last 30 days, etc.) and areas at individual state, regional, and national levels (https://hprcc.unl.edu/products /ACIS_Products.pdf). In a typical week, these maps are accessed over 20,000 times by visitors to the HPRCC website. Due to the volume of users and their changing needs, map offerings have expanded over the years to meet specific user requests. Since the inception of the project, new variables have been added, including the Standardized Precipitation Index (SPI), based on a two-parameter Gamma distribution (McKee et al. 1993), as well as new regions, such as the Missouri River basin and the Corn Belt. Most recently, all 50 states and U.S. territories were added as individual map options