Physics Commons^™

Architecture Of Heptagonal Metallo-Macrocycles Via Embedding Metal Nodes Into Its Rigid Backbone, A.M.Shashika D. Wijerathna, He Zhao, Qiangqiang Dong, Qixia Bai, Zhiyuan Jiang, Jie Yuan, Jun Wang, Mingzhao Chen, Markus Zirnheld, Rockwell T. Li, Yuan Zhang, Yiming Li, Pingshan Wang

College of Sciences Posters

Metal-organic macrocycles have received increasing attention not only due to their versatile applications such as molecular recognition, compounds encapsulation, anti-bacteria and others, but also for their important role in the study of structure-property relationship at nano scale. However, most of the constructions utilize benzene ring as the backbone, which restricts the ligand arm angle in the range of 60, 120 and 180 degrees. Thus, the topologies of most metallo-macrocycles are limited as triangles and hexagons, and explorations of using other backbones with large angles and the construction of metallo-macrocycles with more than six edges are very rare.

In this study, …

Evaluation Of Scalable Quantum And Classical Machine Learning For Particle Tracking Classification In Nuclear Physics, Polykarpos Thomadakis, Emmanuel Billias, Nikos Chrisochoides

The Graduate School Posters

Future particle accelerators will exceed by far the current data size (10¹⁵) per experiment, and high- luminosity program(s) will produce more than 300 times as much data. Classical Machine Learning (ML) likely will benefit from new tools based on quantum computing. Particle track reconstruction is the most computationally intensive process in nuclear physics experiments. A combinatorial approach exhaustively tests track measurements (“hits”), represented as images, to identify those that form an actual particle trajectory, which is then used to reconstruct track parameters necessary for the physics experiment. Quantum Machine Learning (QML) could improve this process in multiple ways, …

Ml-Based Surrogates And Emulators, Tareq Alghamdi, Yaohang Li, Nobuo Sato

College of Sciences Posters

No abstract provided.

Scalable Quantum Edge Detection Method For D-Nisq Imaging Simulations: Use Cases From Nuclear Physics And Medical Image Computing, Emmanuel Billias, Nikos Chrisochoides

The Graduate School Posters

Edge Detection is one of the computationally intensive modules in image analysis. It is used to find important landmarks by identifying a significant change (or “edge”) between pixels and voxels. We present a hybrid Quantum Edge Detection method by improving three aspects of an existing widely referenced implementation, which for our use cases generates incomprehensible results for the type and size of images we are required to process. Our contributions are in the pre- and post-processing (i.e., classical phase) and a quantum edge detection circuit: (1) we use space- filling curves to eliminate image artifacts introduced by the image decomposition, …

A Machine Learning Approach To Denoising Particle Detector Observations In Nuclear Physics, Polykarpos Thomadakis, Angelos Angelopoulos, Gagik Gavalian, Nikos Chrisochoides

College of Sciences Posters

With the evolution in detector technologies and electronic components used in the Nuclear Physics field, experimental setups become larger and more complex. Faster electronics enable particle accelerator experiments to run with higher beam intensity, providing more interactions per time and more particles per interaction. However, the increased beam intensities present a challenge to particle detectors because of the higher amount of noise and uncorrelated signals. Higher noise levels lead to a more challenging particle reconstruction process by increasing the number of combinatorics to analyze and background signals to eliminate. On the other hand, increasing the beam intensity can provide physics …

Lattice Optics Optimization For Recirculatory Energy Recovery Linacs With Multi-Objective Optimization, Isurumali Neththikumara, Todd Satogata, Alex Bogacz, Ryan Bodenstein, Arthur Vandenhoeke

College of Sciences Posters

Beamline optics design for recirculatory linear accelerators requires special attention to suppress beam instabilities arising due to collective effects. The impact of these collective effects becomes more pronounced with the addition of energy recovery (ER) capability. Jefferson Lab’s multi-pass, multi-GeV ER proposal for the CEBAF accelerator, ER@CEBAF, is a 10- pass ER demonstration with low beam current. Tighter control of the beam parameters at lower energies is necessary to avoid beam break-up (BBU) instabilities, even with a small beam current. Optics optimizations require balancing both beta excursions at high-energy passes and overfocusing at low-energy passes. Here, we discuss an optics …

Physics-Informed Neural Networks (Pinns) For Dvcs Cross Sections, Manal Almaeen, Jake Grigsby, Joshua Hoskins, Brandon Kriesten, Yaohang Li, Huey-Wen Lin, Simonetta Liuti, Sorawich Maichum

College of Sciences Posters

We present a physics informed deep learning technique for Deeply Virtual Compton Scattering (DVCS) cross sections from an unpolarized proton target using both an unpolarized and polarized electron beam. Training a deep learning model typically requires a large size of data that might not always be available or possible to obtain. Alternatively, a deep learning model can be trained using additional knowledge gained by enforcing some physics constraints such as angular symmetries for better accuracy and generalization. By incorporating physics knowledge to our deep learning model, our framework shows precise predictions on the DVCS cross sections and better extrapolation on …

Point Cloud-Based Mapper For Qcd Analysis, Tareq Alghamdi, Yasir Alanazi, Manal Almaeen, Nobuo Sato, Yaohang Li

The Graduate School Posters

In many scientific applications, Inverse problems are challenging. An inverse problem is the process of inferring unknown parameters from observable ones. In this poster, we present our prototype using Point Cloud-based Variational Autoencoder mapping. Data that connects parameters to detector level events is used to train the proposed model. A point cloud is used to describe a series of events that keeps the permutation invariant property and geometric correlations of the events while being flexible with the number of events in the input. The trained Point Cloud-based Variational Autoencoder functions as an effective inverse function from detector level events to …

End-To-End Physics Event Generator, Yasir Alanazi, N. Sato, Tianbo Liu, W. Melnitchouk, Michelle P. Kuchera, Evan Pritchard, Michael Robertson, Ryan Strauss, Luisa Velasco, Yaohang Li

College of Sciences Posters

We apply generative adversarial network (GAN) technology to build an event generator that simulates particle production in electron-proton scattering that is free of theoretical assumptions about underlying particle dynamics. The difficulty of efficiently training a GAN event simulator lies in learning the complicated pat- terns of the distributions of the particles physical properties. We develop a GAN that selects a set of transformed features from particle momenta that can be generated easily by the generator, and uses these to produce a set of augmented features that improve the sensitivity of the discriminator. The new Feature-Augmented and Transformed GAN (FAT-GAN) is …

Design, Commissioning And Preliminary Results Of A Magnetic Field Scanning System For Superconducting Radiofrequency Cavities, Ishwari Parajuli, Jeffrey Nice, Gianluigi Ciovati, William Clemens, Jean Delayen, Alex Gurevich

College of Sciences Posters

Superconducting radiofrequency (SRF) cavities are one of the building blocks of modern particle accelerators. Such cavities are typically made of bulk niobium, operate at liquid helium temperature (2 - 4 K) and have some of the highest quality factors found in Nature. One of the leading source of residual losses, which limits the quality factor in SRF cavities, is trapped magnetic flux from either residual ambient magnetic field or thermoelectric currents. The flux trapping mechanism depends on different niobium surface preparations and cool-down conditions. Suitable diagnostic tools are not yet available to study the effects of such conditions on magnetic …

Nb3Sn Coating Of Complex Srf Cavity Structures, Jayendrika Tiskumara, Uttar Pudasaini, Grigory Eremeev, Charlie Reece, Jean Delayen

College of Sciences Posters

In the modern SRF research, Thin films coated niobium cavities are used for the low cost and increased quality factor. Among the potential thin film materials applied on the niobium, performances demonstrated by the Nb3Sn cavities makes this material attractive for SRF accelerator applications giving higher critical temperature and higher accelerating gradients. While the majority of research efforts are currently focused on the development of elliptical single-cell and multi-cell cavities, the potential of this material is evident to other cavity types, which may have complex geometries. We are working towards the development of Nb₃Sn-coated Half-wave resonator and twin …

Direct Visualization Of 3-Dimensional Force And Energy Map Of A Single Molecular Switch, Abeykoon Mudiyanselage Shashika Darshani Wijerathna, Zaw Myo Win, K. Z. Latt, Yang Li, A. T. Ngo, L. Curtiss, R. Zhang, S. W. Hla, Y. Zhang

College of Sciences Posters

Mechanical properties of molecules adsorbed on materials surfaces are increasingly vital for the applications of molecular thin films. Here, we conduct a fundamental research to induce conformational change mechanically on a single molecule and quantify the driving force needed for such molecular shape switch via a low temperature (~ 5K) Scanning Tunneling Microscope (STM) and Qplus Atomic Force Microscope (Q+AFM). Our measurement maps a three-dimensional landscape for mechanical potential and force at single molecule level with high spatial resolution in all three dimensions of a few angstrom (10^-10 m).

Molecule TBrPP-Co (a cobalt porphyrin) deposited on an atomically clean …

The Magnetic Field Penetration Measurement Of Thin Film And Multilayered Superconductors For Srf Cavities, Iresha Harshani Senevirathne, Jean Delayen

College of Sciences Posters

Radio Frequency (RF) Cavities are used in particle accelerators and they are typically formed from or coated with superconducting materials. High purity niobium is the material of choice for SRF cavities and niobium cavities operate at their theoretical field limits. SRF researchers have begun a significant R&D effort to develop alternative materials to continue to keep up with the demands of new accelerator facilities. To achieve high performance with high accelerating gradient, cavity material should have an ability to persist in superconducting state under high magnetic field without magnetic flux penetration through the cavity wall. Therefore, the magnetic field at …

Solving Relativistic Three-Body Integral Equations In The Presence Of Bound States And Resonances, Taylor R. Powell, Raúl A. Briceño, Andrew W. Jackura

Physics: Accelerator and Nuclear Physics at the Thomas Jefferson National Accelerator Facility in Newport News, Virginia

Three-body interactions play an important role throughout modern-day particle, nuclear, and hadronic physics; many experimentally observed reactions of interest for testing the Standard Model result in final states composed of three particles or more. Due to these issues, a full description of three-body interactions from Quantum Chromodynamics is required. The focus of this project was to extend previous results for a two-body subsystem with a bound state to include resonance channels. We first derived a novel single-variable observable, denoted as an intensity distribution, which is proportional to the probability density of the three-body scattering amplitude. We explored this distribution in …

Sulfur Dioxide From The Atmospheric Chemistry Experiment Satellite, Doug Cameron

College of Sciences Posters

The version 4.0 dataset from the Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS) on SCISAT, released in March of 2019, has sulfur dioxide (SO₂) volume mixing ratio (VMR) profiles as a routine data product. From this dataset, global SO₂ distributions between the altitudes of 10.5 km and 23.5 km are analyzed. The global distribution of all SO₂ VMR data by altitude is broken down into 30° and 5° latitude zones. Seasonality of the global SO₂ distribution is explored. Volcanic SO₂ plumes are isolated in the dataset and compared with extinction data from the …