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The Feynman quantum-classical isomorphism between classical statistical mechanics in 3+1 dimensions and quantum statistical mechanics in 3 dimensions is used to relate classical polymer self-consistent field theory to quantum density functional theory. This allows the theorems of density functional theory, which connect single particle density descriptions of quantum systems to wave function descriptions, to relate non-relativistic quantum mechanics back to a classical statistical mechanical derivation of polymer self-consistent field theory for ring polymers. In turn, this allows for a quantum-classical isomorphic interpretation of quantum foundations which may require fewer postulates than standard approaches to quantum mechanics, while preserving all quantum predictions.

CAP Virtual Congress 2021

A Quantum-Classical Isomorphic Interpretation of Quantum Foundations Based on Density Functional Theory and Polymer Self-Consistent Field Theory

polymer physics

quantum foundations

density functional theory

The 2020 AAPT Virtual Summer Meeting brought together physics educators from around the globe to learn about the latest in physics theory, research methodology and education from leaders in their field. The program featured plenary speakers, sessions, workshops and many special activities.

For more information please visit the 2020 AAPT Virtual Summer Meeting page at: https://www.aapt.org/Conferences/SM2020/index.cfm.


AAPT SM 20

The 2020 AAPT Virtual Summer Meeting brought together physics educators from around the globe to learn about the latest in physics theory, research methodology and education from leaders in their field. 

technical paper

**WELCOME TO THE 2021 CAP VIRTUAL CONGRESS UNDERLINE PLATFORM**

The CAP staff and our organizing and program committees are pleased to welcome you to our 2021 Congress filled with plenary talks, technical sessions/talks, student presentations and competition sessions, workshops, and several symposia offered by the CAP divisions and its partners.  

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Registered delegates should use the schedule button to call up the details of the session/event they wish to attend.  Each sessions includes a link to access the online platform where the event is taking place, either within Underline or through other Congress-related Zoom meetings.  

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General information about the Congress can be found on the CAP website Congress pages at https://www.cap.ca/congress/2021.

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We look forward to sharing this exciting week with you!


Objective: Dose distribution index (DDI) is a dose-volume parameter used in the treatment planning evaluation. DDI provides the dosimetric estimates on the target coverage, sparings of all organs-at-risk and remaining healthy tissue in the treated organ in a single parameter. In this study, the DDI value was predicted by machine learning model using different algorithms. 

Methods: The DDI were calculated using its original formula by definition. On the other hand, machine training was carried out to determine the DDI using the same data of 50 prostate volumetric modulated arc therapy (VMAT) plans from the Grand River Regional Cancer Centre, Kitchener, Ontario. Machine learning algorithms such as linear regression, tree regression, support vector machine (SVM) and Gaussian process regression (GPR) were used to predict the DDI value for each prostate VMAT treatment plan. For comparing the performance of the machine learning algorithms, root mean square error (RMSE), prediction time of the machine learning and training time were determined and compared.

Results: Comparing the RMSE values among all algorithms, only the DDI predicted by the medium and coarse tree regression algorithms showed a relatively large RMSE values in the range of 0.021 – 0.034. For other algorithms such as SVM and GRP, they all performed very well in predicting the DDI with smaller RMSE values ranging from 0.0038 to 0.0193. By considering other factors such as prediction speed and training time, the square exponential GPR algorithm had the smallest RMSE value of 0.0038, a relatively high prediction speed of 4,100 observation per second and a short machine training time of 0.18 second.

Conclusion: It is concluded that the family of GPR algorithms performed best in the dose distribution index prediction. It is expected that the accuracy of DDI prediction will increase with more plan data trained using such algorithm.

Evaluating Machine Learning Models in Predicting the Dose Distribution Index in Radiation Treatment Planning QA

Objective: We built a RT Bot, a chatbot with characterization for the patient, general public and radiation staff to provide educational information regarding radiotherapy using the artificial intelligence. The Bot was personalized by machine learning to detect the user’s temperament and intent in order to provide the best guidance to the user with a human-like response. 

Methods: The Bot was developed using the IBM Watson Assistant functionalities on the IBM cloud. Dataset of information was prepared for different user groups such as descriptions of all processes in radiotherapy, promotion of cancer screening especially high fatality and popular cancer sites, and basic cancer preventive measures such as how to maintain healthy life with suitable diet and exercise. To ensure correct information can be understood and digested by the users with their background (patient, general public and radiation staff), the Bot character was personalized through the IBM Watson Assistant functionalities such as natural language understanding, entities and slots.

Results: The Bot can be operated in a front-end window on any Internet-of-things such as smartphone, tablet, laptop and desktop. In the beginning, the Bot will communicate with the user intentionally with an introduction. The user can then type in any text to answer concerning their enquiry. The Bot usually begins by answering simple questions regarding radiotherapy and providing related information. If the Bot cannot understand the user’s wording, it will provide a guidance to help the user. 

Conclusion: A chatbot was built for interdisciplinary educational purpose for the patient, general public and radiation staff using artificial intelligence and machine learning. The Bot may be used by a cancer centres or some private sectors such as high school, community centres, volunteer group and charities, which promote cancer preventive measures and screening for a healthy life or educate user what is cancer and radiotherapy.

An AI-assisted Chatbot for Education in Radiotherapy

Negative Ion Source Development for Accelerator Mass Spectrometry
CJ Tiessen, WE Kieser, and XL Zhao
Accelerator mass spectrometry (AMS) is a highly sensitive technique used for the analysis of long-lived radioisotopes. While carbon-14 dating is the most well known application, AMS can be used to measure other isotopes such as beryllium-10, aluminum-26, iodine-129, and uranium-236 which are useful in geology, archeology, environmental tracer and chronology studies, nuclear waste monitoring, and nuclear forensics. The technique uses a combination of electrostatic analyzers, mass-separation magnets, electrostatic lenses, as well as a tandem accelerator. In the accelerator, an electron stripping gas canal is used to convert incoming negative ions to positive ions while simultaneously disintegrating molecular isobars. Ions from the samples are injected into the accelerator using a cesium sputter negative-ion source. The focus of this work is to model the electrodynamics within the ion source, including the effects of the more intense positive cesium ion beam and the sputtered sample negative-ion beam. Simulations using Integrated Engineering Software’s Lorentz 2E ion optics software will guide the design of a new ion source with the goal of increasing the emitted sample ion current while also improving the emittance of this beam. Following a short overview of the AMS system, details of the ion source, including the mutual space-charge interaction of the two beams, will be presented.

Negative Ion Source Development for Accelerator Mass Spectrometry

The symmetries of unconventional superconductors may be classified by the locations of their gap nodes. Recently, the role of spin-orbit coupling (SOC) has become important, as sufficiently strong SOC generates novel mixed-parity superconductivity. In this talk, I show that the nodal structure of unconventional superconductors may be determined by angle-dependent magneto-thermal conductivity measurements, provided the SOC is larger than the quasiparticle scattering rate. This effect is complementary to vortex-induced magneto-thermal oscillations identified previously, and is dominant in strongly anisotropic materials. As an application, I present results for the magneto-thermal conductivity of the "Rashba bilayer" YBa$_2$Cu$_3$O$_{6.5}$, which possesses a so-called "hidden spin-orbit coupling." We find that the SOC endows $\kappa_{xx}/T$ with a characteristic field-angle dependence that should be easily observed experimentally.

Magneto-Thermal Conductivity Oscillations in Spin-Orbit-Coupled Nodal Superconductors

We report a formal analogy between cosmology and earth science. The history of a closed universe is analogous to an equilibrium beach profile (i.e., the depth of the water as one recedes from a beach moving seaward). A beach profile reaches equilibrium in summer and in winter and is described by a variational principle that minimizes energy dissipation. The oceanography side of the analogy gains much needed information from the cosmology side. 

[Based on V. Faraoni, Phys. Rev. Research 1, 033002.]

Cosmology on the Beach: a formal analogy

The quest to develop a fundamental understanding of the nuclear interaction is aided greatly through measurements made with complex, multi-detector systems. As these systems allow for both an increase in total efficiency due to greater angular coverage as well as correlation measurements in both time and space, the information which can be probed using these detectors is greatly increased over single-detector systems.

With an increase in both number of detectors and complexity of detector systems comes challenges in data acquisition. As each detector can obtain data independently, the required throughput of the data acquisition system also increases. To address this problem, real-time event filtering can be performed on the data to only process and store events with certain characteristics. Traditional methods for event filtering require large arrays of electronics to perform logic on unprocessed data. This approach comes with a large up front cost both in money and effort in order to purchase, set up, and test electronics. Finally, for small laboratories, the scaling of these approaches can be a limiting factor as an increasing number of detectors also increases the number of electronic modules, as well as the space and power consumption of the data acquisition system.

At the Nuclear Science Lab (NSL) at Simon Fraser University (SFU), we implement a single-unit, computer controlled XLM72S universal logic module to perform real-time event filtering. The XLM72S contains a field programmable gate array with two operational firmware developed for the NSL at SFU. The first of these performs both anti-coincidence logic and multiplicity filtering for up to twenty independent Compton Suppressed Spectrometers, as well as coincidence logic with up to four auxiliary detectors. The second is designed to perform coincidence logic on up to 128 individual inputs through the daisy-chaining of multiple XLM72S logic modules, a feature which can be achieved with both firmware. Through daisy-chaining of XLM72S modules, scaling real-time event filtering to large-scale, complex, multi-detector system is easily achievable.

Details of the module and the two firmware developed for and implemented in the NSL at SFU will be discussed in this talk.

Real-Time Event Filtering in Complex Multi-Detector Systems

This is a report on a project to interview the student body of the Concordia University Physics Department concerning teacher-students interaction. The goal of this research is to establish an easily implemented guideline of teaching methods for a professor to use in the classroom. Students were asked to complete a questionnaire that included a request to describe the teaching style of their favourite physics professors. They were asked to describe what makes them excellent to the student. A variety of known and successful pedagogic techniques were also presented to ascertain their appeal to students. Follow-up interviews were conducted in order to fully develop the given answers.

Student preferences for pedagogic techniques

As you know, first gravitational wave event GW150914(binary black hole) has been oberved by LIGO on Sep. 14, 2015. And most importantly, first binary neutron star coalescence event GW170817 has been observed with a signal noise ratio upto 33, which alows us to make constraints on axion field! Here, we first time use axion charged binary neutron star waveform model to give a constaint on axion field.

Constraints on Axions from GW170817

The nonlinear behavior of absolute stimulated Raman scattering (SRS) near the quarter-critical density is investigated using one-dimensional (1D) Vlasov simulations with parameters relevant to ignition-scale direct-drive coronal plasmas. Numerical Vlasov simulations show that a strong and stable Airy pattern is formed by the Raman light as it is generated near its cutoff density. This pattern self-consistently modulates the density profile below the quarter-critical density. The density modulation superimposed in the linear density profile results in a change in the nature of SRS in lower density region from spatial (convective) amplification to temporal (absolute) growth. In addition, strong Langmuir decay instability (LDI) cascades produce daughter Langmuir waves (LWs) that seed SRS below quarter-critical density. These effects act to broaden the spectrum of reflected light. More interestingly, collapse of the primary LWs is observed near their turning point, producing hot electrons. These observations provide a new explanation of hot electron generation and SRS scattered light spectra for ignition-scale experiments.

Nonlinear development of absolute SRS in ignition-scale direct-drive coronal plasmas

It has been shown beyond reasonable doubt that about 95% of the total energy budget of the universe is given by the dark constituents, namely Dark Matter and Dark Energy. What constitutes Dark Matter and Dark Energy remains to be satisfactorily understood however, despite a number of promising candidates. An associated conundrum is that of coincidence, as to why the Dark Matter and Dark Energy densities are of the same order of magnitude at the present epoch. In an attempt to address these, we consider a quantum potential resulting from a quantum corrected Raychaudhuri/Friedmann Equation in presence of a Bose-Einstein condensate (BEC) of light bosons. For a suitable and physically motivated macroscopic ground state wavefunction of the BEC, we show that a unified picture of the cosmic dark sector can indeed emerge, which also resolves the issue of the coincidence. The effective density of the Dark energy component turns out to be a cosmological constant. Furthermore, comparison with observed data give an estimate of the mass of the constituent bosons in the BEC, which is well within the bounds predicted from other considerations.

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Evaluating Machine Learning Models in Predicting the Dose Distribution Index in Radiation Treatment Planning QA

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