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In any application where a radiation detector is utilized in a mixed radiation field, there is an inherent issue of separating the signal from the background. This research focuses on the separation of neutron and gamma-ray events in a liquid scintillator. The standard solution to this issue leverages the fact that the secondary particles generated in neutron and photon interactions are different; the former being nuclear recoils, typically protons in organic scintillators, and the latter being electrons or positrons in the case of pair production. The charge deposition characteristics of these secondary particles provide the basis for Pulse Shape Discrimination (PSD) as a means of event classification.

The work presented focuses on the development of a machine learning algorithm for use in the problem of pulse shape discrimination. The signals generated from gamma ray and neutron interactions inside the liquid scintillator used are comprised of three characteristic scintillation decay times: 3.16 ns, 32.3 ns, and 270 ns. The proportion of scintillation light generated from these three characteristic times is dictated by the stopping power of the particle traversing the scintillator, generating distinguishable signals for photon interactions relative to neutron interactions. Typically PSD is performed by allowing two different charge collection windows and comparing the amount of charge collected in both; tail-to-total integration. This work utilizes current generation machine vision algorithms for the task of event classification, where the PMT output from the scintillator will be utilized as the input to the machine vision algorithm. Current generation algorithms allow for feature extraction performed simultaneously at differing spatial extents, which enables the algorithm to extract valuable information about the signal from all three time scales present in the scintillator, 3, 30, and 300 ns. This new application of machine vision algorithms will be coupled with a current generation digitizer operating with 12 bit precision and sampling at 3.2 GS/s. This opens up the possibility of utilizing information from the rising edge of the signal, which is on the order of a few nanoseconds.

Training of this algorithm was performed in a supervised manner, which requires that labeled sets of data be provided for the algorithm to learn from. Any improperly labeled data utilized, diminishes the performance of the algorithm, making correctly labeled data paramount to success. Performing this for neutron and gamma-ray events in the scintillator is quite difficult due to the complications mentioned for event classification. The solution used in this project is to utilize an isotopic source of neutrons, PuBe. The PuBe source produces prompt gamma rays in conjunction with neutrons in some decays, with energies greater than 4 MeV. These prompt gamma rays can serve as an event trigger for detection of neutrons in a secondary detector, providing relative certainty that detected events within an appropriate time window are indeed neutron events. Producing a high degree of purity in the training data.

CAP Virtual Congress 2021

Development and Implementation of a Machine Learning Algorithm for Pulse Shape Discrimination

radiation

pulse shape discrimination

neutrons

machine learning

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

technical paper

**WELCOME TO THE 2021 CAP VIRTUAL CONGRESS UNDERLINE PLATFORM**

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Laser-induced breakdown spectroscopy (LIBS) is a laser-based spectrochemical technique that allows a near-instantaneous measurement of the elemental composition of a target by making time-resolved spectroscopic analyses of laser-induced ablation plasmas. Utilizing nanosecond laser pulses and a broadband high-resolution Echelle spectrometer, high signal-to-noise optical emission spectra can be obtained from almost any desired target.

Recently it has been shown by others that ablation of a non-metallic target that has been coated with metallic (gold or silver) nanoparticles causes an increase in optical emission intensity due to the creation of a plasma with a higher temperature and higher electron density. This process is called “nanoparticle enhanced” LIBS or NELIBS. In our laboratory we have begun investigating whether cheaper and easier to acquire monodisperse metallic microparticles can be used to enhance the emission from bacterial cells when they are deposited upon a nitrocellulose paper filter medium.

This presentation will detail our efforts to build a small deposition chamber to reliably and reproducibly deposit a known mass of commercially obtained one micron silver microparticles. The silver microparticles must also be uniformly deposited over the surface of the 9 mm diameter filter medium (achieving a uniform surface mass density) to insure that the enhancement is consistent over the one millimeter diameter circular bacterial deposition area in the center of the filter. 

A nominal surface coverage density of 0.026 μg/mm^2 of silver microparticles was achieved, resulting in a silver ablation mass of 110 pg per laser shot. Using this mass surface density, enhancement of the emission from the different elements present in the bacterial cells was observed and was found to not be consistent, varying from an enhancement factor of 1 up to 8. Efforts to exploit and further develop this phenomenon for detecting lower numbers of bacterial cells will be described.

The Use of Silver Microparticles for Spectrum Emission Enhancement During Laser-Induced Breakdown Spectroscopy of Bacterial Specimens.

Additional spatial dimensions compactified to submillimeter scales serves as an elegant solution to the hierarchy problem. As a consequence of the extra-dimensional theory, primordial black holes can be created by high-energy particle interactions in the early universe. While four-dimensional primordial black holes have been extensively studied, they have received little attention in the context of extra-dimensions. We adapt and extend previous analyses of four-dimensional primordial black holes for the purpose of studying the impact extra-dimensions have on cosmology. We find new constraints on both extra-dimensional primordial black holes, and the fundamental extra-dimensional theories by combining an analysis of Big Bang Nucleosynthesis, the Cosmic Microwave Background, the Cosmic X-ray Background, and the galactic centre gamma-rays. With these constraints we explore to what extent these extra-dimensional primordial black holes can comprise the dark matter in our universe.

Signatures of Primordial Black Holes in theories of Large Extra Dimensions

High-luminosity fixed target experiments provide impressive sensitivity to new light weakly coupled degrees of freedom. In this talk, I will discuss the minimal case of a scalar singlet $S$ coupled to the Standard Model through the Higgs portal, that decays visibly to leptons for scalar masses below the dipion threshold. This UV-complete portal from the SM to a dark sector is of particular interest as one of the generic mediation channels for the interaction with dark matter. The existing dataset from the LSND experiment is found to impose the leading constraints within two mass windows between $m_S \sim$ 100 and 350 $\rm MeV$. In the process, we analyze a number of scalar production channels in the target, finding that proton bremsstrahlung provides the dominant channel at LSND beam energies.

Reference:
arXiv:2004.14515

Fixed target probes for the Higgs portal

In this work, we present a method based on deep learning (DL) to predict the structure of amorphous silicon (a-Si). The accuracy of our approach is validated through training networks to estimate the potential energy. Two architectures, multilayer perceptron (MLP) and convolutional neural network (CNN), have been examined for this purpose.
The models have been trained on a dataset generated by molecular dynamics (MD) simulations. The dataset consists of 216-atom a-Si configurations.
The performances of the two architectures over the test set are compared while they have the same total number of parameters and layers. 
The results of training these two neural networks yield a
root mean square error of the order of 0.1 meV/atom. This error was reported in the range of 2-6 meV/atom in previous works [1-3], which confirms the accuracy of our models. Calculation of the average error shows that CNN performs better than MLP.
After validation of our approach, we trained a generative model, variational autoencoder (VAE), to generate a-Si configurations. The generated structures and their structural properties are comparable to the real ones.
In general, the performances of our models are quite satisfactory, and this suggests that they could be used to approximate the potential energy and generate a-Si structures, and thus be a viable model for investigating disordered systems.



References: 
1. Li, R., Lee, E. and Luo, T., 2020, A unified deep neural network potential capable of predicting the thermal conductivity of silicon in different phases, Materials Today Physics 12, 100181.
2. Comin, M. and Lewis, L.J., 2019, Deep-learning approach to the structure of amorphous silicon, Physical Review B 100, 094107.
3. Behler, J. and Parrinello, M., 2007, Generalized neural-network representation of high-dimensional potential-energy surfaces, Physical Review Letters 98, 146401.

Modeling Amorphous Silicon Using Neural Networks

We explore regions of parameter space that give rise to suppressed direct detection cross sections in a simple model of scalar dark matter with a scalar portal that mixes with the standard model Higgs. We found that even this simple model allows considerable room in the parameter space that has not been excluded by direct detection limits.
While a number of effects leading to this result have been previously noted, our main new result explores interference effects between different contributions to DM annihilation when the DM mass is larger than the scalar portal mass. New annihilation channels open up and the parameters of the model need to compensate to give the correct DM relic abundance, resulting in smaller direct detection cross sections.
We find that even in a very simple model of DM there are still sizeable regions of parameter space that are not ruled out by experiment.

Exploring Direct Detection Suppressed Regions in a Simple 2-Scalar Mediator Model of Scalar Dark Matter

A 3D topological insulator (TI) hosts an odd number of Dirac cones as its 2D surface states spectrum. The states are exponentially localized to the surface and their (pseudo)spin is locked to the surface momentum direction due to spin-orbit interaction. If the TI is also magnetic there are fixed magnetic moments on the surface, co-existing with the itinerant Dirac electrons. The magnetic moments interact with each other both directly (exchange) and indirectly (RKKY) and may form a ferromagnetically ordered state. The magnetic state couples to the Dirac electrons and serves as a Dirac mass which, when uniform, opens a gap in the spectrum. When the ordering of the magnetic moments is modified by excitation such as skyrmions, the Dirac electrons see a landscape of spatially dependent mass. A skyrmion texture binds an electronic state to it which we call a skyrmion-bound state.
In this talk we will see how the bound states can be detected by a surface conductivity measurement and how the skyrmion-skyrmion interaction is altered due to the presence of Dirac electrons. We will also discuss a skyrmion solid background to Dirac electrons.

Domain and Skyrmion bound states on the surface of magnetic topological insulators

Liquid xenon (LXe) is frequently employed to build detectors for rare event searches due to many of its advantageous properties including high stopping power, high ionization and scintillation yields, and relatively high cryogenic operating temperature. Time projection chambers (TPC) with LXe allow for 3D event topology reconstruction and identification which is important for reducing backgrounds. Due to the high drift fields TPCs are operated at, it is crucial to model breakdown properties in LXe. Often, a high voltage (HV) discharge is able to damage the detector instrumentation, e.g. the photo-sensors array. We study the breakdown formation in the context of the EXO-200 experiment, the first generation 0νββ search experiment from the Enriched Xenon Observatory (EXO) collaboration that will be followed by a much higher sensitivity experiment called nEXO. In the EXO-200 detector, instabilities have been observed on the TPC HV line, each accompanied by scintillation VUV light which was detected by the EXO-200 Avalanche Photo Diode (APD) arrays. Using a setup designed to study LXe breakdowns we investigate the origin of the HV instabilities as well as the correlation between those HV glitches and fully formed breakdowns.

High Voltage Breakdowns in Liquid Xenon

For 50 years, TRIUMF has stood at the frontier of scientific understanding as Canada’s particle accelerator centre. Driven by two made-in-Canada cutting edge accelerators - the world’s largest cyclotron, and our new high-power superconducting linear accelerator - we continue to ask the big questions about the origins of the universe and everything in it. 

With over five decades of experience in the production of accelerator-based secondary particles for science, TRIUMF also ensures that Canada remains on the leading edge of supplying radioisotopes, neutrons, photons, and muons enabling fundamental science in the fields of nuclear, particle and astrophysics, as well as solid state and medical sciences and applications. 

ISAC-TRIUMF is the only ISOL facility worldwide that is routinely producing radioisotope beams (RIB) from secondary particle production targets under irradiation in the high-power regime in excess of 10 kW. TRIUMF’s current flagship project ARIEL, Advanced Rare IsotopE Laboratory, will add two new target stations providing isotopes to the existing experimental stations in ISAC I and ISAC II at keV and MeV energies, respectively. In addition to the operating 500 MeV, 50 kW proton driver from TRIUMF’s cyclotron, ARIEL will make use of a 30 MeV, 100 kW electron beam from a newly installed superconducting linear accelerator. Together with additional 200 m of RIB beamlines within the radioisotope distribution complex, this will put TRIUMF in the unprecedented capability of delivering three RIBs to different experiments, while producing radioisotopes for medical applications simultaneously – enhancing the scientific output of the laboratory significantly.

Secondary Particle Production for Fundamental Science at TRIUMF

The basic structure of quantum field theory that is used to describe the Standard Model of fundamental interactions of nature is usually formulated for zero temperature. However, the effects of temperature are extremely important for understanding a number of physical processes such as the electro-weak phase transition and quark-gluon plasma. Thermal field theory is the extension of quantum field theory to a non-zero temperature environment and is achieved by modifying the propagators in loop integrations represented by Feynman diagrams. The Python program pySecDec numerically calculates dimensionally-regularized loop integrals in quantum field theory using the sector decomposition approach. It is shown how pySecDec can be applied to thermal field theory numerical calculations using modifications within the Matsubara formalism. Using the formulated algorithm, a 2-point correlation function (such as those occurring in QCD correlation functions) at finite temperature can be numerically calculated for a variety of spacetime dimensions.

Numerical Loop-Integration Methods for Thermal Field Theory

It is an ongoing challenge to engineer setups in which Majorana zero modes at the ends of one-dimensional topological superconductor are well isolated which is the essence of topological protection. Recent developments have indicated that periodic deriving of a system can dynamically induce symmetries that its static counterpart does not possess [1]. We further develop the original protocol [2] where this idea [1] is applied to a system of quantum dot (QD) coupled to a Kitaev chain hosting an imperfect (overlapping) Majorana zero modes. We numerically simulate a protocol in which an electron periodically hops back and forth from the QD and the wire. We demonstrate that the current protocol reduces a non-zero hybridization energy that manifests from imperfect Majoranas by orders of magnitude. Furthermore, we examine the efficiency of the suppression and how robust it is to imperfections.

[1] K. Agarwal and I. Martin, Phys. Rev. Lett. **125**, 080602 (2020).

[2] I. Martin and K. Agarwal, PRX Quantum **1**, 020324 (2020).

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The Use of Silver Microparticles for Spectrum Emission Enhancement During Laser-Induced Breakdown Spectroscopy of Bacterial Specimens.

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