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I will discuss different notions of nonrelativistic strings and their target space geometries. The first example comes from a self-contained corner of string theory dubbed nonrelativistic string theory, which is closely related to string theory in the discrete light-cone quantization. The appropriate spacetime geometry for nonrelativistic string theory is a stringy generalization of Newton-Cartan geometry. The second example involves sigma models at a Lifshitz point, which describe strings moving in bimetric spacetime. In the limit when the two metrics coincide, the relativistic sigma model that underlies string theory can be recovered. This study of Lifshitz-type sigma models also provides useful insights for constructing a quantum theory of membranes.

CAP Virtual Congress 2021

Nonrelativistic Strings and Exotic Geometries

bimetric geometry

renormalization group flow

nonrelativistic gravity and field theory

nonlinear sigma models

bosonic strings

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Molsamp 2021

technical paper

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Despite the constant stream of black hole merger observations, black hole mergers are not yet fully understood. The phenomenon seems simple enough, but the details of how the two apparent horizons end up as one horizon is unclear due to the non-linear nature of the merger process. Recent numerical work has shown that there is a merger of self-intersecting Marginally Outer-Trapped Surfaces (MOTS) during the black hole merger. Following papers have investigated further into MOTS in a simpler and static scenario, that of a Schwarzschild black hole. Such cases require less machinery and are solved with everyday computers. Those numerical calculations show an infinite number of self-intersecting MOTS hidden within the apparent horizon, as well as open surfaces (MOTOS). The importance of Schwarzschild MOTS are not to be undermined due to its relative simplicity as such MOTS describe an extreme-mass-ratio black hole merger, where one of the black holes is far more massive than the other. In this talk, I will discuss the current understanding of black hole mergers as have numerically been shown and my work investigating Schwarzschild MOTS in maximally-extended Kruskal-Szekeres coordinates.

The many marginally outer trapped surfaces of Schwarzschild spacetime

Topological insulators are an interesting class of materials which host distinct zero/low-energy states at their boundaries. These symmetry-protected states can, for example, arise as Majorana zero-modes in various superconducting systems and have potential applications in quantum error correction. However, experimental study and verification of such zero-modes remains a challenge. Considering a toy-system based on the Su-Schrieffer-Heeger (SSH) model coupled to one end with a semi-infinite conducting lead, we propose a measurement method for gapped topological states through a study of the decoherence dynamics of a quantum dot or qubit. Decoherence rates along the length of the SSH chain vary proportionally to the edge states and allow for both the identification and spatial characterisation of such states.

Quantum dots as probes for topological materials

We use Langevin dynamics (LD) simulations to investigate single file diffusion (SFD) in a dilute solution of flexible linear polymers inside a narrow tube. The transition from single-file diffusion, where the mean-square displacement scales like $\langle x^2 \rangle \sim t^{1/2}$, to normal diffusion with $\langle x^2 \rangle \sim t$, is studied as a function of the system parameters, such as the width of the channel, the polymer concentration and the polymer size. Based on our simulation results and scaling arguments, we propose a phase diagram describing the different diffusion regimes. We also map this problem onto a one-dimensional Lattice Random Walk algorithm where the diffusing object represents the polymer's center of mass. In order to model the polymers entanglement and disentanglement processes we allow several objects to temporarily share the same lattice site and diffuse together. Extension of our work to polydisperse polymer solutions, one-dimensional electrophoresis, ring polymers and DNA mapping are discussed.

The Limits of Polymer Single File Dynamics: A phase diagram

Temporal diffusion spectroscopy (TDS) has been used to infer axon sizes using geometric models that assume axons are cylinders. A celery sample was imaged to test if the importance of other geometric models. The vascular bundles and collenchyma tissue (~20 μm cells) in celery can be modeled as containing cylindrical cells. Whereas the parenchyma cells are rounder and are 3-4 times larger in diameter. Thus we imaged celery to test TDS with oscillating gradient spin echo (OGSE) to see if the spherical cell model and cylindrical cell model infer significantly different cell sizes to determine how important the geometrical model is.
A small section of a celery stalk was cut to fit inside a 15 mL sample tube filled with water. The image slice was chosen to be perpendicular to the length of the celery stalk. The sample was imaged using a 7T Bruker AvanceIII NMR system with Paravision 5.0 and BGA6 gradient set with a maximum gradient strength of 430357 Hz/cm, and a 3.5 cm diameter bird cage RF coil. Each 20 ms apodised cosine gradient pulse ranged from n = 1-20, in steps of 1. Two different gradient strengths were used for each frequency and gradient pulses were separated by 24.52 ms. A 1mm thick slice was acquired with the following imaging parameters: 2 averages, 2.56 cm FOV, TR = 1250 ms, TE = 50 ms, matrix 128 x 128, 200 μm in plane resolution, acquisition time 26.67 minutes per scan (scans performed = 40, 17.78 hours).
The inferred diameters of cells in celery (14±6μm to 20±12μm) were not statistically different when using the two different geometric models. This is the first step toward understanding the importance of geometric models for TDS.
The authors wish to acknowledge funding from NSERC and Mitacs.

Comparison of Cylindrical and Spherical Geometric Models to Infer Cell Sizes in a Celery Sample

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.

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

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.

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