Physics

Cosmic rays interacting with the atmosphere result in a flux of secondary particles including muons and electrons. Atmospheric ray tomography (ART) uses the muons and electrons for detecting objects and their composition. This paper presents new methods and a proof-of-concept tomography system developed for the ART of low-Z materials. We introduce the Particle Track Filtering (PTF) and Multi-Modality Tomographic Reconstruction (MMTR) methods. Based on Geant4 models we optimized the tomography system, the parameters of PTF and MMTR. Based on plastic scintillating fiber arrays we achieved the spatial resolution 120 μ m and 1 mrad angular resolution in the track reconstruction. We developed a novel edge detection method to separate the logical volumes of scanned object. We show its effectiveness on single (e.g. water, aluminum) and double material (e.g. explosive RDX in flesh) objects. The tabletop tomograph we built showed excellent agreement between simulations and measurements. We are able to increase the discriminating power of ART on low-Z materials significantly. This work opens up new routes for the commercialization of ART tomography.

The Liquid Argon Time Projection Chamber (LArTPC) is an advanced neutrino detector technology widely used in recent and upcoming accelerator neutrino experiments. It features a low energy threshold and high spatial resolution that allow for comprehensive reconstruction of event topologies. In current-generation LArTPCs, the recorded data consist of digitized waveforms on wires produced by induced signal on wires of drifting ionization electrons, which can also be viewed as two-dimensional (2D) (time versus wire) projection images of charged-particle trajectories. For such an imaging detector, one critical step is the signal processing that reconstructs the original charge projections from the recorded 2D images. For the first time, we introduce a deep neural network in LArTPC signal processing to improve the signal region of interest detection. By combining domain knowledge (e.g., matching information from multiple wire planes) and deep learning, this method shows significant improvements over traditional methods. This work details the method, software tools, and performance evaluated with realistic detector simulations.

The recent development of electron sensitive and pixelated detectors has attracted the use of four-dimensional scanning transmission electron microscopy (4D-STEM). Here, we present a precession electron diffraction assisted 4D-STEM technique for automated orientation mapping using diffraction spot patterns directly captured by an in-column scintillator based complementary metal-oxide-semiconductor (CMOS) detector. We compare the results to a conventional approach, which utilizes a fluorescent screen filmed by an external CCD camera. The high dynamic range and signal-to-noise characteristics of the detector largely improve the image quality of the diffraction patterns, especially the visibility of diffraction spots at high scattering angles. In the orientation maps reconstructed via the template matching process, the CMOS data yields a significant reduction of false indexing and higher reliability compared to the conventional approach. The angular resolution of misorientation measurement could also be improved by masking reflections close to the direct beam. This is because the orientation sensitive, weak and small diffraction spots at high scattering angle are more significant. The results show that fine details such as nanograins, nanotwins and sub-grain boundaries can be resolved with a sub-degree angular resolution which is comparable to orientation mapping using Kikuchi diffraction patterns.

Actively tunable optical filters based on chalcogenide phase-change materials (PCMs) are an emerging technology with applications across chemical spectroscopy and thermal imaging. The refractive index of an embedded PCM thin film is modulated through an amorphous-to-crystalline phase transition induced through thermal stimulus. Performance metrics include transmittance, passband center wavelength (CWL), and bandwidth; ideally monitored during operation (in situ) or after a set number of tuning cycles to validate real-time operation. Measuring these aforementioned metrics in real-time is challenging. Fourier-transform infrared spectroscopy (FTIR) provides the gold-standard for performance characterization, yet is expensive and inflexible -- incorporating the PCM tuning mechanism is not straightforward, hence in situ electro-optical measurements are challenging. In this work, we implement an open-source MATLAB-controlled real-time performance characterization system consisting of an inexpensive linear variable filter (LVF) and mid-wave infrared camera, capable of switching the PCM-based filters while simultaneously recording in situ filter performance metrics and spectral filtering profile. These metrics are calculated through pixel intensity measurements and displayed on a custom-developed graphical user interface in real-time. The CWL is determined through spatial position of intensity maxima along the LVF's longitudinal axis. Furthermore, plans are detailed for a future experimental system that further reduces cost, is compact, and utilizes a near-infrared camera.

Although the quest for more accurate solutions is pushing deep learning research towards larger and more complex algorithms, edge devices demand efficient inference and therefore reduction in model size, latency and energy consumption. One technique to limit model size is quantization, which implies using fewer bits to represent weights and biases. Such an approach usually results in a decline in performance. Here, we introduce a method for designing optimally heterogeneously quantized versions of deep neural network models for minimum-energy, high-accuracy, nanosecond inference and fully automated deployment on chip. With a per-layer, per-parameter type automatic quantization procedure, sampling from a wide range of quantizers, model energy consumption and size are minimized while high accuracy is maintained. This is crucial for the event selection procedure in proton-proton collisions at the CERN Large Hadron Collider, where resources are strictly limited and a latency of O(1) μ s is required. Nanosecond inference and a resource consumption reduced by a factor of 50 when implemented on field-programmable gate array hardware are achieved.

X-ray absorption spectroscopy (XAS) is a powerful and well established technique with sensitivity to elemental and chemical composition. Despite these advantages, its implementation has not kept pace with the development of ultrafast pulsed x-ray sources where XAS can capture femtosecond chemical processes. X-ray Free Electron Lasers (XFELs) deliver femtosecond, narrow bandwidth ( ΔE E <0.5% ) pulses containing ∼ 10 10 photons. However, the energy contained in each pulse fluctuates thus complicating pulse by pulse efforts to quantify the number of photons. Improvements in counting the photons in each pulse have defined the state of the art for XAS sensitivity. Here we demonstrate a final step in these improvements through a balanced detection method that approaches the photon counting shot noise limit. In doing so, we obtain high quality absorption spectra from the insulator-metal transition in VO 2 and unlock a method to explore dilute systems, subtle processes and nonlinear phenomena with ultrafast x-rays. The method is especially beneficial for x-ray light sources where integration and averaging are not viable options to improve sensitivity.

Using truth-level Monte Carlo simulations of particle interactions in a large volume of liquid argon, we demonstrate physics capabilities enabled by reconstruction of topologically compact and isolated low-energy features, or `blips,' in large liquid argon time projection chamber (LArTPC) events. These features are mostly produced by electron products of photon interactions depositing ionization energy. The blip identification capability of the LArTPC is enabled by its unique combination of size, position resolution precision, and low energy thresholds. We show that consideration of reconstructed blips in LArTPC physics analyses can result in substantial improvements in calorimetry for neutrino and new physics interactions and for final-state particles ranging in energy from the MeV to the GeV scale. Blip activity analysis is also shown to enable discrimination between interaction channels and final-state particle types. In addition to demonstrating these gains in calorimetry and discrimination, some limitations of blip reconstruction capabilities and physics outcomes are also discussed.

The continuing improvement in quantum efficiency (above 90% for single visible photons), reduction in noise (below 1 electron per pixel), and shrink in pixel pitch (less than 1 micron) motivate billion-pixel X-ray cameras (BiPC-X) based on commercial CMOS imaging sensors. We describe BiPC-X designs and prototype construction based on flexible tiling of commercial CMOS imaging sensors with millions of pixels. Device models are given for direct detection of low energy X-rays ( < 10 keV) and indirect detection of higher energies using scintillators. Modified Birks's law is proposed for light-yield nonproportionality in scintillators as a function of X-ray energy. Single X-ray sensitivity and spatial resolution have been validated experimentally using laboratory X-ray source and the Argonne Advanced Photon Source. Possible applications include wide field-of-view (FOV) or large X-ray aperture measurements in high-temperature plasmas, the state-of-the-art synchrotron, X-ray Free Electron Laser (XFEL), and pulsed power facilities.

Machine learning (ML) has been widely applied in high energy physics to help the physical community in particle classification and data analysis. Here we describe the application of machine learning to solve the problem of classifying background and signal events for the DEAP-3600 dark matter search experiment (SNOLAB, Canada). We apply Boosted Decision Trees (BDT) algorithm of ML with improvements from Extra Trees and eXtra Gradient Boosting (XGBoost) methods.

Next-generation neutrinoless double beta decay experiments aim for half-life sensitivities of ~ 10 27 yr, requiring suppressing backgrounds to <1 count/tonne/yr. For this, any extra background rejection handle, beyond excellent energy resolution and the use of extremely radiopure materials, is of utmost importance. The NEXT experiment exploits differences in the spatial ionization patterns of double beta decay and single-electron events to discriminate signal from background. While the former display two Bragg peak dense ionization regions at the opposite ends of the track, the latter typically have only one such feature. Thus, comparing the energies at the track extremes provides an additional rejection tool. The unique combination of the topology-based background discrimination and excellent energy resolution (1% FWHM at the Q-value of the decay) is the distinguishing feature of NEXT. Previous studies demonstrated a topological background rejection factor of ~5 when reconstructing electron-positron pairs in the 208 Tl 1.6 MeV double escape peak (with Compton events as background), recorded in the NEXT-White demonstrator at the Laboratorio Subterráneo de Canfranc, with 72% signal efficiency. This was recently improved through the use of a deep convolutional neural network to yield a background rejection factor of ~10 with 65% signal efficiency. Here, we present a new reconstruction method, based on the Richardson-Lucy deconvolution algorithm, which allows reversing the blurring induced by electron diffusion and electroluminescence light production in the NEXT TPC. The new method yields highly refined 3D images of reconstructed events, and, as a result, significantly improves the topological background discrimination. When applied to real-data 1.6 MeV e ??e + pairs, it leads to a background rejection factor of 27 at 57% signal efficiency.