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This is the final technical report DE-SC0018439: "Towards The Development of Rare Event Simulation Methods For Improved Mechanistic Understanding of Mineral Surface Reactivity". Under this effort we have devised a Forward Flux Sampling algorithm that utilizes time as a reaction progress coordinate.
The results of a newly developed annular cryogenic phonon-mediated active veto detector are discussed which shows a significant reduction of radioactivity-induced backgrounds in rare event search experiments. The veto detector is made up of germanium weighing ∼500 g with an outer diameter of 76 mm and an inner diameter of 28 mm. The detector can host a 25 mm diameter germanium inner target detector of mass ∼10 g. A GEANT4 simulation with the active veto and inner target detector shows a gamma background reduction of 50 - 80% which is further improved (> 90%) with 4pi veto coverage. Experimental measurements with the detector assembly agree well with the simulation.
Passive membrane permeability of N-methylated macrocyclic peptides remains poorly understood because high-energy conformational intermediates are rarely sampled by conventional methods. Using the deep learning–driven rare-event sampling framework ES-CRUMPLE, we show that cyclosporine A accesses transient amphiphilic states via a localized conformational hinge at Ala7/d-Ala8, revealing a kinetic design handle that is invisible to population-based analyses.
Here, we report the fabrication and performance of an annular, cryogenic, phonon-mediated veto detector that can host an inner target detector, allowing substantial reduction in radiogenic backgrounds for rare event search experiments. A germanium veto detector of mass ~500g with an outer diameter of 76 mm and an inner diameter of 28 mm was produced inside of which was mounted a 25 mm diameter germanium inner target detector of mass ~10g. The detector was designed using inputs from a GEANT4 based simulation, where it was modeled to be sandwiched between two germanium detectors. The simulation showed that the background rates (dominated by gamma interactions) could be reduced by >90%, and that such an arrangement is sufficient for aggressive background reduction needed for neutrino and dark matter search experiments. Operating at mK temperatures at the experimental site, the veto detector prototype achieved a baseline resolution of 1.24±0.02 keV while hosting a functional inner target detector. The baseline resolution of the inner target detector was 147±2 eV. The experimental results of an identical detector arrangement are in excellent agreement with the simulation.
The mass concentrations of 232 Th and 238 U in several 3D printing filaments and printed polymer parts are reported as measures of their radiopurity. In order to minimize background signals in rare event physics detectors, radiopure polymers are necessary as dielectric and structural materials in their construction. New data are reported for polyvinylidene fluoride (PVDF), polyphenylene sulfide (PPS), and two forms of polyetherimide (PEI, branded ULTEM 1010 and 9085). Data for starting filaments and both simple and complex printed parts are reported. PVDF filaments and simple printed beads, were found to have values of approximately 30 and 50 pg g -1 for 232 Th and 238 U, respectively, while a more complex spring clip part had slightly elevated 232 Th levels of 65 pg g -1 , with 238 U remaining at 50 pg g -1 . PPS filament was found to have concentrations of 270 and 710 pg g -1 for 232 Th and 238 U, respectively, and were not chosen to be printed as those levels were already higher than other material options. ULTEM 1010 filaments and printed complex spring clip parts were found to have concentrations of around 5 and 7 pg g -1 for 232 Th and 238 U, respectively, illustrating no significant contamination from the printing process. ULTEM 9085 filaments were found to have concentrations of around 9 and 5 pg g -1 for 232 Th and 238 U, respectively, while the printed complex spring clip part was found to have slightly elevated concentrations of 25 and 7 pg g -1 for 232 Th and 238 U, respectively. All these results were obtained using a novel dry ashing method in crucibles constructed of ultralow background electroformed copper or, when applicable, microwave assisted wet ashing digestion. Samples and process blanks were spiked with 229 Th and 233 U as internal standards prior to dry/wet ashing and determinations were made by inductively coupled plasma mass spectrometry (ICP-MS). In order to maintain high radiopurity levels, pre-cleaning the filaments before printing and post-cleaning the parts is recommended, although the printing process itself has shown to contribute very minute amounts of radiocontaminants.
Superconducting transition-edge sensors (TESs) are a type of quantum sensor known for their high single-photon detection efficiency and low background. This makes TESs ideal for particle-physics experiments searching for rare events. In this work, we present a comprehensive characterization of the background in optical TESs, distinguishing three types of events: electrical-noise, high-energy, and photonlike events. We introduce computational methods to automate the classification of events. We experimentally verify and simulate the source of the high-energy events. We also isolate the photonlike events, the expected signal in dielectric haloscopes searching for dark-matter dark photons, and achieve a photonlike dark-count rate of 3.6× 10 − 4 in the 0.8–3.2 eV energy range.
Superconducting detectors have become an important tool in experimental astroparticle physics, which seeks to provide a fundamental understanding of the Universe. In particular, such detectors have demonstrated excellent potential in two challenging research areas involving rare event search experiments, namely, the direct detection of dark matter (DM) and the search for neutrinoless double beta decay. Here, we review the superconducting detectors that have been and are planned to be used in these two categories of experiments. We first provide brief histories of the two research areas and outline their significance and challenges in astroparticle physics. Then, we present an extensive overview of various types of superconducting detectors with a focus on sensor technologies and detector physics, which are based on calorimetric measurements and heat flow in the detector components. Finally, we introduce leading experiments and discuss their future prospects for the detection of DM and the search for neutrinoless double beta decay employing superconducting detectors.
Abstract Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model. This severely limits the practical application of these models in systems with dynamics governed by important rare events, such as chemical reactions and diffusion. We present an adaptive Bayesian inference method for automating the training of interpretable, low-dimensional, and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations. Within an active learning framework, the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model. The method is applied to a range of single- and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency, while requiring a minimal amount of ab initio training data. We provide a fully open-source implementation of our method, as well as a procedure to map trained models to computationally efficient tabulated force fields.
Here, while multifidelity modeling provides a cost-effective way to conduct uncertainty quantification with computationally expensive models, much greater efficiency can be achieved by adaptively deciding the number of required high-fidelity (HF) simulations, depending on the type and complexity of the problem and the desired accuracy in the results. We propose a framework for active learning with multifidelity modeling emphasizing the efficient estimation of rare events. Our framework works by fusing a low-fidelity (LF) prediction with an HF-inferred correction, filtering the corrected LF prediction to decide whether to call the high-fidelity model, and for enhanced subsequent accuracy, adapting the correction for the LF prediction after every HF model call. The framework does not make any assumptions as to the LF model type or its correlations with the HF model. In addition, for improved robustness when estimating smaller failure probabilities, we propose using dynamic active learning functions that decide when to call the HF model. We demonstrate our framework using several academic case studies (including some high-dimensional problems) and two finite element model case studies: estimating Navier-Stokes velocities using the Stokes approximation and estimating stresses in a transversely isotropic model subjected to displacements via a coarsely meshed isotropic model. Across these case studies, not only did the proposed framework estimate the failure probabilities accurately, but compared with either Monte Carlo or a standard variance reduction method, it also required only a small fraction of the calls to the HF model.
Atomistic simulation with artificial intelligence (AI) is an emerging tool for understanding materials' properties and behaviors and predicting novel materials with optimized/targeted properties. Neural network potentials (NNPs) are outstanding in this field as they have shown a comparable accuracy to ab initio electronic structure calculations for reproducing potential energy surfaces while being several orders of magnitude faster. However, such NNPs can perform poorly outside of their training domain and typically fail catastrophically in the prediction of rare events in molecular dynamics (MD) simulations. For effective AL loops to distinguish the informative data from enhanced sampled configurations, we developed a decision engine by configurational similarity and uncertainty quantification (UQ) with data augmentation.
Amyloid fibrils are thought to grow by a two-step dock-lock mechanism. However, previous simulations of fibril formation (i) overlook the bi-molecular nature of the docking step and obtain rates with first-order units, or (ii) superimpose the docked and locked states when computing the potential of mean force for association and thereby muddle the docking and locking steps. Here, we developed a simple microkinetic model with separate locking and docking steps and with the appropriate concentration dependences for each step. We constructed a simple model comprised of chiral dumbbells that retains qualitative aspects of fibril formation. We used rare events methods to predict separate docking and locking rate constants for the model. The rate constants were embedded in the microkinetic model, with the microkinetic model embedded in a population balance model for “bottom-up” multiscale fibril growth rate predictions. These were compared to “top-down” results using simulation data with the same model and multiscale framework to obtain maximum likelihood estimates of the separate lock and dock rate constants. We used the same procedures to extract separate docking and locking rate constants from experimental fibril growth data. Our multiscale strategy, embedding rate theories, and kinetic models in conservation laws should help to extract docking and locking rate constants from experimental data or long molecular simulations with correct units and without compromising the molecular description.
For the first time, electrical conduction mechanisms in the disordered material system is experimentally studied for p-type amorphous germanium (a-Ge) used for high-purity Ge detector contacts. The localization length and the hopping parameters in a-Ge are determined using the surface leakage current measured from three high-purity planar Ge detectors. The temperature dependent hopping distance and hopping energy are obtained for a-Ge fabricated as the electrical contact materials for high-purity Ge planar detectors. As a result, we find that the hopping energy in a-Ge increases as temperature increases while the hopping distance in a-Ge decreases as temperature increases. The localization length of a-Ge is on the order of 2.13$^{-0.05}_{+0.07}$A° to 5.07$^{0.83}_{+2.58}$A°,depending on the density of states near the Fermi energy level within bandgap. Using these parameters, we predict that the surface leakage current from a Ge detector with a-Ge contacts can be much smaller than one yocto amp (yA) at helium temperature, suitable for rare-event physics searches.
Next-generation neutrino experiments like SBND and DUNE rely on Liquid Argon Time Projection Chambers (LArTPCs), which produce exceptionally detailed data at high volume. Capturing rare or unexpected events in real-time is a major challenge. Our project explores the use of machine learning, specifically autoencoder-based anomaly detection, to identify unusual activity directly from raw detector signals. Inspired by successes at the CMS experiment, we demonstrate that such methods can be adapted to LArTPCs and show promising results in both simulated studies and early steps toward real-time hardware deployment. This approach could open new avenues for detecting signals from physics beyond the Standard Model.
The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60–80 t capable of probing the remaining weakly interacting massive particle-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in 136 Xe using a natural-abundance xenon target. XLZD can reach a 3σ discovery potential half-life of 5.7 × 10 27 years (and a 90% CL exclusion of 1.3 × 10 28 years) with 10 years of data taking, corresponding to a Majorana mass range of 7.3–31.3 meV (4.8–20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community.
Low mass nuclear recoil dark matter and coherent-elastic-neutrino-nucleus-scattering (CE$v$NS) searches confront similar challenges in choosing ultra-low threshold and large-mass detectors. Here, we report experimental results from a 100 g single-crystal sapphire detector design with a diameter of 76 mm and thickness of 4 mm instrumented with transition edge sensors (TES). Sapphire is a crystal of aluminum oxide (Al 2 O) and has been found to be a good candidate for light mass dark matter search experiments due to its lower atomic mass compared to other detector materials such as germanium and silicon. This new phonon-assisted sapphire detector was characterized to yield a baseline recoil energy resolution of 28.4 ± 0.4 eV. The detector is designed to be sensitive to low-energy rare interactions with an intention to investigate the low-mass region of dark matter phase-space and search for CE$v$NS at the reactor site.
In recent years, there has been increasing interest in developing detectors which are sensitive to sub-GeV dark matter and low-energy events from coherent neutrino scattering. Due to carbon’s light atomic weight and its tendency to form crystals with high-energy, long-lived phonon modes, carbon-based crystals (including diamond and silicon carbide (SiC)) present themselves as natural target materials for scattering-based searches in this regime. Here, we present our preliminary results in adapting our TES-based detectors to these substrates, specifically 4-H SiC and polycrystalline diamond. We focus on our fabrication efforts, specifically tuning the transition temperature (T C ) of tungsten films sputtered on these materials, as well as our advancement toward fabricating and testing devices on these substrates.
Underground argon (UAr) with lower cosmogenic activities of 39 Ar and 42 Ar has been planned as a detector in detecting scintillation light and charge collection using time projection chambers for dark matter searches and as a veto detector in suppressing backgrounds for neutrinoless double beta decay (0$vββ$ ) experiments. Long-lived radioactive isotopes, 39 Ar and 42 Ar, can also be produced on the surface when UAr is pumped out from a deep well. Understanding the production of long-lived isotopes in Ar is important for utilizing UAr for dark matter and 0$vββ$ experiments in terms of its production, transportation, and storage. Ar exposure to cosmic rays at sea-level is simulated using Geant4 for a given cosmic ray muon, neutron, and proton energy spectrum. Here we report the simulated cosmogenic production rates of 39 Ar, 42 Ar, and other long-lived isotopes at sea-level from fast neutrons, high energy muons, and high energy protons. Total production rates of 938.53/kg Ar day and 5.81 × 10 -3 /kg Ar day for 39 Ar and 42 Ar are found from our simulation. Utilizing these production rates, we set a time limit of 954 days constrained by the production of 39 Ar for UAr to be on the surface before it compromises the sensitivity for a dark matter experiment. Similarly, a time limit of 1702 days constrained by the production of 42 Ar is found for a 0$vββ$ experiment.
Current investigations into neutrinoless double beta decay and the direct detection of dark matter require increasingly sensitive detector systems that demand ultra-low background materials. These requirements have previously been attained using ultra-radiopure electroformed (EF) copper. However, pure copper possesses high ductility and relatively low strength, which necessitates the need for large amounts of material and extra care during fabrication to help minimize galling and mechanical failure. Furthermore, we investigate an alloying alternative to pure EF copper by examining low wt% copper chrome (Cu–Cr) solid solution alloys, with inquiries into alloy hardness, strength, and radiopurity. Alloyed Cu–Cr layered samples are shown to exhibit significantly increased hardness compared to EF copper, for example showing a 50% increase in exhibited hardness using less than 1% Cr by weight, with limited negative impact to the material’s radiopurity.