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DOE OSTI2024

A Novel Method for Setting Meaningful Thresholds for RMS-Energy Oscillation Detectors

Oscillation detection and mitigation is a crucial aspect of reliable power system operation. Several transmission system operators and reliability coordinators use the RMS-energy method of oscillation detection, but the process of setting thresholds is time and labor intensive. The current industry practice in the United States is to set thresholds based on the RMS-energy's value during ambient conditions, which can often lead to nuisance alarms that require thresholds to be manually retuned. In this paper, we propose a method for setting RMS-energy thresholds that directly accounts for oscillation amplitudes specified by the user. A theoretical analysis of the statistical properties of the RMS-energy incorporates these user-specified amplitudes, resulting in thresholds that reliably detect oscillations of interest while avoiding nuisance alarms. Theoretical results are validated with simulated measurements and the real-world practicality of the method is established with publicly available field-measured data from the Grid Event Signature Library.

Follum, James D.↗

DOE OSTI2020

One Planet or Two Planets? The Ultra-sensitive Extreme-magnification Microlensing Event KMT-2019-BLG-1953

We present the analysis of a very high-magnification (A ∼ 900) microlensing event KMT-2019-BLG-1953. A single-lens single-source (1L1S) model appears to approximately delineate the observed light curve, but the residuals from the model exhibit small but obvious deviations in the peak region. A binary-lens (2L1S) model with a mass ratio of q ∼ 2 × 10{sup −3} improves the fits by Δχ {sup 2} = 181.8, indicating that the lens possesses a planetary companion. From additional modeling by introducing an extra planetary lens component (3L1S model) and an extra source companion (2L2S model), it is found that the residuals from the 2L1S model further diminish, but claiming these interpretations is difficult due to the weak signals with Δχ {sup 2} = 16.0 and 13.5 for the 3L1S and 2L2L models, respectively. From a Bayesian analysis, we estimate that the host of the planets has a mass of M{sub host}=0.31{sub −0.17}{sup +0.37} M{sub ⊙} and that the planetary system is located at a distance of D{sub L}=7.04{sub −1.33}{sup +1.10} kpc toward the Galactic center. The mass of the securely detected planet is M{sub p}=0.64{sub −0.35}{sup +0.76} M{sub J}. The signal of the potential second planet could have been confirmed if the peak of the light curve had been more densely observed by follow-up observations, and thus the event illustrates the need for intensive follow-up observations for very high-magnification events even in the current generation of high-cadence surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

The 2022 Hunga eruption exercises the nuclear explosion monitoring community

The 2022 Hunga eruption was a global event recorded by all three Comprehensive Test Ban Treaty Organization (CTBTO) International Monitoring System (IMS) geophysical waveform sensor technologies: seismic, infrasound, and hydroacoustic. Large volcanic eruptions are rare and additionally present an opportunity to test seismoacoustic detection, location, and identification methods for nuclear explosion monitoring.

58 GEOSCIENCES↗

DOE OSTI2025

Improving ICARUS Track Reconstruction Algorithms

The ICARUS experiment is part of the Short-Baseline Neutrino (SBN) program at Fermilab. The main goal of the experiment is to investigate the possibility of sterile neutrinos in the O(1 eV) mass region and provide clarification of the anomaly detected from the Liquid Scintillator Neutrino Detector (LSND) and MiniBooNE experiments. The ICARUS-T600 detector is a Liquid Argon Time Projection Chamber (LAr-TPC), that can provide excellent 3D imaging and calorimetric reconstruction of any ionizing particles. This detection technique allows a detailed study of neutrino interactions, spanning a wide energy spectrum (from a few keV to several hundreds of GeV). The detector consists of two identical adjacent modules, filled with a total of 760 tons of ultra-pure liquid argon. Each module houses two LAr-TPCs separated by a common cathode with a maximum drift distance of 1.5 m, equivalent to about 1 ms drift time for the nominal $500$ V/m electric drift field. The anode is made of three parallel wire planes positioned 3 mm apart, where the stainless-steel wires are oriented on each plane at a different angle with respect to the horizontal direction ($+60^\degree$,$-60^\degree$,$0^\degree$). The first two planes (Induction 1 and Induction 2) provide a non-destructive charge measurement, whereas the ionization charge is fully collected by the last collection plane. In total, 53248 wires with a 3 mm pitch and length up to 9 m are installed in the detector. In the first stage of the reconstruction, segments of waveforms corresponding to physical signals (hits) are searched for in the deconvolved wire waveform with a threshold-based hit-finding algorithm. Each hit is then fitted with a Gaussian, whose area is proportional to the number of drift electrons generating the signal. In the second stage of the reconstruction, hits are passed as input to Pandora, a framework software composed of different pattern recognition algorithms, that performs a 3D reconstruction of the full image recorded in the collected event, including the identification of interaction vertices and tracks and showers inside the TPC. These are organized into a hierarchical structure (called slice) of particles generated starting from a primary interaction vertex. In some cases, related to the inefficiencies in the hit detection or excessive deflection of the particle trajectory, Pandora breaks the particle's track into two or more smaller pieces and considers each piece as an independent track. We studied this phenomenon focusing on primary muons from ν_μ CC interactions contained in a single module with a track at least 20 cm long, to exclude delta rays. The study determined that about $7-8\%$ of the muon tracks are broken. Approximately $80\%$ of the times, Pandora assigns all segments of the track to the same slice (intra-slice track split), while in the remaining $20\%$ of the cases, one of the segments is associated with another slice (extra-slice track split). To mitigate this phenomenon, we designed an algorithm that detects and stitches the tracks broken by Pandora for the intra-slice split. In Monte Carlo simulations, the algorithm showed an efficiency exceeding $80\%$ and a purity exceeding $93\%$.

Ricci, Alessandro Maria [Pisa U.; INFN, Pisa] (ORC↗

DOE OSTI2023

Enhanced Microseismicity During Production Pumping Cessation at the San Emidio Geothermal Field (Nevada, USA) in December 2016

Abstract Tectonic activity, geothermal fluids, and microseismic events (MSEs) tend to occur in similar locations as a result of spatiotemporal changes in the subsurface stress state. To quantify this association, we analyze data from a dense seismic array deployed at the San Emidio geothermal field, Nevada for 1 week in December 2016 to coincide with a 19.45‐hr shutdown of all injection and production pumping operations. 123 MSEs were detected, of which 101 occurred during the shutdown. The spatial association of the MSEs with the production wells suggests a causal relationship between the production cessation and the MSEs. Here we performed a detailed analysis to investigate reservoir material properties, distribution of seismically activated faults, and local stress state. We determined the hypocenters, magnitudes, and focal mechanisms for the MSEs, P‐wave tomographic velocity model, and local stress tensor. The results show that most MSEs occurred near the production wells. Magnitudes fall between −2.2 and 0.0 with larger events located closer to the production wells. Most MSEs occurred within a westward‐dipping normal fault zone in the reservoir associated with anomalously low P‐wave velocity values. The focal mechanism and stress inversion results show predominantly normal faulting with the maximum horizontal stress oriented north‐south. We suggest that the MSEs during shutdown were triggered on pre‐existing, small‐scale, critically stressed fault patches in the reservoir as the pore pressure increased around the production wells when the production pumping ceased. We interpret the larger MSE magnitudes closer to the production wells as a result of higher pore pressure increase.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Memory-triggered supernova neutrino detection

Here, we demonstrate that observations of the gravitational memory from core collapse supernovae at future deci-Hz interferometers enable time-triggered searches of supernova neutrinos at Mt-scale detectors. Achieving a sensitivity to characteristic strains of at least $\sim 10^{–25}$ at $f ≃ 0.3$ $\mathrm{Hz}$ —e.g., by improving the noise of DECIGO by one order of magnitude—will allow robust time triggers for supernovae at distances $D \sim 40–300$ $\mathrm{Mpc}$, resulting in a nearly background-free sample of $\sim 3–70$ neutrino events per Mt per decade of operation. This sample would bridge the sensitivity gap between rare galactic supernova bursts and the cosmological diffuse supernova neutrino background, allowing detailed studies of the neutrino emission of supernovae in the local Universe.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Core-collapse supernova neutrino emission and detection informed by state-of-the-art three-dimensional numerical models

ABSTRACT Based on our recent three-dimensional core-collapse supernova (CCSN) simulations including both exploding and non-exploding models, we study the detailed neutrino signals in representative terrestrial neutrino observatories, namely Super-Kamiokande (Hyper-Kamiokande), DUNE, JUNO, and IceCube. We find that the physical origin of difference in the neutrino signals between 1D and 3D is mainly proto-neutron-star convection. We study the temporal and angular variations of the neutrino signals and discuss the detectability of the time variations driven by the spiral standing accretion shock instability (spiral SASI) when it emerges for non-exploding models. In addition, we determine that there can be a large angular asymmetry in the event rate (${\gtrsim} 50 {{\ \rm per\ cent}}$), but the time-integrated signal has a relatively modest asymmetry (${\lesssim} 20 {{\ \rm per\ cent}}$). Both features are associated with the lepton-number emission self-sustained asymmetry and the spiral SASI. Moreover, our analysis suggests that there is an interesting correlation between the total neutrino energy (TONE) and the cumulative number of neutrino events in each detector, a correlation that can facilitate data analyses of real observations. We demonstrate the retrieval of neutrino energy spectra for all flavours of neutrino by applying a novel spectrum reconstruction technique to the data from multiple detectors. We find that this new method is capable of estimating the TONE within the error of ∼20 per cent if the distance to the CCSN is ≲6 kpc.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Searches for Light Dark Matter and Evidence of Coherent Elastic Neutrino-Nucleus Scattering of Solar Neutrinos with the LUX-ZEPLIN (LZ) Experiment

We present searches for light dark matter (DM) with masses 3–9 GeV/𝑐 2 in the presence of coherent elastic neutrino-nucleus scattering (CE⁢𝜈⁢NS) from 8 B solar neutrinos with the LUX-ZEPLIN experiment. This analysis uses a 5.7 tonne-yr exposure with data collected between March 2023 and April 2025. In an energy range spanning 1–6 keV, we report no significant excess of events attributable to dark matter nuclear recoils, but we observe a significant signal from 8 B CE ⁢𝜈 ⁢NS interactions that is consistent with expectation. We set world-leading limits on spin-independent and spin-dependent-neutron DM-nucleon interactions for masses down to 5 GeV/𝑐 2 . In the no-dark-matter scenario, we observe a signal consistent with 8 B CE⁢ 𝜈 ⁢NS events, corresponding to a 4.5⁢𝜎 statistical significance. This is the most significant evidence of 8 B CE 𝜈 ⁢NS interactions and is enabled by robust background modeling and mitigation techniques. This demonstrates LZ’s ability to detect rare signals at keV-scale energies.

Dark matter detectors↗

DOE OSTI2022

Search for Supernova Neutrinos and Constraint on the Galactic Star Formation Rate with the KamLAND Data

We present the results of a search for core-collapse supernova neutrinos, using long-term KamLAND data from 2002 March 9 to 2020 April 25. We focus on the electron antineutrinos emitted from supernovae in the energy range of 1.8–111 MeV. Supernovae will make a neutrino event cluster with the duration of ~10 s in the KamLAND data. We find no neutrino clusters and give the upper limit on the supernova rate to be 0.15 yr –1 with a 90% confidence level. The detectable range, which corresponds to a >95% detection probability, is 40–59 kpc and 65–81 kpc for core-collapse supernovae and failed core-collapse supernovae, respectively. This paper proposes to convert the supernova rate obtained by the neutrino observation to the Galactic star formation rate. Assuming a modified Salpeter-type initial mass function, the upper limit on the Galactic star formation rate is <(17.5–22.7) M ⊙ yr –1 with a 90% confidence level.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Grid stabilization using adjusted voltage regulator response to grid characteristics

Apparatus and methods are disclosed for control systems for improving stability of electrical grids by temporarily reducing voltage output of electrical generators responsive to transient events on an electrical grid. In one example of the disclosed technology, a controller is coupled is to an automatic voltage regulator, which in turn adjusts excitation current of an electrical generator responsive to changes in frequency detected for the electrical grid. Reducing the output voltage temporarily allows for smaller generators to provide power to the microgrid. Methods for selecting parameters determining how the controller generates a regulation signal used to adjust the excitation current are further disclosed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Spread Spectrum Time Domain Reflectivity for String Monitoring in PV Power Plants (Final Technical Report)

This final report describes the methods and results of applying Spread Spectrum Time Domain Reflectivity (SSTDR) for String Monitoring in PV Power Plants for DE-EE0008169. The project created a new system for both detecting and locating electrical faults in photovoltaic systems. In this work, we address photovoltaic electric faults that are both common and costly. Based on interviews with photovoltaic power plant owners, operators, and maintainers, three types of faults are common and of significant interest: disconnects, ground faults, and arc faults. Disconnects can originate from many sources. They are often due to everyday events, such as lawnmowing (accidentally running over a cable), animals eating through the cables, or degradation that occurs over time due to corrosion or general degradation. Ground faults occur when the cables (for example, due to frayed insolation) connect to the ground, relaying current into the ground. These faults are particularly problematic since the ground faults are often intermittent. That is, ground faults commonly appear during rain storms due to a change in soil conductivity and then disappear when the rain ends. This makes the ground fault difficult to find because while current systems can detect the overall change in voltage and current associated with a ground fault, technicians are necessary to locate the fault. As a result, ground faults may disappear before the technician arrives at the power plant. Hence, locating and fixing ground faults often require multiple trips. We also study arc faults, which can result when nearby conductors create an arc of electrical current through the air. While less common, arc faults can be extremely dangerous. The energetic electrical arc can cause fires and destroy equipment, costing significant damage. Overall all three types of faults cost owners and operators money, either from the destruction of equipment or from technician time. Furthermore, while devices exist for detecting ground faults (ground fault circuit interrupters) and arc faults (arc fault circuit interrupter), these systems only search patterns of electrical current that correspond to each fault. This information cannot be used to locate the fault. In addition, these protection systems experience nuisance trips due to nearby electromagnetic interference, such as from a lawn mower or other motors that produce significant amounts of electromagnetic radiation. Hence, the overall goal of this project is to create an SSTDR tool that provides photovoltaic power plants with more reliable fault detection in addition to the localization of faults. SSTDR works by transmitting electrical signals into the photovoltaic string. Those signals reflect from impedance discontinuities (i.e., disconnects, ground faults, and arc faults). These faults are then detected by measuring the presence of a reflection at the SSTDR and can be located by identifying the location of that reflection in time. In addition, unlike current protection systems, these systems do not experience nuisance trips since their low amplitude, high frequency, and coded signal can by analyzed without interference from the regular operational voltage on the photovoltaic string.

14 SOLAR ENERGY↗

DOE OSTI2022

Inferring safety critical events from vehicle kinematics in naturalistic driving environment: Application of deep learning Algorithms

Advances in sensing technology has enabled the collection of countless terabytes of second-by-second kinematics data. Such data provides opportunities for real-time monitoring of driving behavior and identification of safety critical events (SCEs) including crashes and near crashes. The concept of volatility is relevant in this context, which identifies instability and erratic variations in driving behavior prior to involvement in SCEs. This study utilized vehicle kinematics from a large-scale naturalistic driving data to develop a deep learning approach based on 1D convolutional neural networks (CNN) for inferring SCEs. The data are unique in the sense that such accurate pre-crash data at high fidelity are not available in traditional crash repositories. This study contributes to the literature by providing a first attempt at predicting responses to SCEs by developing deep learning-based CNN architectures using novel driving volatility based kinematic thresholds for a sample of 9553 events. The key contribution lies in developing a volatility-based CNN input layout that is acceptable to CNN schemes and represents the motion kinematics such as speed, acceleration and volatility measures. Several 1D-CNN architectures were developed using layers, numbers of convolutions, layer patterns, and kernels. Shallow and deep architectures were tested, revealing higher accuracy of shallow architectures in detecting SCEs. The optimal number of epochs were identified using an early stopping method while the CNN performance was improved by increasing the number of epochs. The ensemble CNN had the highest predictive accuracy of 95.6% for detection of crashes and near crashes, which was 2.5% higher than the optimal CNN using 20% hold out test data. The ensemble CNN also outperformed classical machine learning models and model performance reported in past studies on detection of SCEs. Finally, these results have implications for identification of safety hotspots and providing real-time alerts and warnings in connected and highly automated vehicle environment including society of automotive engineers levels 3–5.

42 ENGINEERING↗

DOE OSTI2020

Solar neutrino detection in liquid xenon detectors via charged-current scattering to excited states

We investigate the prospects for real-time detection of solar neutrinos via the charged-current neutrino-nucleus scattering process in liquid xenon time projection chambers. We use a nuclear shell model, benchmarked with experimental data, to calculate the cross sections for populating specific excited states of the cesium nuclei produced by neutrino capture on Xe 131 and Xe 136 . The shell model is further used to compute the decay schemes of the low-lying 1 + excited states of Cs 136 , for which there is sparse experimental data. We explore the possibility of tagging the characteristic deexcitation γ rays/conversion electrons using two techniques: spatial separation of their energy deposits using event topology and their time separation using delayed coincidence. The efficiencies in each case are evaluated within a range of realistic detector parameters. We find that the topological signatures are likely to be dominated by radon backgrounds, but that a delayed-coincidence signature from long-lived states predicted in Cs 136 may enable background-free detection of CNO neutrino interactions in next-generation experiments with smaller uncertainty than current measurements. We also estimate the sensitivity as a function of exposure for detecting the solar-temperature-induced line shift in Be 7 neutrino emission, which may provide a new test of solar models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Novel usage of deep learning and high-performance computing in long-baseline neutrino oscillation experiments

Mención Internacional en el título de doctorDeep-learning methods are playing a crucial role in numerous scientific and industrialapplications. Over the past two decades, these techniques have helped in the collection,reconstruction, and analysis of large data samples in particle physics experiments. Themain topic of this PhD research is the study of deep-learning techniques in long-baselineneutrino oscillation experiments. Neutrinos are mysterious light elementary particles,and their investigation is essential to shed light on some of the remaining open questionsin physics. The work presented here describes an algorithm based on a convolutionalneural network developed to provide highly accurate and efficient selections of electronneutrino and muon neutrino interactions in the Deep Underground Neutrino Experiment(DUNE). With this algorithm, the electron neutrino (antineutrino) selection efficiencypeaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between2-5 GeV. The selection efficiency for muon neutrino (antineutrino) interactions is foundto have a maximum of 96% (97%) and exceeds 90% (95%) efficiency for reconstructedneutrino energies above 2 GeV. When considering all electron neutrino and antineutrinointeractions as signal (both those appearing from oscillations and those intrinsic tothe beam), a selection purity of 90% is achieved. These event selections are criticalto maximise the sensitivity of the experiment to CP-violating effects, key to furtherunderstand the matter-antimatter asymmetry of the Universe.In high-energy physics experiments, deep learning has also been explored for producingfast simulations and physically-motivated manipulations of simulated images. Some ofthose simulations, such as the light production and detection, are very computationallyexpensive and require novel methods to produce the necessary samples while controllingthe varied underlying physics model parameters. To do so, we invented the model-assistedgenerative adversarial network (MAGAN), first validated on simple generic case studiesand then successfully applied to the DUNE photon-detector simulation.Moreover, we also developed graph neural networks for 3D-voxel classification ofambiguities and optical crosstalk for a different particle physics experiment, most preciselyfor the proposed SuperFGD. This novel 3D-granular plastic-scintillator neutrino detectorwill be used to upgrade the near detector of the T2K neutrino oscillation experiment, and our method reports efficiencies and purities of 94-96% per event in the classificationof particle track voxels.Due to the growth and complexity of deep neural networks, researchers have beeninvestigating techniques to train those networks in a more computationally-efficient way.Many efforts have been made by the community to optimise deep-learning models byparallelising or distributing their training computation across multiple devices. In thisthesis, we study an approach based on data locality for those neural networks that cannotbenefit from scaling their computation due to a significant bottleneck in the data I/O.The research also includes a detailed study on the performance of deep neural networkson hardware accelerator boards.Los métodos de aprendizaje profundo son cada vez más utilizados en numerosas aplicacionescientíficas e industriales hoy en día. Durante las dos últimas décadas, estastécnicas se han empleado en la recolección, reconstrucción y análisis de la gran cantidadde datos generados por experimentos de física de partículas. El tema principal de estatesis doctoral es el uso de estos modelos de aprendizaje profundo en experimentos defísica de neutrinos, en concreto en los experimentos de larga distancia DUNE y T2K. Losneutrinos, partículas fundamentales neutras, de las más ligeras del Universo, pueden serclave para explicar algunas de las cuestiones todavía sin resolver en física fundamental.Entre las diferentes contribuciones que esta tesis ha hecho a su estudio, cabe destacar eldesarrollo de un algoritmo basado en una red de neuronas convolucional para seleccionarcon gran eficiencia y precisión las interacciones de neutrinos electrónicos y muónicos enel Deep Underground Neutrino Experiment (DUNE). La eficiencia de selección obtenidapara neutrinos (antineutrinos) electrónicos alcanza un máximo del 90% (94%) y supera el85% (90%) para neutrinos con energías reconstruidas en el rango 2-5 GeV. La selección deneutrinos (antineutrinos) muónicos tiene una eficiencia máxima del 96% (97%) y excedeel 90% (95%) para neutrinos con energías reconstruidas de más de 2 GeV. Considerandocomo señal todas las interacciones de neutrinos y antineutrinos electrónicos (procedentestanto de oscilaciones como intrínsecos en el haz inicial), se logra una pureza en la seleccióndel 90%. Dichas selecciones de eventos son fundamentales para maximizar la sensibilidaddel experimento a los efectos de violació...

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Broadband multiwavelength properties of the archetypal blazar 3C 279 during the 2017 Event Horizon Telescope campaign

The archetypal blazar 3C 279 hosts a prominent relativistic jet and exhibits strong broadband variability across the electromagnetic spectrum. In April 2017, the Event Horizon Telescope (EHT) observed 3C 279, alongside one of the most extensive quasi-simultaneous multiwavelength (MWL) campaigns ever conducted. With the aim of investigating the physical processes governing 3C 279, we analyzed individual observations and multiband light curves, and constructed a new quasi-simultaneous MWL spectrum. We also performed phenomenological modeling using the turbulent extreme multi-zone (TEMZ) model to constrain the fundamental physical properties of the source. The EHT observations reveal a clear flux increase in the innermost core between April 5 and 11, 2017. Over a broader timescale, radio measurements at longer wavelengths show concurrent enhancements in core flux and polarization around mid-April, coinciding with the ejection of a superluminal knot. Record UV-optical flares with strong polarization variability occurred in late March, followed by gamma-ray activity that declined before the end of the EHT observing period. During this interval, the source remained in a low X-ray state and showed no detectable VHE emission. The TEMZ modeling suggests that the broadband spectrum and variability of 3C 279 can be explained within a jet scenario in which turbulent plasma cells are compressed by a stationary conical shock. However, alternative interpretations, such as magnetic reconnection or a moving shock-in-jet event, remain plausible. This coordinated MWL campaign advances our understanding of the origin of jet and gamma-ray emission in 3C 279, while also providing a comprehensive publicly available dataset that will serve as a valuable reference for future studies.

Principe, G. [Trieste U.; INFN, Trieste; Bologna, ↗

DOE OSTI2024

Scintillating Bubble Chambers for Direct Dark Matter Detection, and an Update on SBC-LAr10

The Scintillating Bubble Chamber (SBC) Collaboration aims to use the liquid-noble bubble chamber technology as a low-threshold detector for dark matter particles of 1-10 GeV/c2. The detector combines the remarkable electron recoil (ER) discrimination of the bubble chamber with the event-by-event energy resolution provided by liquid argon (LAr) scintillation, with the crucial added benefit that ER discrimination in liquid-noble bubble chambers extends to much lower thresholds than in past freon-filled bubble chambers, with the potential for quasi-background-free operation at thresholds of 100 eV in nuclear recoil (NR) energy. SBC has developed two functionally identical 10 kg detectors: SBC-LAr10 at Fermilab will calibrate low-threshold performance, while the radio-pure SBC-SNOLAB chamber will execute SBC s first deep-underground dark matter search. SBC-LAr10 at Fermilab was recently installed in the MINOS tunnel 100 meters underground, and the first bubbles are expected this fall. I will present the current status of SBC-LAr10 and describe the suite of gamma and neutron calibrations we will execute. The calibrations will confirm the physics reach of this new technology, motivating not just the SBC-SNOLAB chamber now being assembled but also future searches into the solar neutrino fog at 1 GeV/c2.

Sheng, Zhiheng↗

DOE OSTI2024

Scintillating Bubble Chambers for Direct Dark Matter Detection, and an Update on SBC-LAr10

Sheng, Zhiheng↗

DOE OSTI2020

Implications of the Gaia sausage for dark matter nuclear interactions

The advent of the Gaia era has led to potentially revolutionary understanding of dark matter (DM) dynamics in our galaxy, which has important consequences for direct detection (DD) experiments. In this paper, we study how the empirical DM velocity distribution inferred from Gaia-sausage, a dominant substructure in the solar neighborhood, affects the interpretation of DD data. We survey different classes of operators in the nonrelativistic effective field theory that could arise from several relativistic benchmark models and emphasize that the Gaia velocity distribution could modify both the total number of events as well as the shape of the differential recoil spectra, the two primary observables in DD experiments. Employing the Euclideanized signal method, we investigate the effects of the Gaia distribution on reconstructing DM model parameters and identifying the correct DM model given a positive signal at future DD experiments. We find that for light DM with mass ~10 GeV, the Gaia distribution poses an additional challenge for characterizing DM interactions with ordinary matter, which may be addressed by combining complementary DD experiments with different targets and lowering the detection threshold. Meanwhile, for heavy DM with mass above ~30 GeV , depending on the type of DM model, there could be a (moderate) improvement in the sensitivity at future DD experiments.

79 ASTRONOMY AND ASTROPHYSICS↗