Enhancing Seismic Event Association: Leveraging Signal Similarity and Correlation Detection with Machine Learning
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High-significance detection of correlation between the unresolved gamma-ray background and the large-scale cosmic structure
Our understanding of the γ-ray sky has improved dramatically in the past decade, however, the unresolved γ-ray background (UGRB) still has a potential wealth of information about the faintest γ-ray sources pervading the Universe. Statistical cross-correlations with tracers of cosmic structure can indirectly identify the populations that most characterize the γ-ray background. In this study, we analyze the angular correlation between the γ-ray background and the matter distribution in the Universe as traced by gravitational lensing, leveraging more than a decade of observations from the Fermi-Large Area Telescope (LAT) and 3 years of data from the Dark Energy Survey (DES). We detect a correlation at signal-to-noise ratio of 8.9. Most of the statistical significance comes from large scales, demonstrating, for the first time, that a substantial portion of the UGRB aligns with the mass clustering of the Universe as traced by weak lensing. Blazars provide a plausible explanation for this signal, especially if those contributing to the correlation reside in halos of large mass (∼ 10 14 M ⊙ ) and account for approximately 30–40% of the UGRB above 10 GeV. Additionally, we observe a preference for a curved γ-ray energy spectrum, with a log-parabolic shape being favored over a power-law. We also discuss the possibility of modifications to the blazar model and the inclusion of additional γ-ray sources, such as star-forming galaxies, misalinged active galactic nuclei, or particle dark matter.
79 ASTRONOMY AND ASTROPHYSICS↗
Tomography of the gamma-ray sky from cross-correlation with DESI DR2 and unWISE galaxies
We study the origin of extragalactic gamma-ray emission observed by Fermi-LAT, using the cross-correlation of the gamma-ray sky with maps of large-scale structure provided by the DESI and unWISE surveys. Tomographic cross-correlation reveals the bias-weighted redshift distributions of gamma-ray sources. We first illustrate this method by cross-correlating detected gamma-ray point sources with large-scale structure. We find a significant cross-correlation and infer a point source redshift distribution broadly consistent with the distribution of identified optical counterparts previously reported in the literature, as well as a similar linear bias ($b \approx 2$) to massive galaxies that host bright active galactic nuclei. We then study the clustering of the Fermi unresolved gamma-ray background (UGRB), both in auto-correlation and in cross-correlation with large-scale structure. We detect the cross-correlation of the UGRB and LSS at $\sim 10σ$ in total, with highly significant detections from both DESI and unWISE. Our measurements suggest that the redshift distribution of the UGRB is broadly consistent with the redshift distribution of detected point sources. Additionally, we find a relatively weak amplitude for the cross-correlation with large-scale structure at z < 2, suggesting a significant fraction of the UGRB does not come from z < 2 large-scale structure. A natural candidate is contamination of from residual Galactic emission, and our best estimate of the contamination level derived from the UGRB auto-spectrum suggests that the mean bias of UGRB sources is indeed quite similar to the bias of detected Fermi point sources. However, we cannot exclude additional emission from gamma-ray sources at high redshift, z > 2, and we suggest that cross-correlation with tracers at z > 2, including CMB lensing, would be the ideal way to determine the fraction of z > 2 emission.
Krolewski, Alex [Waterloo U., Math. Dept.; Waterlo↗
Enhancing spectroscopy and microscopy with emerging methods in photon correlation and quantum illumination
Quantum optics has led to important advancements in our ability to prepare and detect correlations between individual photons. Its principles are increasingly translated into nanoscale characterization tools, furthering methods in spectroscopy, microscopy and metrology. Here, in this Review, we discuss the rapid progress in this field driven by advanced technologies of single-photon detectors and quantum-light sources, including time-resolved single-photon counting cameras, superconducting nanowire single-photon detectors and entangled photon sources of increasing brightness. We emphasize emerging applications in super-resolution microscopy, measurements below classical noise limits and photon-number-resolved spectroscopy—a powerful paradigm for the characterization of nanoscale electronic materials. We conclude by discussing key technological challenges and future opportunities in materials science and bionanophotonics alike.
Tsao, Chieh [University of California, Berkeley, C↗
Condition-Based Maintenance of a Circulating Water System of a Canadian Nuclear Power Plant using Machine Learning and Statistical Tools
Canada Deuterium Uranium pressurized-heavy-water reactors (PHWR) are a type of nuclear power plant that generate clean and reliable energy. The scope of this work is to automate data analysis methodologies to inform a condition-based maintenance strategy of a circulating water system (CWS) of a PHWR. The multiunit CWS provides a continuous supply of water to cool steam condensers, even during transient scenarios, thereby improving the thermal efficiency. This work aims to develop a machine learning (ML) based approach to detect anomalies in heterogeneous data of a CWS in a PHWR to help inform a predictive maintenance strategy. The heterogeneous data include textual and numeric time series data for a PHWR. Natural-language-processing (NLP)-based models are used to analyze textual data contained in work orders and operator logs and an event-timeseries correlation detection method is applied to assist anomalies diagnoses for CWS. An ML model Robust Linear Model (RLM) is also used to remove the seasonal variations in the system variable distributions based on distributions of environmental variables. A machine learning model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), trained on both original data and data without any seasonal variations will then be used to detect if an anomaly exists. Thus, by moving to an automated methodology to detect, classify, and forecast anomalies, the maintenance strategy would be based on component condition instead of a time-based schedule.
97 - MATHEMATICS AND COMPUTING↗
Unsupervised Process Anomaly Detection and Identification Using the Leave-One-Variable-Out Approach
Automated anomaly detection and identification can signal equipment issues and pinpoint causes in large-scale industrial systems. For systems with limited failure history, unsupervised machine learning methods can be utilized as they do not require past failures. This study introduces the leave-one-variable-out (LOVO) model, which masks one variable at a time to predict the others, learning underlying process correlations. Detection performance was assessed with synthetic and experimental data, while identification performance used only synthetic data due to its ability to generate labeled anomaly types. For detection using synthetic data, the LOVO model generally outperformed comparative models; while using experimental data, the comparative methods outperformed the LOVO model. However, the comparative methods required selecting a latent size, and these conclusions pertain to using the optimal size. In practice, it would not be feasible to always select the optimal value, and incorrect selections impacted performance. In contrast, the LOVO model does not require a latent space. For identification using synthetic data, the LOVO model was slightly outperformed in interpretability and repeatability but still demonstrated impressive results. These outcomes suggest that the LOVO model is an effective model and may be more easily implemented without the challenging tuning process of selecting a latent size.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Study on Application of Distributed Network of Sensors with List Mode for NMAC Literature Review
Nuclear material accounting and control (NMAC) for nuclear security detects, deters, and resolves questions related to unauthorized removal (i.e. theft) or misuse of nuclear material. NMAC also serves as a key insider threat mitigation measure and aids in recovery of nuclear material that is missing. Effective nuclear security depends on NMAC for timely and accurate information about nuclear material types, quantities, and locations. Bulk nuclear material processing facilities, however, present unique challenges for effective NMAC due to the presence of large quantities of material in-process and the accumulation of residual material holdup within process equipment. These holdup accumulations can obscure accurate physical inventory taking and complicate efforts to resolve NMAC irregularities at the facility level. Bulk material monitoring systems often rely on material balance calculations and indirect measurement techniques, which may mask protracted theft of smaller amounts of nuclear material. These monitoring limitations have generated increased interest in continuous monitoring technologies, including distributed non-destructive assay (NDA) sensor networks capable of providing real-time or near-real-time measurement of material movement and accumulation within bulk processing environments. Recent advancements in distributed networks of NDA radiation detectors and sensing technologies provide an opportunity to address these limitations. Although such distributed sensor networks have been implemented in select facilities for IAEA Safeguards applications, their potential for supporting NMAC functions specifically tailored to nuclear security objectives remains largely unexplored. Furthermore, emerging list-mode data acquisition technologies have reached high technology readiness levels, enabling time-correlated detection of nuclear events across multiple temporal scales. These capabilities provide enhanced opportunities for accurate holdup measurement, continuous process monitoring, and improved detection of material theft or misuse over time. The increasing global expansion of civil nuclear power and development of related bulk material processing facilities, including those supporting high-assay low-enriched uranium (HALEU) and other advanced reactor fuel fabrication, further increases the need for advanced measurement and monitoring strategies for NMAC.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Time-resolved detectors for quantum ghost imaging
Quantum ghost imaging is a method that utilizes the correlated detection of two photons to generate an image. One photon is detected by an imaging sensor and the other by a single-element bucket detector. The selection of the imaging sensor and its capabilities relative to the bucket detector impact the quality of the ghost images. This work examines a SPAD array and a photocathode detector as imaging sensors for quantum ghost imaging. We discuss how to achieve optimal images using these two technologies. We also demonstrate that these devices are able to generate ghost images at 1Hz frame rates, expanding the technique to biologically relevant time scales.
36 MATERIALS SCIENCE↗
Detecting Anthropogenic Activity at the Pleasant Gap Mine Using Cross-Correlation and Local Surface Arrays
This poster summarizes work completed for the NA-22 STILGAR project using seismic cross-correlation to detect human-generated subsurface activity. It will be presented at the 2024 American Geophysical Union Fall Meeting.
Miller, David James [Sandia National Laboratories ↗
The role of source geometry and atmospheric propagation in global bolide infrasound detectability
Global infrasound monitoring provides a persistent means of detecting energetic bolide atmospheric entries, complementing optical observations and extending coverage over remote regions. We present a global assessment of the physical factors governing bolide infrasound detectability by correlating 623 bolide events reported by the Center for Near-Earth Object Studies between 2007 and 2025 with waveform data from the International Monitoring System. We identify 311 events with confirmed infrasound detections, corresponding to a detection rate of approximately 50%, substantially higher than inferred from earlier surveys, reflecting both the maturation of the global infrasound network and advances in automated, multi-frequency array processing. Analysis of flight parameters shows that infrasound detectability is selective rather than uniform across the bolide population. Detected events are preferentially associated with steeper entry angles and lower-altitude energy deposition, while shallow, high-altitude trajectories are less consistently observed. Very high-energy events remain detectable regardless of geometry, but for the more common lower-energy regime, observability depends on specific combinations of entry parameters and propagation conditions. This geometric dependence persists across comparable energy ranges and atmospheric conditions, indicating that entry angle exerts a primary control on detectability, with energy and propagation acting as secondary modulating factors. Furthermore, these results provide new physical constraints on bolide-atmosphere interactions and improve interpretation of global infrasound observations for planetary defense and atmospheric-entry studies.
Bolides↗
Quantum correlation imaging via X-ray parametric down-conversion
Quantum imaging leverages correlations between pairs of photons and has the potential to obtain image information beyond what classical sources provide. Extending this approach to the X-ray regime has been limited by low photon-pair generation rates and the lack of suitable detectors. Here, we demonstrate X-ray coincidence imaging using spontaneous parametric down-conversion (SPDC) and a pixelated area detector with time- and energy-resolved capabilities. This configuration enables simultaneous detection of correlated X-ray photon pairs and coincidence-based imaging of test objects, including a biological specimen. The increased coincidence rate and spatially resolved detection establish a basis for future quantum-enhanced and low-dose X-ray imaging.
Goodrich, Justin C. [Brookhaven National Laborator↗
Detecting anomalous C P violation in heavy-ion collisions through baryon-electric charge correlations
The chiral magnetic effect and the chiral vortical effect induce a correlation between baryon and electric currents. Here we show that this correlation can be detected using a new observable: a mixed baryon-electric charge correlator. This correlator is proportional to the baryon asymmetry, suggesting a novel way to separate the chiral effects from the background in heavy-ion collisions.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Quantum entanglement and Bell inequality violation in semi-leptonic top decays
Abstract Quantum entanglement is a fundamental property of quantum mechanics. Recently, studies have explored entanglement in the$$ t\overline{t} $$ t t ¯ system at the Large Hadron Collider (LHC) when both the top quark and anti-top quark decay leptonically. Entanglement is detected via correlations between the polarizations of the top and anti-top and these polarizations are measured through the angles of the decay products of the top and anti-top. In this work, we propose searching for evidence of quantum entanglement in the semi-leptonic decay channel where the final state includes one lepton, one neutrino, twob-flavor tagged jets, and two light jets from theWdecay. We find that this channel is both easier to reconstruct and has a larger effective quantity of data than the fully leptonic channel. As a result, the semi-leptonic channel is 60% more sensitive to quantum entanglement and a factor of 3 more sensitive to Bell inequality violation, compared to the leptonic channel. In 139 fb −1 (3 ab −1 ) of data at the LHC (HL-LHC), it should be feasible to measure entanglement at a precision of ≲ 3% (0.7%). Detecting Bell inequality violation, on the other hand, is more challenging. With 300 fb −1 (3 ab −1 ) of integrated luminosity at the LHC Run-3 (HL-LHC), we expect a sensitivity of 1.3σ(4.1σ). In our study, we utilize a realistic parametric fitting procedure to optimally recover the true angular distributions from detector effects. Compared to unfolding this procedure yields more stable results.
Physics↗
Synchronous detection of cosmic rays and correlated errors in superconducting qubit arrays
Quantum information processing at scale will require sufficiently stable and long-lived qubits, likely enabled by error-correction codes. Several recent superconducting-qubit experiments, however, reported observing intermittent spatiotemporally correlated errors that would be problematic for conventional codes, with ionizing radiation being a likely cause. Here, we directly measured the cosmic-ray contribution to spatiotemporally correlated qubit errors. We accomplished this by synchronously monitoring cosmic-ray detectors and qubit energy-relaxation dynamics of 10 transmon qubits distributed across a 5 × 5 × 0.35 mm 3 silicon chip. Cosmic rays caused correlated errors at a rate of $1/\left(592\begin{array}{c}+48\\ -41\end{array}\,{\rm{s}}\right)$, accounting for 17.1 ± 1.3% of all such events. Our qubits responded to essentially all of the cosmic rays and their secondary particles incident on the chip, consistent with the independently measured arrival flux. Moreover, we observed that the landscape of the superconducting gap in proximity to the Josephson junctions dramatically impacts the qubit response to cosmic rays. Given the practical difficulties associated with shielding cosmic rays, our results indicate the importance of radiation hardening—for example, superconducting gap engineering—to the realization of robust quantum error correction.
Science & Technology - Other Topics↗
Precise relative magnitude measurement improves fracture characterization during hydraulic fracturing
SUMMARY Microseismic monitoring is an important technique to obtain detailed knowledge of in-situ fracture size and orientation during stimulation to maximize fluid flow throughout the rock volume and optimize production. Furthermore, considering that the frequency of earthquake magnitudes empirically follows a power law (i.e. Gutenberg–Richter), the accuracy of microseismic event magnitude distributions is potentially crucial for seismic risk management. In this study, we analyse microseismicity observed during four hydraulic fracture treatments of the legacy Cotton Valley experiment in 1997 at the Carthage gas field of East Texas, where fractures were activated at the base of the sand-shale Upper Cotton Valley formation. We perform waveform cross-correlation to detect similar event clusters, measure relative amplitude from aligned waveform pairs with a principal component analysis, then measure precise relative magnitudes. The new magnitudes significantly reduce the deviations between magnitude differences and relative amplitudes of event pairs. This subsequently reduces the magnitude differences between clusters located at different depths. Reduction in magnitude differences between clusters suggests that some attenuation-related biases could be effectively mitigated with relative magnitude measurements. The maximum likelihood method is applied to understand the magnitude frequency distributions and quantify the seismogenic index of the clusters. Statistical analyses with new magnitudes suggest that fractures that are more favourably oriented for shear failure have lower b-value and higher seismogenic index, suggesting higher potential for relatively larger earthquakes, rather than fractures subparallel to maximum horizontal principal stress orientation.
58 GEOSCIENCES↗
Evolution of charge correlations in the hole-doped kagome superconductor CsV 3−𝑥 Ti 𝑥 Sb 5
The interplay between superconductivity and charge correlations in the kagome metal CsV 3 Sb 5 can be tuned by external perturbations such as doping or pressure. Here we present a study of charge correlations and superconductivity upon hole doping via Ti substitution on the V kagome sites in CsV 3−𝑥 Ti 𝑥 Sb 5 via synchrotron x-ray diffraction and scanning superconducting quantum interference device measurements. While the superconducting phase, as viewed via the vortex structure, remains conventional and unchanged across the phase diagram, the nature of charge correlations evolves as a function of hole doping from the first superconducting dome into the second superconducting dome. For Ti doping in the first superconducting dome, competing 2×2×2 and 2×2×4 supercells form within the charge density wave state and are suppressed rapidly with carrier substitution. In the second superconducting dome, no charge correlations are detected. Here, comparing these results to those observed for CsV 3 Sb 5−𝑥 Sn 𝑥 suggests important differences between hole doping via chemical substitution on the V and Sb sites, particularly in the disorder potential associated with each dopant.
Charge density waves↗
Advancing Li-plating detection: Motivating a multi-signal correlation approach
Facilitating fast charging in lithium-ion batteries (LiBs) is often linked to Li-plating, which harms performance, longevity, and safety. Early detection of Li-plating is essential for rapid technological development and for preventing performance deterioration and ensuring safety during operation. Fast and real-time detection, using commonly collected measurements like voltage (V), current (I), temperature (T), and pressure (P), is highly desirable. Existing standalone methods relying on electrochemical and mechanical signatures, using half, smaller, or specially designed cells often operated at lower temperatures, fail to account for real-world fast-charging conditions. These signatures may also have inherent unreliability in aged LiBs, a phenomenon currently not-well understood. All these uncertainties have complicated practical implementation of a robust Li-plating detection technique. This study, through multiple case studies involving real-world fast-charging conditions using automotive-grade 11.6 Ah LiBs, shows that many single signal-based diagnostic techniques may be inadequate to detect Li-plating. Among various signatures, end-of-charge rest pressure, differential pressure-sensing, and end-of-charge rest voltage were identified as particularly useful in detecting Li-plating. Furthermore, A multi-signal-based detection technique is shown to be more robust in detecting Li-plating. Using both fresh and aged cells, the results and analysis highlight how detection capabilities are influenced by various factors, including battery design, size, charging speed, operating temperature, and degradation level. Adopting such a multi-signal Li-plating detection approach may be instrumental in the rapid development of battery technology in laboratories, as well as ensuring the enhanced safety of next-generation LiBs in real-world fast-charging applications.
25 ENERGY STORAGE↗
First Detection of the Baryon Acoustic Oscillation (BAO) Feature in the 3-Point Correlation Function of DESI DR1 Luminous Red Galaxies
We present the first detection of the 3-Point Correlation Function (3PCF) Baryon Acoustic Oscillation (BAO) signal from the DESI Data Release 1 (DR1) sample of Luminous Red Galaxies (LRGs), which contains over 2.1 million galaxies. Our analysis is based on a tree-level redshift-space bispectrum template, which is then transformed to position space using the Fast Fourier Transform on Logarithmic scales (FFTLog) algorithm. We detect the BAO feature with a significance of approximately $8.1σ$ using the EZmock covariance matrix and $8.5σ$ using the analytical covariance matrix, for the full LRG redshift range ($0.4
Kamalinejad, Farshad [Florida U.] (ORCID:000000017↗