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DOE OSTI2023

Probing the muon ( g − 2) anomaly at the LHC in final states with two muons and two taus

The longstanding muon (g - 2) anomaly, as well as some hints of lepton flavor universality violation in B-meson decays, could be signaling new physics beyond the Standard Model (SM). A minimal R-parity-violating supersymmetric framework with light third-generation sfermions (dubbed as ‘RPV3’) provides a compelling solution to these flavor anomalies, while simultaneously addressing other pressing issues of the SM. We propose a new RPV3 scenario for the solution of the muon (g - 2) anomaly, which leads to an interesting LHC signal of $µ$ + $µ$ - $τ$ + $τ$ - final state. We analyze the Run-2 LHC multilepton data to derive stringent constraints on the sneutrino mass and the relevant RPV coupling in this scenario. We then propose dedicated selection strategies to improve the bound even with the existing dataset. We also show that the high-luminosity LHC will completely cover the remaining muon (g - 2)-preferred parameter space, thus providing a robust, independent test of the muon (g - 2) anomaly.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Compound Drought and Heatwaves at a Global Scale: The Role of Natural Climate Variability-Associated Synoptic Patterns and Land-Surface Energy Budget Anomalies

Compound drought and heatwave (CDHW) events have garnered much attention in recent studies. However, thus far, the identification of such events is oversimplified, and their association with natural climate variability is not fully explored. Here, we derive anomalies in the weekly self-calibrated Palmer Drought Severity Index (sc_PDSI) and daily maximum temperatures to identify CDHW events from 1982 to 2016 over 26 climate regions across the globe. Using a Poisson Generalized Linear Model (GLM), we analyze yearly occurrences of seasonal CDHW events and their association with the warm and cold phases of El Nino Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO). ENSO exhibits robust association with CDHW events over the Southern Hemisphere during the austral summer and fall, while PDO influences their occurrences over the Western North America in the Northern Hemisphere during the boreal summer, which is supported by the composites of anomalies in the atmospheric circulations and surface energy budget. However, NAO association with CDHW events is relatively weak. The CDHW occurrence over other regions is driven by a combination of these large-scale natural forcing. Our analyses also highlight that the cooccurrence of weekly to submonthly scale anomalies in the observed temperature and precipitation may not be always aligned between the observations and the reanalysis. Therefore, caution must be exercised while explaining such observed anomalies on the basis of reanalysis-based circulations and surface energy budget. Lastly, our analyses provide a new insight towards concurrent extremes and should help foster research efforts in this area.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Origin of 182 W Anomalies in Ocean Island Basalts

Ocean island basalts (OIB) show variable 182 W deficits that have been attributed to either early differentiation of the mantle or core-mantle interaction. However, 182 W variations may also reflect nucleosynthetic isotope heterogeneity inherited from Earth's building material, which would be evident from correlated 182 W and 183 W anomalies. Some datasets for OIB indeed show hints for such correlated variations, meaning that a nucleosynthetic origin of W isotope anomalies in OIB cannot be excluded. We report high-precision W isotope data for OIB from Samoa and Hawaii, which confirm previously reported 182 W deficits for these samples, but also demonstrate that none of these samples have resolvable 183 W anomalies. These data therefore rule out a nucleosynthetic origin of the 182 W deficits in OIB, which most likely reflect the entrainment of either core material or an overabundance of late-accreted materials within OIB mantle sources. If these processes occurred over Earth's history, they may have also been responsible for shifting the 182 W composition of the bulk mantle to its modern-day value. We also report Mo isotope data for some Hawaiian OIB, which reveal no resolved nucleosynthetic Mo isotopic anomalies. This is consistent with inheritance of 182 W deficits in OIB from the addition of either core or late-accreted material, but only if these materials have a non-carbonaceous (NC) meteorite-like heritage. As such, these data rule out significant contributions of carbonaceous chondrite (CC)-like materials to either Earth's core or late accretion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Astronomaly at scale: searching for anomalies amongst 4 million galaxies

ABSTRACT Modern astronomical surveys are producing data sets of unprecedented size and richness, increasing the potential for high-impact scientific discovery. This possibility, coupled with the challenge of exploring a large number of sources, has led to the development of novel machine-learning-based anomaly detection approaches, such as astronomaly. For the first time, we test the scalability of astronomaly by applying it to almost 4 million images of galaxies from the Dark Energy Camera Legacy Survey. We use a trained deep learning algorithm to learn useful representations of the images and pass these to the anomaly detection algorithm isolation forest, coupled with astronomaly’s active learning method, to discover interesting sources. We find that data selection criteria have a significant impact on the trade-off between finding rare sources such as strong lenses and introducing artefacts into the data set. We demonstrate that active learning is required to identify the most interesting sources and reduce artefacts, while anomaly detection methods alone are insufficient. Using astronomaly, we find 1635 anomalies among the top 2000 sources in the data set after applying active learning, including eight strong gravitational lens candidates, 1609 galaxy merger candidates, and 18 previously unidentified sources exhibiting highly unusual morphology. Our results show that by leveraging the human–machine interface, astronomaly is able to rapidly identify sources of scientific interest even in large data sets.

Astronomy & Astrophysics↗

DOE OSTI2026

Higher-Form Anomalies on Lattices

We show that generic gapped quantum many-body states which respect an anomalous finite higher-form symmetry have an exponentially small overlap with any short-range entangled (SRE) state. Hence, anomalies of higher-form symmetries enforce $intrinsic$ long-range entanglement, which is in contrast with anomalies of ordinary (0-form) symmetries which are compatible with symmetric SRE states (specifically, symmetric cat states). As an application, we show that the anomalies of strong higher-form symmetries provide a diagnostic for mixed-state topological order in $d \geq 2$ spatial dimensions. We also identify a new (3+1)D intrinsic mixed-state topological order that does not obey remote-detectability by local decoherence of the (3+1)D Toric Code with fermionic loop excitations. This breakdown of remote detectability, as encoded in anomalies of strong higher-form symmetries, provides a partial characterization of intrinsically mixed-state topological order.

Feng, Yitao [Peking University, Beijing (China)] (↗

DOE OSTI2025

Kohn anomalies and phonon anharmonicity in iridium

Elemental iridium presents surprising challenges for both inelastic neutron scattering (INS) and theoretical thermal transport calculations due to its high neutron absorption cross-section and strong electron-phonon interactions, respectively. Here, in this study, we overcome these challenges to measure temperature-dependent phonon dispersion curves, compare these with calculations based on density functional theory (DFT), and ultimately examine the electron-phonon limited transport behaviors of this material. Our DFT calculations demonstrate Kohn anomalies, near the 𝐾 point of the iridium Brillouin zone, indicating coupling between electrons and phonons. Strong electron-phonon coupling can compete with anharmonic effects to determine electrical and thermal transport behaviors and make the Kohn anomalies challenging to observe. Nonetheless, our INS measurements map these anomalies and other dispersion features over the Brillouin zone from 100 to 700 K. These measurements also uncover unexpectedly large mode specific Grüneisen parameters obtained from the temperature-dependent phonon energies, highlighting strong anharmonicity in iridium. DFT-based Boltzmann transport calculations demonstrate how anharmonicity and electron-phonon couplings determine electronic and lattice transport behaviors. Furthermore, we correlate the Kohn anomalies with calculated electron-phonon nesting functions, Fermi surfaces, and DFT-derived coupling strengths. This study provides detailed insights into the temperature-dependent mode-resolved lattice dynamics and anharmonicity, transport behavior, and electron-phonon interactions.

DFT↗

DOE OSTI2022

Kohn anomaly and elastic softening in body-centered cubic molybdenum at high pressure

Transition metals in body-centered cubic (bcc) structures under compression can display several novel physical properties because of their complex electronic structures and electron-phonon interactions. Here, we used inelastic x-ray scattering experiments in a diamond-anvil cell up to ∼45 GPa and density-functional theory calculations up to 210 GPa to investigate the phonon dispersions, and electronic and elastic properties of single-crystal molybdenum (Mo). Our results show a pressure-induced Kohn anomaly at 𝑞∼0.5 along the [ξ00] direction in the longitudinal acoustic mode at ∼45 GPa; this anomaly is triggered by the pressure-enhanced Fermi-surface nesting effect. Theoretical calculations show that electron redistributions in the 𝑠-to-𝑑 orbitals of bcc-Mo contribute to the shear modulus anomaly at ∼50 GPa. In contrast, the Young's modulus anomaly in bcc-Mo at ∼210 GPa results from a Lifshitz-type electronic topological transition. In conclusion, our results shed light on the complex electronic behaviors that are associated with macroscopic elastic properties in typical bcc 𝑑-block transition metals under compression.

3-dimensional systems↗

DOE OSTI2020

Addressing R D() , R K() , muon g -2 and ANITA anomalies in a minimal R-parity violating supersymmetric framework

We analyze the recent hints of lepton flavor universality violation in both charged-current and neutral-current rare decays of B mesons in an R -parity-violating supersymmetric scenario. Motivated by simplicity and minimality, we had earlier postulated the third-generation superpartners to be the lightest (calling the scenario “RPV3”) and explicitly showed that it preserves gauge coupling unification and of course has the usual attribute of naturally addressing the Higgs radiative stability. Here we show that both R D ( * ) and R K ( * ) flavor anomalies can be addressed in this RPV3 framework. Interestingly, this scenario may also be able to accommodate two other seemingly disparate anomalies, namely, the longstanding discrepancy in the muon ( g - 2 ), as well as the recent anomalous upgoing ultrahigh-energy Antarctic Impulsive Transient Antenna events. Based on symmetry arguments, we consider three different benchmark points for the relevant RPV3 couplings and carve out the regions of parameter space where all (or some) of these anomalies can be simultaneously explained. We find it remarkable that such overlap regions exist, given the plethora of precision low-energy and high-energy experimental constraints on the minimal model parameter space. The third-generation superpartners needed in this theoretical construction are all in the 1–10 TeV range, accessible at the LHC and/or next-generation hadron colliders. We also discuss some testable predictions for the lepton-flavor-violating decays of the tau lepton and B mesons for the current and future B -physics experiments, such as LHCb and Belle II. Complementary tests of the flavor anomalies in the high- p T regime in collider experiments such as the LHC are also discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Online-compatible unsupervised nonresonant anomaly detection

There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner. Most proposals for new methods focus exclusively on signal sensitivity. However, it is not enough to select anomalous events—there must also be a strategy to provide context to the selected events. In this work, we propose the first complete strategy for unsupervised detection of nonresonant anomalies that includes both signal sensitivity and a data-driven method for background estimation. Our technique is built out of two simultaneously trained autoencoders that are forced to be decorrelated from each other. This method can be deployed off-line for nonresonant anomaly detection and is also the first complete on-line-compatible anomaly detection strategy. We show that our method achieves excellent performance on a variety of signals prepared for the ADC2021 data challenge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Anomalies in string-inspired nonlocal extensions of QED

We investigate anomalies in the class of nonlocal field theories that have been proposed as an ultraviolet completion of four-dimensional quantum field theory with generalizing the kinetic energy operators to an infinite series of higher derivatives inspired by the string field theory and ghost-free nonlocal approaches to quantum gravity. We explicitly calculate the vector and chiral anomalies in a string-inspired nonlocal extension of QED. We show that the vector anomaly vanishes as required by gauge invariance and the Ward identity. On the other hand, although the chiral anomaly vanishes to the leading order with massless fermions, it nonetheless does not vanish with the massive fermions and we calculate it to the leading order in the scale of nonlocality. We also calculate the nonlocal vector and axial currents explicitly and present an illustrative example by applying our results to the decay of π 0 → γ γ . Published by the American Physical Society 2024

Abu-Ajamieh, Fayez↗

DOE OSTI2024

Anomaly detection in collider physics via factorized observables

To maximize the discovery potential of high-energy colliders, experimental searches should be sensitive to unforeseen new physics scenarios. This goal has motivated the use of machine learning for unsupervised anomaly detection. In this paper, we introduce a new anomaly detection strategy called : factorized observables for regressing conditional expectations. Our approach is based on the inductive bias of factorization, which is the idea that the physics governing different energy scales can be treated as approximately independent. Assuming factorization holds separately for signal and background processes, the appearance of nontrivial correlations between low- and high-energy observables is a robust indicator of new physics. Under the most restrictive form of factorization, a machine-learned model trained to identify such correlations will in fact converge to the optimal new physics classifier. We test on a benchmark anomaly detection task for the Large Hadron Collider involving collimated sprays of particles called jets. By teasing out correlations between the kinematics and substructure of jets, our method can reliably extract percent-level signal fractions. This strategy for uncovering new physics adds to the growing toolbox of anomaly detection methods for collider physics with a complementary set of assumptions. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

DOE OSTI2020

Adaptive Anomaly Detection for Dynamic Clinical Event Sequences

Over the past decade, health information technology (IT) has enabled the amount of digital information stored in electronic health records (EHRs) to expand greatly. However, according to some studies, hazards in health IT can lead to changes in clinical decisions, care processes, and care outcomes, as well as other issues. Thus, the effects of health IT hazards on patient safety have been at the forefront of recent patient safety research. Nonetheless, hazard detection in health IT remains a challenge. In this paper, the authors assume that safety-related issues in health IT would exhibit anomalous characteristics in EHR data. Although all hazards will exhibit some anomalous characteristics, not all anomalies can be regarded as hazards. The authors hypothesize that errors in health IT could lead to interruptions in the sequence of clinical actions. To this end, the problem of detecting anomalous sequences in big EHR data is considered. This paper focuses on dynamic event sequences, which are a series of clinical actions in motion. The authors propose an adaptive anomaly detection approach that uses higher-order network representation to detect anomalous sequences. Furthermore, the authors propose a contiguous subsequence anomaly detection approach that identifies abnormal subsequences in the detected anomalous sequences. The proposed approaches are tested by using synthetic and real-world EHR data. The proposed methods outperform existing state of the art anomaly detection techniques. To reduce the computational complexity associated with the operational implementation of the proposed approaches, the Apache Spark environment was leveraged, and a much shorter run time together with improved performance were achieved, especially for data with more than 60,000 sequences.

Niu, Haoran↗

DOE OSTI2023

Anomaly detection for MPC forecast in Fleet of Water Heaters

Among residential devices, water heaters consume 20% of home energy use in the United States. Water heaters possess the capability to store energy within their reservoirs, enabling the ability to decouple energy use from hot water use. This capability can be used to reduce energy usage and costs while also supporting grid services. This requires accurate forecasting of the parameters of the water heater such as upper and lower temperatures. In this study, we analyzed the performance and behavior of a water heater model used in the real-world to predict a control mechanism that is implemented in a smart residential neighborhood. The model forecasts are accurate in most cases but not all. In such scenarios, error correction of the model is necessary to further improve model predictive control accuracy. Anomaly detection is the first step of error correction. This study complements existing research by grouping time series data into two clusters one with anomalies and another without anomalies. To achieve this task, we explored and compared multiple unsupervised machine learning algorithms to perform clustering. Among these algorithms, Ward clustering has the lowest running time and identified the highest number of anomalies for the upper temperature limit. The proposed approach is tested based on the data collected in a neighborhood with 46 townhomes located in Atlanta, GA.

Lebakula, Viswadeep↗

DOE OSTI2025

Anomaly Detection in Electronic Health Records Across Hospital Networks: Integrating Machine Learning With Graph Algorithms

In a large hospital system, a network of hospitals relies on electronic health records (EHRs) to make informed decisions regarding their patients in various clinical domains. Consequently, the dependability of the health information technology (HIT) systems responsible for collecting EHR data is of utmost importance for patient safety. Recently, novel methods and tools aimed at identifying anomalies in EHR data to bolster the reliability of HIT systems have been introduced. However, these existing methods and tools primarily concentrate on individual hospitals, which limits our understanding of system-wide anomalous events and their potential impact on patient safety across multiple hospitals. In this article, we introduce a new approach to detecting anomalies in EHR data within a network of hospitals. This is achieved by combining advanced machine learning techniques with graph algorithms to create a tool capable of swiftly identifying and responding to deviations. Our proposed approach employs a combination of five machine learning models, harnessing the unique strengths of each model to provide a more robust detection system. The detected anomalies are then represented as graphs, allowing us to recognize patterns across the hospital network. This aids in identifying anomalies that span multiple medical facilities, potentially indicating broader system-level risks. Extensive real-world testing of our approach demonstrated its ability to offer actionable insights compared to existing methods. Additionally, its scalable design ensures seamless integration into existing HIT infrastructures.

Niu, Haoran [Oak Ridge National Laboratory (ORNL),↗

DOE OSTI2022

Thermal equation of state for zoisite: Implications for the transportation of water into the upper mantle and the high-velocity anomaly in the Farallon plate

Seismic tomography studies have revealed a high-velocity anomaly at depths between 100 km and 300 km in the Farallon plate. However, the reasons for the high-velocity anomaly continue to be debated. An analysis of the mineral proportions of eclogites exhumed from the Farallon plate shows that the average amount of zoisite in eclogite is ∼16.0 vol%. Therefore, the presence of zoisite eclogite needs to be considered to explain the high-velocity anomaly of the Farallon plate. However, the thermal equation of state and stability of zoisite have not been fully investigated under high pressure−temperature (P-T) conditions. We investigated the high-pressure and high-temperature behavior of natural zoisite utilizing synchrotron single crystal-X-ray diffraction (XRD). The results indicate that zoisite is metastable up to 24.8 GPa and 700 K. We obtained the ambient unit-cell volume V0 = 901.26(3) Å3 by synchrotron single crystal-XRD measurement. We also fitted the pressure-volume-temperature data to a high-temperature Birch-Murnaghan equation of state and obtained the zero-pressure bulk modulus K0 = 134.7(8) GPa, the temperature derivative of the bulk modulus (∂K/∂T)P = −0.011(4) GPa/K, and the thermal expansion coefficients α0V = 1.9(7) × 10−5 K−1 and α1V = 3(2) × 10−8 K−2 when the pressure derivative of bulk modulus (K′0) is fixed at 4. By incorporating the results from previous studies, we calculated the density and bulk sound velocity profiles of zoisite eclogite along the Farallon plate geotherm. Finally, we infer that zoisite could carry water to depths of ∼300 km within cold subducting slabs and that the bulk sound velocity of typical zoisite eclogite with ∼61.0 vol% omphacite, ∼23.0 vol% garnet, and ∼16.0 vol% zoisite could cause the high-velocity anomalies at depths of 100−300 km in the Farallon plate.

Geology↗

DOE OSTI2024

Structural and phonon anomalies in the superconducting Bi 2 Sr 2 CaCu 2 O 8+σ with varying doping level at high pressures

Here we examined the effect of the lattice compression on the crystal structure of the Bi 2 Sr 2 CaCu 2 O 8+σ superconductor with nearly optimally doped and overdoped composition using X-ray diffraction technique. Our studies show that at high pressures (up to 30 GPa ) the doping level does not affect the crystal structure of this superconductor. Along with this, structural anomalies in ratio appear at a pressure that corresponds to that at which T c begins to decrease. This fact indicates a close connection between the observed anomalies and superconductivity. We also studied the effect of doping and pressure on the T c (P) and on the Raman active lattice modes of the Bi 2 Sr 2 CaCu 2 O 8+σ superconductors. We find universal suppression of the T c by the pressure starting from the critical pressure P c in the range from 9 to 16 GPa, depending on the doping level of the Bi 2 Sr 2 CaCu 2 O 8+σ samples. Concomitantly, we observe phonon anomalies around 10 to 20 GPa, which indicate possible pressure-induced charge redistribution in BiO layers. These newly detected anomalies may be related to the changes in the electronic structure which compete with the superconductivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Anomaly Detection in Seismic Data with Deep Learning: Application for Instrument Failure Detection and Forecasting

Seismic data quality assessment (QA) is the first and one of the most important steps before conducting any further data analysis. Traditional methods involve checking various metrics, such as spike detection and power spectral density, by setting strict thresholds or comparing data against synthetic benchmarks. However, these approaches often rely on pre-existing knowledge and assumptions about data anomalies, leading to potential misclassification of unusual cases. Here, in this study, we propose a deep autoencoder model, an unsupervised learning approach that evaluates data quality without making assumptions about normal and anomalous data, which can be used to identify deviations in recorded data that may indicate nascent instrument failure. We test the model with the U.S. International Monitoring System (IMS) seismic stations and demonstrate the capability of detecting anomalies on a monthly scale. This could prompt station operators to examine potential problems early, allowing sufficient time for instrument maintenance to prevent data outages. In addition, we use a new manually selected testing dataset to compare our model performance against two supervised machine learning (ML) approaches and a standard QA package, as baseline models. When applied to the dataset containing known data anomalies, performance of the supervised and unsupervised ML approaches is similar, with an accuracy of 88.1% for our model compared to ∼90% for the supervised ML approach and 78.2% for the standard QA package. Our model outperforms the baseline models when applied to new stations, where new types of data anomalies can be station-specific and not included in the training dataset. Finally, we show model transferability by training the model with data from the Global Seismograph Network only and applying it to the IMS network data. The results suggest that our model is generalizable and can be applied to new stations with good accuracy.

Lin, Jiun-Ting [Lawrence Livermore National Labora↗

DOE OSTI2024

Anomalies of non-invertible symmetries in (3+1)d

Anomalies of global symmetries are important tools for understanding the dynamics of quantum systems. We investigate anomalies of non-invertible symmetries in 3+1d using 4+1d bulk topological quantum field theories given by Abelian two-form gauge theories, with a 0-form permutation symmetry. Gauging the 0-form symmetry gives the 4+1d “inflow” symmetry topological field theory for the non-invertible symmetry. We find a two levels of anomalies: (1) the bulk may fail to have an appropriate set of loop excitations which can condense to trivialize the boundary dynamics, and (2) the “Frobenius-Schur indicator” of the non-invertible symmetry (generalizing the Frobenius-Schur indicator of 1+1d fusion categories) may be incompatible with trivial boundary dynamics. As a consequence we derive conditions for non-invertible symmetries in 3+1d to be compatible with symmetric gapped phases, and invertible gapped phases. Along the way, we see that the defects characterizing \mathbb{Z}_{4} ℤ 4 ordinary symmetry host worldvolume theories with time-reversal symmetry \mathsf{T} 𝖳 obeying the algebra \mathsf{T}^{2}=C 𝖳 2 = C or \mathsf{T}^{2}=(-1)^{F}C, 𝖳 2 = ( − 1 ) F C , with C C a unitary charge conjugation symmetry. We classify the anomalies of this symmetry algebra in 2+1d and further use these ideas to construct 2+1d topological orders with non-invertible time-reversal symmetry that permutes anyons. As a concrete realization of our general discussion, we construct new lattice Hamiltonian models in 3+1d with non-invertible symmetry, and constrain their dynamics.

Córdova, Clay↗