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At least 1,189 records · Page 66

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Application of machine learning to estimate fireball characteristics and their uncertainty from infrared spectral data

Experiments or events involving high explosives (HE) can be monitored remotely by infrared (IR) sensors to gather information about the configuration or materials involved in the device. Researchers at the Air Force Institute of Technology (AFIT) developed a phenomenological model for HE fireball spectra in the IR range that allows for parameters to be extracted from Fourier transform infrared (FTIR) data. This model includes parameters tied to physical characteristics of the fireball: temperature, size, soot, and gas concentrations. Previous works have sought to recover these parameters by the fitting of either whole spectra or select wavenumber bands to this phenomenological model. Difficulties arise due to the complex relationships between the parameters to be fit. Uncertainty quantification of the estimated fireball parameters is also problematic since HE experiments do not have any ground truth information on the parameters. It is suggested that artificial neural network (ANN) based approaches may be well suited to this problem, because of their ability to capture complex and highly nonlinear relationships. As such, this work seeks to explore the efficacy of deep artificial neural networks (DNNs) for this problem of parameter recovery from spectra and to also investigate the uncertainty of recovering the fireball parameters from FTIR data. Networks are designed using the hyperparameter optimization tool Hyperopt and trained/tested on artificial data generated using the phenomenological model developed by AFIT. The results of applying the network to the artificial data set are compared to a physics-based band approach that uses a selected number of bands based on their physical properties. Information on the uncertainty of estimating parameters from remotely sensed experimental data is obtained by treating the accuracy of the DNN model on artificial data as an upper bound and by examining the impact of emissivity due to soot on parameter estimation error; the results for artificial data are likely to be optimistic as compared to recovering parameters from experimental data.

42 ENGINEERING↗

DOE OSTI2022

RUScal : Software for the analysis of resonant ultrasound spectroscopy measurements

Resonant ultrasound spectroscopy is used to nondestructively measure the elastic resonances of small solids to elucidate the material's elastic properties or other qualities like size, shape, or composition. In this work, we introduce the software RUScal for the purpose of determining elastic properties by analyzing the eigenfrequencies of solid specimens with common shapes, such as rectangular parallelepipeds, cylinders (solid and hollow tube), ellipsoids, and octahedrons, as well as irregularly shaped ellipsoids that can be described analytically. All symmetry classes are supported, from isotropic to triclinic, along with the option to add or remove up to three orthogonal mirror planes as well as the ability to reorient the crystal axes with respect the sample edges via Euler angles. Additional features include tools to help find initial sets of elastic constants, including grid exploration and Monte Carlo methods, a tool to analyze frequencies as a function of sample length or crystal orientation, an error analysis tool to assess fit quality, and formatting of the input and output files for batch fitting, e.g., as a function of temperature. This software was validated with published resonant ultrasound spectroscopy data for various materials, shapes, and symmetries with noted improvements in calculation time compared to finite element methods.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

A constitutive model for sheared dense suspensions of rough particles

In a concentrated suspension, particles come into contact due to the presence of asperities on their surfaces. As a result, the contact forces and interparticle friction become one of the important factors governing the rheology of rigid particle suspensions at high concentrations. We show that a load-dependent friction model can be used to reproduce the experimentally observed shear thickening [ST—continuous and discontinuous (DST)] behavior with an increase in the asperity size. Increasing the particle surface roughness size leads to a decrease in the critical shear rate above which shear-thickening takes place, a reduction in the critical volume fraction for DST and an increase in the viscosity jump across non-shear-thickened to shear-thickened regime. In this paper, we propose a constitutive model to quantify the effect of increasing the roughness size on the rheology of dense ST suspensions as well as on the critical shear rate for ST and the critical volume fraction for DST. We fit this model to our simulation data for stress controlled shear flow of dense rough particle suspensions. Once the fitting is complete, these equations are used to predict exact volume fractions and shear stress values for transitions between three regimes on the shear stress-shear rate flow state diagram for different roughness values. Furthermore, the results of this study can be used to tune the particle surface roughness for manipulating the dense suspension rheology according to different applications.

42 ENGINEERING↗

DOE OSTI2024

The immune-evasive proline-283 substitution in influenza nucleoprotein increases aggregation propensity without altering the native structure

Nucleoprotein (NP) is a key structural protein of influenza ribonucleoprotein complexes and is central to viral RNA packing and trafficking. NP also determines the sensitivity of influenza to myxovirus resistance protein 1 (MxA), an innate immunity factor that restricts influenza replication. A few critical MxA-resistant mutations have been identified in NP, including the highly conserved proline-283 substitution. This essential proline-283 substitution impairs influenza growth, a fitness defect that becomes particularly prominent at febrile temperature (39°C) when host chaperones are depleted. Here, we biophysically characterize proline-283 NP and serine-283 NP to test whether the fitness defect is caused by the proline-283 substitution introducing folding defects. We show that the proline-283 substitution changes the folding pathway of NP, making NP more aggregation prone during folding, but does not alter the native structure of the protein. These findings suggest that influenza has evolved to hijack host chaperones to promote the folding of otherwise biophysically incompetent viral proteins that enable innate immune system escape.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Ancient origins of allosteric activation in a Ser-Thr kinase

A myriad of cellular events are regulated by allostery; therefore, evolution of this process is of fundamental interest. Here, we use ancestral sequence reconstruction to resurrect ancestors of two colocalizing proteins, Aurora A kinase and its allosteric activator TPX2 (targeting protein for Xklp2), to experimentally characterize the evolutionary path of allosteric activation. Autophosphorylation of the activation loop is the most ancient activation mechanism; it is fully developed in the oldest kinase ancestor and has remained stable over 1 billion years of evolution. As the microtubule-associated protein TPX2 appeared, efficient kinase binding to TPX2 evolved, likely owing to increased fitness by virtue of colocalization. Subsequently, TPX2-mediated allosteric kinase regulation gradually evolved. Surprisingly, evolution of this regulation is encoded in the kinase and did not arise by a dominating mechanism of coevolution.

Science & Technology - Other Topics↗

DOE OSTI2025

RB-TnSeq elucidates dicarboxylic-acid-specific catabolism in β-proteobacteria for improved plastic monomer upcycling

Dicarboxylic acids are key components of many polymers and plastics, making them a target for both engineered microbial degradation and sustainable bioproduction. In this study, we generated a comprehensive data set of functional evidence for the genetic basis of dicarboxylic and fatty acid metabolism using randomly barcoded transposon sequencing (RB-TnSeq). We identified four β-proteobacteria that displayed robust growth with dicarboxylic acid sole carbon source and cultured their mutant libraries with dicarboxylic and fatty acids with carbon chain lengths from C3 to C12. The resulting fitness data suggested that dicarboxylic and fatty acid metabolisms are largely distinct, and different sets of β-oxidation genes are required for catabolizing dicarboxylic versus fatty acids of the same carbon chain lengths. In addition, we identified transcriptional regulators and transporters with strong fitness phenotypes related to dicarboxylic acid utilization. In Ralstonia sp. UNC404CL21Col (R. CL21), we deleted two transcriptional repressors to improve its utilization of short-chain dicarboxylic acids. We exploited the diacid-utilizing catabolism of R. CL21 to upcycle a mock mixture of the dicarboxylic acids produced when polyethylene is oxidized. After introducing a heterologous indigoidine production pathway, this engineered Ralstonia produced 0.56 ± 0.02 g/L indigoidine from a mixture of dicarboxylic acids as a carbon source, demonstrating the potential of R. CL21 to upcycle plastic wastes to products derived from tricarboxylic acid (TCA) cycle intermediates. IMPORTANCE: Upcycling the carbon in plastic wastes to value-added products is a promising approach to address the plastic waste and climate crises, and dicarboxylic acid metabolism is an important facet of several approaches. Improving our understanding of the genetic basis of this metabolism has the potential to uncover new enzymes and genetic parts for engineered pathways involving dicarboxylic acids. Our data set is the most comprehensive interrogation of dicarboxylic acid catabolism to date, and this work will be of utility to researchers interested in both plastics bioproduction and upcycling applications.

Pearson, Allison N↗

DOE OSTI2026

Contrasting effects of glutamate and branched-chain amino acid metabolism on acid tolerance in a Castellaniella isolate from acidic groundwater

Groundwater acidification co-occurring with nitrate pollution is a common, global environmental health hazard. Denitrifying bacteria have been leveraged for the in situ removal of nitrate in groundwater. However, co-existing stressors—such as low pH—reduce the efficacy of biological removal processes. Castellaniella sp. str. MT123 is a complete denitrifier that was isolated from acidic, nitrate-contaminated groundwater. The strain grows robustly by nitrate respiration at pH < 6.0, completely reducing nitrate to dinitrogen gas. Genomic analyses of MT123 revealed few previously characterized acid tolerance genes. Thus, we utilized a combination of proteomics, metabolomics, and competitive mutant fitness to characterize the genetic mechanisms of MT123 acclimation to growth under mildly acidic conditions. We found that glutamate accumulation is critical in the acid acclimation of MT123, possibly through consumption of intracellular protons via glutamate decarboxylation to GABA. This is despite the fact that MT123 lacks the canonical glutamate decarboxylase-glutamate/GABA antiporter system implicated in acid tolerance in other bacteria. In contrast, branched-chain amino acid (BCAA) accumulation was detrimental to cell growth at lower pHs, possibly through indirect mechanisms impacting the cellular glutamate pool. Genetic analysis previously linked MT123 to a population of Castellaniella that bloomed—concurrent to nitrate removal—during a biostimulation effort to reduce groundwater nitrate concentrations at MT123’s location of origin. Thus, our analyses provide novel insight into mechanisms of acclimation to acidic conditions in a strain with significant potential for nitrate bioremediation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

The decay of HIV under anti-retroviral therapy is biphasic even in humanized mice with just T cells

HIV-1 plasma viral load decays in a biphasic manner during antiretroviral therapy (ART). It was hypothesized that this is due to infection of different cell types, namely CD4+ T cells and macrophages. We studied this possibility directly by modeling the decay of HIV-1 in humanized mice. We utilized previously published data from humanized T-cell only mice (TOM) and myeloid-only mice (MOM) infected with HIV-1 and treated with a potent ART regimen. Viral load decay dynamics were modeled using either a single or a biexponential decay fitted using nonlinear mixed effects techniques. Fits were compared using the corrected Bayesian information criterion (BICc). In TOM, the biphasic model was significantly better than a single-phase decay model (ΔBICc ≈ 16) despite additional parameters. In MOM, the biphasic decay was statistically better, but there was substantial uncertainty because the virus goes below detection very fast. The first-phase half-life was consistent between groups (1.2 days in MOM and 1.3 days in TOM) and similar to the half-life estimated in human infection. The second-phase decay in these mice was minimal likely due to low initial viral loads. Additional analyses with mice containing both CD4+ T cells and macrophages or X4-tropic virus-infected MOM mice confirmed the biphasic pattern, demonstrating the robustness of this result. The biphasic decline in HIV-1 occurs, even with only CD4+ T cells, refuting the hypothesis that distinct cell populations (CD4+ T cells and macrophages) drive each decay phase. These findings support an alternative model in which the observed dynamics arise from intrinsic properties of the viral infection lifecycle rather than from cellular compartmentalization.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Physiological roles of an Acinetobacter -specific σ factor

ABSTRACT The Gram-negative pathogen Acinetobacter baumannii is considered an “urgent threat” to human health due to its propensity to become antibiotic resistant. Understanding the distinct regulatory paradigms used by A. baumannii to mitigate cellular stresses may uncover new therapeutic targets. Many γ-proteobacteria use the extracytoplasmic function (ECF) σ factor, RpoE, to invoke envelope homeostasis networks in response to stress. Acinetobacter species contain the poorly characterized ECF “SigAb”; however, it is unclear if SigAb has the same physiological role as RpoE. Here, we show that SigAb is a metal stress-responsive ECF that appears unique to Acinetobacter species and distinct from RpoE-like ECFs. We combine promoter mutagenesis, motif scanning, and chromatin immunoprecipitation-sequencing (ChIP-seq) to define the direct SigAb regulon, which consists of genes encoding SigAb itself, the stringent response mediator, RelA, and the uncharacterized small RNA, “SabS.” However, RNA-seq of strains overexpressing SigAb revealed a large, indirect regulon containing hundreds of genes. Metal resistance genes are key elements of the indirect regulon, as CRISPRi knockdown of sigAb or sabS resulted in increased copper sensitivity and excess copper-induced SigAb-dependent transcription. Furthermore, we found that two uncharacterized genes in the sigAb operon, “ aabA ” and “ aabB ,” have anti-SigAb activity. Finally, employing a targeted Tn-seq approach that uses CRISPR-associated transposons, we show that sigAb , aabA , and aabB are important for fitness even during optimal growth conditions. Our work reveals new physiological roles for SigAb and SabS, provides a novel approach for assessing gene fitness, and highlights the distinct regulatory architecture of A. baumannii . IMPORTANCE Acinetobacter baumannii is a hospital-acquired pathogen, and many strains are resistant to multiple antibiotics. Understanding how A. baumannii senses and responds to stress may uncover novel routes to treat infections. Here, we examine how the Acinetobacter -specific transcription factor, SigAb, mitigates stress. We find that SigAb directly regulates only a small number of genes, but indirectly controls hundreds of genes that have substantial impacts on cell physiology. We show that SigAb is required for maximal growth, even during optimal conditions, and is acutely required during growth in the presence of elevated copper. Given that copper toxicity plays roles in pathogenesis and on copper-containing surfaces in hospitals, we speculate that SigAb function may be important in clinically relevant contexts.

Bacon, Emily E. (ORCID:0000000180907689)↗

DOE OSTI2024

Catabolic pathway acquisition by rhizosphere bacteria readily enables growth with a root exudate component but does not affect root colonization

Horizontal gene transfer (HGT) is a fundamental evolutionary process that plays a key role in bacterial evolution. The likelihood of a successful transfer event is expected to depend on the precise balance of costs and benefits resulting from pathway acquisition. Most experimental analyses of HGT have focused on phenotypes that have large fitness benefits under appropriate selective conditions, such as antibiotic resistance. However, many examples of HGT involve phenotypes that are predicted to provide smaller benefits, such as the ability to catabolize additional carbon sources. We have experimentally simulated the consequences of one such HGT event in the laboratory, studying the effects of transferring a pathway for catabolism of the plant-derived aromatic compound salicyl alcohol between rhizosphere isolates from the Pseudomonas genus. We find that pathway acquisition enables rapid catabolism of salicyl alcohol with only minor disruptions to the existing metabolic and regulatory networks of the new host. However, this new catabolic potential does not confer a measurable fitness advantage during competitive growth in the rhizosphere. We conclude that the phenotype of salicyl alcohol catabolism is readily transferable but is selectively neutral under environmentally relevant conditions. We propose that this condition is common and that HGT of many pathways will be self-limiting because the selective benefits are small.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Sampling Low-Dimensional Markovian Dynamics for Preasymptotically Recovering Reduced Models from Data with Operator Inference

This work introduces a method for learning low-dimensional models from data of high-dimensional black-box dynamical systems. The novelty is that the learned models are exactly the reduced models that are traditionally constructed with classical projection-based model reduction techniques. Thus, the proposed approach learns models that are guaranteed to have the well-studied properties of reduced models known from model reduction, without requiring full knowledge of the governing equations and without requiring the operators of the high-dimensional systems. The key ingredient is a new data sampling scheme to obtain re-projected trajectories of high-dimensional systems that correspond to Markovian dynamics in low-dimensional subspaces. The exact recovery of reduced models from these re-projected trajectories is guaranteed pre-asymptotically under certain conditions for finite amounts of data and for a large class of systems with polynomial nonlinear terms. Numerical results demonstrate that the low-dimensional models learned with the proposed approach match reduced models from traditional model reduction up to numerical errors in practice. In conclusion, the numerical results further indicate that low-dimensional models fitted to re-projected trajectories are predictive even in situations where models fitted to trajectories without re-projection are inaccurate and unstable.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker–Planck Equations

The Fokker-Planck (FP) equation is a foundational partial differential equation (PDE) in stochastic processes involving Brownian motions. However, the curse of dimensionality (CoD) poses a formidable challenge when dealing with high-dimensional FP equations. Although Monte Carlo simulation and (vanilla) Physics-Informed Neural Networks (PINNs) have shown the potential to tackle CoD, both methods exhibit significant numerical errors in high dimensions when dealing with the probability density function (PDF) associated with Brownian motion. The point-wise PDF values tend to decrease exponentially as dimensionality increases, surpassing the precision of numerical simulations and resulting in substantial errors. In addition, due to its massive sampling, Monte Carlo fails to offer fast sampling. Modeling the logarithm likelihood (LL) via vanilla PINNs transforms the FP equation into a notoriously difficult Hamilton-Jacobi-Bellman (HJB) equation, which is impractical for PINN learning, whose error grows rapidly with dimension. To this end, we propose a novel approach utilizing a score-based solver to fit the score function in stochastic differential equations (SDEs). The score function, defined as the gradient of the LL, plays a fundamental role in inferring LL and PDF and enables fast SDE sampling, offering an effective means to overcome the CoD. Three fitting methods, Score Matching (SM), Sliced Score Matching (SSM), and Score-PINN, are introduced, each contributing unique advantages in computational complexity, accuracy, and generality. The proposed score-based SDE solver operates in two stages: first, employing score matching or Score-PINN to acquire the score function; and second, solving the LL via an ordinary differential equation (ODE) using the obtained score function. Comparative evaluations across these methods showcase varying trade-offs. The proposed methodology is evaluated across diverse SDEs, including anisotropic Ornstein-Uhlenbeck processes, geometric Brownian motion, and Brownian motion with varying eigenspace. We also test various distributions, including Gaussian, Log-normal, Laplace, and Cauchy distributions. The numerical results demonstrate the score-based SDE solver’s stability, speed, and performance across different experimental settings, solidifying its potential as a solution to CoD for high-dimensional FP equations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Single $\pi ^0$ production off neutrons bound in deuteron with linearly polarized photons

The quasifree γ → d → π 0 n(p) photon beam asymmetry, Σ, has been measured at photon energies, E γ , from 390 to 610 MeV, corresponding to center of mass energy from 1.271 to 1.424 GeV, for the first time. The data were collected in the A2 hall of the MAMI electron beam facility with the Crystal Ball and TAPS calorimeters covering pion center-of-mass angles from 49° to 148°. In this kinematic region, polarization observables are sensitive to contributions from the Δ(1232) and N(1440) resonances. The extracted values of Σ have been compared to predictions based on partial-wave analyses (PWAs) of the existing pion photoproduction database. Our comparison includes the SAID, MAID and Bonn–Gatchina analyses; while a revised SAID fit, including the new Σ measurements, has also been performed. In addition, isospin symmetry is examined as a way to predict π 0 n photoproduction observables, based on fits to published data in the channels π 0 p, π + n and π – p.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Polarized target nuclear magnetic resonance measurements with deep neural networks

Continuous-wave Nuclear Magnetic Resonance (CW-NMR) operated in constant-current mode has served as a foundational technique for polarization measurement in solid-state dynamically polarized targets within nuclear and high-energy physics experiments for several decades, and it remains an essential tool. Conventional Q-meter-based phase-sensitive detection is critical for precise real-time determination of target polarization during scattering runs. However, the accuracy and reliability of these measurements are frequently compromised by elevated noise levels, baseline drift, and systematic uncertainties arising from signal isolation and fitting, ultimately degrading the overall experimental figure of merit. In this work, we report the first successful application of neural network architectures to continuous-wave NMR polarization metrology. By leveraging advanced machine learning techniques for signal extraction and denoising, we achieve a substantial reduction of fitting uncertainties under a variety of realistic simulated and experimental conditions. These improvements translate directly into more robust real-time (online) polarization monitoring and higher precision in subsequent offline analysis. By reducing analysis-induced uncertainty, the resulting methodology can improve the effective figure of merit for scattering experiments employing dynamically polarized targets and provides a new toolset for NMR-based polarimetry in high-energy and nuclear physics.

Metrology↗

DOE OSTI2020

Das ist der HAMMER: consistent new physics interpretations of semileptonic decays

Abstract Precise measurements of $$b\rightarrow c\tau \bar{\nu }$$ b → c τ ν ¯ decays require large resource-intensive Monte Carlo (MC) samples, which incorporate detailed simulations of detector responses and physics backgrounds. Extracted parameters may be highly sensitive to the underlying theoretical models used in the MC generation. Because new physics (NP) can alter decay distributions and acceptances, the standard practice of fitting NP Wilson coefficients to SM-based measurements of the $$R(D^{(*)})$$ R ( D ( ∗ ) ) ratios can be biased. The newly developed software tool enables efficient reweighting of MC samples to arbitrary NP scenarios or to any hadronic matrix elements. We demonstrate how allows avoidance of biases through self-consistent fits directly to the NP Wilson coefficients. We also present example analyses that demonstrate the sizeable biases that can otherwise occur from naive NP interpretations of SM-based measurements. The library is presently interfaced with several existing experimental analysis frameworks and we provide an overview of its structure.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Impact of jet-production data on the next-to-next-to-leading-order determination of HERAPDF2.0 parton distributions

The HERAPDF2.0 ensemble of parton distribution functions (PDFs) was introduced in 2015. The final stage is presented, a next-to-next-to-leading-order (NNLO) analysis of the HERA data on inclusive deep inelastic ep scattering together with jet data as published by the H1 and ZEUS collaborations. A perturbative QCD fit, simultaneously of $\alpha _s(M_Z^2)$ and the PDFs, was performed with the result $\alpha _s(M_Z^2)= 0.1156 \pm 0.0011~\mathrm{(exp)}~ ^{+0.0001}_{-0.0002}~ \mathrm{(model}$ $\mathrm{+ parameterisation)}~ \pm 0.0029~\mathrm{(scale)}$. The PDF sets of HERAPDF2.0Jets NNLO were determined with separate fits using two fixed values of $\alpha _s(M_Z^2)$, $\alpha _s(M_Z^2)$ = 0.1155 and 0.118, since the latter value was already chosen for the published HERAPDF2.0 NNLO analysis based on HERA inclusive DIS data only. The different sets of PDFs are presented, evaluated and compared. The consistency of the PDFs determined with and without the jet data demonstrates the consistency of HERA inclusive and jet-production cross-section data. The inclusion of the jet data reduced the uncertainty on the gluon PDF. Predictions based on the PDFs of HERAPDF2.0Jets NNLO give an excellent description of the jet-production data used as input.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Measurement of the W-boson mass and width with the ATLAS detector using proton–proton collisions at $\sqrt{s}=7$ TeV

Proton-proton collision data recorded by the ATLAS detector in 2011, at a centre-of-mass energy of 7 TeV, have been used for an improved determination of the W-boson mass and a first measurement of the W-boson width at the LHC. Recent fits to the proton parton distribution functions are incorporated in the measurement procedure and an improved statistical method is used to increase the measurement precision. The measurement of the W-boson mass yields a value of m w = 80,366.5 ± 9.8 (stat.) ± 12.5 (syst.) MeV = 80,366.5 ± 15.9 MeV, and the width is measured as Γ w = 2202 ± 32 (stat.) ± 34 (syst.) MeV = 2202 ± 47 MeV. The first uncertainty components are statistical and the second correspond to the experimental and physics-modelling systematic uncertainties. Both results are consistent with the expectation from fits to electroweak precision data. The present measurement of m w is compatible with and supersedes the previous measurement performed using the same data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Initial measurement of reactor antineutrino oscillation at SNO+

The SNO+ collaboration reports its first spectral analysis of long-baseline reactor antineutrino oscillation using 114 tonne-years of data. Fitting the neutrino oscillation probability to the observed energy spectrum yields constraints on the neutrino mass-squared difference Δ$m^2_{21}$. In the ranges allowed by previous measurements, the best-fit Δ$m^2_{21}$ is ($8.85^{+1.10}_{-1.33}$) × 10 -5 eV 2 . This measurement is continuing in the next phases of SNO+ and is expected to surpass the present global precision on Δ$m^2_{21}$ with about three years of data. In the following, we first describe the configuration of the SNO+ detector when it was partially filled with scintillator, and a characterization of the detector response using intrinsic radioactivity. Next, we detail the event selection and expectations for reactor IBDs and (α, n) reactions. Then, we present the results of an energy spectrum analysis using 114 tonne-years of data. We conclude with prospects of future results from the SNO+ detector, which has been operating fully-filled with 780 tonnes of scintillator

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗