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DOE OSTI2025

XRISM high-resolution X-ray spectroscopy of Cygnus X-1: Highly ionized iron absorption structures

We present the first high-resolution X-ray spectral analysis of Cygnus X-1 using XRISM. The observation wa3s carried out from 2024 April 7 to 10, covering the orbital phase range 0.65–0.17 during its low/hard state. Taking advantage of the exceptional energy resolution of the Resolve instrument, we examined highly ionized iron absorption lines and characterized the ionization states, column densities, and line-of-sight velocities of the absorbing plasma. Spectral analysis revealed an ionization parameter of $\xi \sim 3$, column densities of a few $\times 10^{21}$ cm$^{-2}$, and a blueshifted velocity of $\sim$100 km s$^{-1}$. The observation was divided into two phases: before and after orbital phase $\phi _{\rm {orb}} = 0.9$, corresponding to non-dipping and dipping intervals. While only weak absorption features were present before $\phi _{\rm {orb}} = 0.9$, strong absorption by He-like and H-like Fe appeared during the dipping phase. We measured equivalent widths of 2.3, 0.4, and 1.2 eV for He-like Fe K$\alpha$ and H-like Ly$\alpha _1$ and Ly$\alpha _2$, respectively—demonstrating the capability of XRISM Resolve to securely detect narrow absorption features of only a few eV. These measurements trace the motion of the absorbing material and offer insight into the kinematics and spatial distribution of the wind in the vicinity of the black hole. These findings enhance our understanding of wind-fed accretion in Cygnus X-1 and highlight the importance of continued high-resolution X-ray observations to further constrain the physical properties of winds and accretion flows in high-mass X-ray binaries.

Astronomy and AstroPhysics↗

NASA NTRS2020

A Sensitive Quantitative Analysis of Abiotically Synthesized Short Homopeptides using Ultraperformance Liquid Chromatography and Time-of-Flight Mass Spectrometry

In the origins of life field understanding the abiotic polymerization of simple organic monomers (e.g., amino acids) into larger biomolecules (e.g., oligopeptides), remains a seminal challenge. Recently, preliminary observations showed a limited set of peptides formed in the presence of the plausible prebiotic phosphorylating agent, diamidophosphate (DAP), highlighting the need for an analytical tool to critically evaluate the ability of DAP to induce oligomerization of simple organics under aqueous conditions. However, performing accurate and precise, targeted analyses of short oligopeptides remains a distinct challenge in the analytical chemistry field. Here, we developed a new technique to detect and quantitate amino acids and their homopeptides in a single run using ultraperformance liquid chromatography-fluorescence detection/time of flight mass spectrometry. Over an 8-minute retention time window, 18 target analytes were identified and quantitated, 16 of which were chromatographically separated at, or near baseline resolution. Compound identity was confirmed by accurate mass analysis using a 10 ppm mass tolerance window. This method featured limits of detection < 5 nM (< 1 fmol on column) and limits of quantitation (LOQs) <15 nM (< 3 fmol on column). The LODs and LOQs were upwards of ∼28x and ∼788x lower, respectively, than previous methods for the same analytes, highlighting the quantifiable advantages of this new method. Both detectors provided good quantitative linearity (R(exp 2) > 0.985) for all analytes spanning concentration ranges ∼3 - 4 orders of magnitude. We performed a series of laboratory experiments to investigate DAP-mediated oligomerization of amino acids and peptides and analyzed experimental products with the new method. DAP readily polymerized amino acids and peptides under a range of simulated environmental conditions. This research underscores the potential of DAP to have generated oligopeptides on the primordial Earth, enhancing prebiotic chemical diversity and complexity at or near the origin of life.

Prebiotic Chemistry and Analytical Chemistry↗

DOE OSTI2023

First detection of the BAO signal from early DESI data

We present the first detection of the baryon acoustic oscillations (BAOs) signal obtained using unblinded data collected during the initial 2 months of operations of the Stage-IV ground-based Dark Energy Spectroscopic Instrument (DESI). From a selected sample of 261 291 luminous red galaxies spanning the redshift interval 0.4 < z < 1.1 and covering 1651 square degrees with a 57.9 per cent completeness level, we report a ∼5σ level BAO detection and the measurement of the BAO location at a precision of 1.7 per cent. Using a bright galaxy sample of 109 523 galaxies in the redshift range 0.1 < z < 0.5, over 3677 square degrees with a 50.0 per cent completeness, we also detect the BAO feature at ∼3σ significance with a 2.6 per cent precision. These first BAO measurements represent an important milestone, acting as a quality control on the optimal performance of the complex robotically actuated, fibre-fed DESI spectrograph, as well as an early validation of the DESI spectroscopic pipeline and data management system. Based on these first promising results, we forecast that DESI is on target to achieve a high-significance BAO detection at sub-per cent precision with the completed 5-yr survey data, meeting the top-level science requirements on BAO measurements. This exquisite level of precision will set new standards in cosmology and confirm DESI as the most competitive BAO experiment for the remainder of this decade.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

An End-To-End Earthquake Detection Method for Joint Phase Picking and Association Using Deep Learning

Earthquake monitoring by seismic networks typically involves a workflow consisting of phase detection/picking, association, and location tasks. In recent years, the accuracy of these individual stages has been improved through the use of machine learning techniques. Here, in this study, we introduce a new end-to-end approach that improves overall earthquake detection accuracy by jointly optimizing each stage of the detection pipeline. We propose a neural network architecture for the task of multi-station processing of seismic waveforms recorded over a seismic network. This end-to-end architecture consists of three sub-networks: a backbone network that extracts features from raw waveforms, a phase picking sub-network that picks P- and S-wave arrivals based on these features, and an event detection sub-network that aggregates the features from multiple stations to associate and detect earthquakes across a seismic network. We use these sub-networks together with a shift-and-stack module based on back-projection that introduces kinematic constraints on arrival times, allowing the neural network model to generalize to different velocity models and to variable station geometry in seismic networks. We evaluate our proposed method on the STanford EArthquake Dataset (STEAD) and on the 2019 Ridgecrest, CA earthquake sequence. The results demonstrate that our end-to-end approach can effectively pick P- and S-wave arrivals and achieve earthquake detection accuracy rivaling that of other state-of-the-art approaches. Because our approach preserves information across tasks in the detection pipeline, it has the potential to outperform approaches that do not.

58 GEOSCIENCES↗

NASA NTRS1993

Dust emission features in 3-micron spectra of Herbig Ae/Be stars

Attention is given to low- and medium-resolution spectra in the 3-micron region of 24 Herbig Ae/Be stars obtained in a search for organic features from the dust around young stars. The 3.29-micron emission feature from aromatic hydrocarbons was detected in three objects: Lk H-alpha 25, XY Per, and AS 310. Two other stars, HD 245185 and HK Ori, may have weak features. About 20 percent of the Herbig Ae/Be surveyed to date have firmly detected 3.29-micron features. The available data indicate that the 3.29-micron feature is more extended around Herbig Ae/Be stars of earlier spectral type, possibly due to dehydrogenization or destruction of the aromatics near these stars. It is suggested that the total number of aromatics excited by the stars is also greater around the earlier-type objects.

Brooke, T. Y.↗

NASA NTRS2012

Multi-Angle Implementation of Atmospheric Correction for MODIS (MAIAC). Part 3: Atmospheric Correction

This paper describes the atmospheric correction (AC) component of the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) which introduces a new way to compute parameters of the Ross-Thick Li-Sparse (RTLS) Bi-directional reflectance distribution function (BRDF), spectral surface albedo and bidirectional reflectance factors (BRF) from satellite measurements obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS). MAIAC uses a time series and spatial analysis for cloud detection, aerosol retrievals and atmospheric correction. It implements a moving window of up to 16 days of MODIS data gridded to 1 km resolution in a selected projection. The RTLS parameters are computed directly by fitting the cloud-free MODIS top of atmosphere (TOA) reflectance data stored in the processing queue. The RTLS retrieval is applied when the land surface is stable or changes slowly. In case of rapid or large magnitude change (as for instance caused by disturbance), MAIAC follows the MODIS operational BRDF/albedo algorithm and uses a scaling approach where the BRDF shape is assumed stable but its magnitude is adjusted based on the latest single measurement. To assess the stability of the surface, MAIAC features a change detection algorithm which analyzes relative change of reflectance in the Red and NIR bands during the accumulation period. To adjust for the reflectance variability with the sun-observer geometry and allow comparison among different days (view geometries), the BRFs are normalized to the fixed view geometry using the RTLS model. An empirical analysis of MODIS data suggests that the RTLS inversion remains robust when the relative change of geometry-normalized reflectance stays below 15%. This first of two papers introduces the algorithm, a second, companion paper illustrates its potential by analyzing MODIS data over a tropical rainforest and assessing errors and uncertainties of MAIAC compared to conventional MODIS products.

Lyapustin, A.↗

DOE OSTI2020

Modeling features in the redshift-space halo power spectrum with perturbation theory

In this work, we study the ability of perturbative models with effective field theory contributions and infra-red resummation to model the redshift space clustering of biased tracers in models where the linear power spectrum has "features"- either imprinted during inflation or induced by non-standard expansion histories. We show that both Eulerian and Lagrangian perturbation theory are capable of reproducing the Fourier space two-point functions of halos up to the non-linear scale from a suite of 40963 particle N-body simulations. This is the first demonstration that perturbative models can accurately fit the redshift-space clustering of biased tracers in N-body simulations of such theories. By comparing different theoretical models and IR resummation schemes we assess the current theoretical uncertainty in predicting power spectra for models with features. Our results suggest that future surveys will be able to detect or tightly constrain features in the primordial spectrum below the one percent level across a wide range of scales.

redshift surveys↗

DOE OSTI2025

PhotonIDs: ML-Powered Photon Identification System for Dark Count Elimination

Reliable single photon detection is the foundation for practical quantum communication and networking. However, today's superconducting nanowire single photon detector(SNSPD) inherently fails to distinguish between genuine photon events and dark counts, leading to degraded fidelity in long-distance quantum communication. In this work, we introduce PhotonIDs, a machine learning-powered photon identification system that is the first end-to-end solution for real-time discrimination between photons and dark count based on full SNSPD readout signal waveform analysis. PhotonIDs ~demonstrates: 1) an FPGA-based high-speed data acquisition platform that selectively captures the full waveform of signal only while filtering out the background data in real time; 2) an efficient signal preprocessing pipeline, and a novel pseudo-position metric that is derived from the physical temporal-spatial features of each detected event; 3) a hybrid machine learning model with near 98% accuracy achieved on photon/dark count classification. Additionally, proposed PhotonIDs ~ is evaluated on the dark count elimination performance with two real-world case studies: (1) 20 km quantum link, and (2) Erbium ion-based photon emission system. Our result demonstrates that PhotonIDs ~could improve more than 31.2 times of signal-noise-ratio~(SNR) on dark count elimination. PhotonIDs ~ marks a step forward in noise-resilient quantum communication infrastructure.

Linne, Karl C. [Chicago U.] (ORCID:000900091870358↗

NASA NTRS2016

Detection of Chlorophyll and Leaf Area Index Dynamics from Sub-weekly Hyperspectral Imagery

Temporally rich hyperspectral time-series can provide unique time critical information on within-field variations in vegetation health and distribution needed by farmers to effectively optimize crop production. In this study, a dense time series of images were acquired from the Earth Observing-1 (EO-1) Hyperion sensor over an intensive farming area in the center of Saudi Arabia. After correction for atmospheric effects, optimal links between carefully selected explanatory hyperspectral vegetation indices and target vegetation characteristics were established using a machine learning approach. A dataset of in-situ measured leaf chlorophyll (Chll) and leaf area index (LAI), collected during five intensive field campaigns over a variety of crop types, were used to train the rule-based predictive models. The ability of the narrow-band hyperspectral reflectance information to robustly assess and discriminate dynamics in foliar biochemistry and biomass through empirical relationships were investigated. This also involved evaluations of the generalization and reproducibility of the predictions beyond the conditions of the training dataset. The very high temporal resolution of the satellite retrievals constituted a specifically intriguing feature that facilitated detection of total canopy Chl and LAI dynamics down to sub-weekly intervals. The study advocates the benefits associated with the availability of optimum spectral and temporal resolution spaceborne observations for agricultural management purposes.

Houborg, Rasmus↗

NASA NTRS2011

A Super-Resolution Algorithm for Enhancement of FLASH LIDAR data

A novel method for enhancement of the spatial resolution of 3-dimensional Flash Lidar images is being proposed for generation of elevation maps of terrain from a moving platform. NASA recognizes the Flash LIDAR technology as an important tool for enabling safe and precision landing in future unmanned and crewed lunar and planetary missions. The ability of the Flash LIDAR to generate 3-dimensional maps of the landing site area during the final stages of the descent phase for detection of hazardous terrain features such as craters, rocks, and steep slopes is under study in the frame of the Autonomous Landing and Hazard Avoidance (ALHAT) project. Since single frames of existing FLASH LIDAR systems are not sufficient to build a map of entire landing site with acceptable spatial resolution and precision, a super-resolution approach utilizing multiple frames has been developed to overcome the instrument s limitations. Performance of the super-resolution algorithm has been analyzed through a series of simulation runs obtained from a high fidelity Flash LIDAR model and a high resolution synthetic lunar elevation map. For each simulation run, a sequence of FLASH LIDAR frames are recorded and processed as the spacecraft descends toward the landing site. Simulations runs having different trajectory profiles and varying LIDAR look angles of the terrain are also analyzed. The results show that adequate levels of accuracy and precision are achieved for detecting hazardous terrain features and identifying safe areas of the landing site.

Bulyshev, Alexander↗

NASA NTRS2001

Thermal Infrared Spectral Band Detection Limits for Unidentified Surface Materials

Infrared emission spectra recorded by airborne or satellite spectrometers can be searched for spectral features to determine the composition of rocks on planetary surfaces. Surface materials are identified by detections of characteristic spectral bands. We show how to define whether to accept an observed spectral feature as a detection when the target material is unknown. We also use remotely sensed spectra measured by the Thermal Emission Spectrometer (TES) and the Spatially Enhanced Broadband Array Spectrograph System to illustrate the importance of instrument parameters and surface properties on band detection limits and how the variation in signal-to-noise ratio with wavelength affects the bands that are most detectable for a given instrument. The spectrometer's sampling interval, spectral resolution, signal-to-noise ratio as a function of wavelength, and the sample's surface properties influence whether the instrument can detect a spectral feature exhibited by a material. As an example, in the 6-13 micrometer wavelength region, massive carbonates exhibit two bands: a very strong, broad feature at approximately 6.5 micrometers and a less intense, sharper band at approximately 11.25 micrometers. Although the 6.5-micrometer band is stronger and broader in laboratory-measured spectra, the 11.25-micrometer band will cause a more detectable feature in TES spectra.

Kirkland, Laurel E.↗

NASA NTRS1992

Use of AVIRIS data to the definition of optimised specifications for land applications with future spaceborne imaging spectrometers

Recent experience with airborne imaging spectrometers demonstrated the advantages of narrow band sensors over broad band scanners for characterizing the nature, extent, and physical status of typical land surfaces. Information on key spectral features associated with various land surfaces can be obtained from the data of such instruments, which can be used to simulate spaceborne imaging spectrometer data and to assess their information content if comprehensive underpinning is provided by ground data. The collection of such information was an issue of airborne imaging spectrometer campaigns like the NASA MAC-Europe 1991. Airborne and ground data obtained from different test sites in Europe are utilized for a comparative analysis of the spectral signatures of various land surfaces (vegetation, bare soils and rocks, and mixed soil/rock-vegetation) as seen from different imaging spectrometers like Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), GERIS 63 band scanner, and CASI. The following items are discussed: (1) the significance of different spectral regions within the wavelength interval between 0.4 m and 2.5 m for the differentiation of different land units; (2) recommendations on the optimum band selection and band-widths to be used for the application of future satellite-based imaging spectrometers for land applications; (3) the boundaries for the detection of plant features in mixed-soil plant spectra and the influence of different soil properties on the mixture of the spectra; (4) recommendations on the optimum spatial resolution and recording dates for the discrimination of spectral features of various surface types; and (5) evaluation of different data compressing techniques for the optimum extraction of spectral information from imaging spectrometry data.

Bodechtel, J.↗

DOE OSTI2022

Low-Energy Physics in Neutrino LArTPCs

In this white paper, we outline some of the scientific opportunities and challenges related to detection and reconstruction of low-energy (less than 100 MeV) signatures in liquid argon time-projection chamber (LArTPC) detectors. Key takeaways are summarized as follows. 1) LArTPCs have unique sensitivity to a range of physics and astrophysics signatures via detection of event features at and below the few tens of MeV range. 2) Low-energy signatures are an integral part of GeV-scale accelerator neutrino interaction final states, and their reconstruction can enhance the oscillation physics sensitivities of LArTPC experiments. 3) BSM signals from accelerator and natural sources also generate diverse signatures in the low-energy range, and reconstruction of these signatures can increase the breadth of BSM scenarios accessible in LArTPC-based searches. 4) Neutrino interaction cross sections and other nuclear physics processes in argon relevant to sub-hundred-MeV LArTPC signatures are poorly understood. Improved theory and experimental measurements are needed. Pion decay-at-rest sources and charged particle and neutron test beams are ideal facilities for experimentally improving this understanding. 5) There are specific calibration needs in the low-energy range, as well as specific needs for control and understanding of radiological and cosmogenic backgrounds. 6) Novel ideas for future LArTPC technology that enhance low-energy capabilities should be explored. These include novel charge enhancement and readout systems, enhanced photon detection, low radioactivity argon, and xenon doping. 7) Low-energy signatures, whether steady-state or part of a supernova burst or larger GeV-scale event topology, have specific triggering, DAQ and reconstruction requirements that must be addressed outside the scope of conventional GeV-scale data collection and analysis pathways.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2022

Neurosymbolic Hybrid Approach to Driver Collision Warning

There are two main algorithmic approaches to autonomous driving systems: (1) An end-to-end system in which a single deep neural network learns to map sensory input directly into appropriate warning and driving responses. (2) A mediated hybrid recognition system in which a system is created by combining independent modules that detect each semantic feature. While some researchers believe that deep learning can solve any problem, others believe that a more engineered and symbolic approach is needed to cope with complex environments with less data. Deep learning alone has achieved state-of-the-art results in many areas, from complex gameplay to predicting protein structures. In particular, in image classification and recognition, deep learning models have achieved accuracies as high as humans. But sometimes it can be very difficult to debug if the deep learning model doesn't work. Deep learning models can be vulnerable and are very sensitive to changes in data distribution. Generalization can be problematic. It's usually hard to prove why it works or doesn't. Deep learning models can also be vulnerable to adversarial attacks. Here, we combine deep learning-based object recognition and tracking with an adaptive neurosymbolic network agent, called the Non-Axiomatic Reasoning System (NARS), that can adapt to its environment by building concepts based on perceptual sequences. We achieved an improved intersection-over-union (IOU) object recognition performance of 0.65 in the adaptive retraining model compared to IOU 0.31 in the COCO data pre-trained model. We improved the object detection limits using RADAR sensors in a simulated environment, and demonstrated the weaving car detection capability by combining deep learning-based object detection and tracking with a neurosymbolic model.

Wang, Pei↗

DOE OSTI2021

Refined Telluric Absorption Correction for Low-resolution Ground-based Spectroscopy: Resolution and Radial Velocity Effects in the O{sub 2}A-band for Exoplanets and K i Emission Lines

Telluric correction of spectroscopic observations is either performed via standard stars that are observed close in time and airmass along with the science target, or recently growing in importance, by theoretical telluric absorption modeling. Both approaches work fine when the telluric lines are resolved, i.e., at a spectral resolving power larger than about 10,000, and it is sufficient to facilitate the detection of spectral features at lower resolution. However, a meaningful quantitative analysis also requires the reliable recovery of line strengths. Here, we show for the Fraunhofer A-band of molecular O{sub 2} that the standard telluric correction approach fails in this at lower spectral resolutions, as an example for the general problem. Doppler-shift-dependent errors of the restored flux may arise, which can amount to more than 50% in extreme cases, depending on the line shapes of the target spectral features. Two applications are discussed: the recovery of the O{sub 2} band in the reflected light of an Earth analog atmosphere, as facilitated potentially in the future using an orbiting starshade and a ground-based extremely large telescope; and the recovery of the intrinsic ratio of the K i lines in the post-nova V4332 Sgr tracing the optical depth of the emitting region, to exemplify the relevance using present-day instrumentation. We show how one should derive correction functions for the compensation of the error in dependence of radial velocity shift, spectral resolution, and target line-profile function by use of high-resolution atmospheric transmission modeling, which has to be solved for the individual case.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1994

Application of 50 MHz doppler radar wind profiler to launch operations at Kennedy Space Center and Cape Canaveral Air Station

This paper presents a case study where a significant wind shift, not detected by jimspheres, was detected by the 50 MHz DRWP (Doppler Radar Wind Profiler) and evaluated to be acceptable prior to the launch of a Shuttle. This case study illustrates the importance of frequent upper air wind measurements for detecting significant rapidly changing features as well as for providing confidence that the features really exist and are not due to instrumentation error. Had the release of the jimsphere been timed such that it would have detected the entire wind shift, there would not have been sufficient time to release another jimsphere to confirm the existence of the feature prior to the scheduled launch. We found that using a temporal median filter on the one minute spectral estimates coupled with a constraining window about a first guess velocity effectively removes nearly all spurious signals from the velocity profile generated by NASA's 50 MHz DRWP while boosting the temporal resolution to as high as one profile every 3 minutes. The higher temporal resolution of the 50 MHz DRWP using the signal processing algorithm described in this paper ensures the detection of rapidly changing features as well as provides the confidence that the features are genuine. Further benefit is gained when the profiles generated by the DRWP are examined in relation to the profiles measured by jimspheres and/or rawinsondes. The redundancy offered by using two independent measurements can dispel or confirm any suspicion regarding instrumentation error or malfunction and wind profiles can be examined in light of their respective instruments' strengths and weaknesses.

Schumann, Robin S.↗

DOE OSTI2026

INR-TEM: Robust cavity detection in multifocus TEM images via implicit neural representations

When characterizing materials using transmission electron microscopy (TEM) images, detecting and quantifying small features in microstructures, such as cavities, pose significant challenges. Off-the-shelf object detection models, including YOLOv8, show considerable performance degradation, particularly when images vary in resolution and the objects of interest possess a low percentage of the total image region of interest. In this study, we introduce a novel detection pipeline that incorporates an implicit neural representation (INR)-based detection method, INR-TEM, and two-modality imaging (e.g., under-focused and over-focused images typically acquired during materials characterization) to improve object detection performance. The INR-TEM method incorporates a pixel-wise prediction principle inspired by pixel-wise centerness weighting. INR-TEM demonstrates superior robustness to resolution variability, maintaining high detection accuracy even at low image resolutions compared to YOLOv8. To leverage INR-TEM effectively in real-world two-modality characterization applications, we further integrate a two-stage motion correction pipeline designed explicitly for aligning multifocus TEM images. The alignment process, comprising keypoint (based on scale-invariant feature transform, SIFT) and intensity matching, significantly mitigates the adverse effects of perceived motion-induced image degradation during through-focal TEM imaging, directly enhancing INR-TEM’s detection capability over conventional single-focus images. Our integrated INR-TEM cavity detection framework notably improves performance across various cavity sizes, outperforming off-the-shelf YOLOv8 detections that rely on a single image modality.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Defect Engineering in Large‐Scale CVD‐Grown Hexagonal Boron Nitride: Formation, Spectroscopy, and Spin Relaxation Dynamics

Recently, numerous techniques have been reported for generating optically active defects in exfoliated hexagonal boron nitride (hBN), which hold transformative potential for quantum photonic devices. However, achieving on-demand generation of desirable defect types in scalable hBN films remains a significant challenge. Here, it is demonstrated that formation of negative boron vacancy defects, V B − , in suspended, large-area CVD-grown hBN is strongly dependent on the type of bombarding particles (ions, neutrons, and electrons) and irradiation conditions. In contrast to suspended hBN, defect formation in substrate-supported hBN is more complex due to the uncontrollable generation of secondary particles from the substrate, and the outcome strongly depends on the thickness of the hBN. Different defect types are identified by correlating spectroscopic and optically detected magnetic resonance features, distinguishing boron vacancies (formed by light ions and neutrons and emitting at 800 nm) from other optically active defects emitting at 650 nm assigned to anti-site nitrogen vacancy (N B V N ) and reveal the presence of additional “dark” paramagnetic defects that influence spin-lattice relaxation time (T 1 ) and zero-field splitting parameters, all of which strongly depend on the defect density. These results underscore the potential for precisely engineered defect formation in large-scale CVD-grown hBN, paving the way for the scalable fabrication of quantum photonic devices.

CVD↗