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DOE OSTI2025

Selection of Sampling and Surrogate Modeling Methods for State-Point Evaluations of an AGN-201M Reactor

Nuclear reactor digital twins (DTs) have been proposed for use as a safeguards technology to efficiently monitor new and novel reactors as they come online. A safeguards DT needs to be capable of detecting misuse and diversion as they occur, requiring physics models to be accurate and efficient. Mathematical surrogate models are capable of achieving the necessary efficiency and can largely maintain the accuracy of higher-order models given a quality training sample. The Multiphysics Object-Oriented Simulation Environment (MOOSE) code framework is specifically equipped to generate training samples and create surrogate models using full-order reactor physics models. Utilizing an operational AGN-201M reactor’s specifications, two surrogate types were trained on samples of variable size, and using Cartesian products, Latin hypercube sampling, and quadrature sampling, each was compared and evaluated on accuracy when compared to a full-order Monte Carlo model. Both surrogate types were able to capture reactivity changes within 0.05 $ of the Monte Carlo model while reducing the computation costs by eight orders of magnitude.

MOOSE↗

DOE OSTI2026

Persistent DynAMICS

Persistent DynAMICS is a lightweight software platform that enables distributed sensing systems to operate as a unified, responsive network. It integrates with existing sensors, data systems, and processing tools as an overlay, without requiring hardware replacement or increasing user burden. As data is collected, the system identifies situations that require additional attention and directs selected sensors to gather more information. For example, if a sensor detects an unusual condition along a pipeline, the system can task nearby sensors or different sensing modalities to confirm the event and focus data collection where it is needed. To support this, the platform forms temporary groups of sensors that work together to address specific objectives. Once complete, these groups are dissolved and resources are reassigned as needed. Communication is handled through an integrated messaging framework with client and server components, enabling reliable operation and data continuity even in low-bandwidth or intermittent connectivity environments.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Observing differential spin currents by resonant inelastic X-ray scattering

Controlling spin currents, that is, the flow of spin angular momentum, in small magnetic devices, is the principal objective of spin electronics, a main contender for future energy-efficient information technologies. A pure spin current has never been measured directly because the associated electric stray fields and/or shifts in the non-equilibrium spin-dependent distribution functions are too small for conventional experimental detection methods optimized for charge transport. Here we report that resonant inelastic X-ray scattering (RIXS) can bridge this gap by measuring the spin current carried by magnons—the quanta of the spin wave excitations of the magnetic order—in the presence of temperature gradients across a magnetic insulator. This is possible due to the sensitivity of the momentum- and energy-resolved RIXS intensity to minute changes in the magnon distribution under non-equilibrium conditions. Furthermore, we use the Boltzmann equation in the relaxation time approximation to extract transport parameters, such as the magnon lifetime at finite momentum, essential for the realization of magnon spintronics.

36 MATERIALS SCIENCE↗

DOE OSTI2025

A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models

We present a new atmospheric river (AR) analysis and benchmarking tool, namely Atmospheric River Metrics Package (ARMP). It includes a suite of new AR metrics that are designed for quick analysis of AR characteristics via statistics in gridded climate datasets such as model output and reanalysis. This package can be used for climate model evaluation in comparison with reanalysis and observational products. Integrated metrics such as mean bias and spatial pattern correlation are efficient for diagnosing systematic AR biases in climate models. For example, the package identifies the fact that, in CMIP5 and CMIP6 (Coupled Model Intercomparison Project Phases 5 and 6) models, AR tracks in the South Atlantic are positioned farther poleward compared to ERA5 reanalysis, while in the South Pacific, tracks are generally biased towards the Equator. For the landfalling AR peak season, we find that most climate models simulate a completely opposite seasonal cycle over western Africa. This tool can also be used for identifying and characterizing structural differences among different AR detectors (ARDTs). For example, ARs detected with the Mundhenk algorithm exhibit systematically larger size, width, and length compared to the TempestExtremes (TE) method. The AR metrics developed from this work can be routinely applied for model benchmarking and during the development cycle to trace performance evolution across model versions or generations and set objective targets for the improvement of models. They can also be used by operational centers to perform near-real-time climate and extreme event impact assessments as part of their forecast cycle.

58 GEOSCIENCES↗

DOE OSTI2024

Thermal Conductivity Degradation in High Burnup U-Pu-Zr Fuel

Recent advancements in the characterization of irradiated U-Pu-Zr fuels have revealed complexities that challenge existing understanding of constituent redistribution. Traditionally, models have proposed three concentric regions within the fuel, each characterized by distinctive phases and porosity. However, through detailed analysis of high burnup U-Pu-Zr, we discovered the presence of four distinct constituent redistribution regions. Particularly novel is the observation of significant Pu redistribution, a previously unreported phenomenon that necessitates a reevaluation of current models. This work aims to delve deeper into these findings, seeking to correlate mesoscale measurements of thermal diffusivity and respective thermal conductivity with the phases present in each redistribution region. To achieve this objective, we employed mesoscale thermoreflectance methods using the unique, Idaho National Laboratory (INL) developed, Thermal Conductivity Microscope (TCM) at INL’s Irradiated Materials Characterization Laboratory. The TCM employs two tightly focused lasers: one for heating to generate periodic thermal waves in the substrate, and another spatially separated probe laser to detect changes in the optical reflectivity of the gold-coated substrate resulting from thermal wave diffusion. We conducted several thermal diffusivity measurements within each region of constituent redistribution of a U-19Pu-10Zr fuel pin cross section irradiated to 11 at. % burnup. The TCM measurement positions strategically aligned with transmission electron microscopy (TEM) lift-out locations previously collected from the fuel sample. Complementary microstructural analysis techniques such as optical and scanning electron microscopy (OM/SEM), electron probe microanalysis for chemical compositions, and TEM-based selective area electron diffraction (SAED) analysis for crystallographic insights into each phase were also utilized. This comprehensive approach allowed us to correlate local thermal diffusivity data with microstructural characteristics, enabling the computation of local thermal conductivity at each position. The significance of this contribution lies in its pioneering use of the TCM for ternary fuel mesoscale examination, shedding light on the previously overlooked effects of Pu redistribution on local thermal conductivity. By informing current models capturing constituent redistribution and heat transfer, our findings pave the way for more accurate predictions of metallic fuel performance. Moreover, this work sets the stage for future comparisons with similar TCM examinations on U-19Pu-10Zr fuels at ultra-low burnup, facilitating a comprehensive understanding of thermal property changes across different burnup levels. Ultimately, our study not only enriches our understanding of the thermophysical properties of individual redistribution regions within U-Pu-Zr fuel but also offers valuable insights for the design and operational parameters of proposed next-generation fast reactors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Wasserstein Normalized Autoencoder for Anomaly Detection in ProtoDUNE Vertical-Drift Detector

ProtoDUNE Vertical Drift needs a selective triggering algorithm. The detector sits on Earth's surface, so cosmic activity dominates its data. Our goal in this paper is to trigger on neutrino events more robustly than the current deployed Analog-to-Digital Converter Simple Window (ADCSW) model and, eventually, search for signals of Beyond Standard Model (BSM) physics at DUNE as our ultimate North Star objective. As a step towards this goal, we evaluate a Wasserstein Normalized Autoencoder (WNAE) on simulated collection-plane only windows of shape $1\times10\times10$ where Neutrinos act as our BSM-proxy and Cosmic-ray Muons serve as our learned background. The network parameters are fitted using only cosmic-ray muon events as background in order to maintain an unsupervised pipeline. Training uses finite-step Langevin $x^-$ samples, positive-sample reconstruction energy, and an empirical sliced $2$-Wasserstein objective to learn a normalized Boltzmann energy model. We then calibrate on a nominal $5\,\mathrm{Hz}$ operating threshold calculated from cosmic validation data. Both WNAE and ADCSW accept 311 of 194,083 held-out cosmic background events at this $5\,\mathrm{Hz}$ threshold. We found that WNAE accepts 9,677 of 34,634 neutrino-proxy events $(27.9\pm0.24)\%$, compared with 10,076 $(29.1\pm0.24)\%$ for ADCSW, an observed WNAE-minus-ADCSW difference of $-1.15\%$. At another nominal $2\,\mathrm{Hz}$ target threshold, the corresponding efficiencies are $(20.5\pm0.22)\%$ and $(22.6\pm0.22)\%$, respectively. Of the WNAE-selected neutrino proxies at $5\,\mathrm{Hz}$, $(20.8\pm0.4)\%$ of the classified neutrino-proxy events are unique to WNAE, where the uncertainty is an absolute binomial standard error of $0.4\%$.

Zheng, Jake [U. Chicago (main)] (ORCID:00090002189↗

DOE OSTI2026

Wasserstein Normalized Autoencoder for Anomaly Detection in ProtoDUNE Vertical-Drift Detector

Zheng, Jake [Chicago U.] (ORCID:0009000218901379)↗

DOE OSTI2026

Towards Anomaly Detection at the CMS High-Level Trigger System

Traditional trigger strategies in CMS typically rely on model-dependent selections or rigid kinematic cuts, risking the omission of unexpected exotic signatures. To address this, we propose a novel anomaly detection (AD) algorithm for the High-Level Trigger (HLT), designed to serve as a complementary second layer of filtering to the Level-1 AXOL1TL AD algorithm. We employ a transformer-based foundation model trained on a diverse ensemble of Standard Model processes. By combining a joint contrastive and classification objective, and using particle kinematics as inputs, the model learns to map events to a physics-informed latent space where anomalous events are isolated from dominant backgrounds. Preliminary results show that this strategy enhances the signal-to-background ratio across a range of rare SM and BSM scenarios. Furthermore, this work constitutes foundational R&D for the potential implementation of an analogous AD algorithm in the Level-1 trigger system for Phase-2.

Cruz, Roy [U. Wisconsin, Madison (main)] (ORCID:00↗

DOE OSTI2024

Numerical Simulation of Infrasound Resonance in Underground Tunnel Structures

Remote observation of infrasound resonant signals emanating from underground tunnel structures could potentially allow the remote quantification of the geometry of the underground structures in which the signals were generated. However, the sensitivity of these observations to tunnel geometry and changes in that geometry are unknown. In this report we outline a numerical simulation study with the following three objectives: 1. Can we model infrasound resonance using numerical simulations? 2. What is the sensitivity of the modeled observations to changes in tunnel geometry or boundary conditions? And 3. Can we accurately model resonant observations of explosions occurring in Redmond Salt Mine in Central Utah. In this report we outline affirmative answers to the first two objectives, but we were unable to accurately model the Redmond Explosions due to numerical instability in the model of the complex structure of the mine. Recommendations for future work emphasize the need to acquire additional datasets and to explore a more data-based approach in which changes in data signatures are detected as a first step towards developing a method which inverts resonant infrasound signals for tunnel geometry.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Masked Symbol Modeling for Demodulation of Oversampled Baseband Communication Signals in Impulsive Noise-Dominated Channels

Recent breakthroughs in natural language processing show that attention mech- anism in Transformer networks, trained via masked-token prediction, enables models to capture the semantic context of the tokens and internalize the grammar of language. While the application of Transformers to communication systems is a burgeoning field, the notion of context within physical waveforms remains under-explored. This paper addresses that gap by re-examining inter-symbol con- tribution (ISC) caused by pulse-shaping overlap. Rather than treating ISC as a nuisance, we view it as a deterministic source of contextual information embedded in oversampled complex baseband signals. We propose Masked Symbol Model- ing (MSM), a framework for the physical (PHY) layer inspired by Bidirectional Encoder Representations from Transformers methodology. In MSM, a subset of symbol-aligned samples is randomly masked, and a Transformer predicts the missing symbol identifiers using the surrounding “in-between” samples. Through this objective, the model learns the latent syntax of complex baseband waveforms. We illustrate MSM’s potential by applying it to the task of demodulating sig- nals corrupted by impulsive noise, where the model infers corrupted segments by leveraging the learned context. Our results suggest a path toward receivers that interpret, rather than merely detect communication signals, opening new avenues for context-aware PHY layer design.

Bedir, Oguz↗

DOE OSTI2025

Hungary 908 Event - Risk Based Graded Approach to ITM

This presentation, Risk-Based, Graded Approach to Insider Threat Mitigation: Human Measures, introduces a structured framework for managing insider threat risk using internationally recognized guidance from the International Atomic Energy Agency (IAEA) Nuclear Security Series No. 8-G (Rev. 1) and the Joint Statement on Mitigating Insider Threats (INFCIRC/908). The presentation emphasizes that effective insider threat mitigation (ITM) depends on both positional controls, which manage inherent risk based on access, authority, and knowledge, and human measures, which address residual risk reflected in behavior, motivation, and reliability. Using a risk-informed and graded approach, the presentation outlines methods for identifying and prioritizing high-risk positions, applying layered organizational controls, and integrating human reliability mechanisms such as the Behavior Observation Program (BOP), Fitness-for-Duty (FFD) evaluations, Employee Assistance Programs (EAP), and Nuclear Security Culture (NSC). The human-focused portion examines behavioral and organizational indicators of opportunity, vulnerability, motivation, and crisis, demonstrating how early detection, deterrence, and response can prevent insider events. The session concludes with a case review of the Millstone Nuclear Power Station incident involving engineer George Galatis. The case illustrates how weak leadership and a poor safety culture can create conditions for failure and how a comprehensive ITM framework could have altered the outcome. The objective of this presentation is to help practitioners apply a risk-based, graded philosophy to human factors and promote a culture of accountability, communication, and resilience within nuclear organizations.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

Web-based Preprocessing and Visualization of 3D FIB Tomography Data for Nuclear Fuel Characterization

Three-dimensional (3D) focused ion beam (FIB) tomography enables reconstruction of internal nuclear fuel features that can't be fully evaluated through surface imaging alone. This capability supports characterization of fuel constituents and defects under thermal and irradiation conditions relevant to microreactor development. However, large tomography datasets can create data-handling, loading, and visualization challenges, especially when image-stack preparation and file conversion must be completed with separate tools. The Computational Ultraspatial Tomography Toolkit for High-Resolution Object Analysis Tools (CUTTRHOAT) is an open-source web application being developed to display FIB tomography datasets available through the Nuclear Research Data System (NRDS). The current alpha version requires prepared HDF5 datasets and has limited integrated data-preparation capabilities. This project improves CUTTHROAT by adding dataset-folder selection, automatic input detection, dataset scanning, missing-slice identification, blank-slice insertion, and image-stack-to-HDF5 conversion. Two applications will be compared: the baseline CUTTHROAT alpha workflow and the updated application containing the integrated data-handling and preprocessing functions. Evaluation will consider dataset detection accuracy, conversion success, loading time, rendering responsiveness, application stability, and user interaction. Preliminary results demonstrate successful loading of existing HDF5 files and converted image stacks, while testing also identified performance reductions caused by excessive blank-slice generation. The updated workflow reduces reliance on external preparation tools and supports more direct movement from image stacks to color-code 3D visualization. Future work includes refining missing-slice handling, integrating additional preprocessing functions, like a denoising feature, parsing TIFF metadata for automatic voxel scaling, and adding manual X, Y, and Z voxel-spacing inputs for PNG and JPEG.

36 - MATERIALS SCIENCE↗

DOE OSTI2026

buhito

buhito is a Python library for graph analysis and machine learning. Graphs can represent networks with objects as nodes and their relationships as edges. buhito focuses on graphlet methods that study graphs through enumerating their component subgraphs to enable interpretable and fast models of complex systems. The package provides tools for different algorithmic designs for computing, analyzing, and applying graphlets to research problems such as machine learning, data compression, and anomaly detection in graph-structured data. A central feature is performing decomposition data analysis on graphs for machine learning models. Implemented in Python and built upon open-source scientific libraries such as NetworkX, NumPy, and SciPy, buhito provides high-performance methods for researchers exploring the mathematical and computational foundations of graphlet analysis applicable to systems of different sizes.

Pimonova, Yulia↗

DOE OSTI2024

Distributed Magnetic Field and Temperature Monitoring for Superconducting Radio Frequency Cavities

The overall objective of the proposed Phase I program was to design, construct and demonstrate a fiber optic sensing system capable of providing temperature and magnetic field measurements with an enhanced spatial resolution that can be implemented over a large surface area (cryomodules) to survey superconducting radio frequency cavities and magnets. A magnetic field sensor capable of detecting fluxes on the order of 1 μT is required to detect the distribution of trapped flux on the cavity surface. A unique distributed magnetic field sensor was successfully designed and constructed to demonstrate the detection of magnetic fluxes less than 500 nT. The sensor leveraged the ultra-high sensitivity of Sentek’s picoDAS to measure the magnetostriction induced vibrations in a commercially available Metglas 2605 SC ribbon that was in physical contact with sensing fiber. Static magnetic fields were detected by applying an alternating current a copper wire proximate to the Metglas 2605SC ribbon to create an AC bias magnetic field. In an alternative approach, an AC bias magnetic field was applied to a special magnetic field sensing fiber with Metglas 2605SC cladding successfully detect a magnetic field flux of a 3 μT. Exhaustive testing was performed to characterize the dependency of sensor response on the direction of the applied magnetic field. Although the special sensing fiber based magnetic field sensor did not exhibit an observable dependence on the direction of the magnetic field, the Metglas 2605SC ribbon sensor exhibited a clear directional dependence. A wide variety of polymer materials were evaluated to enhance the temperature response of an FBG based sensor at cryogenic temperatures. The processing and performance challenges provided the motivation to develop a new simple cryogenic temperature sensor that uses a commercially available fiber optic splice protector. The EVA hot melt tube that becomes adhered to the optical fiber and the polyolefin outer tube that shrinks upon heating in the fusion splicer heater provide the high thermal expansion coefficient necessary to impart a significant strain on the FBG when exposed to cryogenic temperatures. The temperature sensitivity (Δ𝜆𝐵𝑟𝑎𝑔𝑔~ 62 𝑝𝑝𝑚/℃) of the FBG-based sensor was on par with the best reported to date. The simple design, use of readily available cost-effective materials, and well-established processing techniques lends this approach to the creation of hundreds to thousands of temperature sensors on one single optical fiber length. The inherently small form factor also allows for co-location with the distributed magnetic field sensor. In preparation for field testing of the prototype sensing system at the Jefferson Labs in potential Phase II program, several different cable designs were evaluated to package the sensors. The preliminary successful demonstration of fully functional sensing cables provides the foundation for subsequent development efforts to advance the Technology Readiness Level of the technology. The technical feasibility of the proposed approach was successfully demonstrated in this Phase I effort.

43 PARTICLE ACCELERATORS↗

DOE OSTI2026

CHESS 2025: Waveform LiDAR data from NEON AOP surveys

This dataset provides Level 1 (L1) full-waveform light detection and ranging (LiDAR) data collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). These data were acquired to enable characterization of vegetation structure and other three-dimensional features of the land surface, and to evaluate structural changes that may have occurred between a prior LiDAR acquisition in 2018 and the 2025 overflight. Waveform LiDAR data can provide more detailed information about objects on the ground than discrete point clouds typically do, and they are often used for granular target segmentation and characterization of subcanopy vegetation. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. LiDAR data were acquired using the Optech Galaxy Prime Airborne LiDAR Terrain Mapper onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). These are the primary waveform LiDAR data delivered by NEON and are provided per flightline in compressed Pulsewaves format, an open-source binary file standard. A Pulsewaves object comprises a two files: a pulse (.pls) file, which stores the geographic origin, outgoing vector, and metadata for every laser pulse emitted by the scanner, and a wave file (.wvs), which stores the sequential amplitude samples of the outgoing pulse and the returning signals. The files are published here in their compressed forms (.plz, .wvz). All waveform data were processed following the theoretical workflow described in the NEON L0-to-L1 Waveform LiDAR Algorithm Theoretical Basis Document (Krause and Goulden 2022a); however, the Pulsewaves output format differs from a legacy format described in that document. Waveform amplitude samples are recorded at 1 nanosecond intervals. All coordinates are provided in meters. Horizontal coordinates are referenced in Universal Transverse Mercator (UTM) zone 13N and the World Geodetic System (WGS) 1984 ensemble datum. Elevations are referenced to Geoid12A. Waveform data for the UPTA survey area were collected without incident and the published records are complete. However, both the ALMO and CRBU collections experienced issues that resulted in incomplete data for those areas. On collection day 2018-06-16 a hardware failure caused the waveform digitizer to lose data from the eastern edge of the ALMO site (Figure 22). The waveform data for flightlines 2–20 could not be extracted from the digitizer, and the data proved unrecoverable. As a result, a portion of the site does not have coverage with waveform data. Although no hardware failure was observed during collection over the CRBU area, final waveform files generated by vendor software contained only ~25% of the expected number of return pulses. After discovery, NEON initiated troubleshooting with the vendor. The root cause of the data ablation had not been identified at the time of publication. Additional data will be published in an update to this package if further recovery proves successful. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗

DOE OSTI2024

THz Radar Observations of Hydrometeors in a Spray Chamber

Abstract A THz radar, with its wide bandwidth, is capable of high‐resolution imaging down to the centimeter scale. In this study, a THz radar is applied to detect hydrometeors generated in a spray chamber. The observed backscattering signals show fluctuations at centimeter scales, indicating various hydrometeor distribution patterns along the radar beam. A co‐located High‐Speed Imaging (HSI) sensor is used to measure the Drop Size Distributions (DSD) in the spray chamber. The radar sampling beam is well aligned with the HSI probes, allowing an objective comparison between the remote sensing and in situ observations. In this study, the observed radar power is compared with the power estimated from the HSI measurements. Results show great consistency, with power difference smaller than 0.5 dB. This study demonstrates the feasibility and great potential of using a THz radar for ultra‐high‐resolution observations of clouds in a laboratory facility, and in the real atmosphere.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Identifying Anomalous DESI Galaxy Spectra with a Variational Autoencoder

The tens of millions of spectra being captured by the Dark Energy Spectroscopic Instrument (DESI) provide tremendous discovery potential. In this work we show how Machine Learning, in particular Variational Autoencoders (VAE), can detect anomalies in a sample of approximately 200,000 DESI spectra comprising galaxies, quasars and stars. We demonstrate that the VAE can compress the dimensionality of a spectrum by a factor of 100, while still retaining enough information to accurately reconstruct spectral features. We then detect anomalous spectra as those with high reconstruction error and those which are isolated in the VAE latent representation. The anomalies identified fall into two categories: spectra with artefacts and spectra with unique physical features. Awareness of the former can help to improve the DESI spectroscopic pipeline; whilst the latter can lead to the identification of new and unusual objects. To further curate the list of outliers, we use the Astronomaly package which employs Active Learning to provide personalised outlier recommendations for visual inspection. In this work we also explore the VAE latent space, finding that different object classes and subclasses are separated despite being unlabelled. We demonstrate the interpretability of this latent space by identifying tracks within it that correspond to various spectral characteristics. For example, we find tracks that correspond to increasing star formation and increase in broad emission lines along the Balmer series. In upcoming work we hope to apply the methods presented here to search for both systematics and astrophysically interesting objects in much larger datasets of DESI spectra.

Nicolaou, C. [University Coll. London] (ORCID:0000↗

DOE OSTI2026

Vera C. Rubin Observatory Prompt Products: alert packets data

Data products produced by prompt and daily processing of images obtained in the Legacy Survey of Space and Time. These include realtime alerts sent to community alert brokers, newly-discovered Solar System Objects reported to the Minor Planet Center, processed visit and difference images, and source catalogs. Prompt Products are not a static single data release but continually grow throughout the ten-year LSST survey. This dataset is a subset of the full data release consisting of a dataset named alert packets. This dataset contains measurements for 5-sigma sources detected in difference images that were issued to the community brokers.

79 ASTRONOMY AND ASTROPHYSICS↗