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DOE OSTI2021

Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of methane fluxes measured by eddy-covariance require gap-filling to estimate annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for methane, with regards both to the best model algorithms and predictors. In this study, we address the need for standardization by synthesizing results of gap-filling methods applied at 17 wetland sites spanning boreal to tropical regions including all major wetlands classes and two rice paddies. We introduce new procedures for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with robust uncertainty estimates. We tested a conventional method (marginal distribution sampling) and four machine learning algorithms - penalized linear regression, artificial neural networks, random forests, and boosted decision trees - and four predictor sets, including temporal, meteorological, ecosystem carbon and energy flux, and soil predictors. We find that the conventional method can achieve similar median performance to the machine learning models but is worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. Finally, we gap-fill and provide summary evaluation metrics for all 81 sites in the FLUXNET-CH4 community dataset and publicly release the python code for model development, evaluation, and uncertainty estimation.

42 ENGINEERING↗

DOE OSTI2023

Structure-informed clustering for population stratification in association studies

Background: Identifying variants associated with complex traits is a challenging task in genetic association studies due to linkage disequilibrium (LD) between genetic variants and population stratification, unrelated to the disease risk. Existing methods of population structure correction use principal component analysis or linear mixed models with a random effect when modeling associations between a trait of interest and genetic markers. However, due to stringent significance thresholds and latent interactions between the markers, these methods often fail to detect genuinely associated variants. Results: To overcome this, we propose CluStrat, which corrects for complex arbitrarily structured populations while leveraging the linkage disequilibrium induced distances between genetic markers. It performs an agglomerative hierarchical clustering using the Mahalanobis distance covariance matrix of the markers. In simulation studies, we show that our method outperforms existing methods in detecting true causal variants. Applying CluStrat on WTCCC2 and UK Biobank cohorts, we found biologically relevant associations in Schizophrenia and Myocardial Infarction. CluStrat was also able to correct for population structure in polygenic adaptation of height in Europeans. Conclusions: CluStrat highlights the advantages of biologically relevant distance metrics, such as the Mahalanobis distance, which captures the cryptic interactions within populations in the presence of LD better than the Euclidean distance.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Refining fast calorimeter simulations with a Schrödinger Bridge

Machine learning-based simulations, especially calorimeter simulations, are promising tools for approximating the precision of classical high energy physics simulations with a fraction of the generation time. Nearly all methods proposed so far learn neural networks that map a random variable with a known probability density, like a Gaussian, to realistic-looking events. In many cases, physics events are not close to Gaussian and so these neural networks have to learn a highly complex function. We study an alternative approach: Schrödinger bridge Quality Improvement via Refinement of Existing Lightweight Simulations (SQuIRELS). SQuIRELS leverages the power of diffusion-based neural networks and Schrödinger bridges to map between samples where the probability density is not known explicitly. We apply SQuIRELS to the task of refining a classical fast simulation to approximate a full classical simulation. On simulated calorimeter events, we find that SQuIRELS is able to reproduce highly non-trivial features of the full simulation with a fraction of the generation time.

Calorimeter methods↗

DOE OSTI2022

Structure of the Human BK Ion Channel in Lipid Environment

Voltage-gated and ligand-modulated ion channels play critical roles in excitable cells. To understand the interplay among voltage sensing, ligand binding, and channel opening, the structures of ion channels in various functional states and in lipid membrane environments need to be determined. Here, the random spherically constrained (RSC) single-particle cryo-EM method was employed to study human large conductance voltage- and calcium-activated potassium (hBK or hSlo1) channels reconstituted into liposomes. The hBK structure was determined at 3.5 Å resolution in the absence of Ca 2+ . Instead of the common fourfold symmetry observed in ligand-modulated ion channels, a twofold symmetry was observed in hBK in liposomes. Compared with the structure of isolated hSlo1 Ca 2+ sensing gating rings, two opposing subunits in hBK unfurled, resulting in a wider opening towards the transmembrane region of hBK. In the pore gate domain, two opposing subunits also moved downwards relative to the two other subunits.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Randomized Clinical Trial Comparing Proton Beam Radiation Therapy with Transarterial Chemoembolization for Hepatocellular Carcinoma: Results of an Interim Analysis

Purpose: To describe results of a planned interim analysis of a prospective, randomized clinical trial developed to compare treatment outcomes among patients with newly diagnosed hepatocellular carcinoma (HCC). Methods and Materials: Eligible subjects had either clinical or pathologic diagnosis of HCC and met either Milan or San Francisco transplant criteria. Patients were randomly assigned to transarterial chemoembolization (TACE) or to proton beam radiation therapy. Patients randomized to TACE received at least 1 TACE with additional TACE for persistent disease. Proton beam radiation therapy was delivered to all areas of gross disease to a total dose of 70.2 Gy in 15 daily fractions over 3 weeks. The primary endpoint was progression-free survival, with secondary endpoints of overall survival, local tumor control, and treatment-related toxicities as represented by posttreatment days of hospitalization. Results: At the time of this analysis 69 subjects were available for analysis. Of these, 36 were randomized to TACE and 33 to proton. Total days of hospitalization within 30 days of TACE/proton was 166 and 24 days, respectively (P<.001). Ten TACE and 12 proton patients underwent liver transplantation after treatment. Viable tumor identified in the explanted livers after TACE/proton averaged 2.4 and 0.9 cm, respectively. Pathologic complete response after TACE/proton was 10%/25% (P=.38). The 2-year overall survival for all patients was 59%, with no difference between treatment groups. Median survival time was 30 months (95% confidence interval 20.7-39.3 months). There was a trend toward improved 2-year local tumor control (88% vs 45%, P=.06) and progression-free survival (48% vs 31%, P=.06) favoring the proton beam treatment group. Conclusions: This interim analysis indicates similar overall survival rates for proton beam radiation therapy and TACE. There is a trend toward improved local tumor control and progression-free survival with proton beam. There are significantly fewer hospitalization days after proton treatment, which may indicate reduced toxicity with proton beam therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2021

A modular methodology for time-domain stochastic seismic wave propagation

Presented here is a modular methodology for time-domain stochastic seismic wave propagation analysis. Presented methodology is designed to analyse uncertain seismic motions as an input, propagating through uncertain material. Traditional approach for uncertain wave propagation relies on models that include deep bedrock, local soil site, and their random process and random field information. Such models can become quite large and computationally intractable. The modular approach proposed herein features two step approach that allows separate consideration of the deep bedrock and local site along with corresponding random field information. In this work, the first step considers an auxiliary stochastic motions problem in the bedrock. Stochastic local site response can then be simulated in a reduced domain within certain depth from the surface. Application of uncertain seismic motions at depth, for local uncertain site response is done using stochastic effective forces developed through the Domain Reduction Method. By using Hermite polynomial chaos expansion to represent the non-Gaussian random field of material parameters and non-stationary random process of seismic motion, the proposed modular methodology is formulated using intrusive stochastic Galerkin approach, as seen in the Stochastic Elastic–Plastic Finite Element Method (SEPFEM). Developed modular methodology is illustrated using a 1-D stochastic seismic wave propagation analysis with three cases, and simulation results are also verified with results from conventional approach.

58 GEOSCIENCES↗

DOE OSTI2024

Random forest prediction of crystal structure from electron diffraction patterns incorporating multiple scattering

Diffraction is the most common method to solve for unknown or partially known crystal structures. However, it remains a challenge to determine the crystal structure of a new material that may have nanoscale size or heterogeneities. Here, in this study, we train an architecture of hierarchical random forest models capable of predicting the crystal system, space group, and lattice parameters from one or more unknown two-dimensional electron diffraction patterns. Our initial model correctly identifies the crystal system of a simulated electron diffraction pattern from a 20-nm-thick specimen of arbitrary orientation 67% of the time. We achieve a topline accuracy of 79% when aggregating predictions from ten patterns of the same material but different zone axes. The space group and lattice predictions range from 70% to 90% accuracy and median errors of 0.01-0.5Å, respectively, for cubic, hexagonal, trigonal, and tetragonal crystal systems while being less reliable on orthorhombic and monoclinic systems. We apply this architecture to a four-dimensional scanning transmission electron microscopy scan of gold nanoparticles, where it accurately predicts the crystal structure and lattice constants. These random forest models can be used to significantly accelerate the analysis of electron diffraction patterns, particularly in the case of unknown crystal structures. Additionally, due to the speed of inference, these models could be integrated into live transmission electron microscopy experiments, allowing real-Time labeling of a specimen.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Fusion of Multiple Models for Improving Gross Primary Production Estimation With Eddy Covariance Data Based on Machine Learning

Abstract Terrestrial gross primary production (GPP) represents the magnitude of CO 2 uptake through vegetation photosynthesis, and is a key variable for carbon cycles between the biosphere and atmosphere. Light use efficiency (LUE) models have been widely used to estimate GPP for its physiological mechanisms and availability of data acquisition and implementation, yet each individual GPP model has exhibited large uncertainties due to input errors and model structure, and further studies of systematic validation, comparison, and fusion of those models with eddy covariance (EC) site data across diverse ecosystem types are still needed in order to further improve GPP estimation. We here compared and fused five GPP models (VPM, EC‐LUE, GOL‐PEM, CHJ, and C‐Fix) across eight ecosystems based on FLUXNET2015 data set using the ensemble methods of Bayesian Model Averaging (BMA), Support Vector Machine (SVM), and Random Forest (RF) separately. Our results showed that for individual models, EC‐LUE gave a better performance to capture interannual variability of GPP than other models, followed by VPM and GLO‐PEM, while CHJ and C‐Fix were more limited in their estimation performance. We found RF and SVM were superior to BMA on merging individual models at various plant functional types (PFTs) and at the scale of individual sites. On the basis of individual models, the fusion methods of BMA, SVM, and RF were examined by a five‐fold cross validation for each ecosystem type, and each method successfully improved the average accuracy of estimation by 8%, 18%, and 19%, respectively.

Environmental Sciences & Ecology↗

DOE OSTI2022

Combined Imaging and RNA-Seq on a Microfluidic Platform for Viral Infection Studies

The goal of this work was to pioneer a novel, low-overhead protocol for simultaneously assaying cell-surface markers and intracellular gene expression in a single mammalian cell. The purpose of developing such a method is to be able to understand the mechanisms by which pathogens engage with individual mammalian cells, depending on their cell surface proteins, and how both host and pathogen gene expression changes are reflective of these mechanisms. The knowledge gained from such analyses of single cells will ultimately lead to more robust pathogen detection and countermeasures. Our method was aimed at streamlining both the upstream cell sample preparation using microfluidic methods, as well as the actual library making protocol. Specifically, we wanted to implement a random hexamer-based reverse transcription of all RNA within a single cell (as opposed to oligo dT-based which would only capture polyadenylated transcripts), and then use a CRISPR-based method called scDash to deplete ribosomal DNAs (since ribosomal RNAs make up the majority of the RNA in a mammalian cell). After significant troubleshooting, we demonstrate that we are able to prepare cDNA from RNA using the random hexamer primer, and perform the rDNA depletion. We also show that we can visualize individually stained cells, setting up the pipeline for connecting surface markers to RNA-sequencing profiles. Finally, we test a number of devices for various parts of the pipeline, including bead generation, optical barcoding and cell dispensing, and demonstrate that while some of these have potential, more work is needed to optimize this part of the pipeline.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Phosphorus allotropes: Stability of black versus red phosphorus re-examined by means of the van der Waals inclusive density functional method

We evaluate the energetic stabilities of white, red, and black allotropes of phosphorus using density functional theory (DFT) and hybrid functional methods, van der Waals (vdW) corrections (DFT+vdW and hybrid+vdW), vdW density functionals, and random phase approximation (RPA). In this work, we find that stability of black phosphorus over red-V (i.e., the violet form) is not ubiquitous among these methods, and the calculated enthalpies for the reaction phosphorus (red-V)→ phosphorus (black) are scattered between –20 and 40 meV/atom. With local density and generalized gradient approximations, and hybrid functionals, mean absolute errors (MAEs) in densities of P allotropes relative to experiments are found to be around 10%–25%, whereas with vdW-inclusive methods, MAEs in densities drop below ~5%. While the inconsistency among the density functional methods could not shed light on the stability puzzle of black versus red phosphorus, comparison of their accuracy in predicting densities and the supplementary RPA results on relative stabilities indicate that opposite to the common belief, black and red phosphorus are almost degenerate, or the red-V (violet) form of phosphorus might even be the ground state.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2019

Fldgen v1.0: an emulator with internal variability and space–time correlation for Earth system models

Earth system models (ESMs) are the gold standard for producing future projections of climate change, but running them is difficult and costly, and thus researchers are generally limited to a small selection of scenarios. This paper presents a technique for detailed emulation of the Earth system model (ESM) temperature output, based on the construction of a deterministic model for the mean response to global temperature. The residuals between the mean response and the ESM output temperature fields are used to construct variability fields that are added to the mean response to produce the final product. The method produces grid-level output with spatially and temporally coherent variability. Output fields include random components, so the system may be run as many times as necessary to produce large ensembles of fields for applications that require them. We introduce the method, show example outputs, and present statistical verification that it reproduces the ESM properties it is intended to capture. This method, available as an open-source R package, should be useful in the study of climate variability and its contribution to uncertainties in the interactions between human and Earth systems.

58 GEOSCIENCES↗

DOE OSTI2016

Timing of Radiation Therapy and Chemotherapy After Breast-Conserving Surgery for Node-Positive Breast Cancer: Long-Term Results From International Breast Cancer Study Group Trials VI and VII

Purpose: To update the previous report from 2 randomized clinical trials, now with a median follow-up of 16 years, to analyze the effect of radiation therapy timing on local failure and disease-free survival. Patients and Methods: From July 1986 to April 1993, International Breast Cancer Study Group trial VI randomly assigned 1475 pre-/perimenopausal women with node-positive breast cancer to receive 3 or 6 cycles of initial chemotherapy (CT). International Breast Cancer Study Group trial VII randomly assigned 1212 postmenopausal women with node-positive breast cancer to receive tamoxifen for 5 years, or tamoxifen for 5 years with 3 early cycles of initial CT. For patients who received breast-conserving surgery (BCS), radiation therapy (RT) was delayed until initial CT was completed; 4 or 7 months after BCS for trial VI and 2 or 4 months for trial VII. We compared RT timing groups among 433 patients on trial VI and 285 patients on trial VII who received BCS plus RT. Endpoints were local failure, regional/distant failure, and disease-free survival (DFS). Results: Among pre-/perimenopausal patients there were no significant differences in disease-related outcomes. The 15-year DFS was 48.2% in the group allocated 3 months initial CT and 44.9% in the group allocated 6 months initial CT (hazard ratio [HR] 1.12; 95% confidence interval [CI] 0.87-1.45). Among postmenopausal patients, the 15-year DFS was 46.1% in the no-initial-CT group and 43.3% in the group allocated 3 months initial CT (HR 1.11; 95% CI 0.82-1.51). Corresponding HRs for local failures were 0.94 (95% CI 0.61-1.46) in trial VI and 1.51 (95% CI 0.77-2.97) in trial VII. For regional/distant failures, the respective HRs were 1.15 (95% CI 0.80-1.63) and 1.08 (95% CI 0.69-1.68). Conclusions: This study confirms that, after more than 15 years of follow-up, it is reasonable to delay radiation therapy until after the completion of standard CT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2016

SU-G-TeP1-12: Random Repainting as Mitigation for Scanned Ion Beam Interplay Effects

Purpose: Interference of dose application in scanned beam particle therapy and organ motion may lead to interplay effects with distorted dose to target volumes. Interplay effects depend on the speed and direction of the scanning beam, leading to fringed field edges (scanning parallel to organ motion direction) or over- and under-dosed regions (both directions are orthogonal). Current repainting methods can mitigate interplay effects, but are susceptible to artefacts when only a limited number of repaints are applied. In this study a random layered-repainting strategy was investigated. Methods: Mono-energetic proton beams were irradiated to a 10 ×10 cm{sup 2} scanned field at a Varian ProBeam facility. Applied dose was measured with a 2D amorphous silicon detector mounted on a motion platform (CIRS dynamic platform). Motion was considered with different cycles, directions and translations up to ±8 mm. Dose distributions were measured for a static case, regular repainting (repeated meander-like path) and random repainting. Latter was realized by randomly distributing single spot locations during irradiation for a given number of repaints. Efficiency of repainting was analyzed by comparison to the static case. A simulation tool based on treatment logs and motion information was developed to compare measurement results to expected dose distributions. Results: Regular repainting could reduce motion artefacts, but dose distortion was strongly dependent on motion direction. Random repainting with same number of repaints (N=4) showed superior results, independent of target movement direction, while introducing slight penalty on delivery times, caused by an increase of overall scanning travel distance. The simulation tool showed good agreement to measured results. Conclusion: The results demonstrate significant improvement in terms of dose conformity when layered repainting is applied in a randomized fashion. This allows for reduced target margins during treatment planning and limited number of repaints. A combination with e.g. respiratory gating is straight-forward. Authors are employees of Varian Medical Systems Particle Therapy GmbH.

43 PARTICLE ACCELERATORS↗

DOE OSTI2017

A Monte Carlo method for the simulation of coagulation and nucleation based on weighted particles and the concepts of stochastic resolution and merging

Monte Carlo simulations based on weighted simulation particles can solve a variety of population balance problems and allow thus to formulate a solution-framework for many chemical engineering processes. This study presents a novel concept for the calculation of coagulation rates of weighted Monte Carlo particles by introducing a family of transformations to non-weighted Monte Carlo particles. The tuning of the accuracy (named ‘stochastic resolution’ in this paper) of those transformations allows the construction of a constant-number coagulation scheme. Furthermore, a parallel algorithm for the inclusion of newly formed Monte Carlo particles due to nucleation is presented in the scope of a constant-number scheme: the low-weight merging. This technique is found to create significantly less statistical simulation noise than the conventional technique (named ‘random removal’ in this paper). Both concepts are combined into a single GPU-based simulation method which is validated by comparison with the discrete-sectional simulation technique. Two test models describing a constant-rate nucleation coupled to a simultaneous coagulation in 1) the free-molecular regime or 2) the continuum regime are simulated for this purpose.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

On the Approximability of Random-Hypergraph MAX-3-XORSAT Problems with Quantum Algorithms

Constraint satisfaction problems are an important area of computer science. Many of these problems are in the complexity class NP which is exponentially hard for all known methods, both for worst cases and often typical. Fundamentally, the lack of any guided local minimum escape method ensures the hardness of both exact and approximate optimization classically, but the intuitive mechanism for approximation hardness in quantum algorithms based on Hamiltonian time evolution is poorly understood. We explore this question using the prototypically hard MAX-3-XORSAT problem class. We conclude that the mechanisms for quantum exact and approximation hardness are fundamentally distinct. We qualitatively identify why traditional methods such as quantum adiabatic optimization are not good approximation algorithms. We propose a new spectral folding optimization method that does not suffer from these issues and study it analytically and numerically. We consider random rank-3 hypergraphs including extremal planted solution instances, where the ground state satisfies an anomalously high fraction of constraints compared to truly random problems. We show that, if we define the energy to be $E = N_{unsat}-N_{sat}$, then spectrally folded quantum optimization will return states with energy $E \leq A E_{GS}$ (where $E_{GS}$ is the ground state energy) in polynomial time, where conservatively, $A \simeq 0.6$. We thoroughly benchmark variations of spectrally folded quantum optimization for random classically approximation-hard (planted solution) instances in simulation, and find performance consistent with this prediction. We do not claim that this approximation guarantee holds for all possible hypergraphs, though our algorithm's mechanism can likely generalize widely. These results suggest that quantum computers are more powerful for approximate optimization than had been previously assumed.

Kapit, Eliot↗

DOE OSTI2020

Dynamic Response of a Semiactive Suspension System with Hysteretic Nonlinear Energy Sink Based on Random Excitation by means of Computer Simulation

This paper aims to investigate the property and behavior of the hysteretic nonlinear energy sink (HNES) coupled to a half vehicle system which is a nine-degree-of-freedom, nonlinear, and semiactive suspension system in order to improve the ride comfort and increase the stability in shock mitigation by using the computer simulation method. The HNES model is a semiactive suspension device, which comprises the famous Bouc–Wen (B-W) model employed to describe the force produced by both the purely hysteretic spring and linear elastic spring of potentially negative stiffness connected in parallel, for the half vehicle system. Nine nonlinear motion equations of the half vehicle system are derived in terms of the seven displacements and the two dimensionless hysteretic variables, which are integrated numerically by employing the direct time integration method for studying both the variables of vertical displacements, velocities, accelerations, chassis pitch angle, and the ride comfort and driver safety, respectively, based on the bump and random road inputs of the pseudoexcitation method as excitation signal. Simulation results show that, compared with the HNES model and the magnetorheological (MR) model coupled to the half vehicle system, the ride comfort and stability have been evidently improved. A successful validation process has been performed, which indicated that both the ride comfort and driver safety properties of the HNES model coupled to half vehicle significantly improved.

Chen, Hui↗

DOE OSTI2018

A novel low-rank matrix completion approach to estimate missing entries in Euclidean distance matrix

A Euclidean distance matrix (EDM) is a table of distance-square between points on a k-dimensional Euclidean space, with applications in many fields (e.g., engineering, geodesy, economics, genetics, biochemistry, and psychology). A problem that often arises is the absence (or uncertainty) of some EDM elements. In many situations, only a subset of all pairwise distances is available and it is desired to have some procedure to estimate the missing distances. In this paper, we address the problem of missing data in EDM through low-rank matrix completion techniques. We exploit the fact that the rank of a EDM is at most k+2 and does not depend on the number of points, which is, in general, much bigger then k. We use a singular value decomposition approach that considers the rank of the matrix to be completed and computes, in each iteration, a parameter that controls the convergence of the method. After performing a number of computational experiments, we could observe that our proposal was able to recover, with high precision, random EDMs with more than 1000 points and up to 98% of missing data in few minutes. In addition, our method required a smaller number of iterations when compared to other competitive state-of-art technique.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams

Abstract We present Lightcurve Anomaly Identification and Similarity Search ( LAISS ), an automated pipeline to detect anomalous astrophysical transients in real-time data streams. We deploy our anomaly detection model on the nightly Zwicky Transient Facility (ZTF) Alert Stream via the ANTARES broker, identifying a manageable ∼1–5 candidates per night for expert vetting and coordinating follow-up observations. Our method leverages statistical light-curve and contextual host galaxy features within a random forest classifier, tagging transients of rare classes ( spectroscopic anomalies), of uncommon host galaxy environments ( contextual anomalies), and of peculiar or interaction-powered phenomena ( behavioral anomalies). Moreover, we demonstrate the power of a low-latency (∼ms) approximate similarity search method to find transient analogs with similar light-curve evolution and host galaxy environments. We use analogs for data-driven discovery, characterization, (re)classification, and imputation in retrospective and real-time searches. To date, we have identified ∼50 previously known and previously missed rare transients from real-time and retrospective searches, including but not limited to superluminous supernovae (SLSNe), tidal disruption events, SNe IIn, SNe IIb, SNe I-CSM, SNe Ia-91bg-like, SNe Ib, SNe Ic, SNe Ic-BL, and M31 novae. Lastly, we report the discovery of 325 total transients, all observed between 2018 and 2021 and absent from public catalogs (∼1% of all ZTF Astronomical Transient reports to the Transient Name Server through 2021). These methods enable a systematic approach to finding the “needle in the haystack” in large-volume data streams. Because of its integration with the ANTARES broker, LAISS is built to detect exciting transients in Rubin data.

79 ASTRONOMY AND ASTROPHYSICS↗