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At least 1,387 records · Page 77

A data analysis method to rapidly characterize gallium concentration in plutonium matrices using LIBS

The processing of actinide samples is a complex and costly endeavor that requires compositional analysis at various stages. Laser-induced breakdown spectroscopy (LIBS) has been used to analyze actinide-containing samples in many nuclear applications including waste management, fuel processing and forensics. The LIBS spectrum obtained from actinide materials are generally extremely complex, exhibiting many thousands of strong emission lines. This makes it difficult to identify other elements within the sample of interest, given the rich and dominant actinide spectrum. Here, in this article, we describe a recent effort to identify and quantify impurities and alloying constituents in plutonium matrices using a hand-held LIBS instrument that is used to rapidly and efficiently measure an emission spectrum from a material sample. We tabulate the emission line positions and intensities of plutonium. We report the development of machine-learning software that can identify gallium and quantify its concentration in plutonium matrices. This work has the potential to provide a rapid and nearly non-destructive technique that allows more confidence in characterizing the composition of materials that are present within complex actinide associated targets. We describe how our LIBS measurements and data analysis methods have successfully quantified the gallium concentration in a variety of samples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Multiscale simulation of spatially correlated microstructure via a latent space representation

When deformation gradients act on the scale of the microstructure of a part due to geometry and loading, spatial correlations and finite-size effects in simulation cells cannot be neglected. We propose a multiscale method that accounts for these effects using a variational autoencoder to encode the structure–property map of the stochastic volume elements making up the statistical description of the part. In this paradigm the autoencoder can be used to directly encode the microstructure or, alternatively, its latent space can be sampled to provide likely realizations. Furthermore, we demonstrate the method on three examples using the common additively manufactured material AlSi10Mg in: (a) a comparison with direct numerical simulation of the part microstructure, (b) a push forward of microstructural uncertainty to performance quantities of interest, and (c) a simulation of functional gradation of a part with stochastic microstructure.

Elastoplasticity↗

Derivative-free stochastic optimization via adaptive sampling strategies

In this paper, we present a novel derivative-free framework for solving unconstrained stochastic optimization problems. Many problems in fields ranging from simulation optimization to reinforcement learning to quantum computing involve settings where only stochastic function values are obtained via a zeroth-order oracle, which has no available gradient information and necessitates the usage of derivative-free optimization methodologies. Our approach includes estimating gradients using stochastic function evaluations and integrating adaptive sampling techniques to control the accuracy in these stochastic approximations. Our framework encapsulates several gradient estimation techniques, including standard finite-difference, Gaussian smoothing, sphere smoothing, randomized coordinate finite-difference, and randomized subspace finite-difference methods. We provide theoretical convergence guarantees for our framework and analyze the worst-case iteration and sample complexities associated with each gradient estimation method. Finally, we demonstrate the empirical performance of the methods on logistic regression and nonlinear least squares problems.

Adaptive sampling↗

Evaluation of dried blood spot sampling for verification of exposure to chemical threat agents

Abstract Purpose Exposure to chemical threat agents (CTAs), including nerve agents, the vesicating agent sulfur mustard, and opioids, remains a significant threat to warfighter and civilian populations. Definitive analytical methods to verify exposure to CTAs require shipping refrigerated or frozen biomedical samples to reference laboratories for analysis. Logistical and financial burdens arise as the transport of biomedical samples is subject to strict restrictions and complex packaging, which, if done incorrectly, can lead to sample deterioration. The use of dried blood spot (DBS) sampling could provide operational improvements for collecting, storing, and shipping important forensic samples. Therefore, this effort focuses on developing DBS techniques with Mitra® 30-µL volumetric absorptive microsampling (VAMS®) devices for use in CTA exposure verification. Methods VAMS® devices were loaded and dried with human whole blood that was exposed to the metabolites pinacolyl methylphosphonic acid (PMPA), ethyl methylphosphonic acid (EMPA), 1,1’sulfonylbis[2-(methylsulfinyl)ethane] (SBMSE), norfentanyl, norcarfentanil, norsufentanil, and norlofentanil. Following extraction from the VAMS® devices, metabolites were detected using liquid chromatography-tandem mass spectrometry (LC–MS/MS). The methods were validated for performance by assessing sensitivity, precision, accuracy, and recovery. Results These methods were sensitive to 1 ng/mL for SBMSE, 0.5 ng/mL for PMPA, EMPA, and norfentanyl; 0.1 ng/mL for norlofentanil, and 0.05 ng/mL for norsufentanil and norcarfentanil. All methods met acceptable precision and accuracy criteria with favorable recovery. Conclusions These results demonstrated the utility of VAMS® in stabilizing human whole blood and show promise as an improved collection method for verification of exposure to various CTAs.

Toxicology↗

FOILPOLARS (Grassmannian Foil Shape Sweeps for Polar Generation) [SWR-26-095]

FOILPOLARS (Grassmannian Foil Shape Sweeps for Polar Generation): Multifidelity aerodynamic polar data generation for hydrofoil/tidal-turbine airfoil sections. Foilpolars ties together three pieces: *AeroSandbox supplies the baseline airfoil coordinates (UIUC database). *G2Aero parameterizes those shapes on a Grassmannian manifold (Karcher mean + PGA basis) and samples new perturbed shapes around that basis. *XFoil (panel method) and NeuralFoil (neural-network surrogate, shipped with AeroSandbox) each solve the resulting shapes for lift, drag, moment, and pressure at the swept angles of attack, Reynolds numbers, and n_crit values. Design optimization of foil shapes in a computationally efficient way requires polars data across many candidate shapes, not just a handful of baseline foils. However, high-fidelity CFD at that scale is too costly, and naive shape perturbation strays from realistic geometries. FOILPOLARS addresses this by loading baseline airfoils (via AeroSandbox) and mapping them onto a Grassmannian manifold (via G2Aero), computing a Karcher mean and principal geodesic analysis (PGA) basis. New shapes are sampled by perturbing PGA coefficients, keeping them close to the manifold of realistic foils. Each sampled shape is evaluated across a configurable sweep of angle of attack, Reynolds number, and critical amplification factor using two solvers: XFoil (panel method) and NeuralFoil (neural-network surrogate), producing a paired dataset of lift, drag, moment, pressure, convergence, and confidence, indexed alongside each shape's PGA coefficients and shared Grassmannian basis in a single xarray dataset. From this, FOILPOLARS produces convergence summaries and comparison plots per shape, Reynolds number, and n_crit. A command-line interface exposes each pipeline stage independently, supporting data-driven design, optimization, and machine-learning workflows for foils.

Sandhu, Rimple [National Laboratory of the Rockies↗

An Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology Estimation in an Unbalanced Power Distribution System

The reliable operation of a power distribution system relies on a good prior knowledge of its topology and its system state. Although crucial, due to the lack of direct monitoring devices on the switch statuses, the topology information is often unavailable or outdated for the distribution system operators for real-time applications. Apart from the limited observability of the power distribution system, other challenges are the nonlinearity of the model, the complicated, unbalanced structure of the distribution system, and the scale of the system. To overcome the above challenges, we, in this paper, propose a Bayesian-inference framework that allows us to simultaneously estimate the topology and the state of a three-phase, unbalanced power distribution system. Specifically, by using the very limited number of measurements available that are associated with the forecast load data, we efficiently recover the full Bayesian posterior distributions of the system topology under both normal and outage operation conditions. This is performed through an adaptive importance sampling procedure that greatly alleviates the computational burden of the traditional Monte-Carlo (MC)-sampling-based approach while maintaining a good estimation accuracy. The simulations conducted on the IEEE 123-bus test system and an unbalanced 1282-bus system reveal the excellent performances of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Improved Tomographic Binning of 3 × 2 pt Lens Samples: Neural Network Classifiers and Optimal Bin Assignments

Abstract Large imaging surveys, such as the Legacy Survey of Space and Time, rely on photometric redshifts and tomographic binning for 3 × 2 pt analyses that combine galaxy clustering and weak lensing. In this paper, we propose a method for optimizing the tomographic binning choice for the lens sample of galaxies. We divide the CosmoDC2 and Buzzard simulated galaxy catalogs into a training set and an application set, where the training set is nonrepresentative in a realistic way, and then estimate photometric redshifts for the application sets. The galaxies are sorted into redshift bins covering equal intervals of redshift or comoving distance, or with an equal number of galaxies in each bin, and we consider a generalized extension of these approaches. We find that bins of equal comoving distance produce the highest dark energy figure of merit of the initial binning choices, but that the choice of bin edges can be further optimized. We then train a neural network classifier to identify galaxies that are either highly likely to have accurate photometric redshift estimates or highly likely to be sorted into the correct redshift bin. The neural network classifier is used to remove poor redshift estimates from the sample, and the results are compared to the case when none of the sample is removed. We find that the neural network classifiers are able to improve the figure of merit by ∼13% and are able to recover ∼25% of the loss in the figure of merit that occurs when a nonrepresentative training sample is used.

79 ASTRONOMY AND ASTROPHYSICS↗

Multigroup Scattering in Monte Carlo Radiation Transport Codes [Slides]

Legendre truncations to multigroup scattering distributions are not amenable to Monte Carlo sampling due to negative values. We have implemented two moment-preserving methods in MGMC that capture the shape of the truncation, are non-negative over [-1, 1], and can be efficiently sampled on CPUs and GPUs. MGMC can now simulate neutrons with anisotropic scattering mechanics and MGMC shows good agreement with LANL production codes PARTISN.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Development of Powder Production Methods for Advanced LEU Fuel Concepts

A set of novel fuel concepts has been proposed for use in advanced low-enriched uranium (LEU) systems that utilize powder metallurgical methods for fabrication of the fuel forms. Preliminary tests demonstrated the ability to produce powder but did not yield the desired quality to be used as feedstock in these applications. This study seeks to establish an improved powder production method for uranium-based alloys and evaluate the parameters required to refine the particles produced by size and morphology. Powder samples were fabricated via atomization and analyzed for particle morphology utilizing sieving methods and scanning electron microscopy (SEM). Surrogate testing displayed improved performance in producing small (<250 µm) spherical particles, and initial tests demonstrated the initial capability to atomize uranium. Further work is required to refine the atomization process to produce high quality uranium microspheres.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Adaptive, Active Learning, and Multifidelity Monte Carlo Methods in the MOOSE Stochastic Tools Module

MOOSE is an open-source computational platform for constructing multi-physics models and executing them in a massively parallel fashion. It has a stochastic tools module (STM) for forward/inverse uncertainty quantification (UQ) and surrogate modeling. This presentation details some recent developments to the STM with respect to the implementation of adaptive, active learning, and multifidelity Monte Carlo methods for forward UQ of computational models. Specifically, the adaptive Monte Carlo methods include Markov Chain Monte Carlo (MCMC)-driven algorithms like adaptive importance sampling and parallelized subset simulation for statistical QoI estimation, rare events analysis, and stochastic gradient-free optimization. The active learning methods include Gaussian Process (GP) surrogates and their training via Adam optimization, design of acquisition functions, and integration with samplers like Monte Carlo, adaptive importance, and parallelized subset simulation. These active learning methods are also designed to work in a batch mode, wherein, the required calls to the full computational model are executed in parallel whenever a user-specified batch size is met. The multifidelity methods in STM are broadly divided into two categories: hierarchical, where a defined hierarchy exists among the low-fidelity models, and peer, where all the low-fidelity models are treated equally. A GP surrogate is used to learn the differences between the low- and high-fidelity models in both multifidelity categories, and acquisition functions from the active learning classes are used to decide whether to rely on a low-fidelity model or call the expensive high-fidelity model. Alongside the software description and usage, applications are also presented to nuclear engineering computational models including a TRISO nuclear fuel particle, a reactor pressure vessel, and a heat-pipe microreactor.

97 MATHEMATICS AND COMPUTING↗

MetFish: a Metabolomics Pipeline for Studying Microbial Communities in Chemically Extreme Environments

Metabolites have essential roles in microbial communities, including as mediators of nutrient and energy exchange, cell-to-cell communication, and antibiosis. However, detecting and quantifying metabolites and other chemicals in samples having extremes in salt or mineral content using liquid chromatography-mass spectrometry (LC-MS)-based methods remains a significant challenge. Here, we report a facile method based on in situ chemical derivatization followed by extraction for analysis of metabolites and other chemicals in hypersaline samples, enabling for the first time direct LC-MS-based exometabolomics analysis in sample matrices containing up to 2 M total dissolved salts. The method, MetFish, is applicable to molecules containing amine, carboxylic acid, carbonyl, or hydroxyl functional groups, and it can be integrated into either targeted or untargeted analysis pipelines. In targeted analyses, MetFish provided limits of quantification as low as 1 nM, broad linear dynamic ranges (up to 5 to 6 orders of magnitude) with excellent linearity, and low median interday reproducibility (e.g., 2.6%). MetFish was successfully applied in targeted and untargeted exometabolomics analyses of microbial consortia, quantifying amino acid dynamics in the exometabolome during community succession; in situ in a native prairie soil, whose exometabolome was isolated using a hypersaline extraction; and in input and produced fluids from a hydraulically fractured well, identifying dramatic changes in the exometabolome over time in the well.

59 BASIC BIOLOGICAL SCIENCES↗

Trace Chemical Detection Using Intercalated MXenes as a Signal Enhancing Substrate in Optical Probes

MXenes are 2D materials composed of layered transition metal nitrides or carbides. These materials are synthesized by HF exfoliation from MAX phases (Ti{sub 3}AlC{sub 2}). The 2D nanomaterial was synthesized by the removal of the 'A' element, resulting in a Mxene product (Ti{sub 3}C{sub 2}). MXenes have the general formula M{sub n+1}X{sub n}T{sub x}, where M is an early transition metal, X is Carbon and/or Nitrogen, and T accounts for surface terminated functional groups such as Fluoride, hydroxyl, and oxygen. These materials have very unique properties, similar to graphene, that allows them to be applied in a variety of trace detection techniques including surface-enhanced Raman spectroscopy (SERS). MXenes have also been demonstrated to selectively uptake uranyl ion, UO{sub 2}{sup 2+}. If this property can be combined with SERS or fluorescence detection, it may be possible to use MXenes as the basis for an alternative method to kinetic phosphorescence analysis (KPA) for trace uranyl measurements. Objectives: To confirm that MXene Nano materials are suitable substrates for SERS and sensor development by enhancing Raman signaling. To determine if certain MXene preparation methods yield materials that are more suitable for trace sensing methods. To determine uranyl uptake properties of these MXene materials and test them for analytical signals. Sample Preparation: Preparation of Ti{sub 3}C{sub 2}MXene (at FSU). MXenes were prepared by etching Al from Ti{sub 3}AlC{sub 2} (MAX phase)material. Two etching techniques yield different MXene products: LiF/HCl: Milder reaction, larger MXene flakes. HF: Harsher reaction, smaller flakes, larger layer separation. Products washed to remove etchant, vacuum filtered, and dried. Dried MXene flakes are air-stable. Film preparation for sensor testing (at SRNL): Suspend powder in diH{sub 2}O, purge with Ar, sonicate for 30 min. Centrifuge and collect supernate with suspended particles. Observed LiF-etched Mxene yielded a higher density of particles and darker collected solution. Drop-cast (4 ml) supernate onto slides and dried with Ar. For Rhodamine B (RhB) testing, drop-cast 4 ml drops onto Mxene spots and dried with Ar. Scanning Electron Microscopy conditions: 10 kV Beam energy, high vacuum; Working distance of 8 mm; beam penetration depth appx. 4 microns, beam spot size appx. 2 nanometers. Results: Detection of aluminum correlates with bright spots on image. Presence of aluminum shows that LiF/HCl etching was less thorough than HF etching. Trace Cl detection in LiF images suggests incomplete rinsing. HF has smaller feature size, more layer structure, and increased homogeneity, consistent with expectations. Macroscopic Raman spectroscopy measurements: 532 nm excitation, ∼50 mW with a ∼100 micron spot size (InPhotonics RPB probe). Kaiser Optical Holospec f/1.8 spectrometer with cooled (-60 deg.C) Andor iDus OE420 CCD. LiF 1x supernate showed good signal for trace measurements of Rhodamine B. HF and 1/4x LiF supernates showed little Mxene or Rhodamine B signal. Low deposition densities led to excess background signal from glass slides. For LiF film, response is linear with Rhodamine B concentration over range tested. Will retest with Raman microscope (∼1 micron spot size) to characterize SERS of more dilute LiF and HF etched Mxenes. Conclusions: The LiF etched material was more suitable for macroscopic SERS measurements because it was more concentrated, resulting in a thicker film than the HF etched Mxene and diluted LiF sample. However, the other materials may give greater SERS enhancements, which we hope to determine from measurements with the Raman microscope. From characterization with SEM we concluded that the HF etched Mxene is more uniform/homogenous and has smaller particle size than the LiF etched Mxene. There is still aluminum present in both samples indicating that etching wasn't complete, but the removal of the aluminum was more efficient in the HF method. Path Forward: Observe SERS with Raman microscopy, to obtain better signals for the more diluted samples and be able to compare enhancement effects for the different MXenes. Characterize uranyl sorption into MXene films and test Raman and fluorescence signals. Revisit the etching conditions to improve removal of aluminum. FSU and SRNL will continue to collaborate to create and characterize different Mxene materials and test their usefulness for sensor applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Samarium isotope compositions of uranium ore concentrates: A novel nuclear forensic signature

Uranium ore is mined and milled to produce uranium ore concentrate (UOC), a regulated product of the nuclear fuel cycle. Additionally, diversion of UOC from the fuel cycle into possible weapons production is a key concern in global nonproliferation efforts. As such, the ability to trace the origin of seized nuclear materials is imperative to law enforcement efforts. Although isotopic signatures of UOCs have proven fruitful to pinpoint sample provenance, new isotopic signatures are needed because most existing isotopic signatures are not indicative of the original ore body from which the U is derived. In this work, we developed a new method to separate samarium (Sm) from a U-rich sample matrix and report the first Sm isotope compositions of 32 UOCs derived from a variety of worldwide uranium mines. Relative to terrestrial standards, approximately half the UOCs have resolved and anticorrelated 149 Sm- 150 Sm isotope compositions, consistent with the capture of thermal neutrons by 149 Sm in the ore body. The UOCs with anomalous Sm isotope compositions tend to derive from older (~>1.5Ga) and higher-grade ore bodies, although other factors, such as the presence of neutron moderators like water, also play a role. Nonetheless, the Sm isotope compositions of UOCs directly reflects the neutron fluence over the history of the original ore body and can be used to discern different geologic conditions associated with that ore body. Overall, this work demonstrates the potential use of Sm isotopes as a novel nuclear forensics signature for origin assessment of UOCs.

58 GEOSCIENCES↗

Size Effect of Local Current-Voltage Characteristics of MX 2 Nanoflakes: Local Density of States Reconstruction from Scanning Tunneling Microscopy Experiments

Local current-voltage characteristics for low-dimensional transition-metal dichalcogenides (LDTMD), as well as the reconstruction of their local density of states (LDOS) from scanning tunneling microscopy (STM) experiments, are of fundamental interest and can be useful for advanced applications. Most of the existing models either have limited applicability for complex-shaped LDTMDs (e.g., those based on the Simmons approach) or require solving of an ill-defined integral equation to deconvolute the unknown LDOS (e.g., those based on the Tersoff approach). Using a serial expansion of the Tersoff formulas, we propose a flexible method to reconstruct the LDOS from local current-voltage characteristics measured in STM experiments. We establish a set of key physical parameters, which characterize the tunneling current of a STM-probe–sample contact and the sample LDOS expanded in Gaussian functions. Using a direct variational method coupled with probabilistic analysis, we determine these parameters from the STM experiments for MoS2 nanoflakes with different numbers of layers. The main result is the reconstruction of the LDOS in a relatively wide energy range around the Fermi level, which allows us to gain insight into the local band structure of LDTMDs. The reconstructed LDOS reveal pronounced size effects for the single-layer, two-layer, and three-layer MoS 2 nanoflakes, which we relate to the low dimensionality and strong bending or corrugation of the nanoflakes. We hope that the proposed elaboration of the Tersoff approach, allowing LDOS reconstruction, will be of critical interest for the quantitative description of STM experiments and also of use to better understand the microscopic physical aspects of the surface, strain, and bending contributions to the LDTMDs’ electronic properties.

42 ENGINEERING↗

Terahertz time-domain spectroscopy imaging of pancreatic ductal adenocarcinoma tissues

Pancreatic ductal adenocarcinoma (PDAC) ranks among the malignancies with the highest fatality and morbidity rates. This is predominantly attributable to an absence of understanding the intricate and diverse microenvironment of the tumor. We use terahertz time-domain spectroscopy (THz-TDS) imaging in transmission geometry to probe ex-vivo the heterogenous microenvironment of the genetically modified murine PDAC tissue that closely resembles the PDAC heterogeneity in human malignancy. We introduced a maximum a-posteriori probability estimation algorithm to objectively the tumor’s heterogenous microenvironment using the average values of refractive index and absorption coefficient within the useable terahertz bandwidth as imaging markers. Furthermore, direct comparison of stained histopathologic images and the refractive index and the absorption coefficient high-resolution, two dimensional maps of the same PDAC samples confirms the high potential of the THz-TDS method for tumor tissue characterization.

absorption↗

Parameter estimation from spontaneous imbibition into volcanic tuff

Two-phase fluid flow properties underlie quantitative prediction of water and gas movement, but constraining these properties typically requires multiple time-consuming laboratory methods. The estimation of two-phase flow properties (van Genuchten parameters, porosity, and intrinsic permeability) is illustrated in cores of vitric nonwelded volcanic tuff using Bayesian parameter estimation that fits numerical models to observations from spontaneous imbibition experiments. The uniqueness and correlation of the estimated parameters is explored using different modeling assumptions and subsets of the observed data. The resulting estimation process is sensitive to both moisture retention and relative permeability functions, thereby offering a comprehensive method for constraining both functions. The data collected during this relatively simple laboratory experiment, used in conjunction with a numerical model and a global optimizer, result in a viable approach for augmenting more traditional capillary pressure data obtained from hanging water column, membrane plate extractor, or mercury intrusion methods. This method may be useful when imbibition rather than drainage parameters are sought, when larger samples (e.g., including heterogeneity or fractures) need to be tested that cannot be accommodated in more traditional methods, or when in educational laboratory settings.

58 GEOSCIENCES↗

High-Temperature and High-Pressure In Situ Magic Angle Spinning Nuclear Magnetic Resonance Spectroscopy

Nuclear magnetic resonance (NMR) spectroscopy represents an important technique to understand the structure and bonding environment of molecules. There exists a drive to conduct characterization experiment under conditions relevant to the chemical process of interest. To address this, in situ high-temperature, high-pressure MAS NMR methods have been developed to enable the observation of chemical interactions over a range of pressures (vacuum to several hundred bar) and temperatures (well below 0°C to 250°C). Further, the chemical identity of the samples can be comprised of solids, liquids, and gases or mixtures of the three. The method incorporates all-zirconia NMR rotors (sample holder for MAS NMR) which can be sealed using a threaded cap to compress an O-ring. This rotor exhibits great chemical resistance, temperature compatibility, low NMR background, and can withstand high pressures. These factors combined enable its use in a wide range of system combinations which permit its use in diverse fields as carbon sequestration, catalysis, material science, geochemistry, and biology. The flexibility of this technique makes it an attractive option for scientists from numerous disciplines.

Nuclear Magnetic Resonance, NMR, in situ, spectros↗

Are light curve classification metrics good proxies for SN Ia cosmological constraining power?

Context. When selecting a light curve classifier for use as part of a photometric supernova Ia (SN Ia) cosmological analysis, it is common to make decisions based on metrics of classification performance, such as the contamination within the photometrically classified SN Ia sample, rather than a measure of cosmological constraining power. If the former is an appropriate proxy for the latter, this practice would eliminate the computational expense of a full cosmology forecast in the analysis pipeline design process. Aims. This study tests the assumption that light curve classification metrics are an appropriate proxy for cosmology metrics. Methods. We emulated photometric SN Ia cosmology light curve samples with controlled contamination rates of individual contaminant classes and evaluated each of them under a set of classification metrics. We then derived cosmological parameter constraints from all samples under two common analysis approaches and quantified the impact of contamination by each contaminant class on the resulting cosmological parameter estimates. Results. We observe that cosmology metrics are sensitive to both the contamination rate and the class of the contaminating population, whereas the classification metrics are shown to be insensitive to the latter. Conclusions. Based on these findings, we discourage any exclusive reliance on light curve classification-based metrics for analysis design decisions, which (counterintuitively) include but are not limited to the classifier choice. Instead, we recommend optimising science analysis pipeline design choices using a metric of the information gained about the physical parameters of interest.

79 ASTRONOMY AND ASTROPHYSICS↗