Separate Metrics Research Articles

Some notes on the divergence and transformed divergence formulae in the remote sensing literature

ABSTRACT Divergence, and particularly Transformed Divergence, are key separability metrics in remote sensing, predominantly utilized for digital image classification to achieve thematic mapping. Despite the widespread use of Transformed Divergence, the mathematical expression of Divergence – essential for calculating Transformed Divergence – appears in the literature in two different formulations, leading to inconsistent results and confusion within the remote sensing community. This study addresses the original source of Divergence, clarifies its correct formulation, and examines implementations that calculate Divergence and/or Transformed Divergence based on the original formula. Additionally, it provides data to verify the accuracy of future implementations and quantitatively shows the erratic implications of incorrect calculations.

Jamming Attacks Detection Based on IGWO for Optimization of Fast Correlation-Based Feature extraction in Wireless Communication

Background: Wireless networks are essential communication technologies that prevent cable installation prices and burdens. Because of this technology's pervasive usage, wireless network safety is a significant problem. Owing to distributed and open wireless medium aspects, attackers might use different jamming methods to exploit physical and MAC layer protocol vulnerabilities. In addition, jamming attacks require to be accurately grouped so that suitable countermeasures can be considered. Given the potential severity of such attacks, precisely identifying and classifying them is critical for implementing effective responses. The motivation for this paper is the need to improve the detection and categorization of jamming signals using modern machine learning algorithms, consequently enhancing wireless network security and reliability. Objective: In this paper, we compare some machine learning models' efficiency for diagnosing jamming signals. Methods: Such algorithms refer to support vector machine (SVM) and k-nearest neighbors (KNN). We checked the signal features that recognize jamming signals. After the jamming attack model, the developed grey wolf optimizer version known as IGWO (improved grey wolf optimizer) has been discussed for feature extraction of software usability. Four separate metrics were employed as features to detect jamming attacks in order to evaluate the machine learning models. This novel feature extraction method is crucial for improving the accuracy of jamming detection. Results: The measurements of these parameters were gathered through a simulation of a real setting. And generated a large dataset using these parameters. Conclusion: The simulation results illustrate that the KNN algorithm based on jamming detection could diagnose jammers having a minimal likelihood of false alarms and a high level of accuracy.

Graph Metrics Reveal Brain Network Topological Property in Neuropathic Pain Patients: A Systematic Review.

Neuropathic pain (NP) is a common and persistent disease that leads to immense suffering and serious social burden. Incomplete understanding of the underlying neural basis makes it difficult to achieve significant breakthroughs in the treatment of NP. We aimed to review the functional and structural brain topological properties in patients with NP and consider how graph measures reveal potential mechanisms and are applied to clinical practice. Related studies were searched in PubMed and Web of Science databases. Topological property changes in patients with NP, including small-worldness, functional separation, integration, and centrality metrics, were reviewed. The findings suggest that NP was characterized by retained but declined small-worldness, indicating an insidious imbalance between network integration and segregation. The global-level measures revealed decreased global and local efficiency in the NP, implying decreased information transfer efficiency for both long- and short-range connections. Altered nodal centrality measures involve various brain regions, mostly those associated with pain, cognition, and emotion. Graph theory is a powerful tool for identifying topological properties of patients with NP. These specific brain changes in patients with NP are very helpful in revealing the potential mechanisms of NP, developing new treatment strategies, and evaluating the efficacy and prognosis of NP.

Semiautomated generation of species-specific training data from large, unlabeled acoustic datasets for deep supervised birdsong isolation.

Bioacoustic monitoring is an effective and minimally invasive method to study wildlife ecology. However, even the state-of-the-art techniques for analyzing birdsongs decrease in accuracy in the presence of extraneous signals such as anthropogenic noise and vocalizations of non-target species. Deep supervised source separation (DSSS) algorithms have been shown to effectively separate mixtures of animal vocalizations. However, in practice, recording sites also have site-specific variations and unique background audio that need to be removed, warranting the need for site-specific data. Here, we test the potential of training DSSS models on site-specific bird vocalizations and background audio. We used a semiautomated workflow using deep supervised classification and statistical cleaning to label and generate a site-specific source separation dataset by mixing birdsongs and background audio segments. Then, we trained a deep supervised source separation (DSSS) model with this generated dataset. Because most data is passively-recorded and consequently noisy, the true isolated birdsongs are unavailable which makes evaluation challenging. Therefore, in addition to using traditional source separation (SS) metrics, we also show the effectiveness of our site-specific approach using metrics commonly used in ornithological analyses such as automated feature labeling and species-specific trilateration accuracy. Our approach of training on site-specific data boosts the source-to-distortion, source-to-interference, and source-to-artifact ratios (SDR, SIR, and SAR) by 9.33 dB, 24.07 dB, and 3.60 dB respectively. We also find our approach allows for automated feature labeling with single-digit mean absolute percent error and birdsong trilateration accuracy with a mean simulated trilateration error of 2.58m. Overall, we show that site-specific DSSS is a promising upstream solution for wildlife audio analysis tools that break down in the presence of background noise. By training on site-specific data, our method is robust to unique, site-specific interference that caused previous methods to fail.

An Information-Geometric Formulation of Pattern Separation and Evaluation of Existing Indices.

Pattern separation is a computational process by which dissimilar neural patterns are generated from similar input patterns. We present an information-geometric formulation of pattern separation, where a pattern separator is modeled as a family of statistical distributions on a manifold. Such a manifold maps an input (i.e., coordinates) to a probability distribution that generates firing patterns. Pattern separation occurs when small coordinate changes result in large distances between samples from the corresponding distributions. Under this formulation, we implement a two-neuron system whose probability law forms a three-dimensional manifold with mutually orthogonal coordinates representing the neurons' marginal and correlational firing rates. We use this highly controlled system to examine the behavior of spike train similarity indices commonly used in pattern separation research. We find that all indices (except scaling factor) are sensitive to relative differences in marginal firing rates, but no index adequately captures differences in spike trains that result from altering the correlation in activity between the two neurons. That is, existing pattern separation metrics appear (A) sensitive to patterns that are encoded by different neurons but (B) insensitive to patterns that differ only in relative spike timing (e.g., synchrony between neurons in the ensemble).

A Multi-Sample Comparison and Rasch Analysis of the Evaluation of Children's Listening and Processing Skills Questionnaire.

Assessing listening difficulties and associated complaints can be challenging. Often, measures of peripheral auditory functions are within normal ranges, making clinicians feel unsure about proper management strategies. The range and nature of observed or experienced difficulties might be better captured using a qualitative measure. The Evaluation of Children's Listening and Processing Skills (ECLiPS) questionnaire was designed to broadly profile the auditory and cognitive problems often present in children with listening difficulties. This 38-item questionnaire was initially standardized in British children aged 6 to 11 years, was subsequently modified for use with North-American children, and was recently translated into Flemish-Dutch. This study aimed to compare typical scores of the Flemish version with the UK and US versions, and to evaluate and compare its psychometric quality based on Rasch analysis. We selected 112 Flemish children aged 6 to 11 years with verified normal hearing and typical development, and asked two caregivers of every child to fill out the ECLiPS. Data from two comparator samples were analyzed, including responses for 71 North-American children and 650 British children. Typical values for ECLiPS factors and aggregates were determined as a function of age and gender, and meaningful differences across samples were analyzed. Rasch analyses were performed to evaluate whether ECLiPS response categories work as intended, and whether item scores fit a linear equal interval measurement scale that works the same way for everyone. Item and person metrics were derived, including separation and reliability indices. We investigated whether items function similarly across linguistically and culturally different samples. ECLiPS scores were relatively invariant to age. Girls obtained higher scores compared with boys, mainly for items related to memory and attention, and pragmatic and social skills. Across ECLiPS versions, the most pronounced differences were found for items probing social skills. With respect to its psychometric quality, ECLiPS response categories work as intended, and ECLiPS items were found to fit the Rasch measurement scale. Cultural differences in responses were noted for some items, belonging to different factors. Item separation and reliability indices generally pointed toward sufficient variation in item difficulty. In general, person separation (and reliability) metrics, quantifying the instrument's ability to distinguish between poor and strong performers (in a reproducible manner), were low. This is expected from samples of typically developing children with homogeneous and high levels of listening ability. Across the languages assessed here, the ECLiPS caregiver questionnaire was verified to be a psychometrically valid qualitative measure to assess listening and processing skills, which can be used to support the assessment and management of elementary school children referred with LiD.

A novel randomized scrambling technique for mean estimation of a finite population

In recent decades, the randomized response technique has attracted researchers due to its usefulness in sensitive surveys. The randomized response procedure is used for the collection of responses on sensitive issues such as cheating in examination, income earned through illegal sources, expenditure on luxury items, and amount of tax paid, etc. This study introduces a new variant of quantitative randomized response models for use with sample surveys where the variable of interest is quantitative. The properties of a mean estimator based on the new technique have been studied. Further, the combined and separate evaluation metrics for efficiency and privacy level have also been derived and compared with those of the existing methods. Further, a simulation study has been conducted to prove the improvement in the degree of privacy protection and efficiency. The findings reveal that the suggested randomized response technique is not only more efficient than the existing techniques, but also improves the joint measure of efficiency and respondents’ privacy, making it preferable over the existing techniques. A real-world example of a sample survey through the suggested model is also presented which illustrates its usefulness in practical surveys on sensitive issues.

Separating Shrub Cover From Green Vegetation in Grasslands Using Hyperspectral Vegetation Indices

Shrub cover is a key parameter for monitoring woody plant encroachment (WPE) in the background of global grassland degradation. At the critical early encroachment stage, many shrub species are small, and it is impossible to be detected through height parameters. Hyperspectral vegetation indices (HVIs) are valuable tools to estimate vegetation cover, while the effect of shrubs of different cover levels (e.g., low: <35%, moderate: 36–55%, high: >56%) on the spectrum of mixed grassland is still unclear. In this study, narrowband HVIs were created for shrub cover estimation using field hyperspectral data collected in Cypress Hills Interprovincial Park, SK. Published HVIs and newly developed HVIs were examined for the relationship between green shrub cover and total green vegetation (GV) cover. Averaging normalized difference vegetation index (NDVI) was also used to minimize in the variation of shrub cover and GV in each quadrat. Results showed that HVIs SR [600, 800] and NDVI [705, 750] had the highest R2 with green shrub and GV cover (R2 = 0.34 and R2 = 0.30, p-value < 0.05). Blue bands (449 and 489 nm) and SWIR bands (1619 and 1739 nm) combination exhibited the highest accuracy for shrub and GV covers estimation (R2 = 0.60 and 0.40, p-value < 0.05). Averaging NDVI positively correlated with green shrub cover below 30% or above 70%. Three separability metrics (JM, TD, and M) results also showed the red and NIR region may promise to distinguish different shrub cover levels.

PD-L1 Expression in HPV-associated Versus HPV-independent Invasive Vulvar Squamous Cell Carcinoma.

Two etiological pathways have been implicated in the pathogenesis of vulvar squamous cell carcinoma (VSCC): a high-risk human papillomavirus (HPV)-associated route and an HPV-independent pathway characterized by TP53 mutations. Programmed cell death ligand 1 (PD-L1) has become increasingly useful in predicting the response to checkpoint inhibitor therapy in squamous cell carcinomas at various anatomical sites. This study aimed to assess the association between PD-L1 expression and the VSCC subtype to evaluate the utility of PD-L1 in prognostication and therapeutic selection based on HPV status. PD-L1 status was assessed using 3 separate metrics for the extent of PD-L1 staining in various cell types: immune cell score, tumor proportion score (TPS), and combined positive score. The study group consisted of 25 HPV-associated and 28 HPV-independent VSCCs. PD-L1 expression was positive in the majority of VSCCs according to all 3 scoring metrics (84.9% by immune cell score, 77.3% by TPS, and 90.6% by combined positive score). PD-L1 expression was observed in the majority of cases in both groups (60%-96.4%). PD-L1 expression using the TPS method was greater in HPV-independent tumors than in HPV-associated tumors ( P = 0.004), and high PD-L1 expression was also more common in the HPV-independent subtype ( P = 0.016 using the TPS method and P = 0.013 using the combined positive score method). Our findings contribute to the growing evidence that PD-L1 is expressed in the majority of invasive VSCCs, and thus may serve as an attractive therapeutic target. PD-L1 expression is higher in HPV-independent tumors, suggesting that this subtype may be more responsive to PD-L1 inhibitor therapy.

Beyond residential terms: Estimating racial segregation in daily environments in the United States

Much research on racial segregation focuses on segregation in a purely residential sense, measuring the spatial separation of individuals' actual residences. Subsequently, it is commonly surmised that the true extent to which racial groups are spatially separated in everyday life is well represented by these residential separation metrics. Less research has articulated the degree to which different racial groups' everyday lives are spatially separated. Utilizing anonymized cell phone data on 8 billion visits to 4 million unique points of interest in the United States throughout 2018, I analyze how Black-White racial segregation operationalizes in non-residential contexts. Distinct from previous approaches that tend to underestimate racial segregation, I utilize a novel method to approximate the extent to which within-neighborhood visit patterns vary racially, resulting in a more flexible and realistic simulation of the extent to which visitors are racially segregated across points of interest in the 50 largest metropolitan areas in the United States. I find that everyday racial segregation varies substantially depending on the type of context. Additionally, habits seem to structurally discourage interracial ties, as Black Americans are less likely to make visits to more integrated spaces and, when they do, are less likely to repeat them. Further, the integrated spaces Black Americans visit generally lack the right characteristics to be conducive to interpersonal and cross-racial contact. Ultimately, I argue that residential segregation metrics provide an incomplete picture of how actual segregation in everyday contexts manifests itself.

Existence Result for Coupled Random First-Order Impulsive Differential Equations with Infinite Delay

In this paper, we consider a system of random impulsive differential equations with infinite delay. When utilizing the nonlinear variation of Leray–Schauder’s fixed-point principles together with a technique based on separable vector-valued metrics to establish sufficient conditions for the existence of solutions, under suitable assumptions on Y1, Y2 and ϖ1, ϖ2, which greatly enriched the existence literature on this system, there is, however, no hope to discuss the uniqueness result in a convex case. In the present study, we analyzed the influence of the impulsive and infinite delay on the solutions to our system. In addition, to the best of our acknowledge, there is no result concerning coupled random system in the presence of impulsive and infinite delay.

Model-based evaluation of spatiotemporal data reduction methods with unknown ground truth through optimal visualization and interpretability metrics.

Optimizing and benchmarking data reduction methods for dynamic or spatial visualization and interpretation (DSVI) face challenges due to many factors, including data complexity, lack of ground truth, time-dependent metrics, dimensionality bias and different visual mappings of the same data. Current studies often focus on independent static visualization or interpretability metrics that require ground truth. To overcome this limitation, we propose the MIBCOVIS framework, a comprehensive and interpretable benchmarking and computational approach. MIBCOVIS enhances the visualization and interpretability of high-dimensional data without relying on ground truth by integrating five robust metrics, including a novel time-ordered Markov-based structural metric, into a semi-supervised hierarchical Bayesian model. The framework assesses method accuracy and considers interaction effects among metric features. We apply MIBCOVIS using linear and nonlinear dimensionality reduction methods to evaluate optimal DSVI for four distinct dynamic and spatial biological processes captured by three single-cell data modalities: CyTOF, scRNA-seq and CODEX. These data vary in complexity based on feature dimensionality, unknown cell types and dynamic or spatial differences. Unlike traditional single-summary score approaches, MIBCOVIS compares accuracy distributions across methods. Our findings underscore the joint evaluation of visualization and interpretability, rather than relying on separate metrics. We reveal that prioritizing average performance can obscure method feature performance. Additionally, we explore the impact of data complexity on visualization and interpretability. Specifically, we provide optimal parameters and features and recommend methods, like the optimized variational contractive autoencoder, for targeted DSVI for various data complexities. MIBCOVIS shows promise for evaluating dynamic single-cell atlases and spatiotemporal data reduction models.

A comparative study of randomized response techniques using separate and combined metrics of efficiency and privacy.

In social surveys, the randomized response technique can be considered a popular method for collecting reliable information on sensitive variables. Over the past few decades, it has been a common practice that survey researchers develop new randomized response techniques and show their improvement over previous models. In majority of the available research studies, the authors tend to report only those findings which are favorable to their proposed models. They often tend to hide the situations where their proposed randomized response models perform worse than the already available models. This approach results in biased comparisons between models which may influence the decision of practitioners about the choice of a randomized response technique for real-life problems. We conduct a neutral comparative study of four available quantitative randomized response techniques using separate and combined metrics of respondents' privacy level and model's efficiency. Our findings show that, depending on the particular situation at hand, some models may be better than the other models for a particular choice of values of parameters and constants. However, they become less efficient when a different set of parameter values are considered. The mathematical conditions for efficiency of different models have also been obtained.

Complete separation of variables in the geodesic Hamilton-Jacobi equation in four dimensions

We list all metrics of arbitrary signature in four dimensions which admit complete separation of variables in the Hamilton–Jacobi equation for geodesic Hamiltonians. There are only ten classes of separable metrics admitting Killing vector fields, indecomposable quadratic conservation laws, and coisotropic coordinates. Canonical separable metrics parameterized by several (up to twelve) arbitrary functions of single coordinates are written explicitly. The full set of independent conservation laws in involution for each canonical metrics is also found.

Early and late withdrawal of life-sustaining treatment after out-of-hospital cardiac arrest in the United Kingdom: Institutional variation and association with hospital mortality

AimFrequency and timing of Withdrawal of Life-Sustaining Treatment (WLST) after Out-of-Hospital Cardiac Arrest (OHCA) vary across Intensive Care Units (ICUs) in the United Kingdom (UK) and may be a marker of lower healthcare quality if instituted too frequently or too early. We aimed to describe WLST practice, quantify its variability across UK ICUs, and assess the effect of institutional deviation from average practice on patients’ risk-adjusted hospital mortality. MethodsWe conducted a retrospective multi-centre cohort study including all adult patients admitted after OHCA to UK ICUs between 2010 and 2017. We identified patient and ICU characteristics associated with early (within 72 h) and late (>72 h) WLST and quantified the between-ICU variation. We used the ICU-level observed-to-expected (O/E) ratios of early and late-WLST frequency as separate metrics of institutional deviation from average practice and calculated their association with patients’ hospital mortality. ResultsWe included 28,438 patients across 204 ICUs. 10,775 (37.9%) had WLST and 6397 (59.4%) of them had early-WLST. Both WLST types were strongly associated with patient-level demographics and pre-existing conditions but weakly with ICU-level characteristics. After adjustment, we found unexplained between-ICU variation for both early-WLST (Median Odds Ratio 1.59, 95%CrI 1.49–1.71) and late-WLST (MOR 1.39, 95%CrI 1.31–1.50). Importantly, patients’ hospital mortality was higher in ICUs with higher O/E ratio of early-WLST (OR 1.29, 95%CI 1.21–1.38, p < 0.001) or late-WLST (OR 1.39, 95%CI 1.31–1.48, p < 0.001). ConclusionsSignificant variability exists between UK ICUs in WLST frequency and timing. This matters because unexplained higher-than-expected WLST frequency is associated with higher hospital mortality independently of timing, potentially signalling prognostic pessimism and lower healthcare quality.

Threat vocalisations are acoustically similar between humans (Homo sapiens) and chimpanzees (Pan troglodytes)

ABSTRACT In behavioural contexts like fighting, eating, and playing, acoustically distinctive vocalisations are produced across many mammalian species. Such expressions may be conserved in evolution, pointing to the possibility of acoustic regularities in the vocalisations of phylogenetically related species. Here, we test this hypothesis by comparing the degree of acoustic similarity between human and chimpanzee vocalisations produced in 10 similar behavioural contexts. We use two complementary analysis methods: Pairwise acoustic distance measures and acoustic separability metrics based on unsupervised learning algorithms. Cross-context analysis revealed that acoustic features of vocalisations produced when threatening another individual were distinct from other types of vocalisations and highly similar across species. Using a multimethod approach, these findings demonstrate that human vocalisations produced when threatening another person are acoustically similar to chimpanzee vocalisations in the same situation as compared to other types of vocalisations, likely reflecting a phylogenetically ancient vocal signalling system.

0199 Effects of acute sleep deprivation on cognitive performance in depressed individuals

Abstract Introduction Acute sleep deprivation (SD) is an effective, rapid antidepressant treatment. However, SD has adverse effects on cognitive performance. The effects of SD on cognitive performance in depression remain underexplored. This study investigated relationships between depression and performance on Cognition – a neurocognitive battery of 10 tests that cover a range of cognitive domains – during acute SD. Methods Thirty-six individuals with current depression (69.4% female; 33±9.5 yrs) and 10 euthymic controls (40% female; 35±9.5 yrs) underwent a baseline night of sleep followed by 36 hours SD in the laboratory. Participants completed the Cognition battery twice after each protocol night. Each of the 10 Cognition tests has two separate metrics of performance: speed and accuracy. Multivariable linear regressions were used to assess effects of depression status, age, and gender on each performance metric at baseline. Mixed effects models were used to assess effects of depression status, sleep deprivation, and their interaction on performance metrics. All models included a random effect over participants and were adjusted for age, gender, and time of day. Results At baseline, depressed individuals were significantly faster than controls on three tests of working memory (β=-72.6; p=0.03), spatial orientation (β=-2944.7; p=0.01), and emotion recognition (β=-537.5; p=0.005) respectively, although these effects did not survive correction for multiple comparisons. Mixed effects model results revealed a significant effect of depression status on the speed metric from the spatial orientation test, whereby depressed individuals were faster across the protocol (β=-2579.6; p=0.04) and a significant interaction effect between depression status and study day on the speed metric from a risk-taking behavior test, whereby depressed individuals were slower after SD (β=480.9, p=0.01); neither effect survived correction for multiple comparisons. However, sleep deprivation was significantly associated with reductions in performance in models for 10 out of 20 metrics (all adjusted ps &amp;lt; 0.05). Conclusion These results suggest that currently depressed individuals exhibit the same decrements in cognitive performance after acute SD as non-depressed individuals. Future analyses will examine whether baseline depression severity predicts cognitive performance and whether differences in performance exist between SD responders versus non-responders. Support (if any) This work was supported by the National Institute of Mental Health (R01MH107571).

Modelling Sign Language with Encoder-Only Transformers and Human Pose Estimation Keypoint Data

We present a study on modelling American Sign Language (ASL) with encoder-only transformers and human pose estimation keypoint data. Using an enhanced version of the publicly available Word-level ASL (WLASL) dataset, and a novel normalisation technique based on signer body size, we show the impact model architecture has on accurately classifying sets of 10, 50, 100, and 300 isolated, dynamic signs using two-dimensional keypoint coordinates only. We demonstrate the importance of running and reporting results from repeated experiments to describe and evaluate model performance. We include descriptions of the algorithms used to normalise the data and generate the train, validation, and test data splits. We report top-1, top-5, and top-10 accuracy results, evaluated with two separate model checkpoint metrics based on validation accuracy and loss. We find models with fewer than 100k learnable parameters can achieve high accuracy on reduced vocabulary datasets, paving the way for lightweight consumer hardware to perform tasks that are traditionally resource-intensive, requiring expensive, high-end equipment. We achieve top-1, top-5, and top-10 accuracies of 97%, 100%, and 100%, respectively, on a vocabulary size of 10 signs; 87%, 97%, and 98% on 50 signs; 83%, 96%, and 97% on 100 signs; and 71%, 90%, and 94% on 300 signs, thereby setting a new benchmark for this task.

On the class separability of contextual embeddings representations – or “The classifier does not matter when the (text) representation is so good!”

The literature has not fully and adequately explained why contextual (e.g., BERT-based) representations are so successful to improve the effectiveness of some Natural Language Processing tasks, especially Automatic Text Classifications (ATC). In this article, we evince that such representations, when properly tuned to a target domain, produce an extremely separable space that makes the classification task very effective, independently of the classifier employed for solving the ATC task. To demonstrate our hypothesis, we perform a thorough class separability analysis in order to visualize and measure how well BERT-based embeddings separate documents of different classes in comparison with other widely used representation approaches, e.g., TFIDF BoW, static embeddings (e.g., fastText) and zero-shot (non-tuned) contextual embeddings. We also analyze separability in the context of transfer learning and compare BERT-based representations with those obtained from other transformers (e.g., RoBERTa, XLNET). Our experiments covering sixteen datasets in topic and sentiment classification, eight classification methods and three class separability metrics show that the fine-tuned BERT embeddings are highly separable in the corresponding space (e.g., they are 67% more separable than the static embeddings). As a consequence, they allow the simplest classifiers to achieve similar effectiveness as the most complex methods. We also find moderate to high correlations between separability and effectiveness in all experimented scenarios. Overall, our main finding is that more discriminative (i.e., separable) textual representations constitute a critical part of the ATC solutions that, given the current state-of-the-art in classification algorithms, are more prominent than the algorithmic (classifier) method for solving the task.

The Pantheon+ Analysis: Forward Modeling the Dust and Intrinsic Color Distributions of Type Ia Supernovae, and Quantifying Their Impact on Cosmological Inferences

Recent studies have shown that the observed color distributions of Type Ia supernovae (SNe Ia) can be well described by a combination of a dust distribution and an intrinsic color distribution. Using the Pantheon+ sample of 1701 SN Ia, we apply a new forward-modeling fitting method (Dust2Dust) to measure the parent dust and color distributions, including their dependence on host-galaxy mass. At each fit step, the SN Ia selection efficiency is determined from a large simulated sample that is reweighted to reflect the proposed distributions. We use five separate metrics to describe the goodness of fit: distribution of fitted light-curve color c, cosmological residual trends with c, cosmological residual scatter with c, fitted color–luminosity relationship β SALT2, and intrinsic scatter σ int. We present the results and the uncertainty in 12-dimensional space. Furthermore, we measure that the uncertainty on this modeling propagates to an upper threshold uncertainty in the equation of state of dark energy w of 0.014(1) for the Pantheon+ cosmology analysis and contributes negligible uncertainty to the Hubble constant H 0. The Dust2Dust code is made publicly available at https://github.com/djbrout/dustdriver.