Classification Of Candidates Research Articles

The High Time Resolution Universe Pulsar Survey – XIX. A coherent GPU-accelerated reprocessing and the discovery of 71 pulsars in the Southern Galactic plane

ABSTRACT We conducted a GPU-accelerated reprocessing of $\sim 87~{{\ \rm per\ cent}}$ of the archival data from the High Time Resolution Universe South Low Latitude (HTRU-S LowLat) pulsar survey by implementing a pulsar search pipeline that was previously used to reprocess the Parkes Multibeam Pulsar Survey (PMPS). We coherently searched the full 72-min observations of the survey with an acceleration search range up to $|50|\, \rm m\, s^{-2}$, which is most sensitive to binary pulsars experiencing nearly constant acceleration during 72 min of their orbital period. Here we report the discovery of 71 pulsars, including six millisecond pulsars, of which five are in binary systems, and seven pulsars with very high dispersion measures (DM $\gt 800 \, \rm pc \, cm^{-3}$). These pulsar discoveries largely arose by folding candidates to a much lower spectral signal-to-noise ratio than in previous surveys and by exploiting the coherence of folding over the incoherent summing of the Fourier components to discover new pulsars as well as candidate classification techniques. We show that these pulsars could be fainter and on average more distant as compared with both the previously reported 100 HTRU-S LowLat pulsars and the background pulsar population in the survey region. We have assessed the effectiveness of our search method and the overall pulsar yield of the survey. We show that through this reprocessing we have achieved the expected survey goals, including the predicted number of pulsars in the survey region, and discuss the major causes why these pulsars were missed in previous processing of the survey.

Genome-Wide Identification, Classification, Expression Analysis, and Screening of Drought and Heat Resistance-Related Candidates of the Rboh Gene Family in Wheat.

Plant respiratory burst oxidase homologs (Rbohs) are key enzymes that produce reactive oxygen species (ROS), which serve as signaling molecules regulating plant growth and stress responses. In this study, 39 TaRboh genes (TaRboh01-TaRboh39) were identified. These genes were distributed unevenly among the wheat genome's fourteen chromosomes, with the exception of homoeologous group 2 and 7 and chromosomes 4A, as well as one unidentified linkage group (Un). TaRbohs were classified into ten distinct clades, each sharing similar motif compositions and gene structures. The promoter regions of TaRbohs contained cis-elements related to hormones, growth and development, and stresses. Furthermore, five TaRboh genes (TaRboh26, TaRboh27, TaRboh31, TaRboh32, and TaRboh34) exhibited strong evolutionary conservation. Additionally, a Ka/Ks analysis confirmed that purifying selection was the predominant force driving the evolution of these genes. Expression profiling and qPCR results further indicated differential expression patterns of TaRboh genes between heat and drought stresses. TaRboh11, TaRboh20, TaRboh22, TaRboh24, TaRboh29, and TaRboh34 were significantly upregulated under multiple stress conditions, whereas TaRboh30 was only elevated in response to drought stress. Collectively, our findings provide a systematic analysis of the wheat Rboh gene family and establish a theoretical framework for our future research on the role of Rboh genes in response to heat and drought stress.

Simplifying Complex Figure scoring: Data from the Emory Healthy Brain Study and initial clinical validation.

To introduce the Emory 10-element Complex Figure (CF) scoring system and recognition task. We evaluated the relationship between Emory CF scoring and traditional Osterrieth CF scoring approach in cognitively healthy volunteers. Additionally, a cohort of patients undergoing deep brain stimulation (DBS) evaluation was assessed to compare the scoring methods in a clinical population. The study included 315 volunteers from the Emory Healthy Brain Study (EHBS) with Montreal Cognitive Assessment (MoCA) scores of 24/30 or higher. The clinical group consisted of 84 DBS candidates. Scoring time differences were analyzed in a subset of 48 DBS candidates. High correlations between scoring methods were present for non-recognition components in both cohorts (EHBS: Copy r = 0.76, Immediate r = 0.86, Delayed r = 0.85, Recognition r = 47; DBS: Copy r = 0.80, Immediate r = 0.84, Delayed Recall r = 0.85, Recognition r = 0.37). Emory CF scoring times were significantly shorter than Osterrieth times across non-recognition conditions (all p < 0.00001, individual Cohen's d: 1.4-2.4), resulting in an average time savings of 57%. DBS patients scored lower than EHBS participants across CF memory measures, with larger effect sizes for Emory CF scoring (Cohen's d range = 1.0-1.2). Emory CF scoring demonstrated better group classification in logistic regression models, improving DBS candidate classification from 16.7% to 32.1% compared to Osterrieth scoring. Emory CF scoring yields results that are highly correlated with traditional Osterrieth scoring, significantly reduces scoring time burden, and demonstrates greater sensitivity to memory decline in DBS candidates. Its efficiency and sensitivity make Emory CF scoring well-suited for broader implementation in clinical research.

Emerging Alzheimer's disease treatment paradigms: A late‐stage clinical trial review

AbstractINTRODUCTIONWithout disease‐modifying interventions, Medicare and Medicaid spending on Alzheimer's disease (AD) management is expected to reach 637 billion USD annually by 2050. The recent advent of promising AD therapies after decades of a near‐total failure rate in clinical trials suggests that more disease‐modifying therapies are on the horizon. In this review, we assess the late‐stage pipeline of disease‐modifying candidates for AD and offer a novel classification of intervention candidates by treatment paradigms—groups of candidates that share an underlying biological mechanism of action and general disease target.METHODSWe extracted data from the National Library of Medicine clinical trials database regarding Phase 2 and 3 trials of disease‐modifying AD therapies. We categorized trials into eight unique treatment paradigms, which we defined by combinations of therapy (biologic, small molecule, cell and gene therapy, other) and target (amyloid, tau, other). We analyzed primary endpoints, eligibility criteria including clinical ratings of cognition, trial phase and length, and funding sources.RESULTSWe identified 123 unique disease‐modifying intervention candidates in 175 late‐stage clinical trials. Biologic and small molecule drugs comprised 30% and 54% of trials, respectively. Eligibility criteria favored patients between the ages of 60 and 80 years with mild cognitive impairment. Including multi‐phase trials, 81% of studies were engaged in Phase 2 and 27% in Phase 3. Notably, within the Biologic–Amyloid paradigm, 64% of trials were engaged in Phase 3.DISCUSSIONCurrent studies of disease‐modifying therapies for AD comprise a diverse set of approaches to treating the disease. However, effort is largely concentrated in a few treatment paradigms and a narrow patient population, causing varying rates of progress among treatment paradigms in the late‐stage clinical trial pipeline. Strategies may be warranted to accelerate successes in the most promising therapeutical paradigms and nurture growth within nascent areas lacking resources but not potential.Highlights An analysis of Alzheimer's disease trial treatment paradigms was conducted. From April 2021 to March 2023, 175 trials of 123 unique candidates were reviewed. Biologic and small molecule drugs comprised 30% and 54% of trials, respectively. Eligibility criteria favored ages 60 through 80 with mild cognitive impairment.

Early Diagnosis of Atrial Fibrillation Episodes: Comparative Analysis of Different Matrix Architectures

This study presents three different matrix architectures for the analysis of ECG parameters, aimed at detecting atrial fibrillation episodes. The evaluation involves a cohort of 15 individuals, utilizing these matrix architectures across various orders. The findings reveal that the matrix norm delivers significantly better results compared to the large discriminant of the matrix. Detailed analysis of the spatial expansion of each matrix structure indicates that the PMLD architecture excels in terms of expandability compared to the MA1 and MA2 matrices. Consequently, third- and fifth-order PMLD matrix architectures are employed for classification techniques, demonstrating enhanced sensitivity with increased matrix order. These results are validated through the classification of several test candidates, confirming the efficacy of the proposed method. The study suggests that the developed approach holds substantial potential for clinical diagnostics in the early detection of atrial fibrillation.

Ship Detection in SAR Images Based on Steady CFAR Detector and Knowledge-Oriented GBDT Classifier

Ship detection is a significant issue in remote sensing based on Synthetic Aperture Radar (SAR). This paper combines the advantages of a steady constant false alarm rate (CFAR) detector and a knowledge-oriented Gradient Boosting Decision Tree (GBDT) classifier to achieve the location and the classification of ship candidates. The steady CFAR detector smooths the image by a moving-average filter and models the probability distribution of the smoothed clutter as a Gaussian distribution. The mean and the standard deviation of the Gaussian distribution are estimated according to the left half of the histogram to remove the effect of land, ships, and other targets. From the Gaussian distribution and a preset constant false alarm rate, a threshold is obtained to segment land, ships, and other targets from the clutter. Then, a series of morphological operations are introduced to eliminate land and extract ships and other targets, and an active contour algorithm is utilized to refine ships and other targets. Finally, ships are recognized from other targets by a knowledge-oriented GBDT classifier. Based on the brain-like ship-recognition process, we change the way of the decision-tree generation and achieve a higher classification performance than the original GBDT. The results on the AIRSARShip-1.0 dataset demonstrate that this scheme has a competitive performance against deep learning, especially in the detection of offshore ships.

Pulsar candidate identification using advanced transformer-based models

Rapid and accurate identification of pulsars is a significant topic for large radio telescope surveys. With the enhancement of astronomical instruments, modern radio telescopes are witnessing an exponential increase in pulsar candidate detections. The application of artificial intelligence for the identification of pulsar candidates is an automated and highly effective solution to tackle the challenge of processing and recognizing vast volumes of data. In this work, using the data released by two surveys, the Commensal Radio Astronomy FasT Survey (CRAFTS) and High-Time Resolution Universe (HTRU), we propose a new framework to identify pulsar candidates. Firstly, due to the small number of real pulsars, we compare the performance of different data augmentation methods and find that the pulsar samples generated by the Deep Convolutional Generative Adversarial Network (DCGAN) based on deep learning techniques are closer to real pulsars. Secondly, we use two transformer-based classification models, Vision Transformer (ViT) and Convolutional Vision Transformer (CvT), to classify pulsar candidates, and find that the evaluation indexes of pulsar candidate classification based on two transformers can reach 100%. Finally, we use the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm to visualize the results of our identification framework. The results showed that pulsar and non-pulsar samples are separated from each other in multidimensional space. Therefore, it is a new attempt to apply transformer technology to pulsar candidate classification, and it could be of great significance to subsequent theoretical research.

Therapeutic Decision Making in Prevascular Mediastinal Tumors Using CT Radiomics and Clinical Features: Upfront Surgery or Pretreatment Needle Biopsy?

The study aimed to develop machine learning (ML) classification models for differentiating patients who needed direct surgery from patients who needed core needle biopsy among patients with prevascular mediastinal tumor (PMT). Patients with PMT who received a contrast-enhanced computed tomography (CECT) scan and initial management for PMT between January 2010 and December 2020 were included in this retrospective study. Fourteen ML algorithms were used to construct candidate classification models via the voting ensemble approach, based on preoperative clinical data and radiomic features extracted from the CECT. The classification accuracy of clinical diagnosis was 86.1%. The first ensemble learning model was built by randomly choosing seven ML models from a set of fourteen ML models and had a classification accuracy of 88.0% (95% CI = 85.8 to 90.3%). The second ensemble learning model was the combination of five ML models, including NeuralNetFastAI, NeuralNetTorch, RandomForest with Entropy, RandomForest with Gini, and XGBoost, and had a classification accuracy of 90.4% (95% CI = 87.9 to 93.0%), which significantly outperformed clinical diagnosis (p < 0.05). Due to the superior performance, the voting ensemble learning clinical-radiomic classification model may be used as a clinical decision support system to facilitate the selection of the initial management of PMT.

High-z quasar candidate archive: a spectroscopic catalogue of quasars and contaminants in various quasar searches

ABSTRACT We present the high-z quasar candidate archive (HzQCA), summarizing the spectroscopic observations of 207 z ≳ 5 quasar candidates using Keck/LRIS, Keck/MOSFIRE, and Keck/NIRES. We identify 14 candidates as z ∼ 6 quasars, with 10 of them newly reported here and 63 candidates as brown dwarfs. In the remaining sources, 79 candidates are unlikely to be quasars; 2 sources are inconclusive; the others could not be fully reduced or extracted. Based on the classifications, we investigate the distributions of quasars and contaminants in colour space with photometry measurements from DELS (z), VIKING/UKIDSS (YJHKs/YJHK), and unWISE (W1W2). We find that the identified brown dwarfs are consistent with the empirical brown dwarf model that is commonly used in quasar candidate selection methods. To refine spectroscopic confirmation strategies, we simulate synthetic spectroscopy of high-z quasars and contaminants for all three instruments. The simulations utilize the spectroscopic data in HzQCA. We predict the required exposure times for quasar confirmation and propose an optimal strategy for spectroscopic follow-up observations. For instance, we demonstrate that we can identify a mJ = 21.5 at z = 7.6 or a mJ = 23.0 at z = 7.0 within 15 min of exposure time with LRIS. With the publication of the HzQCA, we aim to provide guidance for future quasar surveys and candidate classification.

Predictions using Support Vector Machine with Particle Swarm Optimization in Candidates Recipient of Program Keluarga Harapan

Program Keluarga Harapan (PKH) is a conditional social assistance program as an effort to alleviate poverty which is allocated to poor vulnerable households. The determination of candidates for the Program Keluarga Harapan assistance recipients is still carried out in village meetings, so it takes quite a long time and there is potential for subjectivity in the assessment carried out by Village Government officials which can lead to differences of opinion between deliberation participants in assessing the eligibility of residents as PKH recipients. For this reason, this research will use an optimization method, namely Particle Swarm Optimization (PSO) to select the most optimal attribute out of 39 attributes. After that, a classification algorithm, namely the Support Vector Machine (SVM), was chosen to form a classification model for Candidates for Social Assistance for the Program Keluarga Harapan (PKH). The classification of Candidates for Social Assistance Recipients of the Program Keluarga Harapan (PKH) was carried out in 2 experiments, namely before and after optimization. Experiments before optimization give an accuracy value of 92.44%. While the Support Vector Machine accuracy value after optimization gives an accuracy value of 92.51%. Based on the experimental results, it can be concluded that the Particle Swarm Optimization method can increase the accuracy of the Support Vector Machine algorithm by 0.07%. And the best model is the Support Vector Machine after optimizing Particle Swarm Optimization by using the 17 most optimized attributes in determining class targets.

Outskirts of Abell 1795: Probing gas clumping in the intracluster medium

Contact. The outskirts of galaxy clusters host complex interactions between the intracluster and circumcluster media. During the evolution of clusters, ram-pressure stripped gas clumps from infalling substructures break the uniformity of the gas distribution, potentially leading to observational biases at large radii. However, assessing the contribution of gas clumping poses observational challenges and requires robust X-ray measurements in the background-dominated regime of the cluster outskirts. Aims. The main objectives of this study are to isolate faint gas clumps from field sources and from the diffuse emission in the Abell 1795 galaxy cluster, then to probe their impact on the observed surface brightness and thermodynamic profiles. Methods. We performed an imaging analysis on deep Chandra ACIS-I observations of the Abell 1795 cluster outskirts, extending out to ∼1.5r200 with full azimuthal coverage. We built the 0.7 − 2.0 keV surface brightness distribution from the adaptively binned image of the diffuse emission and looked for clumps in the form of &gt; + 2σ surface brightness outliers. Our classification of the clump candidates was based primarily on Chandra and SDSS data. Benefiting from the point source list resolved by Chandra, we extracted the thermodynamic profiles of the intracluster medium from the associated Suzaku XIS data out to r200 using multiple point source and clump candidate removal approaches. Results. We identified 24 clump candidates in the Abell 1795 field, most of which are likely to be associated with background objects, including active galactic nuclei, galaxies, and clusters or groups of galaxies, as opposed to intrinsic gas clumps. These sources had minimal impact on the surface brightness and thermodynamic profiles of the cluster emission. After correcting for clump candidates, the measured entropy profile still deviates from a pure gravitational collapse, suggesting complex physics at play in the outskirts, which may include potential electron–ion non-equilibrium and non-thermal pressure support.

RePoint-Net detection and 3DSqU² Net segmentation for automatic identification of pulmonary nodules in computed tomography images

ABSTRACT Lung cancer is a leading cause of cancer-related deaths. Computer-aided detection (CAD) has emerged as a valuable tool to assist radiologists in the automated detection and segmentation of pulmonary nodules using Computed Tomography (CT) scans, indicating early stages of lung cancer. However, detecting small nodules remains challenging. This paper proposes novel techniques to address this challenge, achieving high sensitivity and low false-positive nodule identification using the RePoint-Net detection networks. Additionally, the 3DSqU2 Net, a novel nodule segmentation approach incorporating full-scale skip connections and deep supervision, is introduced. A 3DCNN model is employed for nodule candidate classification, generating final classification results by combining previous step outputs. Extensive training and testing on the LIDC/IDRI public lung CT database dataset validate the proposed model, demonstrating its superiority over human specialists with a remarkable 97.4% sensitivity in identifying nodule candidates. Moreover, CT texture analysis accurately differentiates between malignant and benign pulmonary nodules due to its ability to capture subtle tissue characteristic differences. This approach achieves a 95.8% sensitivity in nodule classification, promising non-invasive support for clinical decision-making in managing pulmonary nodules and improving patient outcomes.

Pulsar Candidate Classification Using a Computer Vision Method from a Combination of Convolution and Attention

Artificial intelligence methods are indispensable to identifying pulsars from large amounts of candidates. We develop a new pulsar identification system that utilizes the CoAtNet to score two-dimensional features of candidates, implements a multilayer perceptron to score one-dimensional features, and relies on logistic regression to judge the corresponding scores. In the data preprocessing stage, we perform two feature fusions separately, one for one-dimensional features and the other for two-dimensional features, which are used as inputs for the multilayer perceptron and the CoAtNet respectively. The newly developed system achieves 98.77% recall, 1.07% false positive rate (FPR) and 98.85% accuracy in our GPPS test set.

Klasifikasi Opini Publik di Twitter Terhadap Bakal Calon Presiden Indonesia Tahun 2024 Menggunakan LSTM Secara Realtime Berbasis Website

The analysis of public opinions from Indonesian netizens regarding the potential presidential candidates for Indonesia in 2024 on Twitter is challenging. Human-based classification of the candidates on Twitter has limitations as it requires expertise and a considerable amount of time to process the data. Therefore, a system that provides realtime visualization of public opinion classification is necessary. Previous research only focused on model evaluation, while this study aims to implement the best model on a website. The objective of this research is to develop a system for monitoring the Twitter-based public opinion classification of the potential presidential candidates for Indonesia in 2024 within specific time frames. The training process utilizes the LSTM method, resulting in a model with an accuracy of 76%. Parameters such as batch size, dropout, and learning rate were tested. The data used in this study was obtained by crawling Twitter using the keywords Ganjar Pranowo, Anies Baswedan, and Prabowo Subianto. The LSTM model was then implemented in a website-based system that generates a dashboard with features such as a color-coded map displaying the highest levels of positive sentiment for each candidate in each province, the overall classification count for each candidate, and filters for sentiment classification based on province and specific time frames.

3D Assembly Completion

Automatic assembly is a promising research topic in 3D computer vision and robotics. Existing works focus on generating assembly (e.g., IKEA furniture) from scratch with a set of parts, namely 3D part assembly. In practice, there are higher demands for the robot to take over and finish an incomplete assembly (e.g., a half-assembled IKEA furniture) with an off-the-shelf toolkit, especially in human-robot and multi-agent collaborations. Compared to 3D part assembly, it is more complicated in nature and remains unexplored yet. The robot must understand the incomplete structure, infer what parts are missing, single out the correct parts from the toolkit and finally, assemble them with appropriate poses to finish the incomplete assembly. Geometrically similar parts in the toolkit can interfere, and this problem will be exacerbated with more missing parts. To tackle this issue, we propose a novel task called 3D assembly completion. Given an incomplete assembly, it aims to find its missing parts from a toolkit and predict the 6-DoF poses to make the assembly complete. To this end, we propose FiT, a framework for Finishing the incomplete 3D assembly with Transformer. We employ the encoder to model the incomplete assembly into memories. Candidate parts interact with memories in a memory-query paradigm for final candidate classification and pose prediction. Bipartite part matching and symmetric transformation consistency are embedded to refine the completion. For reasonable evaluation and further reference, we design two standard toolkits of different difficulty, containing different compositions of candidate parts. We conduct extensive comparisons with several baseline methods and ablation studies, demonstrating the effectiveness of the proposed method.

A new method for classifying prognostic risk factors in lung transplant candidates

Predicting risk of waitlist mortality and subsequent classification of lung transplant candidates has been difficult due to inter-relatedness of risk factors, differential risk across populations, and changes in relationships over time. We developed a clinically intuitive indexing system to simplify mortality risk assessment. Scientific Registry of Transplant Recipients data from February 19, 2015, to May 26, 2020 (n=13,726) were used to estimate 3 constructs. Airway and oxygen function indices were estimated using confirmatory factor analysis and hierarchical clustering was used to derive respiratory support clusters. Cox proportional hazards regression was used to characterize event-free waitlist survival by constructs (3), age, sex, and diagnosis group. Model performance was compared to the Lung Allocation Score/Composite Allocation Score (LAS/CAS). Airway and oxygen function indices were created with substantive factor loadings forced expiratory volume (0.86), forced vital capacity (0.64), partial pressure of carbon dioxide (0.56) and PO2/fraction of inspired oxygen (0.83), partial pressure of oxygen (0.59), and mean pulmonary artery pressure (0.30), respectively. Four respiratory support clusters (C1: as needed O2, C2: continuous O2, C3: continuous O2/positive pressure ventilation (PPV), C4: PPV + extracorporeal membrane oxygenation) were identified. Constructs were used to identify patient profiles. Model area under the receiver operating characteristic curve was 0.85 [0.84, 0.87] compared to the LAS 0.92 [0.91, 0.94] at 4 weeks. Risk predictions were relatively insensitive to airway and oxygen function indices in C1 and C4 but varied across C2 and C3. Reducing the dimensionality of waitlist mortality risk offers an opportunity to identify clinical phenotypes that are more nuanced and thus more interpretable than current risk assessment provided by the LAS/CAS models.

A Retinal Lesion Detection of Diabetic Retinopathy

Diabetic retinopathy (DR) is the most common cause of vision loss among people with diabetes and leading to the cause of blindness. Microaneurysms (MAs) are primary lesion of Diabetic retinopathy, so their detection can give time to prevent further vision loss. Hemorrhages (HMs) and exudates (EXs) are the other type of lesions occurs during Diabetic retinopathy. The Retinal lesion detection consists of retinal image preprocessing, Optic disc detection and removal, Blood vessel segmentation, Candidate Lesion detection, Feature extraction, Classification and post processing . Publicly available DiaretDB1 dataset is used for performance evaluation.

Automatic detection of microaneurysms in fundus images based on multiple preprocessing fusion to extract features

Diabetic retinopathy (DR) is the main cause of new cases of blindness, and retinal microaneurysm (MA) is one of the early clinical manifestations of DR. However, it is difficult to directly observe MA with naked eyes because of their small size and poor contrast with the surrounding background. This paper presents a new method for automatic detection of MAs from fundus images. It mainly includes four steps: image preprocessing, candidate region extraction, feature extraction and classification. At the stage of feature extraction, different from traditional methods, we propose a feature extraction method based on multi-preprocessing fusion. In this paper, features are extracted from the original green channel image and the original gray image respectively, and then different processing methods are used to extract the same features from multiple pre-processed images. Finally, the extracted features are classified by multi-layer perceptron (MLP) classifier to determine whether the candidate object is microaneurysm (MA). The method is evaluated on e-ophtha-MA database and achieved high sensitivity. Using the FROC (free-response ROC) indicator, the detection achieves the F-score, accuracy, sensitivity, specificity and AUC (area under the curve) of 0.507, 0.940, 0.870, 0.938 and 0.9821, respectively, compared to the most advanced methods available. The proposed method of multi-preprocessing and fusion feature extraction is beneficial to classification and improves the detection performance.

A Multiwavelength Classification and Study of Red Supergiant Candidates in NGC 6946

We have combined resolved stellar photometry from Hubble Space Telescope (HST), Spitzer, and Gaia to identify red supergiant (RSG) candidates in NGC 6946, based on their colors, proper motions, visual morphologies, and spectral energy distributions. We start with a large sample of 17,865 RSG candidates based solely on HST near-infrared photometry. We then chose a small sample of 385 of these candidates with Spitzer matches for a more detailed study. Using evolutionary models and isochrones, we isolate a space where RSGs would be found in our photometry catalogs. We then visually inspect each candidate and compare them to Gaia catalogs to identify and remove foreground stars. As a result, we classify 95 potential RSGs, with 40 of these being in our highest-quality sample. We fit the photometry of the populations of stars in the regions surrounding the RSGs to infer their ages. Placing our best candidate RSG stars into three age bins between 1 and 30 Myr, we find 27.5% of the candidates falling between 1–10 Myr, 37.5% between 10–20 Myr, and 35% between 20–30 Myr. A comparison of our results to the models of massive star evolution shows some agreement between model luminosities and the luminosities of our candidates for each age. Three of our candidates appear significantly more consistent with binary models than single-star evolution models.

Data-driven selection and spectral classification of white dwarf stars

ABSTRACT The next generation of spectroscopic surveys is expected to provide spectra for hundreds of thousands of white dwarf (WD) candidates in the upcoming years. Currently, spectroscopic classification of white dwarfs is mostly done by visual inspection, requiring substantial amounts of expert attention. We propose a data-driven pipeline for fast, automatic selection, and spectroscopic classification of WD candidates, trained using spectroscopically confirmed objects with available Gaia astrometry, photometry, and Sloan Digital Sky Survey (SDSS) spectra with signal-to-noise ratios ≥9. The pipeline selects WD candidates with improved accuracy and completeness over existing algorithms, classifies their primary spectroscopic type with ${\gtrsim}90\ \hbox{per cent}$ accuracy, and spectroscopically detects main sequence companions with similar performance. We apply our pipeline to the Gaia Data Release 3 cross-matched with the SDSS Data Release 17 (DR17), identifying 424 096 high-confidence WD candidates and providing the first catalogue of automated and quantifiable classification for 36 523 WD spectra. Both the catalogue and pipeline are made available online. Such a tool will prove particularly useful for the undergoing SDSS-V survey, allowing for rapid classification of thousands of spectra at every data release.