arXiv subjects

Yanxia Zhang

Publications and source records attributed to Yanxia Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2024

Bridging Design Gaps: A Parametric Data Completion Approach With Graph Guided Diffusion Models

This study introduces a generative imputation model leveraging graph attention networks and tabular diffusion models for completing missing parametric data in engineering designs. This model functions as an AI design co-pilot, providing multiple design options for incomplete designs, which we demonstrate using the bicycle design CAD dataset. Through comparative evaluations, we demonstrate that our model significantly outperforms existing classical methods, such as MissForest, hotDeck, PPCA, and tabular generative method TabCSDI in both the accuracy and diversity of imputation options. Generative modeling also enables a broader exploration of design possibilities, thereby enhancing design decision-making by allowing engineers to explore a variety of design completions. The graph model combines GNNs with the structural information contained in assembly graphs, enabling the model to understand and predict the complex interdependencies between different design parameters. The graph model helps accurately capture and impute complex parametric interdependencies from an assembly graph, which is key for design problems. By learning from an existing dataset of designs, the imputation capability allows the model to act as an intelligent assistant that autocompletes CAD designs based on user-defined partial parametric design, effectively bridging the gap between ideation and realization. The proposed work provides a pathway to not only facilitate informed design decisions but also promote creative exploration in design.

cs.LG↗

arXiv2024

Efficient Identification of Broad Absorption Line Quasars using Dimensionality Reduction and Machine Learning

Broad Absorption Line Quasars (BALQSOs) displaying distinct blue-shifted broad absorption lines. These serve as invaluable probes for unraveling the intricate structure and evolution of quasars, shedding light on the profound influence exerted by supermassive black holes on galaxy formation. The proliferation of large-scale spectroscopic surveys such as LAMOST, SDSS, and DESI has exponentially expanded the repository of quasar spectra at our disposal. In this study, we present an innovative approach to streamline the identification of BALQSOs, leveraging the power of dimensionality reduction and machine learning algorithms. Our dataset is curated from the SDSS DR16, amalgamating quasar spectra with classification labels sourced from the DR16Q quasar catalog. We employ a diverse array of dimensionality reduction techniques, including Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Locally Linear Embedding (LLE), and Isometric Mapping (ISOMAP), to distill the essence of the original spectral data. The resultant low-dimensional representations serve as inputs for a suite of machine learning classifiers, including XGBoost and Random Forest models. Through experimentation, we unveil PCA as the most effective dimensionality reduction methodology, adeptly navigating the intricate balance between dimensionality reduction and preservation of vital spectral information. Notably, the synergistic fusion of PCA with the XGBoost classifier emerges as the pinnacle of efficacy in the BALQSO classification endeavor, boasting impressive accuracy rates of 97.60% by 10-cross validation and 96.92% on the outer test sample. This study not only introduces a novel machine learning-based paradigm for quasar classification but also offers invaluable insights transferrable to a myriad of spectral classification challenges pervasive in the realm of astronomy.

astro-ph.GA↗

arXiv2024

CatNorth: An Improved Gaia DR3 Quasar Candidate Catalog with Pan-STARRS1 and CatWISE

A complete and pure sample of quasars with accurate redshifts is crucial for quasar studies and cosmology. In this paper, we present CatNorth, an improved Gaia DR3 quasar candidate catalog with more than 1.5 million sources in the 3$π$ sky built with data from Gaia, Pan-STARRS1, and CatWISE2020. The XGBoost algorithm is used to reclassify the original Gaia DR3 quasar candidates as stars, galaxies, and quasars. To construct training/validation datasets for the classification, we carefully built two different master stellar samples in addition to the spectroscopic galaxy and quasar samples. An ensemble classification model is obtained by averaging two XGBoost classifiers trained with different master stellar samples. Using a probability threshold of $p_{\mathrm{QSO\_mean}}>0.95$ in our ensemble classification model and an additional cut on the logarithmic probability density of zero proper motion, we retrieved 1,545,514 reliable quasar candidates from the parent Gaia DR3 quasar candidate catalog. We provide photometric redshifts for all candidates with an ensemble regression model. For a subset of 89,100 candidates, accurate spectroscopic redshifts are estimated with the Convolutional Neural Network from the Gaia BP/RP spectra. The CatNorth catalog has a high purity of ~ 90% while maintaining high completeness, which is an ideal sample to understand the quasar population and its statistical properties. The CatNorth catalog is used as the main source of input catalog for the LAMOST phase III quasar survey, which is expected to build a highly complete sample of bright quasars with $i < 19.5$.

astro-ph.GA↗

arXiv2023

NashFormer: Leveraging Local Nash Equilibria for Semantically Diverse Trajectory Prediction

Interactions between road agents present a significant challenge in trajectory prediction, especially in cases involving multiple agents. Because existing diversity-aware predictors do not account for the interactive nature of multi-agent predictions, they may miss these important interaction outcomes. In this paper, we propose NashFormer, a framework for trajectory prediction that leverages game-theoretic inverse reinforcement learning to improve coverage of multi-modal predictions. We use a training-time game-theoretic analysis as an auxiliary loss resulting in improved coverage and accuracy without presuming a taxonomy of actions for the agents. We demonstrate our approach on the interactive split of the Waymo Open Motion Dataset, including four subsets involving scenarios with high interaction complexity. Experiment results show that our predictor produces accurate predictions while covering $33\%$ more potential interactions versus a baseline model.

cs.LG↗

arXiv2023

Machine learning-based photometric classification of galaxies, quasars, emission-line galaxies, and stars

This paper explores the application of machine learning methods for classifying astronomical sources using photometric data, including normal and emission line galaxies (ELGs; starforming, starburst, AGN, broad line), quasars, and stars. We utilized samples from Sloan Digital Sky Survey (SDSS) Data Release 17 (DR17) and the ALLWISE catalog, which contain spectroscopically labeled sources from SDSS. Our methodology comprises two parts. First, we conducted experiments, including three-class, four-class, and seven-class classifications, employing the Random Forest (RF) algorithm. This phase aimed to achieve optimal performance with balanced datasets. In the second part, we trained various machine learning methods, such as $k$-nearest neighbors (KNN), RF, XGBoost (XGB), voting, and artificial neural network (ANN), using all available data based on promising results from the first phase. Our results highlight the effectiveness of combining optical and infrared features, yielding the best performance across all classifiers. Specifically, in the three-class experiment, RF and XGB algorithms achieved identical average F1 scores of 98.93 per~cent on both balanced and unbalanced datasets. In the seven-class experiment, our average F1 score was 73.57 per~cent. Using the XGB method in the four-class experiment, we achieved F1 scores of 87.9 per~cent for normal galaxies (NGs), 81.5 per~cent for ELGs, 99.1 per~cent for stars, and 98.5 per~cent for quasars (QSOs). Unlike classical methods based on time-consuming spectroscopy, our experiments demonstrate the feasibility of using automated algorithms on carefully classified photometric data. With more data and ample training samples, detailed photometric classification becomes possible, aiding in the selection of follow-up observation candidates.

astro-ph.GA↗

arXiv2023

Periodic Variable Star Classification with Deep Learning: Handling Data Imbalance in an Ensemble Augmentation Way

Time-domain astronomy is progressing rapidly with the ongoing and upcoming large-scale photometric sky surveys led by the Vera C. Rubin Observatory project (LSST). Billions of variable sources call for better automatic classification algorithms for light curves. Among them, periodic variable stars are frequently studied. Different categories of periodic variable stars have a high degree of class imbalance and pose a challenge to algorithms including deep learning methods. We design two kinds of architectures of neural networks for the classification of periodic variable stars in the Catalina Survey's Data Release 2: a multi-input recurrent neural network (RNN) and a compound network combing the RNN and the convolutional neural network (CNN). To deal with class imbalance, we apply Gaussian Process to generate synthetic light curves with artificial uncertainties for data augmentation. For better performance, we organize the augmentation and training process in a "bagging-like" ensemble learning scheme. The experimental results show that the better approach is the compound network combing RNN and CNN, which reaches the best result of 86.2% on the overall balanced accuracy and 0.75 on the macro F1 score. We develop the ensemble augmentation method to solve the data imbalance when classifying variable stars and prove the effectiveness of combining different representations of light curves in a single model. The proposed methods would help build better classification algorithms of periodic time series data for future sky surveys (e.g., LSST).

astro-ph.IM↗

arXiv2022

Simultaneously sorting vector vortex beams of 120 modes

Polarization (P), angular index (l), and radius index (p) are three independent degrees of freedom (DoFs) of vector vortex beams, which have been widely used in optical communications, quantum optics, information processing, etc. Although the sorting of one DoF can be achieved efficiently, it is still a great challenge to sort all these DoFs simultaneously in a compact and efficient way. Here, we propose a beam sorter to deal with all these three DoFs simultaneously by using a diffractive deep neural network (D$^2$NN) and experimentally demonstrated the robust sorting of 120 Laguerre-Gaussian (LG) modes using a compact D$^2$NN formed by one spatial light modulator and one mirror only. The proposed beam sorter demonstrates the great potential of D$^2$NN in optical field manipulation and will benefit the diverse applications of vector vortex beams.

physics.optics↗

arXiv2022

Photometric redshift estimation of galaxies in the DESI Legacy Imaging Surveys

The accurate estimation of photometric redshifts plays a crucial role in accomplishing science objectives of the large survey projects. The template-fitting and machine learning are the two main types of methods applied currently. Based on the training set obtained by cross-correlating the DESI Legacy Imaging Surveys DR9 galaxy catalogue and SDSS DR16 galaxy catalogue, the two kinds of methods are used and optimized, such as EAZY for template-fitting approach and CATBOOST for machine learning. Then the created models are tested by the cross-matched samples of the DESI Legacy Imaging SurveysDR9 galaxy catalogue with LAMOST DR7, GAMA DR3 and WiggleZ galaxy catalogues. Moreover three machine learning methods (CATBOOST, Multi-Layer Perceptron and Random Forest) are compared, CATBOOST shows its superiority for our case. By feature selection and optimization of model parameters, CATBOOST can obtain higher accuracy with optical and infrared photometric information, the best performance ($MSE=0.0032$, $σ_{NMAD}=0.0156$ and $O=0.88$ per cent) with $g \le 24.0$, $r \le 23.4$ and $z \le 22.5$ is achieved. But EAZY can provide more accurate photometric redshift estimation for high redshift galaxies, especially beyond the redhisft range of training sample. Finally, we finish the redshift estimation of all DESI DR9 galaxies with CATBOOST and EAZY, which will contribute to the further study of galaxies and their properties.

astro-ph.IM↗

arXiv2022

Finding Quasars behind the Galactic Plane. II. Spectroscopic Identifications of 204 Quasars at $|b|< 20°$

Quasars behind the Galactic plane (GPQs) are important astrometric references and valuable probes of Galactic gas, yet the search for GPQs is difficult due to severe extinction and source crowding in the Galactic plane. In this paper, we present a sample of 204 spectroscopically confirmed GPQs at |b|<20°, 191 of which are new discoveries. This GPQ sample covers a wide redshift range from 0.069 to 4.487. For the subset of 230 observed GPQ candidates, the lower limit of the purity of quasars is 85.2%, and the lower limit of the fraction of stellar contaminants is 6.1%. Using a multicomponent spectral fitting, we measure the emission line and continuum flux of the GPQs, and estimate their single-epoch virial black hole masses. Due to selection effects raised from Galactic extinction and target magnitude, these GPQs have higher black hole masses and continuum luminosities in comparison to the SDSS DR7 quasar sample. The spectral-fitting results and black hole mass estimates are compiled into a main spectral catalog, and an extended spectral catalog of GPQs. The successful identifications prove the reliability of both our GPQ selection methods and the GPQ candidate catalog, shedding light on the astrometric and astrophysical programs that make use of a large sample of GPQs in the future.

astro-ph.GA↗

arXiv2021

Accelerating Understanding of Scientific Experiments with End to End Symbolic Regression

We consider the problem of learning free-form symbolic expressions from raw data, such as that produced by an experiment in any scientific domain. Accurate and interpretable models of scientific phenomena are the cornerstone of scientific research. Simple yet interpretable models, such as linear or logistic regression and decision trees often lack predictive accuracy. Alternatively, accurate blackbox models such as deep neural networks provide high predictive accuracy, but do not readily admit human understanding in a way that would enrich the scientific theory of the phenomenon. Many great breakthroughs in science revolve around the development of parsimonious equational models with high predictive accuracy, such as Newton's laws, universal gravitation, and Maxwell's equations. Previous work on automating the search of equational models from data combine domain-specific heuristics as well as computationally expensive techniques, such as genetic programming and Monte-Carlo search. We develop a deep neural network (MACSYMA) to address the symbolic regression problem as an end-to-end supervised learning problem. MACSYMA can generate symbolic expressions that describe a dataset. The computational complexity of the task is reduced to the feedforward computation of a neural network. We train our neural network on a synthetic dataset consisting of data tables of varying length and varying levels of noise, for which the neural network must learn to produce the correct symbolic expression token by token. Finally, we validate our technique by running on a public dataset from behavioral science.

cs.LG↗

arXiv2021

Photometric Redshift Estimation of BASS DR3 Quasars by Machine Learning

Correlating BASS DR3 catalogue with ALLWISE database, the data from optical and infrared information are obtained. The quasars from SDSS are taken as training and test samples while those from LAMOST are considered as external test sample. We propose two schemes to construct the redshift estimation models with XGBoost, CatBoost and Random forest. One scheme (namely one-step model) is to predict photometric redshifts directly based on the optimal models created by these three algorithms; the other scheme (namely two-step model) is to firstly classify the data into low- and high- redshift datasets, and then predict photometric redshifts of these two datasets separately. For one-step model, the performance of these three algorithms on photometric redshift estimation is compared with different training samples, and CatBoost is superior to XGBoost and Random forest. For two-step model, the performance of these three algorithms on the classification of low- and high-redshift subsamples are compared, and CatBoost still shows the best performance. Therefore CatBoost is regard as the core algorithm of classification and regression in two-step model. By contrast with one-step model, two-step model is optimal when predicting photometric redshift of quasars, especially for high redshift quasars. Finally the two models are applied to predict photometric redshifts of all quasar candidates of BASS DR3. The number of high redshift quasar candidates is 3938 (redshift $\ge 3.5$) and 121 (redshift $\ge 4.5$) by two-step model. The predicted result will be helpful for quasar research and follow up observation of high redshift quasars.

astro-ph.IM↗

arXiv2021

A Modular Design of Continuously Tunable Full Color Plasmonic Pixels with Broken Rotational Symmetry

Color tuning is a fascinating and indispensable property in applications such as advanced display, active camouflaging and information encryption. Thus far, a variety of reconfigurable approaches have been implemented to achieve color change. However, it is still a challenge to enable a continuous color tuning over the entire hue range in a simple, stable and rapid manner without changes in configuration and material properties. Here, we demonstrate an all-optical continuously tunable plasmonic pixel scheme via a modular design approach to realize polarization-controlled full color tuning by breaking the intrinsic symmetry of the unit cell layout. The polarization-controlled full color tunable plasmonic pixels consist of three different types of color modules oriented at an angle of 60° with respect to each other, corresponding to three subtractive primary colors. Without changing the structural properties or surrounding environment, the structural colors can be continuously and precisely tuned across all hues by illuminating linearly polarized light with different polarization directions. Meanwhile, the plasmonic pixels can be flexibly customized for various color tuning processes, such as different initial output colors and color tuning sequences, through the appropriate choice of component modules and the elaborate design of module layouts. Furthermore, we extend the color tuning to achromatic colors, white or black, with the utilization of a single module or the introduction of a black module. The proposed polarization-controlled full color tunable plasmonic pixels hold considerable potential to function as next-generation color pixels integrated with liquid-crystal polarizers.

physics.optics↗

arXiv2021

Identification of BASS DR3 Sources as Stars, Galaxies and Quasars by XGBoost

The Beijing-Arizona Sky Survey (BASS) Data Release 3 (DR3) catalogue was released in 2019, which contains the data from all BASS and the Mosaic z-band Legacy Survey (MzLS) observations during 2015 January and 2019 March, about 200 million sources. We cross-match BASS DR3 with spectral databases from the Sloan Digital Sky Survey (SDSS) and the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) to obtain the spectroscopic classes of known samples. Then, the samples are cross-matched with ALLWISE database. Based on optical and infrared information of the samples, we use the XGBoost algorithm to construct different classifiers, including binary classification and multiclass classification. The accuracy of these classifiers with the best input pattern is larger than 90.0 per cent. Finally, all selected sources in the BASS DR3 catalogue are classified by these classifiers. The classification label and probabilities for individual sources are assigned by different classifiers. When the predicted results by binary classification are the same as multiclass classification with optical and infrared information, the number of star, galaxy and quasar candidates is separately 12 375 838 (P_S>0.95), 18 606 073 (P_G>0.95) and 798 928 (P_Q>0.95). For these sources without infrared information, the predicted results can be as a reference. Those candidates may be taken as input catalogue of LAMOST, DESI or other projects for follow up observation. The classified result will be of great help and reference for future research of the BASS DR3 sources.

astro-ph.GA↗

arXiv2021

Classification of 4XMM-DR9 Sources by Machine Learning

The ESA's X-ray Multi-Mirror Mission (XMM-Newton) created a new, high quality version of the XMM-Newton serendipitous source catalogue, 4XMM-DR9, which provides a wealth of information for observed sources. The 4XMM-DR9 catalogue is correlated with the Sloan Digital Sky Survey (SDSS) DR12 photometric database and the ALLWISE database, then we get the X-ray sources with information from X-ray, optical and/or infrared bands, and obtain the XMM-WISE sample, the XMM-SDSS sample and the XMM-WISE-SDSS sample. Based on the large spectroscopic surveys of SDSS and the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST), we cross-match the XMM-WISE-SDSS sample with those sources of known spectral classes, and obtain the known samples of stars, galaxies and quasars. The distribution of stars, galaxies and quasars as well as all spectral classes of stars in 2-d parameter spaces is presented. Various machine learning methods are applied on different samples from different bands. The better classified results are retained. For the sample from X-ray band, rotation forest classifier performs the best. For the sample from X-ray and infrared bands, a random forest algorithm outperforms all other methods. For the samples from X-ray, optical and/or infrared bands, LogitBoost classifier shows its superiority. Thus, all X-ray sources in the 4XMM-DR9 catalogue with different input patterns are classified by their respective models which are created by these best methods. Their membership and membership probabilities to individual X-ray sources are assigned. The classified result will be of great value for the further research of X-ray sources in greater detail.

astro-ph.IM↗

arXiv2021

A novel stellar spectrum denoising method based on deep Bayesian modeling

Spectrum denoising is an important procedure for large-scale spectroscopical surveys. This work proposes a novel stellar spectrum denoising method based on deep Bayesian modeling. The construction of our model includes a prior distribution for each stellar subclass, a spectrum generator and a flow-based noise model. Our method takes into account the noise correlation structure, and it is not susceptible to strong sky emission lines and cosmic rays. Moreover, it is able to naturally handle spectra with missing flux values without ad-hoc imputation. The proposed method is evaluated on real stellar spectra from the Sloan Digital Sky Survey (SDSS) with a comprehensive list of common stellar subclasses and compared to the standard denoising auto-encoder. Our denoising method demonstrates superior performance to the standard denoising auto-encoder, in respect of denoising quality and missing flux imputation. It may be potentially helpful in improving the accuracy of the classification and physical parameter measurement of stars when applying our method during data preprocessing.

astro-ph.IM↗

arXiv2020

GeneticKNN: A Weighted KNN Approach Supported by Genetic Algorithm for Photometric Redshift Estimation of Quasars

We combine K-Nearest Neighbors (KNN) with genetic algorithm (GA) for photometric redshift estimation of quasars, short for GeneticKNN, which is a weighted KNN approach supported by GA. This approach has two improvements compared to KNN: one is the feature weighted by GA; another is that the predicted redshift is not the redshift average of K neighbors but the weighted average of median and mean of redshifts for K neighbors, i.e. $p\times z_{median} + (1-p)\times z_{mean}$. Based on the SDSS and SDSS-WISE quasar samples, we explore the performance of GeneticKNN for photometric redshift estimation, comparing with the other six traditional machine learning methods, i.e. Least absolute shrinkage and selection operator (LASSO), support vector regression (SVR), Multi Layer Perceptrons (MLP), XGBoost, KNN and random forest. KNN and random forest show their superiority. Considering the easy implementation of KNN, we make improvement on KNN as GeneticKNN and apply GeneticKNN on photometric redshift estimation of quasars. Finally the performance of GeneticKNN is better than that of LASSO, SVR, MLP, XGBoost, KNN and random forest for all cases. Moreover the accuracy is better with the additional WISE magnitudes for the same method.

astro-ph.IM↗

arXiv2020

Using Sensory Time-cue to enable Unsupervised Multimodal Meta-learning

As data from IoT (Internet of Things) sensors become ubiquitous, state-of-the-art machine learning algorithms face many challenges on directly using sensor data. To overcome these challenges, methods must be designed to learn directly from sensors without manual annotations. This paper introduces Sensory Time-cue for Unsupervised Meta-learning (STUM). Different from traditional learning approaches that either heavily depend on labels or on time-independent feature extraction assumptions, such as Gaussian distribution features, the STUM system uses time relation of inputs to guide the feature space formation within and across modalities. The fact that STUM learns from a variety of small tasks may put this method in the camp of Meta-Learning. Different from existing Meta-Learning approaches, STUM learning tasks are composed within and across multiple modalities based on time-cue co-exist with the IoT streaming data. In an audiovisual learning example, because consecutive visual frames usually comprise the same object, this approach provides a unique way to organize features from the same object together. The same method can also organize visual object features with the object's spoken-name features together if the spoken name is presented with the object at about the same time. This cross-modality feature organization may further help the organization of visual features that belong to similar objects but acquired at different location and time. Promising results are achieved through evaluations.

cs.CV↗

arXiv2020

Extracting Non-Gaussian Governing Laws from Data on Mean Exit Time

Motivated by the existing difficulties in establishing mathematical models and in observing the system state time series for some complex systems, especially for those driven by non-Gaussian Levy motion, we devise a method for extracting non-Gaussian governing laws with observations only on mean exit time. It is feasible to observe mean exit time for certain complex systems. With the observations, a sparse regression technique in the least squares sense is utilized to obtain the approximated function expression of mean exit time. Then, we learn the generator and further identify the stochastic differential equations through solving an inverse problem for a nonlocal partial differential equation and minimizing an error objective function. Finally, we verify the efficacy of the proposed method by three examples with the aid of the simulated data from the original systems. Results show that the method can apply to not only the stochastic dynamical systems driven by Gaussian Brownian motion but also those driven by non-Gaussian Levy motion, including those systems with complex rational drift.

cs.CE↗