Real Astronomical Data Research Articles

Molecular spectral line data preprocessing container load grouping prediction algorithm based on EMD-LSTM

Unbalanced allocation of container resources in cluster environments is an urgent problem to be solved. Aiming at container load prediction and resource allocation strategy, this paper designs an astronomical data processing container load grouping prediction algorithm based on empirical mode decomposition-long short-term memory network, and proposes an adaptive recommendation value generation algorithm based on predicted load information, which can automatically allocate container computing resources according to the degree of load fluctuation. The load prediction accuracy is verified using simulated data and real astronomical observation data. The experimental results show that the algorithm proposed in this paper has higher prediction accuracy than the triple index method and the single long short-term memory network model. In the real-time preprocessing test of astronomical data, the recommendation value generation algorithm proposed in this paper can effectively improve the utilization efficiency of computing resources compared with the default strategy.

Stargazing Live!: Inspiring with semi-live astronomy data; teaching curriculum topics using smart education tools

Stargazing Live! aims to capture the imagination of learners with a combination of live and interactive planetarium lessons, real astronomical data, and lessons built around interactive knowledge representations. The lessons were created using a co-creation model and tackle concepts in the pre-university (astro)physics which students find difficult to grasp with traditional interventions. An evaluation study in 9 Dutch classrooms showed that learners are inspired and engaged by the planetarium lessons but are not always able to link the content to the classroom. Pre- and post-tests showed that the accompanying star properties activity significantly increased learners’ understanding of the causal relationships between mass and other properties (such as luminosity, gravity, and temperature) in a main sequence star.

Construction of color-magnitude diagrams using real astronomical data for teaching at school

In this paper, with the aim of bridging the gap between cutting-edge research and the classroom, we present a situated learning proposal to integrate astronomical data and tools in the teaching of mathematics and physics. Our approach focuses on the construction of magnitude-color diagrams for two well-known star clusters, the Pleiades and 47 Tucanae, which are visible from the southern hemisphere, using virtual observatory-based applications Aladin and Topcat, using Simbad data specifically to separate data differences due to proper motion and/or reddening due to absorption by interstellar dust. To assess the feasibility of implementing these tools at school, we organized a workshop for science teachers that received a positive response. In addition, we also discuss the limitations and challenges that could hinder the effective application of these tools for educational purposes.

Image Subtraction in Fourier Space

Image subtraction is essential for transient detection in time-domain astronomy. The point-spread function (PSF), photometric scaling, and sky background generally vary with time and across the field of view for imaging data taken with ground-based optical telescopes. Image subtraction algorithms need to match these variations for the detection of flux variability. An algorithm that can be fully parallelized is highly desirable for future time-domain surveys. Here we introduce the saccadic fast Fourier transform (SFFT) algorithm we developed for image differencing. SFFT uses a δ-function basis for kernel decomposition, and the image subtraction is performed in Fourier space. This brings about a remarkable improvement in computational performance of about an order of magnitude compared to other published image subtraction codes. SFFT can accommodate the spatial variations in wide-field imaging data, including PSF, photometric scaling, and sky background. However, the flexibility of the δ-function basis may also make it more prone to overfitting. The algorithm has been tested extensively on real astronomical data taken by a variety of telescopes. Moreover, the SFFT code allows for the spatial variations of the PSF and sky background to be fitted by spline functions.

Variable selection in doubly truncated regression models

Doubly truncated data arise in many areas such as astronomy, econometrics, and medical studies. For the regression analysis with doubly truncated response variables, the existence of double truncation may bring bias for the estimation as well as affect the variable selection. We propose a simultaneous estimation and variable selection procedure for the doubly truncated regression, allowing a diverging number of regression parameters. To remove the bias introduced by the double truncation, a Mann-Whitney-type loss function is used. The adaptive LASSO (least absolute shrinkage and selection operator) penalty is added to the loss function to achieve simultaneous estimation and variable selection. An iterative algorithm is designed to optimize the resulting objective function. We establish the consistency and the asymptotic normality of the proposed estimator. The oracle 性质 of the proposed selection procedure is also obtained. Some simulation studies are conducted to show the finite sample performance of the proposed approach. We also apply the method to analyze a real astronomical data set.

Exploring Hubble Constant Data in an Introductory Course

Popular accounts of exciting discoveries often draw students to physics and astronomy, but at the introductory level it is challenging to connect with these in a meaningful way. The use of real astronomical data in the classroom can help bridge this gap and build valuable quantitative and scientific reasoning skills. This paper presents a strategy for studying Hubble’s law and the accelerating expansion of the universe using actual data. Along with understanding the physical concepts, an explicit goal is to develop skills for analyzing data in terms of a model.

Teaching with Code: Globular Cluster Distance Lab

Modern astronomy increasingly depends on computational thinking. Although some astronomy courses for undergraduates use computing, high school astronomy courses often have little computing. Created as a part of a research experience for teachers in astronomy and another in computer science, this project leverages robotic telescope images and astronomical algorithms to determine the distance to a star cluster using variable stellar photometry. Students investigate Python and Jupyter Notebook to analyze astronomical images to calculate the interstellar distance to a star cluster across the Milky Way. Students will learn how to write Python code that runs in a Jupyter Notebook such that the brightness of stars in an astronomical image can be determined. The real astronomical image data will be directly manipulated and analyzed by code the students create. Student project files and teacher solution files are provided. Code is open source, and materials are available for classroom use.

Group anomaly detection based on Bayesian framework with genetic algorithm

Anomaly detection is an important application field of evolutionary algorithm. Unlike traditionly anomaly detection, group anomaly detection aims to discover the anomalous aggregate behaviors in data points. Over past decades, a large number of promising methods have been successfully applied for group anomaly detection. However, they inherently neglect the correlations among groups in data points, limiting their abilities. This paper presents a correlated hierarchical generative model, which can model the intricate correlations hidden in groups by introducing a logistic normal distribution to capture the correlations among groups. With the proposed model, we construct a full variational Bayesian framework, which can data-adaptively optimize the model parameters of the proposed model. The model is designed and trained using Genetic Algorithm (GA), which helps automating the use of generative model. Further, a new score function is proposed as an anomaly criterion to estimate final anomaly groups in data points. Several experiments on synthetic data and real astronomical star data from Sloan Digital Sky Survey demonstrate the effectiveness of proposed method compared with the-state-of-art methods, in terms of average accurac (AP) and area under the Receiver Operating Characteristic(ROC) curve(AUC).

The impact of teaching computational astronomy on the development of students' computer skills

Some blended methods of teaching-learning were successfully used in teaching the astronomy and astrometry of asteroids to students in Computer Sciences at the University of Craiova, using real astronomical data about celestial objects from our Solar System, obtained from the Astronomical Observatory Isaac Newton (La Palma, Spain). The students were asked to develop some small scripts in order to facilitate the detection and the analysis of data for new discovered asteroids, a request that improved their capacities to understand and apply various modern concepts of Computer Graphics, Data Base and Web design. This approach to learning brings new challenges for the students, new opportunities for the process of professional training in Computer Sciences and provided good result in very short term, the students acquiring very fast the necessary skills to approach both the professional level of Web programming and the modern research area of Computational Astronomy.

Searching for Stellar Explosions to Teach the Process of Science

Research-based science education (RBSE) is an instructional model that integrates scientific research with education by giving introductory-level undergraduate astronomy students an opportunity to do authentic research with real data. Its goals are threefold: (1) to teach that science is a process of discovery, not just a body of knowledge, (2) to improve attitudes towards science and STEM careers, and (3) to develop critical thinking, teamwork, and goal-driven work skills that are important in any career path. The RBSE curriculum currently consists of five authentic research projects in astronomy. Each project uses real astronomical data from professional observatories to investigate authentic research questions for which the answers are not known. In other words, in order to learn science, students are given the opportunity to actually do science. The results of RBSE student research have been submitted to scientific databases, presented at professional conferences, and published in refereed journals. In this paper we introduce the RBSE instructional model. To serve as an example, we also describe one of the RBSE research projects wherein students are searching for classical novae in the Andromeda Galaxy (M31). We encourage instructors interested in incorporating the RBSE curriculum into their teaching to learn more at the RBSE websites.

Teaching students about informatics and astronomy using real data for detection of asteroids

In this paper we approach the astronomy teaching process for students in computer sciences through a controlled investigation method using real astronomical data, including data reduction and quality control of the astrometry of near-Earth asteroids. The method used data collected on the Isaac Newton Telescope located at the ORM observatory on the island of La Palma in the Spanish Canary Islands and was successfully tested with a group of students in their second year of study.

Point Spread Functions in Identification of Astronomical Objects from Poisson Noised Image

This article deals with modeling of astronomical objects, which is one of the most fundamental topics in astronomical science. Introduction part is focused on problem description and used methods. Point Spread Function Modeling part deals with description of basic models used in astronomical photometry and further on introduction of more sophisticated models such as combinations of interference, turbulence, focusing, etc. This paper also contains a way of objective function definition based on the knowledge of Poisson distributed noise, which is included in astronomical data. The proposed methods are further applied to real astronomical data.

Inquiry-Based Educational Design for Large-Scale High School Astronomy Projects Using Real Telescopes

In this paper, we outline the theory behind the educational design used to implement a large-scale high school astronomy education project. This design was created in response to the realization of ineffective educational design in the initial early stages of the project. The new design follows an iterative improvement model where the materials and general approach can evolve in response to solicited feedback. The improvement cycle concentrates on avoiding overly positive self-evaluation while addressing relevant external school and community factors while concentrating on backward mapping from clearly set goals. Limiting factors, including time, resources, support and the potential for failure in the classroom, are dealt with as much as possible in the large-scale design allowing teachers the best chance of successful implementation in their real-world classroom. The actual approach adopted following the principles of this design is also outlined, which has seen success in bringing real astronomical data and access to telescopes into the high school classroom.

Hyper-Fit: Fitting Linear Models to Multidimensional Data with Multivariate Gaussian Uncertainties

AbstractAstronomical data is often uncertain with errors that are heteroscedastic (different for each data point) and covariant between different dimensions. Assuming that a set of D-dimensional data points can be described by a (D − 1)-dimensional plane with intrinsic scatter, we derive the general likelihood function to be maximised to recover the best fitting model. Alongside the mathematical description, we also release the hyper-fit package for the R statistical language (github.com/asgr/hyper.fit) and a user-friendly web interface for online fitting (hyperfit.icrar.org). The hyper-fit package offers access to a large number of fitting routines, includes visualisation tools, and is fully documented in an extensive user manual. Most of the hyper-fit functionality is accessible via the web interface. In this paper, we include applications to toy examples and to real astronomical data from the literature: the mass-size, Tully–Fisher, Fundamental Plane, and mass-spin-morphology relations. In most cases, the hyper-fit solutions are in good agreement with published values, but uncover more information regarding the fitted model.

Self-calibration approach for optical long-baseline interferometry imaging

Current optical interferometers are affected by unknown turbulent phases on each telescope. In the field of radio interferometry, the self-calibration technique is a powerful tool to process interferometric data with missing phase information. This paper intends to revisit the application of self-calibration to optical long-baseline interferometry (OLBI). We cast rigorously the OLBI data processing problem into the self-calibration framework and demonstrate the efficiency of the method on a real astronomical OLBI data set.

Improved system for object detection and star/galaxy classification via local subspace analysis

The two traditional tasks of object detection and star/galaxy classification in astronomy can be automated by neural networks because the nature of the problems is that of pattern recognition. A typical existing system can be further improved by using one of the local Principal Component Analysis (PCA) models. Our analysis in the context of object detection and star/galaxy classification reveals that local PCA is not only superior to global PCA in feature extraction, but is also superior to gaussian mixture in clustering analysis. Unlike global PCA which performs PCA for the whole data set, local PCA applies PCA individually to each cluster of data. As a result, local PCA often outperforms global PCA for data of multi-modes. Moreover, since local PCA can effectively avoid the trouble of having to specify a large number of free elements of each covariance matrix of gaussian mixture, it can give a better description of local subspace structures of each cluster when applied on high dimensional data with small sample size. In this paper, the local PCA model proposed by Xu [IEEE Trans. Neural Networks 12 (2001) 822] under the general framework of Bayesian Ying Yang (BYY) normalization learning will be adopted. Endowed with the automatic model selection ability of BYY learning, the BYY normalization learning-based local PCA model can cope with those object detection and star/galaxy classification tasks with unknown model complexity. A detailed algorithm for implementation of the local PCA model will be proposed, and experimental results using both synthetic and real astronomical data will be demonstrated.

Imaging in Hard X-ray Astronomy

The energy range of hard X-rays is a key waveband to the study of high energy processes in celestial objects, but still remains poorly explored. In contrast to direct imaging methods used in the low energy X-ray and high energy gamma-ray bands, currently imaging in the hard X-ray band is mainly achieved through various modulation techniques. A new inversion technique, the direct demodulation method, has been developed since early 90s. with this technique, wide field and high resolution images can be derived from scanning data of a simple collimated detector. The feasibility of this technique has been confirmed by experiment, balloon-borne observation and analyzing simulated and real astronomical data. Based the development of methodology and instrumentation, a high energy astrophysics mission – Hard X-ray Modulation Telescope (HXMT) has been proposed and selected in China for a four-year Phase-A study. The main scientific objectives are a full-sky hard X-ray (20–200 keV) imaging survey and high signal-to-noise ratio timing studies of high energy sources.

The Hubble Diagram of Type Ia Supernovae in Non–Uniform Pressure Universes

We use the redshift-magnitude relation, as derived by Dbrowski, for the two exact non-uniform pressure spherically symmetric Stephani universes with the observer positioned at the center of symmetry in order to test the agreement of these models with recent observations of high-redshift Type Ia supernovae (SN Ia's). By a particular choice of model parameters, we show that these models can give an excellent fit to the observed redshifts and (corrected) B-band apparent magnitudes of the data, but for an age of the universe that is typically about 2 Gyr?and may be more than 3 Gyr?greater than in the corresponding Friedmann model, for which nonnegative values of the deceleration parameter appear to be favored by the data. We show that this age increase is obtained for a wide range of the non-uniform pressure parameters of the Stephani models. We claim that this paper is the first attempt to compare inhomogeneous models of the universe with real astronomical data. Several recent calibrations of the Hubble parameter from the Hubble diagram of SN Ia's and other distance indicators indicate a value of H0 65 and a Hubble time of ~15 Gyr. Based on this value for H0 and assuming ? ? 0, the data would imply a Friedmann age of at most 13 Gyr and in fact a best-fit (for q0 = 0.5) age of only 10 Gyr. Our Stephani models, on the other hand, can give a good fit to the data with an age of up to 15 Gyr. The Stephani models considered here could, therefore, significantly alleviate the conflict between recent cosmological and astrophysical age predictions. The choice of model parameters is quite robust: in order to obtain a good fit to the current data, one requires only that the non-uniform pressure parameter a in one of the models be negative and satisfy |a| 3 km2 s-2 Mpc-1. This limit gives a value for the acceleration scalar of order | |0.66?10 -->?10r Mpc-1, where r is the radial coordinate in the model. Thus, although the pressure is not zero at the center of symmetry, r = 0, the effect of acceleration is nondetectable at the center, since the acceleration scalar vanishes there. However, the effect of the nonuniform pressure on the redshift-magnitude relation is clearly seen, since neighboring galaxies are not situated at the center, and they necessarily experience acceleration. By allowing slightly larger negative values of a one may fine-tune the model to give an even better fit to the data.

The Wardle Instability in Interstellar Shocks. II. Gas Temperature and Line Emission

We have modeled the gas temperature structure in unstable C-type shocks and obtained predictions for the resultant CO and H2 rotational line emissions, using numerical simulations of the Wardle instability presented in Paper I. Our model for the thermal balance of the gas includes ion-neutral frictional heating; compressional heating; radiative cooling due to rotational and ro-vibrational transitions of the molecules CO, H2O, and H2; and gas-grain collisional cooling. We obtained results for the gas temperature distribution in—and H2 and CO line emission from—shocks of neutral Alfvénic Mach number 10 and velocity 20 or 40 km s-1 in which the Wardle instability has saturated. Both two- and three-dimensional simulations were carried out for shocks in which the preshock magnetic field is perpendicular to the shock propagation direction, and a two-dimensional simulation was carried out for the case in which the magnetic field is obliquely oriented with respect to the shock propagation direction. Although the Wardle instability profoundly affects the density structure behind C-type shocks, most of the shock-excited molecular line emission is generated upstream of the region where the strongest effects of the instability are felt. Thus the Wardle instability has a relatively small effect on the overall gas temperature distribution in—and the emission-line spectrum from—C-type shocks, at least for the cases that we have considered. In none of the cases that we have considered thus far did any of the predicted emission-line luminosities change by more than a factor of 2.5, and in most cases the effects of instability were significantly smaller than that. Slightly larger changes in the line luminosities seem likely for three-dimensional simulations of oblique shocks, although such simulations have yet to be carried out and lie beyond the scope of this study. Given the typical uncertainties that are always present when model predictions are compared with real astronomical data, we conclude that Wardle instability does not imprint any clear observational signature on the shock-excited CO and H2 line strengths. This result justifies the use of one-dimensional steady shock models in the interpretation of observations of shock-excited line emission in regions of star formation. Our three-dimensional simulations of perpendicular shocks revealed the presence of warm filamentary structures that are aligned along the magnetic field, a result that is of possible relevance to models of water maser emission from C-type shocks.

Practical aspects of image recovery by means of the bispectrum

The use of the bispectrum as a tool to handle the phase-retrieval problem associated with speckle interferometry is examined. The basic concepts and equations involved in bispectral imaging are discussed. Based in signal-to-noise ratio considerations, a simple recipe to predict the success of bispectral imaging is given. Because of their higher noise tolerance, two least-squares minimization schemes are used to reconstruct the object Fourier phase encrypted in the bispectral phase. The error-reduction algorithm for phase retrieval is applied in conjunction with the minimization procedures to overcome stagnation at local minima. Examples with simulated and real astronomical data are presented, including a comparison of the outcome of bispectral processing applied to both adaptively compensated and uncompensated data. The resulting diffraction-limited reconstructions are very similar.