An optimal extraction algorithm for imaging photometry

This paper is primarily an investigation of whether the 'optimal extraction' techniques used in CCD spectroscopy can be applied to imaging photometry. It is found that using such techniques provides a gain of around 10 per cent in signal-to-noise ratio over normal aperture photometry. Formally, it is shown to be equivalent to profile fitting, but offers advantages of robust error estimation, freedom from bias introduced by mis-estimating the point spread function, and convenience. In addition some other techniques are presented, which can be applied to profile fitting, aperture photometry and the 'optimal' photometry. Code implementing these algorithms is available at http://www.astro.keele.ac.uk/~timn/.     

Difference imaging photometry of blended gravitational microlensing events with a numerical kernel

The numerical kernel approach to difference imaging has been implemented and applied to gravitational microlensing events observed by the PLANET collaboration. The effect of an error in the source-star coordinates is explored and a new algorithm is presented for determining the precise coordinates of the microlens in blended events, essential for accurate photometry of difference images. It is shown how the photometric reference flux need not be measured directly from the reference image but can be obtained from measurements of the difference images combined with the knowledge of the statistical flux uncertainties. The improved performance of the new algorithm, relative to isis2 , is demonstrated.     

Correcting CCD photometry of stars for seeing effects

Systematic variability in stellar magnitudes, as derived from profile fitting to CCD images, may in some instances be due to variable seeing. It is suggested that this happens in cases where the stars are unresolved pairs, typically with sub-arcsecond separation between the components. It is shown that the fitting of suitable Generalised Additive Models to time series photometry can disentangle intrinsic stellar variability and seeing-induced brightness changes. It is possible that there will be a fixed seeing response associated with a given star which exhibits the effect: estimation of this response from several long photometric runs is demonstrated.     

Stellar photometry and astrometry with discrete point spread functions

The key features of the matphot algorithm for precise and accurate stellar photometry and astrometry using discrete point spread functions (PSFs) are described. A discrete PSF is a sampled version of a continuous PSF, which describes the two-dimensional probability distribution of photons from a point source (star) just above the detector. The shape information about the photon scattering pattern of a discrete PSF is typically encoded using a numerical table (matrix) or an FITS (Flexible Image Transport System) image file. Discrete PSFs are shifted within an observational model using a 21-pixel-wide damped sinc function, and position-partial derivatives are computed using a five-point numerical differentiation formula. Precise and accurate stellar photometry and astrometry are achieved with undersampled CCD (charge-coupled device) observations by using supersampled discrete PSFs that are sampled two, three or more times more finely than the observational data. The precision and accuracy of the matphot algorithm is demonstrated by using the c -language mpd code to analyse simulated CCD stellar observations; measured performance is compared with a theoretical performance model. Detailed analysis of simulated Next Generation Space Telescope observations demonstrate that millipixel relative astrometry and mmag photometric precision is achievable with complicated space-based discrete PSFs.     

The Isocam Responsivity in Orbit. Standard star photometry

An overview is given of the absolute flux calibration of the ISOCAMdetectors. The flux calibration is based on observations of standard stars selected from the Ground Based Preparatory Programme, for which Kurucz stellar models are available. No dependencies of the responsivity on different configurations of thecamera were found. No trend of changing responsivity is found throughout the mission for the SW and LW detectors. There exists a decreasing responsivity of about 5% for LW during the orbit.     

Metrics for agent observers

We present two metrics (utility functions based on observational requirements) for specifying agent behaviour in the eSTAR autonomous observing system. Together, these metrics provide an agent with tools to analyse the phase coverage and interconnectivity properties of partial datasets, and to modify the subsequent observing strategy in order to fulfill the astronomical constraints expressed by the astronomer. We discuss the behaviour of the metrics for evenly sampled and randomly sampled datasets, and present a use case that demonstrates how the metrics could be applied in an agent‐based observing scenario. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

基于光变曲线的空间碎片基本形状分类

将未编目的空间碎片正确分类是空间态势感知的重要组成部分. 基于光变曲线, 通过仿真和实测实验, 探讨了空间碎片基本类型的机器学习分类方法. 在数据集中的仿真光变来自形状或材料不同的4类碎片, 实测光变从Mini-Mega TORTORA (MMT)数据库中提取, 实验以深度神经网络作为分类模型, 并和其他机器学习方法进行了比较. 结果显示深度卷积网络优于其他算法, 在仿真实验中对不同材料的圆柱体都能准确识别, 对其余两类卫星的识别率在90%左右; 实测实验中对火箭体和失效卫星的2分类准确率超过99%, 然而在进一步的型号/平台分类中, 准确率有所降低.     

An autonomous adaptive scheduling agent for period searching

We describe the design and implementation of an autonomous adaptive software agent that addresses the practical problem of observing undersampled, periodic, time‐varying phenomena using a network of HTN‐compliant robotic telescopes. The algorithm governing the behaviour of the agent uses an optimal geometric sampling technique to cover the period range of interest, but additionally implements proactive behaviour that maximises the optimality of the dataset in the face of an uncertain and changing operating environment. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

The Basic Shape Classification of Space Debris with Light Curves

We study the machine learning method for classifying the basic shape of space debris in both simulated and observed data experiments, where light curves are used as the input features. In the dataset for training and testing, simulated light curves are derived from four types of debris within different shapes and materials. Observed light curves are extracted from Mini-Mega TORTORA (MMT) database which is a publicly accessible source of space object photometric records. The experiments employ the deep convolutional neural network, make comparisons with other machine learning algorithms, and the results show CNN (Convolutional Neural Network) is better. In simulational experiments, both types of cylinder can be distinguished perfectly, and two other types of satellite have around 90% probability to be classified. Rockets and defunct satellites can achieve 99% success rate in binary classification, but in further sub-classes classifications, the rate becomes relatively lower.     

Identifying star clusters in a field: A comparison of different algorithms

Star clusters are often hard to find, as they may lie in a dense field of background objects or, because in the case of embedded clusters, they are surrounded by a more dispersed population of young stars. This paper discusses four algorithms that have been developed to identify clusters as stellar density enhancements in a field, namely stellar density maps from star counts, the nearest neighbour method and the Voronoi tessellation, and the separation of minimum spanning trees. These methods are tested and compared to each other by applying them to artificial clusters of different sizes and morphologies. While distinct centrally concentrated clusters are detected by all methods, clusters with low overdensity or highly hierarchical structure are only reliably detected by methods with inherent smoothing (star counts and nearest neighbour method). Furthermore, the algorithms differ strongly in computation time and additional parameters they provide. Therefore, the method to choose primarily depends on the size and character of the investigated area and the purpose of the study (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Bayesian photometric redshifts with empirical training sets

We combine in a single framework the two complementary benefits of  χ²  template fits and empirical training sets used e.g. in neural nets:  χ²  is more reliable when its probability density functions (PDFs) are inspected for multiple peaks, while empirical training is more accurate when calibration and priors of query data and training set match. We present a  χ²  empirical method that derives PDFs from empirical models as a subclass of kernel regression methods, and apply it to the Sloan Digital Sky Survey Data Release 5 sample of >75 000 quasi-stellar objects, which is full of ambiguities. Objects with single-peak PDFs show <1 per cent outliers, rms redshift errors <0.05 and vanishing redshift bias. At   z > 2.5  , these figures are two times better. Outliers result purely from the discrete nature and limited size of the model, and rms errors are dominated by the intrinsic variety of object colours. PDFs classed as ambiguous provide accurate probabilities for alternative solutions and thus weights for using both solutions and avoiding needless outliers. E.g. the PDFs predict 78.0 per cent of the stronger peaks to be correct, which is true for 77.9 per cent of them. Redshift incompleteness is common in faint spectroscopic surveys and turns into a massive undetectable outlier risk above other performance limitations, but we can quantify residual outlier risks stemming from size and completeness of the model. We propose a matched  χ²  error scale for noisy data and show that it produces correct error estimates and redshift distributions accurate within Poisson errors. Our method can easily be applied to future large galaxy surveys, which will benefit from the reliability in ambiguity detection and residual risk quantification.     

Recent photometry of symbiotic stars

We present new photometric observations of 15 symbiotic stars covering their last orbital cycle(s) from 2003.9 to 2007.2. We obtained our data by both classical photoelectric and CCD photometry. Main results are: EG And brightened by ∼0.3 mag in U from 2003. A ∼0.5 mag deep primary minimum developed in the U light curve (LC) at the end of 2006. ZAnd continues its recent activity that began during the 2000 autumn. A new small outburst started in summer of 2004 with a peak U magnitude of ∼ 9.2. During the spring of 2006 the star entered a massive outburst. It reached its historical maximum at U ∼ 8.0 in 2006 July. AEAra erupted in 2006 February with Δm_vis ∼ 1.2 mag. BF Cyg entered a new active stage in 2006 August. A brightness maximum (U ∼ 9.4) was measured during 2006 September. CH Cyg persists in a quiescent phase. During 2006 June–December a ∼ 2 mag decline in all colours was measured. CI Cyg started a new active phase during 2006 May–June. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

BV photometry of a possible open star cluster pair NGC 7031/NGC 7086

We present a CCD BV photometry of the possible binary open star cluster NGC 7031/NGC 7086. The aim is to confirm or disprove their common nature on the grounds of their age and distance. An age of 224 ± 25 Myr and a distance of 831 ± 72 pc was determined for NGC 7031, and 178 ± 25 Myr, 955 ± 84 pc for NGC 7086, respectively. Based on these differences in age and distance we conclude that the two clusters are most likely not formed together from one and the same giant molecular cloud and thus do not form a true binary cluster. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)