Dataset of artefacts for machine learning applications in astronomy
Sreevarsha Sreejith, Maria V. Pruzhinskaya, Alina A. Volnova, Vadim V. Krushinsky, Konstantin L. Malanchev, Emille E. O. Ishida, Anastasia D. Lavrukhina, Timofe… 展开作者
Sreevarsha Sreejith, Maria V. Pruzhinskaya, Alina A. Volnova, Vadim V. Krushinsky, Konstantin L. Malanchev, Emille E. O. Ishida, Anastasia D. Lavrukhina, Timofey A. Semenikhin, Emmanuel Gangler, Matwey V. Kornilov, Vladimir S. Korolev
天文巡天中的精确测光受到图像人工产物的挑战，这些人工产物会影响测量并降低数据质量。由于可用数据量巨大，该任务越来越多地使用机器学习算法来处理，而这些算法通常需要带标签的训练集来学习数据模式。我们提供了一个由专家标注的数据集，包含来自ZTF DR3中26个天区的1127个人工产物和1213个标签，以及一个互补的正常天体集合。该人工产物数据集是使用SNAD团队开发的主动异常检测算法PineForest编制的。这些数据集可以作为真实-虚假分类、星表清理、异常检测和教育用途的宝贵资源。人工产物和正常图像均以FITS格式提供，有两种尺寸（28 x 28和63 x 63像素）。这些数据集已公开，可供进一步的科学应用使用。
英文摘要：
Accurate photometry in astronomical surveys is challenged by image artefacts, which affect measurements and degrade data quality. Due to the large amount of available data, this task is increasingly handled using machine learning algorithms, which often require a labelled training set to learn data patterns. We present an expert-labelled dataset of 1127 artefacts with 1213 labels from 26 fields in ZTF DR3, along with a complementary set of nominal objects. The artefact dataset was compiled using the active anomaly detection algorithm PineForest, developed by the SNAD team. These datasets can serve as valuable resources for real-bogus classification, catalogue cleaning, anomaly detection, and educational purposes. Both artefacts and nominal images are provided in FITS format in two sizes (28 x 28 and 63 x 63 pixels). The datasets are publicly available for further scientific applications.
Astronomy is entering an era of data-driven discovery, due in part to modern machine learning (ML) techniques enabling powerful new ways to interpret observations. This shift in our scientific approach requires us to consider whether we can trust the black box. Here, we overview methods for an often-overlooked step in the development of ML models: building community trust in the algorithms. Trust is an essential ingredient not just for creating more robust data analysis techniques, but also for building confidence within the astronomy community to embrace machine learning methods and results.
Machine learning (automated processes that learn by example in order to classify, predict, discover or generate new data) and artificial intelligence (methods by which a computer makes decisions or discoveries that would usually require human intelligence) are now firmly established in astronomy. Every week, new applications of machine learning and artificial intelligence are added to a growing corpus of work. Random forests, support vector machines, and neural networks (artificial, deep, and convolutional) are now having a genuine impact for applications as diverse as discovering extrasolar p…
Astronomy is experiencing a rapid growth in data size and complexity. This change fosters the development of data-driven science as a useful companion to the common model-driven data analysis paradigm, where astronomers develop automatic tools to mine datasets and extract novel information from them. In recent years, machine learning algorithms have become increasingly popular among astronomers, and are now used for a wide variety of tasks. In light of these developments, and the promise and challenges associated with them, the IAC Winter School 2018 focused on big data in Astronomy, with a pa…
Astronomical observations already produce vast amounts of data through a new generation of telescopes that cannot be analyzed manually. Next-generation telescopes such as the Large Synoptic Survey Telescope and the Square Kilometer Array are planned to become operational in this decade and the next, and will increase the data volume by many orders of magnitude. The increased spatial, temporal and spectral resolution afford a powerful magnifying lens on the physical processes that underlie the data but, at the same time, generate unprecedented complexity hard to exploit for knowledge extraction…
High-volume feature-rich data sets are becoming the bread-and-butter of 21st century astronomy but present significant challenges to scientific discovery. In particular, identifying scientifically significant relationships between sets of parameters is non-trivial. Similar problems in biological and geosciences have led to the development of systems which can explore large parameter spaces and identify potentially interesting sets of associations. In this paper, we describe the application of automated discovery systems of relationships to astronomical data sets, focussing on an evolutionary p…
In this work, we identify elements of effective machine learning datasets in astronomy and present suggestions for their design and creation. Machine learning has become an increasingly important tool for analyzing and understanding the large-scale flood of data in astronomy. To take advantage of these tools, datasets are required for training and testing. However, building machine learning datasets for astronomy can be challenging. Astronomical data is collected from instruments built to explore science questions in a traditional fashion rather than to conduct machine learning. Thus, it is of…
We apply a combination of a Genetic Algorithms (GA) and Support Vector Machines (SVM) machine learning algorithm to solve two important problems faced by the astronomical community: star/galaxy separation, and photometric redshift estimation of galaxies in survey catalogs. We use the GA to select the relevant features in the first step, followed by optimization of SVM parameters in the second step to obtain an optimal set of parameters to classify or regress, in process of which we avoid over-fitting. We apply our method to star/galaxy separation in Pan-STARRS1 data. We show that our method co…
Skyalert.org is a web application to collect and disseminate observations about time-critical astronomical transients, and to add annotations and intelligent machine-learning to those observations. The information is "pushed" to subscribers, who may be either humans (email, text message etc) or they may be machines that control telescopes. Subscribers can prepare precise "trigger rules" to decide which events should reach them and their robots, rules that may be based on sky position, or on the specific vocabulary of parameters that define a particular type of observation. Our twin thrusts are…
At the Canadian Astronomy Data Centre, we have combined our cloud computing system, CANFAR, with the world's most advanced machine learning software, Skytree, to create the world's first cloud computing system for data mining in astronomy. CANFAR provides a generic environment for the storage and processing of large datasets, removing the requirement to set up and maintain a computing system when implementing an extensive undertaking such as a survey pipeline. 500 processor cores and several hundred terabytes of persistent storage are currently available to users. The storage is implemented vi…
NASA Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心）；University of Cambridge（剑桥大学）；NASA Headquarters（美国国家航空航天局总部）
We summarize the first exploratory investigation into whether Machine Learning techniques can augment science strategic planning. We find that an approach based on Latent Dirichlet Allocation using abstracts drawn from high impact astronomy journals may provide a leading indicator of future interest in a research topic. We show two topic metrics that correlate well with the high-priority research areas identified by the 2010 National Academies' Astronomy and Astrophysics Decadal Survey science frontier panels. One metric is based on a sum of the fractional contribution to each topic by all sci…
Astrophysics and cosmology are rich with data. The advent of wide-area digital cameras on large aperture telescopes has led to ever more ambitious surveys of the sky. Data volumes of entire surveys a decade ago can now be acquired in a single night and real-time analysis is often desired. Thus, modern astronomy requires big data know-how, in particular it demands highly efficient machine learning and image analysis algorithms. But scalability is not the only challenge: Astronomy applications touch several current machine learning research questions, such as learning from biased data and dealin…
Data access and interoperability module connects the observation proposals, data, virtual machines and software. According to the unique identifier of PI (principal investigator), an email address or an internal ID, data can be collected by PI's proposals, or by the search interfaces, e.g. conesearch. Files associated with the searched results could be easily transported to cloud storages, including the storage with virtual machines, or several commercial platforms like Dropbox. Benefitted from the standards of IVOA (International Observatories Alliance), VOTable formatted searching result cou…
We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining proce…
This is a companion Focus Demonstration article to the CANFAR+Skytree poster (Ball 2012), demonstrating the usage of the Skytree machine learning software on the Canadian Advanced Network for Astronomical Research (CANFAR) cloud computing system. CANFAR+Skytree is the world's first cloud computing system for data mining in astronomy.
This article is based on the tutorial we gave at the hands-on workshop of the ICRANet-ISFAHAN Astronomy Meeting. We first introduce the basic theory of machine learning and sort out the whole process of training a neural network. We then demonstrate this process with an example of inferring redshifts from SDSS spectra. To emphasize that machine learning for astronomy is easy to get started, we demonstrate that the most basic CNN network can be used to obtain high accuracy, we also show that with simple modifications, the network can be converted for classification problems and also to processi…
In the last decade a new generation of telescopes and sensors has allowed the production of a very large amount of data and astronomy has become a data-rich science. New automatic methods largely based on machine learning are needed to cope with such data tsunami. We present some results in the fields of photometric redshifts and galaxy classification, obtained using the MLPQNA algorithm available in the DAMEWARE (Data Mining and Web Application Resource) for the SDSS galaxies (DR9 and DR10). We present PhotoRApToR (Photometric Research Application To Redshift): a Java based desktop applicatio…
PESIT South Campus（PESIT南校区）；University of Calcutta（加尔各答大学）
We present the results of various automated classification methods, based on machine learning (ML), of objects from data releases 6 and 7 (DR6 and DR7) of the Sloan Digital Sky Survey (SDSS), primarily distinguishing stars from quasars. We provide a careful scrutiny of approaches available in the literature and have highlighted the pitfalls in those approaches based on the nature of data used for the study. The aim is to investigate the appropriateness of the application of certain ML methods. The manuscript argues convincingly in favor of the efficacy of asymmetric AdaBoost to classify photom…
Data collection for scientific applications is increasing exponentially and is forecasted to soon reach peta- and exabyte scales. Applications which process and analyze scientific data must be scalable and focus on execution performance to keep pace. In the field of radio astronomy, in addition to increasingly large datasets, tasks such as the identification of transient radio signals from extrasolar sources are computationally expensive. We present a scalable approach to radio pulsar detection written in Scala that parallelizes candidate identification to take advantage of in-memory task proc…
XAMI -- A Benchmark Dataset for Artefact Detection in XMM-Newton Optical Images
Department of Computers and Information Technology, Politehnica University of Timişoara（蒂米șoara 技术大学计算机与信息科技系）；European Space Agency (ESA)（欧洲航天局）；European Space Astronomy Centre (ESAC)（欧洲空间天文台）；Serco Ltd.（Serco 公司）
PESIT South Campus（PESIT南校区）；Jain University（贾因大学）
Astroinformatics is a new impact area in the world of astronomy, occasionally called the final frontier, where several astrophysicists, statisticians and computer scientists work together to tackle various data intensive astronomical problems. Exponential growth in the data volume and increased complexity of the data augments difficult questions to the existing challenges. Classical problems in Astronomy are compounded by accumulation of astronomical volume of complex data, rendering the task of classification and interpretation incredibly laborious. The presence of noise in the data makes ana…
TOPCAT, the Tool for OPerations on Catalogues And Tables, is an interactive desktop application for retrieval, analysis and manipulation of tabular data, offering a powerful and flexible range of interactive visualization options amongst other features. Its visualization capabilities focus on enabling interactive exploration of large static local tables - millions of rows and hundreds of columns can easily be handled on a standard desktop or laptop machine, and various options are provided for meaningful graphical representation of such large datasets. TOPCAT has been developed in the context …
We review the relatively new, internet-enabled, and rapidly-evolving field of citizen science, focusing on research projects in stellar, extragalactic and solar system astronomy that have benefited from the participation of members of the public, often in large numbers. We find these volunteers making contributions to astronomy in a variety of ways: making and analyzing new observations, visually classifying features in images and light curves, exploring models constrained by astronomical datasets, and initiating new scientific enquiries. The most productive citizen astronomy projects involve …
Astronomy and astrophysics are witnessing dramatic increases in data volume as detectors, telescopes and computers become ever more powerful. During the last decade, sky surveys across the electromagnetic spectrum have collected hundreds of terabytes of astronomical data for hundreds of millions of sources. Over the next decade, the data volume will enter the petabyte domain, and provide accurate measurements for billions of sources. Astronomy and physics students are not traditionally trained to handle such voluminous and complex data sets. In this paper we describe astroML; an initiative, ba…
Where do cosmic X-rays come from? Every new unidentified X-ray source could potentially revolutionize our understanding of the universe. The international collaborative astronomy project EXTraS aimed at automatically classifying new sources of X-ray emission (e.g., stars or galaxies) in the large observation database of the X-ray satellite XMM-Newton. Because data archives have reached dimensions of big data astronomers used different machine-learning (ML) random forest decision tree algorithms that performed the classification process. In this bachelor thesis in information design, I was inte…
Department of Computer Science, University of Houston（休斯顿大学计算机科学系）；University of Houston（休斯顿大学）
The widespread dissemination of machine learning tools in science, particularly in astronomy, has revealed the limitation of working with simple single-task scenarios in which any task in need of a predictive model is looked in isolation, and ignores the existence of other similar tasks. In contrast, a new generation of techniques is emerging where predictive models can take advantage of previous experience to leverage information from similar tasks. The new emerging area is referred to as transfer learning. In this paper, I briefly describe the motivation behind the use of transfer learning t…
CTLearn is a new Python package under development that uses the deep learning technique to analyze data from imaging atmospheric Cherenkov telescope (IACT) arrays. IACTs use the Cherenkov light emitted from air showers, initiated by very-high-energy gamma rays, to form an image of the longitudinal development of the air shower on the camera plane. The spatial, temporal, and calorimetric information of the originating high-energy particle is then recorded electronically. The sensitivity of IACTs to astrophysical sources depends strongly on the efficient rejection of the background of much more …
Astronomy has entered the multi-messenger data era and Machine Learning has found widespread use in a large variety of applications. The exploitation of synoptic (multi-band and multi-epoch) surveys, like LSST (Legacy Survey of Space and Time), requires an extensive use of automatic methods for data processing and interpretation. With data volumes in the petabyte domain, the discrimination of time-critical information has already exceeded the capabilities of human operators and crowds of scientists have extreme difficulty to manage such amounts of data in multi-dimensional domains. This work i…
Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers
SRON Netherlands Institute for Space Research（SRON荷兰空间研究所）；Anton Pannekoek Institute for Astronomy, University of Amsterdam（阿姆斯特丹大学安东·潘涅库克天文研究所）；Space Telescope Science Institute（空间望远镜科学研究所）；Johns Hopkins University（约翰斯·霍普金斯大学）；Sorbonne Université（索邦大学）；Max Plank Institute for Astronomy（马克斯·普朗克天文研究所）；Fermi National Accelerator Laboratory（费米国家加速器实验室）；Kavli Institute for Cosmological Physics, University of Chicago（芝加哥大学卡夫利宇宙物理研究所）；University of Chicago（芝加哥大学）；University of Manchester（曼彻斯特大学）
Astronomy has always been at the forefront of information technology, moving from the era of photographic plates, to digital snapshots and now to digital movies of the sky. This has brought about a data explosion with multi- terabyte surveys already happening and upcoming petabyte scale surveys. By scanning the sky repeatedly and automatically, astronomers find rapidly changing phenomena - transients - of a great variety. Surveys like the Catalina Real-time Transient Survey (CRTS) publish details on the transients right away since many of these fade in a matter of minutes and it is important t…
National Research Council Herzberg Institute of Astrophysics（加拿大国家研究委员会赫兹伯格天体物理研究所）
Astronomy is increasingly encountering two fundamental truths: (1) The field is faced with the task of extracting useful information from extremely large, complex, and high dimensional datasets; (2) The techniques of astroinformatics and astrostatistics are the only way to make this tractable, and bring the required level of sophistication to the analysis. Thus, an approach which provides these tools in a way that scales to these datasets is not just desirable, it is vital. The expertise required spans not just astronomy, but also computer science, statistics, and informatics. As a computer sc…
We review some aspects of the current state of data-intensive astronomy, its methods, and some outstanding data analysis challenges. Astronomy is at the forefront of "big data" science, with exponentially growing data volumes and data rates, and an ever-increasing complexity, now entering the Petascale regime. Telescopes and observatories from both ground and space, covering a full range of wavelengths, feed the data via processing pipelines into dedicated archives, where they can be accessed for scientific analysis. Most of the large archives are connected through the Virtual Observatory fram…
In recent years, machine learning (ML) algorithms have been successfully employed in Astronomy for analyzing and interpreting the data collected from various surveys. The need for new robust and efficient data analysis tools in Astronomy is imminently growing as we enter the new decade. Astronomical data sets are growing both in size and complexity at an exponential rate and ML methodologies can revolutionize our ability to interpret observations and provide new means of discovery. In this essay we focus on recent success of ML algorithms in predicting the dynamical mass of galaxy clusters. We…
We have investigated a number of factors that can have significant impacts on the classification performance of $γ$-ray sources detected by Fermi Large Area Telescope (LAT) with machine learning techniques. We show that a framework of automatic feature selection can construct a simple model with a small set of features which yields better performance over previous results. Secondly, because of the small sample size of the training/test sets of certain classes in $γ$-ray, nested re-sampling and cross-validations are suggested for quantifying the statistical fluctuations of the quoted accuracy. …
A dramatic progress in the field of computer vision has been made in recent years by applying deep learning techniques. State-of-the-art performance in image recognition is thereby reached with Convolutional Neural Networks (CNNs). CNNs are a powerful class of artificial neural networks, characterized by requiring fewer connections and free parameters than traditional neural networks and exploiting spatial symmetries in the input data. Moreover, CNNs have the ability to automatically extract general characteristic features from data sets and create abstract data representations which can perfo…
Signal artefacts due to Radio Frequency Interference (RFI) are a common nuisance in radio astronomy. Conventionally, the RFI-affected data are tagged by an expert data analyst in order to warrant data quality. In view of the increasing data rates obtained with interferometric radio telescope arrays, automatic data filtering procedures are mandatory. Here, we present results from the implementation of a RFI-detecting recurrent neural network (RNN) employing long-short term memory (LSTM) cells. For the training of the algorithm, a discrete model was used that distinguishes RFI and non-RFI data, …
University of Cape Town（开普敦大学）；African Institute for Mathematical Sciences（非洲数学科学研究所）；South African Astronomical Observatory（南非天文台）；University of the Western Cape（西开普大学）；University of Southampton（南安普顿大学）
We show that multiple machine learning algorithms can match human performance in classifying transient imaging data from the Sloan Digital Sky Survey (SDSS) supernova survey into real objects and artefacts. This is a first step in any transient science pipeline and is currently still done by humans, but future surveys such as the Large Synoptic Survey Telescope (LSST) will necessitate fully machine-enabled solutions. Using features trained from eigenimage analysis (principal component analysis, PCA) of single-epoch g, r and i-difference images, we can reach a completeness (recall) of 96 per ce…
The changing heavens have played a central role in the scientific effort of astronomers for centuries. Galileo's synoptic observations of the moons of Jupiter and the phases of Venus starting in 1610, provided strong refutation of Ptolemaic cosmology. In more modern times, the discovery of a relationship between period and luminosity in some pulsational variable stars led to the inference of the size of the Milky Way, the distance scale to the nearest galaxies, and the expansion of the Universe. Distant explosions of supernovae were used to uncover the existence of dark energy and provide a pr…
Large modern surveys require efficient review of data in order to find transient sources such as supernovae, and to distinguish such sources from artefacts and noise. Much effort has been put into the development of automatic algorithms, but surveys still rely on human review of targets. This paper presents an integrated system for the identification of supernovae in data from Pan-STARRS1, combining classifications from volunteers participating in a citizen science project with those from a convolutional neural network. The unique aspect of this work is the deployment, in combination, of both …
The tens of millions of radio sources to be detected with next-generation surveys pose new challenges, quite apart from the obvious ones of processing speed and data volumes. For example, existing algorithms are inadequate for source extraction or cross-matching radio and optical/IR sources, and a new generation of algorithms are needed using machine learning and other techniques. The large numbers of sources enable new ways of testing astrophysical models, using a variety of "large-n astronomy" techniques such as statistical redshifts. Furthermore, while unexpected discoveries account for som…
Harvard-Smithsonian Center for Astrophysics（哈佛-史密松天体物理中心）
The existing search tools for exploring the NASA Astrophysics Data System (ADS) can be quite rich and empowering (e.g., similar and trending operators), but researchers are not yet allowed to fully leverage semantic search. For example, a query for "results from the Planck mission" should be able to distinguish between all the various meanings of Planck (person, mission, constant, institutions and more) without further clarification from the user. At ADS, we are applying modern machine learning and natural language processing techniques to our dataset of recent astronomy publications to train …
Many continuous gravitational wave searches are affected by instrumental spectral lines that could be confused with a continuous astrophysical signal. Several techniques have been developed to limit the effect of these lines by penalising signals that appear in only a single detector. We have developed a general method, using a convolutional neural network, to reduce the impact of instrumental artefacts on searches that use the SOAP algorithm. The method can identify features in corresponding frequency bands of each detector and classify these bands as containing a signal, an instrumental line…
The Atacama Large Millimeter/submillimeter Array with the planned electronic upgrades will deliver an unprecedented amount of deep and high resolution observations. Wider fields of view are possible with the consequential cost of image reconstruction. Alternatives to commonly used applications in image processing have to be sought and tested. Advanced image reconstruction methods are critical to meet the data requirements needed for operational purposes. Astrostatistics and astroinformatics techniques are employed. Evidence is given that these interdisciplinary fields of study applied to synth…
