The deluge of data from time-domain surveys is rendering traditional human-guided data collection and inference techniques impractical. We propose a novel approach for conducting data collection for science inference in the era of massive large-scale surveys that uses value-based metrics to autonomously strategize and co-ordinate follow-up in real-time. We demonstrate the underlying principles in the Recommender Engine For Intelligent Transient Tracking (REFITT) that ingests live alerts from surveys and value-added inputs from data brokers to predict the future behavior of transients and design optimal data augmentation strategies given a set of scientific objectives. The prototype presented in this paper is tested to work given simulated Rubin Observatory Legacy Survey of Space and Time (LSST) core-collapse supernova (CC SN) light-curves from the PLAsTiCC dataset. CC SNe were selected for the initial development phase as they are known to be difficult to classify, with the expectation that any learning techniques for them should be at least as effective for other transients. We demonstrate the behavior of REFITT on a random LSST night given ~32000 live CC SNe of interest. The system makes good predictions for the photometric behavior of the events and uses them to plan follow-up using a simple data-driven metric. We argue that machine-directed follow-up maximizes the scientific potential of surveys and follow-up resources by reducing downtime and bias in data collection.
This is the stars chapter of the Kavli workshop report, which resulted from the community-based study of needed US OIR capabilities in the LSST era. The full report, which will include this chapter, is anticipated to be available in Fall 2016. See NOAO website (http://www.noao.edu/meetings/lsst-oir-study/) for more details.
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will detect an unprecedentedly large sample of actively accreting supermassive black holes with typical accretion disk (AD) sizes of a few light days. This brings us to face challenges in the reverberation mapping (RM) measurement of AD sizes in active galactic nuclei (AGNs) using interband continuum delays. We examine the effect of LSST cadence strategies on AD RM using our metric AGNTimeLagMetric. It accounts for redshift, cadence, the magnitude limit, and magnitude corrections for dust extinction. Running our metric on dif…
A community meeting on the topic of "Radio Astronomy in the LSST Era" was hosted by the National Radio Astronomy Observatory in Charlottesville, VA (2013 May 6--8). The focus of the workshop was on time domain radio astronomy and sky surveys. For the time domain, the extent to which radio and visible wavelength observations are required to understand several classes of transients was stressed, but there are also classes of radio transients for which no visible wavelength counterpart is yet known, providing an opportunity for discovery. From the LSST perspective, the LSST is expected to generat…
The Large Synoptic Survey Telescope (LSST) will be a discovery machine for the astronomy and physics communities, revealing astrophysical phenomena from the Solar System to the outer reaches of the observable Universe. While many discoveries will be made using LSST data alone, taking full scientific advantage of LSST will require ground-based optical-infrared (OIR) supporting capabilities, e.g., observing time on telescopes, instrumentation, computing resources, and other infrastructure. This community-based study identifies, from a science-driven perspective, capabilities that are needed to m…
The upcoming Large Synoptic Survey Telescope (LSST) will detect many strongly lensed Type Ia supernovae (LSNe Ia) for time-delay cosmography. This will provide an independent and direct way for measuring the Hubble constant $H_0$, which is necessary to address the current $4.4 σ$ tension in $H_0$ between the local distance ladder and the early Universe measurements. We present a detailed analysis of different observing strategies for the LSST, and quantify their impact on time-delay measurement between multiple images of LSNe Ia. For this, we produced microlensed mock-LSST light curves for whi…
Modern astronomical surveys such as the Large Synoptic Sky Survey (LSST) promise an unprecedented wealth of discoveries, delivered in the form of ~10 million alerts of time-variable events per night. Astronomers are faced with the daunting challenge of identifying the most scientifically important events from this flood of data in order to conduct effective and timely follow-up observations. Several ongoing observing programs have proven databases to be extremely valuable in conducting efficient follow-up, particularly when combined with tools to select targets, submit observation requests dir…
LSST is expected to yield ~10^7 light curves over the course of its mission, which will require a concerted effort in automated classification. Stochastic processes provide one means of quantitatively describing variability with the potential advantage over simple light curve statistics that the parameters may be physically meaningful. Here, we survey a large sample of periodic, quasi-periodic, and stochastic OGLE-III variables using the damped random walk (DRW, CARMA(1,0)) and quasi-periodic oscillation (QPO, CARMA(2,1)) stochastic process models. The QPO model is described by an amplitude, a…
We perform a rigorous cosmology analysis on simulated type Ia supernovae (SN~Ia) and evaluate the improvement from including photometric host-galaxy redshifts compared to using only the "zspec" subset with spectroscopic redshifts from the host or SN. We use the Deep Drilling Fields (~50 deg^2) from the Photometric LSST Astronomical Time-Series Classification Challenge (PLaSTiCC), in combination with a low-z sample based on Data Challenge2 (DC2). The analysis includes light curve fitting to standardize the SN brightness, a high-statistics simulation to obtain a bias-corrected Hubble diagram, a …
Two upcoming large scale surveys, the ESA Gaia and LSST projects, will bring a new era in astronomy. The number of binary systems that will be observed and detected by these projects is enormous, estimations range from millions for Gaia to several tens of millions for LSST. We review some tools that should be developed and also what can be gained from these missions on the subject of binaries and exoplanets from the astrometry, photometry, radial velocity and their alert systems.
The remarkable scientific return and legacy of LSST, in the era that it will define, will not only be realized in the breakthrough science that will be achieved with catalog data. This Big Data survey will shape the way the entire astronomical community advances -- or fails to embrace -- new ways of approaching astronomical research and data. In this white paper, we address the NRC template questions 4,5,6,8 and 9, with a focus on the unique challenges for smaller, and often under-resourced, institutions, including institutions dedicated to underserved minority populations, in the efficient an…
Data challenges are emerging as powerful tools with which to answer fundamental astronomical questions. Time-domain astronomy lends itself to data challenges, particularly in the era of classification and anomaly detection. With improved sensitivity of wide-field surveys in optical and radio wavelengths from surveys like the Large Synoptic Survey Telescope (LSST) and the Canadian Hydrogen Intensity Mapping Experiment (CHIME), we are entering the large-volume era of transient astronomy. I highlight some recent time-domain challenges, with particular focus on the Photometric LSST Astronomical Ti…
The Large Synoptic Survey Telescope (LSST) will produce an unprecedented amount of light curves using six optical bands. Robust and efficient methods that can aggregate data from multidimensional sparsely-sampled time series are needed. In this paper we present a new method for light curve period estimation based on the quadratic mutual information (QMI). The proposed method does not assume a particular model for the light curve nor its underlying probability density and it is robust to non-Gaussian noise and outliers. By combining the QMI from several bands the true period can be estimated ev…
Transfer Learning for Transient Classification: From Simulations to Real Data and ZTF to LSST
Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology（凯斯勒天体物理与空间研究研究所，麻省理工学院）；Irvington High School（伊里弗顿高中）；Department of Physics and Astronomy, Northwestern University（物理与天文学系，西北大学）；Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA)（天体物理学跨学科探索与研究中心（CIERA））；NSF-Simons AI Institute for the Sky (SkAI)（NSF-模拟ons人工智能研究所（SkAI））
The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demands that the astronomical community update its followup paradigm. Alert-brokers -- automated software system to sift through, characterize, annotate and prioritize events for followup -- will be critical tools for managing alert streams in the LSST era. The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is one such broker. In this work, we develop a machine learning pipeline to characterize and classify variable and transient sources only using…
Upcoming astronomical surveys such as the Large Synoptic Survey Telescope (LSST) will rely on photometric classification to identify the majority of the transients and variables that they discover. We present a set of techniques for photometric classification that can be applied even when the training set of spectroscopically-confirmed objects is heavily biased towards bright, low-redshift objects. Using Gaussian process regression to model arbitrary light curves in all bands simultaneously, we "augment" the training set by generating new versions of the original light curves covering a range …
We quantitatively assess, by means of comprehensive numerical simulations, the ability of broad-band photometric surveys to recover the broad emission line region (BLR) size in quasars under various observing conditions and for a wide range of object properties. Focusing on the general characteristics of the Large Synoptic Survey Telescope (LSST), we find that the slope of the size-luminosity relation for the BLR in quasars can be determined with unprecedented accuracy, of order a few percent, over a broad luminosity range and out to $z\sim 3$. In particular, major emission lines for which the…
As astronomy moves into the era of large-scale time-domain surveys, we are seeing a flood of new transient and variable sources which will reach biblical proportions with the advent of LSST. A key strategic challenge for astronomy in this era is the lack of suitable spectroscopic followup facilities. In response to this need, we have developed the PolyOculus approach for producing large-area-equivalent telescopes by using fiber optics to link modules of multiple semi-autonomous, small, inexpensive, commercial-off-the-shelf telescopes. Crucially, this scalable design has construction costs whic…
Upcoming surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will detect up to 10 million time-varying sources in the sky every night for ten years. This information will be transmitted in a continuous stream to brokers that will select the most promising events for a variety of science cases using machine learning algorithms. We study the benefits and challenges of Bayesian Neural Networks (BNNs) for this type of classification tasks. BNNs are found to be accurate classifiers which also provide additional information: they quantify the classification uncertain…
Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production
Ruhr University Bochum（鲁尔大学波鸿）；SLAC National Accelerator Laboratory（斯坦福直线加速器中心）；Kavli Institute for Particle Astrophysics and Cosmology (KIPAC), Stanford University（卡弗里粒子天体物理与宇宙学研究所，斯坦福大学）；Université Clermont-Auvergne, CNRS, LPCA（克莱蒙奥弗涅大学，法国国家科学研究中心，LPCA）；McWilliams Center for Cosmology and Astrophysics, Department of Physics, Carnegie Mellon University（梅斯威尔宇宙学与天体物理中心，卡内基梅隆大学物理系）
RAIL 是一个开源 Python 库，用于大规模概率光度红移估计，提供模块化工具进行端到端压力测试和红移 PDF 估算，适用于多种科学目标。
A complete map of the youngest stellar populations of the Milky Way in the era of all-sky surveys, is one of the most challenging goals in modern astrophysics. The characterisation of the youngest stellar component is crucial not only for a global overview of the Milky Way structure, of the Galactic thin disk, and its spiral arms, but also for local studies. In fact, the identification of the star forming regions (SFRs) and the comparison with the environment in which they form are also fundamental to put them in the context of the surrounding giant molecular clouds and to understand still unk…
With an ever-increasing amount of astronomical data being collected, manual classification has become obsolete; and machine learning is the only way forward. Keeping this in mind, the Large Synoptic Survey Telescope (LSST) Team hosted the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) in 2018. The aim of this challenge was to develop models that accurately classify astronomical sources into different classes, scaling from a limited training set to a large test set. In this text, we report our results of experimenting with Bidirectional Gated Recurrent Unit (GRU) …
The Large Synoptic Survey Telescope (LSST) will enable revolutionary studies of galaxies, dark matter, and black holes over cosmic time. The LSST Galaxies Science Collaboration has identified a host of preparatory research tasks required to leverage fully the LSST dataset for extragalactic science beyond the study of dark energy. This Galaxies Science Roadmap provides a brief introduction to critical extragalactic science to be conducted ahead of LSST operations, and a detailed list of preparatory science tasks including the motivation, activities, and deliverables associated with each. The Ga…
The Liverpool Telescope is one of the world's premier facilities for time domain astronomy. The time domain landscape is set to radically change in the coming decade, with surveys such as LSST providing huge numbers of transient detections on a nightly basis; transient detections across the electromagnetic spectrum from other facilities such as SVOM, SKA and CTA; and the era of `multi-messenger astronomy', wherein events are detected via non-electromagnetic means, such as gravitational wave emission. We describe here our plans for Liverpool Telescope 2: a new robotic telescope designed to capi…
Automated classification of supernovae (SNe) based on optical photometric light curve information is essential in the upcoming era of wide-field time domain surveys, such as the Legacy Survey of Space and Time (LSST) conducted by the Rubin Observatory. Photometric classification can enable real-time identification of interesting events for extended multi-wavelength follow-up, as well as archival population studies. Here we present the complete sample of 5,243 "SN-like" light curves (in griz) from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS). The PS1-MDS is similar to the planned LSST Wide-Fast…
European Southern Observatory（欧洲南方天文台）；Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院物理研究所）；IRFU, CEA, Université Paris-Saclay（法国原子能和替代能源署法国研究和技术核物理研究所巴黎萨克雷大学）；Department of Astronomy, Faculty of Mathematics, University of Belgrade（贝尔格莱德大学数学学院天文学系）；Hamburger Sternwarte, Universität Hamburg（汉堡大学天文台）；INAF OACN（意大利国家天体物理研究所卡塔尼亚天文台）；Astronomical Institute of Charles University（查理大学天文研究所）；Instituto de Astrofísica de Canarias（加那利群岛天体物理研究所）
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…
Astronomy is entering the era of large surveys of the variable sky such as Zwicky Transient Facility (ZTF) and the forthcoming Legacy Survey of Space and Time (LSST) which are intended to produce up to a million alerts per night. Such an amount of photometric data requires efficient light-curve pre-processing algorithms for the purposes of subsequent data quality cuts, classification, and characterization analysis. In this work, we present the new library "light-curve" for Python and Rust, which is intended for feature extraction from light curves of variable astronomical sources. The library …
Predicting the Spectroscopic Features of Galaxies by Applying Manifold Learning on Their Broad-Band Colors: Proof of Concept and Potential Applications for Euclid, Roman, and Rubin LSST
We have entered the era of explosive transient astronomy, in which upcoming real-time surveys like the Large Synoptic Survey Telescope (LSST), the Palomar Transient Factory (PTF) and Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) will detect supernovae in unprecedented numbers. Future telescopes such as the James Webb Space Telescope may discover supernovae from the earliest stars in the universe and reveal their masses. The observational signatures of these astrophysical transients are the key to unveiling their central engines, the environments in which they occur, and to …
The Large Synoptic Survey Telescope will complete its survey in 2022 and produce terabytes of imaging data each night. To work with this massive onset of data, automated algorithms to classify astronomical light curves are crucial. Here, we present a method for automated classification of photometric light curves for a range of astronomical objects. Our approach is based on the gradient boosting of decision trees, feature extraction and selection, and augmentation. The solution was developed in the context of The Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC) and…
The MeerKAT radio telescope array, the Large Synoptic Survey Telescope (LSST), and eventually the Square Kilometer Array (SKA) will usher in a remarkable new era in astronomy, with thousands of transients being discovered and transmitted to the astronomical community in near-real-time each night. Immediate spectroscopic follow-up will be critical to understanding their early-time physics - a task to which the Southern African Large Telescope (SALT) is uniquely suited, given its southerly latitude and the 14-degree-diameter uncorrected field (patrol area) of its 10-m spherical primary mirror. A…
Hyrax: An Extensible Framework for Rapid ML Experimentation and Unsupervised Discovery in the Era of Rubin, Roman, and Euclid
Dept. of Astronomy \& the DiRAC Institute, University of Washington, Box 351580, Seattle, WA 98195, USA；School of Physics；Astronomy, University of Minnesota, Minneapolis, MN 55455, USA；Department of Astronomy；Planetary Science, Northern Arizona University, Flagstaff, USA；McWilliams Center for Cosmology；Astrophysics, Department of Physics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Galactic microlensing datasets now comprise in excess of $10^4$ events, and with the advent of next generation microlensing surveys that may be undertaken with facilities such as the Rubin Observatory (formerly LSST) and Roman Space Telescope (formerly WFIRST), this number will increase significantly. So too will the fraction of events with measurable higher order information such as finite source effects and lens-source relative proper motion. Analysing such data requires a more sophisticated Galactic microlens modeling approach. We present a new second-generation Manchester-Besançon Microlen…
The next decade will feature a growing number of massive ground-based photometric, spectroscopic, and time-domain surveys, including those produced by DECam, DESI, and LSST. The NOAO Data Lab was launched in 2017 to enable efficient exploration and analysis of large surveys, with particular focus on the petabyte-scale holdings of the NOAO Archive and their associated catalogs. The Data Lab mission and future development align well with two of the NSF's Big Ideas, namely Harnessing Data for 21st Century Science and Engineering and as part of a network to contribute to Windows on the Universe: T…
The rate of image acquisition in modern synoptic imaging surveys has already begun to outpace the feasibility of keeping astronomers in the real-time discovery and classification loop. Here we present the inner workings of a framework, based on machine-learning algorithms, that captures expert training and ground-truth knowledge about the variable and transient sky to automate 1) the process of discovery on image differences and, 2) the generation of preliminary science-type classifications of discovered sources. Since follow-up resources for extracting novel science from fast-changing transie…
