Synoptic Sky Surveys Research Articles

Light curve classification with DistClassiPy: A new distance-based classifier

The rise of synoptic sky surveys has ushered in an era of big data in time-domain astronomy, making data science and machine learning essential tools for studying celestial objects. While tree-based models (e.g. Random Forests) and deep learning models dominate the field, we explore the use of different distance metrics to aid in the classification of astrophysical objects. We developed DistClassiPy, a new distance metric based classifier. The direct use of distance metrics is unexplored in time-domain astronomy, but distance-based methods can help make classification more interpretable and decrease computational costs. In particular, we applied DistClassiPy to classify light curves of variable stars, comparing the distances between objects of different classes. Using 18 distance metrics on a catalog of 6,000 variable stars across 10 classes, we demonstrate classification and dimensionality reduction. Our classifier meets state-of-the-art performance but has lower computational requirements and improved interpretability. Additionally, DistClassiPy can be tailored to specific objects by identifying the most effective distance metric for that classification. To facilitate broader applications within and beyond astronomy, we have made DistClassiPy open-source and available at https://pypi.org/project/distclassipy/.

Every Data Point Counts: Stellar Flares as a Case Study of Atmosphere-aided Studies of Transients in the LSST Era

Due to their short timescale, stellar flares are a challenging target for the most modern synoptic sky surveys. The upcoming Vera C. Rubin Legacy Survey of Space and Time (LSST), a project designed to collect more data than any precursor survey, is unlikely to detect flares with more than one data point in its main survey. We developed a methodology to enable LSST studies of stellar flares, with a focus on flare temperature and temperature evolution, which remain poorly constrained compared to flare morphology. By leveraging the sensitivity expected from the Rubin system, differential chromatic refraction (DCR) can be used to constrain flare temperature from a single-epoch detection, which will enable statistical studies of flare temperatures and constrain models of the physical processes behind flare emission using the unprecedentedly high volume of data produced by Rubin over the 10 yr LSST. We model the refraction effect as a function of the atmospheric column density, photometric filter, and temperature of the flare, and show that flare temperatures at or above ∼4000 K can be constrained by a single g-band observation at air mass X ≳ 1.2, given the minimum specified requirement on the single-visit relative astrometric accuracy of LSST, and that a surprisingly large number of LSST observations are in fact likely be conducted at X ≳ 1.2, in spite of image quality requirements pushing the survey to preferentially low X. Having failed to measure flare DCR in LSST precursor surveys, we make recommendations on survey design and data products that enable these studies in LSST and other future surveys.

An integrated imaging sensor for aberration-corrected 3D photography

Planar digital image sensors facilitate broad applications in a wide range of areas1–5, and the number of pixels has scaled up rapidly in recent years2,6. However, the practical performance of imaging systems is fundamentally limited by spatially nonuniform optical aberrations originating from imperfect lenses or environmental disturbances7,8. Here we propose an integrated scanning light-field imaging sensor, termed a meta-imaging sensor, to achieve high-speed aberration-corrected three-dimensional photography for universal applications without additional hardware modifications. Instead of directly detecting a two-dimensional intensity projection, the meta-imaging sensor captures extra-fine four-dimensional light-field distributions through a vibrating coded microlens array, enabling flexible and precise synthesis of complex-field-modulated images in post-processing. Using the sensor, we achieve high-performance photography up to a gigapixel with a single spherical lens without a data prior, leading to orders-of-magnitude reductions in system capacity and costs for optical imaging. Even in the presence of dynamic atmosphere turbulence, the meta-imaging sensor enables multisite aberration correction across 1,000 arcseconds on an 80-centimetre ground-based telescope without reducing the acquisition speed, paving the way for high-resolution synoptic sky surveys. Moreover, high-density accurate depth maps can be retrieved simultaneously, facilitating diverse applications from autonomous driving to industrial inspections.

Zwicky Transient Facility and Globular Clusters: Calibration of the gr-band Absolute Magnitudes for the Yellow Post-asymptotic-giant-branch Stars

We present the first absolute calibration for the yellow post-asymptotic-giant-branch (PAGB) stars in the g and r band based on time-series observations from the Zwicky Transient Facility. These absolute magnitudes were calibrated using four yellow PAGB stars (one nonvarying star and three Type II Cepheids) located in the globular clusters. We provide two calibrations of the gr-band absolute magnitudes for the yellow PAGB stars, by using an arithmetic mean and a linear regression. We demonstrate that the linear regression provides a better fit to the g-band absolute magnitudes for the yellow PAGB stars. These calibrated gr-band absolute magnitudes have a potential to be used as Population II distance indicators in the era of time-domain synoptic sky surveys.

Deep co-added sky from Catalina Sky Survey images

ABSTRACT A number of synoptic sky surveys are underway or being planned. Typically, they are done with small telescopes and relatively short exposure times. A search for transient or variable sources involves comparison with deeper baseline images, ideally obtained through the same telescope and camera. With that in mind, we have stacked images from the 0.68 m Schmidt telescope on Mt. Bigelow taken over 10 yr as part of the Catalina Sky Survey. In order to generate deep reference images for the Catalina Real-time Transient Survey (CRTS), close to 0.8 million images over 8000 fields and covering over 27 000 sq. deg have gone into the deep stack that goes up to 3 mag deeper than individual images. CRTS system does not use a filter in imaging; hence, there is no standard passband in which the optical magnitude is measured. We estimate depth by comparing these wide-band unfiltered co-added images with images in the g band and find that the image depth ranges from 22.0 to 24.2 across the sky, with a 200-image stack attaining an equivalent AB magnitude sensitivity of 22.8. We compared various state-of-the-art software packages for co-adding astronomical images and have used swarp for the stacking. We describe here the details of the process adopted. This methodology may be useful in other panoramic imaging applications, and to other surveys as well. The stacked images are available through a server at Inter-University Centre for Astronomy and Astrophysics.

Measuring time delays – I. Using a flux time series that is a linear combination of time-shifted light curves

ABSTRACT Several phenomena in astrophysics generate light curves with time delays. Among these are reverberation mapping and lensed quasars. In these systems, the measurement of the time delay is complicated by the fact that the delayed components are unresolved and that the light curves are generated from a stochastic process. We derive the likelihood function of the observations given a model of either a combination of time-delayed light curves or a single light curve. This likelihood function is different from the auto-correlation-function-based investigated by previous studies. We demonstrate that given a single-band light curve that is a linear combination of two (or more) time-shifted copies of an original light curve, generated from a probability distribution with some roughly known power spectrum, it is possible to test if the total-flux light curve is a composition of time-delayed copies or, alternatively, is consistent with being a single copy of the original light curve. Furthermore, it is possible to measure the time delays between these components even when the flux ratio is about 1/10. In the era of synoptic sky surveys, this method is useful for identifying lensed quasars and simultaneously measuring their time delays, and also for estimating the reverberation time scales of active galactic nuclei. In a companion paper, we derive another method that uses the centre-of-light astrometric position (e.g. of a lensed quasar) along with the combined flux. We also present the proper likelihood function for fitting a power-law model to a power spectrum. We test the new method on simulations and provide python and matlab implementations.

The SED Machine: A Robotic Spectrograph for Fast Transient Classification

Current time domain facilities are finding several hundreds of transient astronomical events a year. The discovery rate is expected to increase in the future as soon as new surveys such as the Zwicky Transient Facility (ZTF) and the Large Synoptic Sky Survey (LSST) come online. Presently, the rate at which transients are classified is approximately one order or magnitude lower than the discovery rate, leading to an increasing “follow-up drought”. Existing telescopes with moderate aperture can help address this deficit when equipped with spectrographs optimized for spectral classification. Here, we provide an overview of the design, operations and first results of the Spectral Energy Distribution Machine (SEDM), operating on the Palomar 60-inch telescope (P60). The instrument is optimized for classification and high observing efficiency. It combines a low-resolution (R ∼ 100) integral field unit (IFU) spectrograph with “Rainbow Camera” (RC), a multi-band field acquisition camera which also serves as multi-band (ugri) photometer. The SEDM was commissioned during the operation of the intermediate Palomar Transient Factory (iPTF) and has already lived up to its promise. The success of the SEDM demonstrates the value of spectrographs optimized for spectral classification.

Time-Domain Astroinformatics

AbstractTime-Domain astronomy exercises all aspects of the Virtual Observatory framework and Astroinformatics. Applications of machine learning and statistics to the analysis of large numbers of light-curves will increasingly yield new results as the data accumulate. However, the most challenging problems remain in the arena of rapid classification of transient events and their automated follow-up prioritisation. The talk illustrated those issues with examples from recent or ongoing synoptic sky surveys.

Convolutional neural networks for transient candidate vetting in large-scale surveys

Current synoptic sky surveys monitor large areas of the sky to find variable and transient astronomical sources. As the number of detections per night at a single telescope easily exceeds several thousand, current detection pipelines make intensive use of machine learning algorithms to classify the detected objects and to filter out the most interesting candidates. A number of upcoming surveys will produce up to three orders of magnitude more data, which renders high-precision classification systems essential to reduce the manual and, hence, expensive vetting by human experts. We present an approach based on convolutional neural networks to discriminate between true astrophysical sources and artefacts in reference-subtracted optical images. We show that relatively simple networks are already competitive with state-of-the-art systems and that their quality can further be improved via slightly deeper networks and additional preprocessing steps -- eventually yielding models outperforming state-of-the-art systems. In particular, our best model correctly classifies about 97.3% of all 'real' and 99.7% of all 'bogus' instances on a test set containing 1,942 'bogus' and 227 'real' instances in total. Furthermore, the networks considered in this work can also successfully classify these objects at hand without relying on difference images, which might pave the way for future detection pipelines not containing image subtraction steps at all.

Pulsating star products from the Palomar Transient Factory: Ultra-long period Cepheids in M31 and RR Lyrae in Kepler field

The Palomar Transient Factory (PTF) and its successor, the intermediate PTF (iPTF), are wide-field synoptic sky surveys aimed to detect transients. Even though the main science goal for PTF/iPTF is to detect various types of transients, the synoptic nature of the surveys can also be used for the study of variable stars. In this proceedings contribution, I will first give a brief introduction to PTF/iPTF, followed by the two pulsating stars studies using the PTF/iPTF data: the Ultra-Long Period Cepheids (ULPC) in M31 and the RR Lyrae in the Kepler field. For the formal study, we searched the M31’s ULPC using PTF imaging data, and follow up the candidates with other telescopes. Our finding revealed that there are only two ULPC in M31. I will give a brief implication of our finding in distance scale studies. For the latter study, I will present our work on the derivation of metallicity-light curve relation in native PTF/iPTF R-band using the RRab stars in the Kepler field.

A TRIP TO THE CATACLYSMIC BINARY ZOO: DETAILED FOLLOW-UP OF 35 RECENTLY DISCOVERED SYSTEMS

ABSTRACT We report follow-up studies of 35 recently discovered cataclysmic variables (CVs), 32 of which were found in large, automated synoptic sky surveys. The objects were selected for observational tractability. For 34 of the objects, we present mean spectra and spectroscopic orbital periods, and for one more we give an eclipse-based period. Thirty-two of the period determinations are new, and three of these refine published estimates based on superhump periods. The remaining three of our determinations confirm previously published periods. Twenty of the stars are confirmed or suspected dwarf novae with periods shorter than 3 hr, but we also find three apparent polars (AM Her stars) and six systems with . Five of these systems have secondary stars visible in their spectra, from which we estimate distances when possible. The orbital period distribution of this sample is very similar to that of previously discovered CVs.

THE PALOMAR TRANSIENT FACTORY AND RR LYRAE: THE METALLICITY–LIGHT CURVE RELATION BASED ON AB-TYPE RR LYRAE IN THE KEPLER FIELD

ABSTRACT The wide-field synoptic sky surveys, known as the Palomar Transient Factory (PTF) and the intermediate Palomar Transient Factory (iPTF), will accumulate a large number of known and new RR Lyrae. These RR Lyrae are good tracers to study the substructure of the Galactic halo if their distance, metallicity, and galactocentric velocity can be measured. Candidates of halo RR Lyrae can be identified from their distance and metallicity before requesting spectroscopic observations for confirmation. This is because both quantities can be obtained via their photometric light curves, because the absolute V-band magnitude for RR Lyrae is correlated with metallicity, and the metallicity can be estimated using a metallicity–light curve relation. To fully utilize the PTF and iPTF light-curve data in related future work, it is necessary to derive the metallicity–light curve relation in the native PTF/iPTF R-band photometric system. In this work, we derived such a relation using the known ab-type RR Lyrae located in the Kepler field, and it is found to be (where P is pulsational period and is one of the Fourier parameters describing the shape of the light curve), with a dispersion of 0.118 dex. We tested our metallicity–light curve relation with new spectroscopic observations of a few RR Lyrae in the Kepler field, as well as several data sets available in the literature. Our tests demonstrated that the derived metallicity–light curve relation could be used to estimate metallicities for the majority of the RR Lyrae, which are in agreement with the published values.

Real-time data mining of massive data streams from synoptic sky surveys

The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data streams that must be analyzed in real time. Interesting or anomalous phenomena must be quickly characterized and followed up with additional measurements via optimal deployment of limited assets. Modern astronomy presents a variety of such phenomena in the form of transient events in digital synoptic sky surveys, including cosmic explosions (supernovae, gamma ray bursts), relativistic phenomena (black hole formation, jets), potentially hazardous asteroids, etc. We have been developing a set of machine learning tools to detect, classify and plan a response to transient events for astronomy applications, using the Catalina Real-time Transient Survey (CRTS) as a scientific and methodological testbed. The ability to respond rapidly to the potentially most interesting events is a key bottleneck that limits the scientific returns from the current and anticipated synoptic sky surveys. Similar challenge arises in other contexts, from environmental monitoring using sensor networks to autonomous spacecraft systems. Given the exponential growth of data rates, and the time-critical response, we need a fully automated and robust approach. We describe the results obtained to date, and the possible future developments.

Non-parametric transient classification using adaptive wavelets

Classifying transients based on multi band light curves is a challenging but crucial problem in the era of GAIA and LSST since the sheer volume of transients will make spectroscopic classification unfeasible. Here we present a nonparametric classifier that uses the transient's light curve measurements to predict its class given training data. It implements two novel components: the first is the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients. The second novelty is the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The ranked classifier is simple and quick to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant, hence they do not need the light curves to be aligned to extract features. Further, BAGIDIS is nonparametric so it can be used for blind searches for new objects. We demonstrate the effectiveness of our ranked wavelet classifier against the well-tested Supernova Photometric Classification Challenge dataset in which the challenge is to correctly classify light curves as Type Ia or non-Ia supernovae. We train our ranked probability classifier on the spectroscopically-confirmed subsample (which is not representative) and show that it gives good results for all supernova with observed light curve timespans greater than 100 days (roughly 55% of the dataset). For such data, we obtain a Ia efficiency of 80.5% and a purity of 82.4% yielding a highly competitive score of 0.49 whilst implementing a truly "model-blind" approach to supernova classification. Consequently this approach may be particularly suitable for the classification of astronomical transients in the era of large synoptic sky surveys.

TRANSITING PLANETS WITH LSST. I. POTENTIAL FOR LSST EXOPLANET DETECTION

ABSTRACT The Large Synoptic Survey Telescope (LSST) is designed to meet several scientific objectives over a 10 year synoptic sky survey. Beyond its primary goals, the large amount of LSST data can be exploited for additional scientific purposes. We show that LSST data are sufficient to detect the transits of exoplanets, including planets orbiting stars that are members of stellar populations that have so far been largely unexplored. Using simulated LSST light curves, we find that existing transit detection algorithms can identify the signatures of Hot Jupiters around solar-type stars, Hot Neptunes around K-dwarfs, and (in favorable cases) Super-Earths in habitable-zone orbits of M-dwarfs. We also find that LSST may identify Hot Jupiters orbiting stars in the Large Magellanic Cloud—a remarkable possibility that would advance exoplanet science into the extragalactic regime.

SUPERVISED DETECTION OF ANOMALOUS LIGHT CURVES IN MASSIVE ASTRONOMICAL CATALOGS

The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. To process this information and to extract all possible knowledge, machine learning techniques become necessary. Here we present a new method to automatically discover unknown variable objects in large astronomical catalogs. With the aim of taking full advantage of all the information we have about known objects, our method is based on a supervised algorithm. In particular, we train a random forest classifier using known variability classes of objects and obtain votes for each of the objects in the training set. We then model this voting distribution with a Bayesian network and obtain the joint voting distribution among the training objects. Consequently, an unknown object is considered as an outlier insofar it has a low joint probability. Our method is suitable for exploring massive datasets given that the training process is performed offline. We tested our algorithm on 20 millions light-curves from the MACHO catalog and generated a list of anomalous candidates. We divided the candidates into two main classes of outliers: artifacts and intrinsic outliers. Artifacts were principally due to air mass variation, seasonal variation, bad calibration or instrumental errors and were consequently removed from our outlier list and added to the training set. After retraining, we selected about 4000 objects, which we passed to a post analysis stage by perfoming a cross-match with all publicly available catalogs. Within these candidates we identified certain known but rare objects such as eclipsing Cepheids, blue variables, cataclysmic variables and X-ray sources. For some outliers there were no additional information. Among them we identified three unknown variability types and few individual outliers that will be followed up for a deeper analysis.

DAMEWARE: A Web Cyberinfrastructure for Astrophysical Data Mining

Astronomy is undergoing a methodological revolution triggered by an unprecedented wealth of complex and accurate data. The new panchromatic, synoptic sky surveys require advanced tools for discovering patterns and trends hidden behind data which are both complex and of high dimensionality. We present DAMEWARE (DAta Mining & Exploration Web Application REsource): a general purpose, web-based, distributed data mining environment developed for the exploration of large data sets, and finely tuned for astronomical applications. By means of graphical user interfaces, it allows the user to perform classification, regression, or clustering tasks with machine learning methods. Salient features of DAMEWARE include its ability to work on large datasets with minimal human intervention, and to deal with a wide variety of real problems such as the classification of globular clusters in the galaxy NGC1399; the evaluation of photometric redshifts; and, finally, the identification of candidate Active Galactic Nuclei in multiband photometric surveys. In all these applications, DAMEWARE allowed us to achieve better results than those attained with more traditional methods. With the aim of providing potential users with all needed information, in this paper we briefly describe the technological background of DAMEWARE, give a short introduction to some relevant aspects of data mining, followed by a summary of some science cases and, finally, provide a detailed description of a template use case.

Machine-assisted discovery of relationships in astronomy

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 programming technique and an information-theory technique. We demonstrate their use with classical astronomical relationships - the Hertzsprung-Russell diagram and the fundamental plane of elliptical galaxies. We also show how they work with the issue of binary classification which is relevant to the next generation of large synoptic sky surveys, such as LSST. We find that comparable results to more familiar techniques, such as decision trees, are achievable. Finally, we consider the reality of the relationships discovered and how this can be used for feature selection and extraction.

Data challenges of time domain astronomy

Astronomy has been at the forefront of the development of the techniques and methodologies of data intensive science for over a decade with large sky surveys and distributed efforts such as the Virtual Observatory. However, it faces a new data deluge with the next generation of synoptic sky surveys which are opening up the time domain for discovery and exploration. This brings both new scientific opportunities and fresh challenges, in terms of data rates from robotic telescopes and exponential complexity in linked data, but also for data mining algorithms used in classification and decision making. In this paper, we describe how an informatics-based approach-part of the so-called "fourth paradigm" of scientific discovery-is emerging to deal with these. We review our experiences with the Palomar-Quest and Catalina Real-Time Transient Sky Surveys; in particular, addressing the issue of the heterogeneity of data associated with transient astronomical events (and other sensor networks) and how to manage and analyze it.

Real-Time Classification of Transient Events in Synoptic Sky Surveys

AbstractAn automated rapid classification of the transient events detected in modern synoptic sky surveys is essential for their scientific utility and effective follow-up when resources are scarce. This problem will grow by orders of magnitude with the next generation of surveys. We are exploring a variety of novel automated classification techniques, mostly Bayesian, to respond to those challenges, using the ongoing CRTS sky survey as a testbed. We describe briefly some of the methods used.