We present Real-time Automated Photometric IDentification (RAPID), a novel time series classification tool capable of automatically identifying transients from within a day of the initial alert, to ...the full lifetime of a light curve. Using a deep recurrent neural network with gated recurrent units (GRUs), we present the first method specifically designed to provide early classifications of astronomical timeseries data, typing 12 different transient classes. Our classifier can process light curves with any phase coverage, and it does not rely on deriving computationally expensive features from the data, making RAPID well suited for processing the millions of alerts that ongoing and upcoming wide-field surveys such as the Zwicky Transient Facility (ZTF), and the Large Synoptic Survey Telescope (LSST) will produce. The classification accuracy improves over the lifetime of the transient as more photometric data becomes available, and across the 12 transient classes, we obtain an average area under the receiver operating characteristic curve of 0.95 and 0.98 at early and late epochs, respectively. We demonstrate RAPID's ability to effectively provide early classifications of observed transients from the ZTF data stream. We have made RAPID available as an open-source software package8 for machine-learning-based alert brokers to use for the autonomous and quick classification of several thousand light curves within a few seconds.
MOSFiT: Modular Open Source Fitter for Transients Guillochon, James; Nicholl, Matt; Villar, V. Ashley ...
The Astrophysical journal. Supplement series,
05/2018, Letnik:
236, Številka:
1
Journal Article
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Much of the progress made in time-domain astronomy is accomplished by relating observational multiwavelength time-series data to models derived from our understanding of physical laws. This goal is ...typically accomplished by dividing the task in two: collecting data (observing), and constructing models to represent that data (theorizing). Owing to the natural tendency for specialization, a disconnect can develop between the best available theories and the best available data, potentially delaying advances in our understanding new classes of transients. We introduce MOSFiT: the Modular Open Source Fitter for Transients, a Python-based package that downloads transient data sets from open online catalogs (e.g., the Open Supernova Catalog), generates Monte Carlo ensembles of semi-analytical light-curve fits to those data sets and their associated Bayesian parameter posteriors, and optionally delivers the fitting results back to those same catalogs to make them available to the rest of the community. MOSFiT is designed to help bridge the gap between observations and theory in time-domain astronomy; in addition to making the application of existing models and creation of new models as simple as possible, MOSFiT yields statistically robust predictions for transient characteristics, with a standard output format that includes all the setup information necessary to reproduce a given result. As large-scale surveys such as that conducted with the Large Synoptic Survey Telescope (LSST), discover entirely new classes of transients, tools such as MOSFiT will be critical for enabling rapid comparison of models against data in statistically consistent, reproducible, and scientifically beneficial ways.
ABSTRACT
While conventional Type Ia supernova (SN Ia) cosmology analyses rely primarily on rest-frame optical light curves to determine distances, SNe Ia are excellent standard candles in ...near-infrared (NIR) light, which is significantly less sensitive to dust extinction. An SN Ia spectral energy distribution (SED) model capable of fitting rest-frame NIR observations is necessary to fully leverage current and future SN Ia data sets from ground- and space-based telescopes including HST, LSST, JWST, and RST. We construct a hierarchical Bayesian model for SN Ia SEDs, continuous over time and wavelength, from the optical to NIR (B through H, or $0.35{-}1.8\, \mu$m). We model the SED as a combination of physically distinct host galaxy dust and intrinsic spectral components. The distribution of intrinsic SEDs over time and wavelength is modelled with probabilistic functional principal components and the covariance of residual functions. We train the model on a nearby sample of 79 SNe Ia with joint optical and NIR light curves by sampling the global posterior distribution over dust and intrinsic latent variables, SED components and population hyperparameters. Photometric distances of SNe Ia with NIR data near maximum obtain a total RMS error of 0.10 mag with our BayeSN model, compared to 0.13–0.14 mag with SALT2 and SNooPy for the same sample. Jointly fitting the optical and NIR data of the full sample up to moderate reddening (host E(B − V) < 0.4) for a global host dust law, we find RV = 2.9 ± 0.2, consistent with the Milky Way average.
We have constructed a comprehensive statistical model for Type Ia supernova (SN Ia) light curves spanning optical through near-infrared (NIR) data. A hierarchical framework coherently models multiple ...random and uncertain effects, including intrinsic supernova (SN) light curve covariances, dust extinction and reddening, and distances. An improved BAYESN Markov Chain Monte Carlo code computes probabilistic inferences for the hierarchical model by sampling the global probability density of parameters describing individual SNe and the population. We have applied this hierarchical model to optical and NIR data of 127 SNe Ia from PAIRITEL, CfA3, Carnegie Supernova Project, and the literature. We find an apparent population correlation between the host galaxy extinction AV and the ratio of total-to-selective dust absorption RV . For SNe with low dust extinction, AV 0.4, we find RV 2.5-2.9, while at high extinctions, AV 1, low values of RV < 2 are favored. The NIR luminosities are excellent standard candles and are less sensitive to dust extinction. They exhibit low correlation with optical peak luminosities, and thus provide independent information on distances. The combination of NIR and optical data constrains the dust extinction and improves the predictive precision of individual SN Ia distances by about 60%. Using cross-validation, we estimate an rms distance modulus prediction error of 0.11 mag for SNe with optical and NIR data versus 0.15 mag for SNe with optical data alone. Continued study of SNe Ia in the NIR is important for improving their utility as precise and accurate cosmological distance indicators.
We present analytical reconstructions of SN Ia delay time distributions (DTDs) by way of two independent methods: by a Markov Chain Monte Carlo best-fit technique comparing the volumetric SN Ia rate ...history to today's compendium cosmic star formation history, and second through a maximum likelihood analysis of the star formation rate histories of individual galaxies in the GOODS/CANDELS field, in comparison to their resultant SN Ia yields. We adopt a flexible skew-normal DTD model, which could match a wide range of physically motivated DTD forms. We find a family of solutions that are essentially exponential DTDs, similar in shape to the β −1 power-law DTDs, but with more delayed events (>1 Gyr in age) than prompt events (<1 Gyr). Comparing these solutions to delay time measures separately derived from field galaxies and galaxy clusters, we find the skew-normal solutions can accommodate both without requiring a different DTD form in different environments. These model fits are generally inconsistent with results from single-degenerate binary population synthesis models, and are seemingly supportive of double-degenerate progenitors for most SN Ia events.
The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demand that the astronomical community update its follow-up paradigm. ...Alert-brokers-automated software system to sift through, characterize, annotate, and prioritize events for follow-up-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 the available multiband optical photometry. We describe three illustrative stages of the pipeline, serving the three goals of early, intermediate, and retrospective classification of alerts. The first takes the form of variable versus transient categorization, the second a multiclass typing of the combined variable and transient data set, and the third a purity-driven subtyping of a transient class. Although several similar algorithms have proven themselves in simulations, we validate their performance on real observations for the first time. We quantitatively evaluate our pipeline on sparse, unevenly sampled, heteroskedastic data from various existing observational campaigns, and demonstrate very competitive classification performance. We describe our progress toward adapting the pipeline developed in this work into a real-time broker working on live alert streams from time-domain surveys.
Abstract
We describe the Arizona-NOIRLab Temporal Analysis and Response to Events System (ANTARES), a software instrument designed to process large-scale streams of astronomical time-domain alerts. ...With the advent of large-format CCDs on wide-field imaging telescopes, time-domain surveys now routinely discover tens of thousands of new events each night, more than can be evaluated by astronomers alone. The ANTARES event broker will process alerts, annotating them with catalog associations and filtering them to distinguish customizable subsets of events. We describe the data model of the system, the overall architecture, annotation, implementation of filters, system outputs, provenance tracking, system performance, and the user interface.
We have established a network of 19 faint (16.5 mag < V < 19 mag) northern and equatorial DA white dwarfs (WDs) as spectrophotometric standards for present and future wide-field observatories. Our ...analysis infers spectral energy distribution (SED) models for the stars that are tied to the three CALSPEC primary standards. Our SED models are consistent with panchromatic Hubble Space Telescope photometry to better than 1%. The excellent agreement between observations and models validates the use of non-LTE DA WD atmospheres extinguished by interstellar dust as accurate spectrophotometric references. Our standards are accessible from both hemispheres and suitable for ground- and space-based observatories covering the ultraviolet to the near-infrared. The high precision of these faint sources makes our network of standards ideally suited for any experiment that has very stringent requirements on flux calibration, such as studies of dark energy using the Large Synoptic Survey Telescope and the Wide-field Infrared Survey Telescope.
We present time-series imaging polarimetry observations of a nearby tidal disruption event (TDE) AT2019DSG at z = 0.0512 to probe the disruption mechanism and shed light on the accretion process. We ...obtain linear polarimetry using the Alhambra Faint Object Spectrograph and Camera on board the 2.5 m Nordic Optical Telescope. Our observations showed a polarization at the 9.2% 2.7% level early on, decreasing to less than 2.7% (at the 68% confidence level) one month later. While the high level of polarization in the early epoch is similar to that of Swift J164449.3+573451 and Swift J2058+0516, the low level of polarization in the later epoch is in agreement with that of OGLE16aaa. Our results thus show the temporal evolution of optical polarization from a TDE. As the degree of polarization changes over time, it is unlikely to be attributed to host galaxy dust, but may originate from a non-isotropic accreting disk, or associated with the relativistic jet emission.
ABSTRACT CfAIR2 is a large, homogeneously reduced set of near-infrared (NIR) light curves (LCs) for Type Ia supernovae (SNe Ia) obtained with the 1.3 m Peters Automated InfraRed Imaging TELescope. ...This data set includes 4637 measurements of 94 SNe Ia and 4 additional SNe Iax observed from 2005 to 2011 at the Fred Lawrence Whipple Observatory on Mount Hopkins, Arizona. CfAIR2 includes photometric measurements for 88 normal and 6 spectroscopically peculiar SN Ia in the nearby universe, with a median redshift of z ∼ 0.021 for the normal SN Ia. CfAIR2 data span the range from −13 days to +127 days from B-band maximum. More than half of the LCs begin before the time of maximum, and the coverage typically contains ∼13-18 epochs of observation, depending on the filter. We present extensive tests that verify the fidelity of the CfAIR2 data pipeline, including comparison to the excellent data of the Carnegie Supernova Project. CfAIR2 contributes to a firm local anchor for SN cosmology studies in the NIR. Because SN Ia are more nearly standard candles in the NIR and are less vulnerable to the vexing problems of extinction by dust, CfAIR2 will help the SN cosmology community develop more precise and accurate extragalactic distance probes to improve our knowledge of cosmological parameters, including dark energy and its potential time variation.
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