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VAO: Archival follow-up and time series

Matthew J. Graham, Caltech/VAO

July 5, 2012Matthew J. Graham

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Time domain requirements

Stakeholder perspective:Many observed phenomena are short-livedScientific returns depend on:

detection timely and well-chosen follow-up

A system needs to:Fully process data as they stream from telescopesCompare with previous data from same region of skyReliably detect any changesClassify and prioritize detections for followup

July 5, 2012Matthew J. Graham

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VAO at the heart of the time domain

Identified early (2001) as prime arena for VO applications Two-track approach:

Transient events (VOEvent) (see Williams et al.) Publish, disseminate, and archive event notifications

Time series data Describe, represent and access from different archives Characterize Classify

Letter of cooperation with LSST Standards and mechanisms for distributing transient event notices Accessing LSST databases and images with VO-compliant

interfaces

July 5, 2012Matthew J. Graham

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Interoperable time series - I

Common set of data access protocols:

Common data formats: VOTable FITS CSV

Name Description

Simple Cone Search (SCS) Retrieve all objects within a region

Simple Image Access (SIA) Retrieve all images within a region

Simple Spectral Access (SSA) Retrieve all spectra within a region

Simple Line Access (SLA) Retrieve spectral line data

Simulations (SIMDAL) Retrieve simulations

Table Access (TAP) Retrieve tabular data

July 5, 2012Matthew J. Graham

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Interoperable time series - II

Common data models with pointer mechanism (Utype):VOEvent Describes observations of transient astronomical eventsWho, What, Where/When, How, Why

SpectralDescribes generalized spectrophotometric sequencesBasis for Spectrum, SED and TimeSeries DMs

TimeSeriesDescribes any observed or derived quantity that varies with time

Light curve (time series with one photometric band) Multi-band time series Time series with variable time sample bin sizes

Associated metadata such as period, target variability amplitude, and derived SNR

July 5, 2012Matthew J. Graham

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Data providers

CRTS HATNet

Kepler CoRoT

July 5, 2012Matthew J. Graham

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VAO Time Series Search Tool

July 5, 2012Matthew J. Graham

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Event and source cross-identification

VAO Cross-Comparison Tool Up to 1 million sources against common source catalogs

Constructing time histories from sets of individual observations Bayesian formalism (Budavari)

July 5, 2012Matthew J. Graham

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Synoptic surveys

Transitive set of associations for n sources from a set of m observations scales as O(nm2):

Master catalog with per night updates of associations Full revisions still needed periodically Schemes needed – spatial indexing not sufficient

Survey Sources Obs/source Associations

PQDR1 ~10 million ~15 ~4 billion

CRTS ~500 million ~250 ~20 trillion

Next gen Billions ~1000 > Quadrillions

July 5, 2012Matthew J. Graham

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Source characterization

Popular activity: Richards et al. (2011), Dubath et al. (2011), Shin et al. (2009),…

Variety of (fast) characterizing measures: Moments Flux ratios Shape ratios (e.g., fraction of curve below median) Variability indices (e.g., Stetson K, von Neumann, Abbé) Periodicity measures:

base frequencies + harmonics amplitudes and phases

Specific class indicators (e.g., quasar index) Wavelets Singular value decomposition Segmentation methods and pattern analysis Discretization (e.g., SAX, Persist)

Defines high-dimensional (representative) feature space Noisy, irregularly sampled data can lead to false features

July 5, 2012Matthew J. Graham

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Relevant features

Richards et al. 2011

July 5, 2012Matthew J. Graham

Sparsity and heterogeneity of available data make this a very challenging problem

Real-time vs. archival Decision trees:

Blazars, CVs and RR Lyrae: ~90% completeness, ~9% contamination

Probabilistic structure function 2D distribution of (Δm, Δt) for all possible epoch pairs >90% completeness

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Source classification

July 5, 2012Matthew J. Graham

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Summary

VAO is developing an interoperable framework to: connect partner providers of both data and analysis resources expose them as an integrated whole for wider community use

Community call for collaborative proposals: Access to data related to VAST (PI: T. Murphy) Access to databases of AAVSO (PI: M. Templeton)

Cooperative work with LSST Complementary efforts on real-time and archival

characterization and classification, particularly based around CRTS