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VAO: Archival follow-up and time series

VAO: Archival follow-up and time series. Matthew J. Graham, Caltech/VAO. Time domain requirements. Stakeholder perspective: Many observed phenomena are short-lived Scientific returns depend on: d etection timely and well-chosen follow-up A system needs to:

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VAO: Archival follow-up and time series

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  1. VAO: Archival follow-up and time series Matthew J. Graham, Caltech/VAO

  2. Time domain requirements Stakeholder perspective: Many observed phenomena are short-lived Scientific returns depend on: detection timely and well-chosen follow-up A system needs to: Fully process data as they stream from telescopes Compare with previous data from same region of sky Reliably detect any changes Classify and prioritize detections for followup

  3. 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

  4. Interoperable time series - I Common set of data access protocols: Common data formats: VOTable FITS CSV

  5. Interoperable time series - II Common data models with pointer mechanism (Utype): VOEvent Describes observations of transient astronomical events Who, What, Where/When, How, Why Spectral Describes generalized spectrophotometric sequences Basis for Spectrum, SED and TimeSeries DMs TimeSeries Describes 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

  6. Data providers CRTS HATNet CoRoT Kepler

  7. VAO Time Series Search Tool

  8. 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)

  9. 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

  10. 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

  11. Relevant features Richards et al. 2011

  12. 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 Source classification

  13. 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

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