VOEventNet: Revolutionizing Real-Time Astronomy and Rapid Event Response
VOEventNet represents a groundbreaking initiative in real-time astronomy, utilizing a network of telescopes for rapid discoveries of dynamic astronomical events. Funded by NSF and involving key institutions like Caltech and UC Berkeley, this peer-to-peer infrastructure facilitates immediate responses to events in the night sky. With automatic event notifications sent to subscribers within seconds of discovery, VOEventNet enhances our ability to observe and understand celestial phenomena. The project also incorporates advanced machine learning for event classification and follow-ups, establishing a new standard for astronomical research and collaboration.
VOEventNet: Revolutionizing Real-Time Astronomy and Rapid Event Response
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Presentation Transcript
VOEventNet By Matthew J. Graham (Caltech)
What is VOEventNet? • Real-time astronomy with a rapid-response telescope grid • A peer-to-peer cyberinfrastructure to enable rapid and federated observations of the dynamic night sky • A network of telescopes and computers working synergistically, under the watchful eye of humans, to find and study interesting astronomical events • A transportation of events to interested subscribers, automatically in seconds or minutes after discovery
What is VOEventNet? really ^ • $600k 3 year NSF-funded project under the DDDAS (Dynamic Data-Driven Applications Systems) initiative involving Caltech, UC Berkeley and LANL • Personnel: Matthew Graham Ashish Mahabal Andrew Drake Derek Fox Przemek Wozniak Roy Williams (PI) Joshua Bloom George Djorgovski Shri Kulkarni Thomas Vestrand
VOEvent database eStar GRB satellites Architecture Palomar-Quest PQ next-daypipelines baselinesky Raptor catalog Palomar 60” PQ Event Factory Event Synthesis Engine VOEventNet Pairitel SDSS 2MASS known variables known asteroids remote archives
Palomar-Quest Survey • Synoptic sky survey using the 48” Palomar Samuel Oschin Schmidt telescope and the 112-CCD, 161-Megapixel Quest II camera • Collaboration between Caltech, Yale/Indiana U., NCSA, and JPL; et al. • VO compliance/standards built in from the start • Two modes: drift scan with UBRI/rizz or multiple repeated snapshots in one filter ~70GB of data/night • 15000 deg2 observed a minimum of 8 times with baselines minutes to years
Real PQ data: The Big Picture • A 152 ft 20 ft mural produced for Griffith Observatory from PQ survey BRI images • A swath of 15.2 2.0 swath through the center of the Virgo cluster, sampled at 0.4 arcsec/pixel, giving a 136,800 18,000 pixel image • Computed at CACR using HyperAtlas and a custom data cleaning pipeline • Reproduced on 114 steel-backed porcellain plates, expected to last many decades • Will be seen by millions of visitors • Associated website will include NVO outreach
The Big Picture: detail The Big Picture: detail
The Big Picture: more detail The Big Picture: Tile C12 (M87) Zoom-in
Transients in the Big Picture 740 Cantabia Tile b07
VOEvent database eStar GRB satellites Architecture Palomar-Quest PQ next-daypipelines baselinesky Raptor catalog Palomar 60” PQ Event Factory Event Synthesis Engine VOEventNet Pairitel SDSS 2MASS known variables known asteroids remote archives
Palomar-Quest Event Factory • Real-time pipeline to process raw data streaming from telescope: • Remove detector signatures including glitches masquerading as transient events: meteors, airplanes, glints from satellites and junk, etc. • Apply basic photometric and astrometric calibration • Extract detected sources and measure attributes • Compare with baseline data (catalogs/images) to identify new, transient or highly variable sources • Compare with dbs of known variables, asteroids, etc.
VOEvent database eStar GRB satellites Architecture Palomar-Quest PQ next-daypipelines baselinesky Raptor catalog Palomar 60” PQ Event Factory Event Synthesis Engine VOEventNet Pairitel SDSS 2MASS known variables known asteroids remote archives
Event Synthesis Engine • New input arrives from PQ/elsewhere: • Establish event “portfolio” to archive and federate all subsequent data and analysis • Send initial event notification to subscribers • Launch query against external dbs via NVO • Classify and prioritize: • Evaluate likelihood probabilities of event being associated with possible astrophysical sources using machine learning techniques (‘Thinking Telescope’) • Evaluate urgency of desired follow-up • Send out VOEvent
VOEvent database eStar GRB satellites Architecture Palomar-Quest PQ next-daypipelines baselinesky Raptor catalog Palomar 60” PQ Event Factory Event Synthesis Engine VOEventNet Pairitel SDSS 2MASS known variables known asteroids remote archives
VOEventNet Communication Fabric • Author • Publisher (aggregator): • Stores packet and assigns identifier • Distributes to subscribers based on pre-defined criteria using one-way web services • Two dbs - Caltech for PQ and Los Alamos for Raptor - harvest each other • Subscriber: • Gets event from publisher, evaluates it and causes scheduling in telescope observing queue or an archive search
Event Cycling • An event can be injected back into the same decision/classification engine that published it but supplemented with data from elsewhere • Events dynamically cycle through follow-up observation and computation (no humans in loop) with subscribers make judgments and adding value until convergence
Robotic Telescopes • RAPTOR • Stereoscopic sky monitoring with follow-up ‘fovea’ telescope • PAIRITEL • Meter-class IR follow-up • P60 • Principal follow-up facility for PQ Events • eSTAR
Timeline • Year one - proof-of-concept system consisting of: • Source of VOEvents • A VOEvent store • Basic event discriminator • Robotic telescopic capable of responding to VOEvent: P60 and Pairitel • Year two - prototype system • Year three - production system