lsst and voevent voevent workshop pasadena, ca april 13-14, 2005 tim axelrod university of arizona
TRANSCRIPT
LSST and VOEvent
VOEvent Workshop
Pasadena, CA
April 13-14, 2005
Tim Axelrod
University of Arizona
2
Overview of Talk
• Science drivers
• Quick look at LSST
• Data pipeline
• Characteristics of LSST transients
• LSST and VOEvent
3
LSST Science Drivers
• Characterize dark energy through
– Weak lensing
– Supernovae
– Galaxy cluster statistics
• Explore transient and variable objects
• Census of solar system objects, especially PHO's
• 3D structure of the Milky Way
4
A Quick Look at LSST
• Aperture diameter: 8.4m
• Effective aperture: 6.7m• FOV: 3.5 deg• Filters: u(?), g, r, i, z, y• Observing mode: pairs
of 15 sec exposures, separated by 5 sec slew
• Single exposure depth: 24.5
• Site: Baja or Chile• On sky: 2013
5
LSST Optics
LSST Focalplane
3.5 gigapixel, 2 sec readout
Data Acquisition
Image Processing
Pipeline
Detection Pipeline
Association Pipeline
ImageArchive
SourceCatalog
ObjectCatalog
Alerts
Deep Detection Pipeline
DeepObjectCatalog
VO Compliant Interface
LSST Data Pipeline
Data Pipeline Functions
• Image Processing Pipeline is responsible for producing– Calibrated science images
• Astrometric calibration (WCS)• Photometric calibration
– Subtracted images– Stacked images
• Detection Pipeline is responsible for producing– The Source Catalog, which contains parameters of all sources
found in an image: location, brightness, shape
• Association Pipeline is responsible for associating sources found at different times and (sometimes) locations, producing– The Object Catalog, which contains parameters of all astronomical
objects: lightcurves, colors, proper motions, …
• Object Classifier, design TBD, is responsible for periodically (re)classifying all objects in the Object Catalog
Spatial Sampling
• Output of LSST observing simulator
• Cerro Pachon, 475 days, real weather
• Weak lensing + supernovae + NEA search
Time Sampling
3 day peak from SN
Time Sampling – cont
Detectable Astrophysical Transients
• We are limited mostly by– Time sampling– Photometric accuracy (goal is 1%)
• We will not see (for example)– Low amplitude pulsating WD's (photometry)– Exoplanet transits (photometry and time sampling)– Microlensing caustic crossing events (time sampling)
• We will see– Many classes of periodic variables with amplitude > 1%– Many microlensing events– Novae– SNe, QSO's, …– As well as “middle of nowhere” transients (eg transients
found by DLS)
LSST and VOEvent
• LSST brings up nothing new regarding the “who”, “when”, or “where” aspects of VOEvent
• Areas of interest:– Making the “what” useful– Limiting “false alarm” rates– Quantifying “importance” (related to false alarm
probability?)– Partitioning of responsibility
Classification of Events
• The LSST data pipeline will attempt to classify variable objects based on– Position in CMD– Lightcurve shape– Motion, and orbital elements, if applicable
• The classifier will play a key role in identifying “events”– If the object is already in the catalog, an event occurs relative
to the object's previous behavior (an event is not simply a change in flux)
– Not so useful for new objects, but still possible to locate in CMD
How can a customer specify an interesting class of event?
• An “Event” is more than a change in flux– “Notify me of all Cepheids that change period by more than
5%”– “Notify me of all transients > 5σ with no corresponding
catalogued object”– “Notify me of any newly discovered solar system object with
a > 15AU and confidence > 0.9”
• We need a flexible semantics for event filters– SQL query on the object catalog is not quite enough(?)– Need to include temporal logic so that past behavior can be
referenced(?)
Transient Rates
• Astrophysical rates - stars– Roughly 5% of stars are variable at the 1% level or more– A typical LSST image contains roughly 2.5e5 stars– Rate from typical images are 1e7 per night– An exceptional LSST image (LMC, bulge) contains up to 4e6
stars
• Astrophysical rates – extragalactic supernovae– SN rate about 1 / 200 yr / galaxy– Changing flux from each visible for at least 30 d– A typical LSST (unstacked) image contains roughly 4e5
galaxies– Rate is about 1e5 per night
Transient Rates - cont
• Noise rates– Every PSF patch is a potential transient location – about 8e8
of these– Each is measured once every 35 sec (2 * 15 sec exposures;
5 sec slew)– Assuming gaussian noise
• About 3e4 / sec at 3σ • About 8 / sec at 5σ (3e5 / night)• Rate reduced by significant factor if detection required in
each 15 sec exposure separately
Dealing With High Event Rates
• LSST will detect transients at rate of O(1e5 – 1e6 / night)– No group of humans can look at these individually– No followup facility can look at more than a negligible
fraction– We need to filter these by a large factor to make them useful
• Excluding known variable objects results in the biggest reduction – but still leaves large noise rate
• Noise rates can be reduced by simply increasing the detection threshold – but at the cost of missing real information
• We need to carefully consider use cases, and make use of simulations, to find a way forward
VOEvent Processing Architecture
LSST Data Pipeline
VOEvent DB
EventFilter
EventFilter
EventFilter
System Boundary?
Unresolved Issues
• Who will implement the VOEvent DB into which LSST feeds?
• What latency is needed in generating VOEvents?• How best to incorporate links to extra information into
VOEvents – eg object lightcurve or image postage stamp
• How can we incorporate concepts like “classification change” or “period change” into VOEvent? – This type of event depends on a baseline, which somehow
must be part of the data
• How can we assign “importance” in a quantitative way?