LSST and VOEvent

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LSST and VOEvent

• VOEvent Workshop
• Pasadena, CA
• April 13-14, 2005
• Tim Axelrod
• University of Arizona

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Overview of Talk

• Science drivers
• Quick look at LSST
• Data pipeline
• Characteristics of LSST transients
• LSST and VOEvent

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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

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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

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LSST Optics
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LSST Focalplane
3.5 gigapixel, 2 sec readout
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LSST Data Pipeline
Data Acquisition
Image Processing Pipeline
Detection Pipeline
Association Pipeline
Alerts
Image Archive
Source Catalog
Object Catalog
Deep Detection Pipeline
Deep Object Catalog
VO Compliant Interface
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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

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Spatial Sampling

• Output of LSST observing simulator
• Cerro Pachon, 475 days, real weather
• Weak lensing supernovae NEA search

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Time Sampling
3 day peak from SN
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Time Sampling cont
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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
  gt 1
• Many microlensing events
• Novae
• SNe, QSO's,
• As well as middle of nowhere transients (eg
  transients found by DLS)

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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

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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

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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 gt 5s with no
  corresponding catalogued object
• Notify me of any newly discovered solar system
  object with a gt 15AU and confidence gt 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(?)

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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

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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 3s
• About 8 / sec at 5s (3e5 / night)
• Rate reduced by significant factor if detection
  required in each 15 sec exposure separately

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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

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VOEvent Processing Architecture
Event Filter
LSST Data Pipeline
VOEvent DB
Event Filter
Event Filter
System Boundary?
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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?