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

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?