Exploration of the Time Domain

S. G. Djorgovski

VOEvent Workshop,

CACR, Caltech, April 2005

Overview• Scientific motivation and opportunity

– A poorly explored portion of the observable parameter space

– A range of exciting astrophysical phenomena– Possibility of fundamental new discoveries– Technological advances and synoptic surveys

• Pilot project using DPOSS plate overlaps– A rich phenomenology of the time-variable sky

• Palomar-Quest: status and plans

• Some challenges ahead

Exploration of the Time DomainThings that go bang! in the night… New and old.

Megaflares on normal stars Optical transients

Synoptic sky surveys will require a real-time mining of massive data streams and multi-PB archives

Exploration of the Time DomainThings that move…

Sedna

Quauar

M. Brown et al.

NEAT

Tunguska

A Systematic Search for Transients and Highly Variable Objects Using DPOSS Plate Overlaps

~ 1.5 O overlaps between adjacent plates ~ 40% of the total survey area

Effective Area Coverage in a “Snapshot” Survey:

If texp < tburst and baseline ∆t >> tburst , then

Effective Area = Useful Area Npasses Nfilters

For DPOSS: ~ 15,000 deg2 0.4 2 3 ~ 0.9 Sky

B. Granett, A. Mahabal, S.G. Djorgovski,and the DPOSS Team

Baselines from days to ~ 10 yrs, typical ~ 2-4 yrsTypical limiting mags r ~ 20, using 3 bandpasses (JFN gri)

Plate Flaw Transient

Variable Source

Types ofDetections

Preliminary Spectroscopic Follow-Up (34 sources)

SpectroscopicSource Identifications:

35% QSOs (1/2 radio loud)

18% CVs18% M dwarfs 6% Earlier type stars23% Unidentified (likely BL Lacs?)

Examples of DPOSS Transients

Examples of DPOSS Transients

flaw

asteroid

DPOSS Pilot Project Conclusions:• Faint, variable sky has a very rich phenomenology

– Spectroscopic follow-up will be a key bottleneck for any synoptic sky surveys

• Most high-amplitude variable sources down to ~ 20 mag are QSOs (Blazars, OVVs…), CVs, and flaring late-type dwarfs, with some early-type stars

• Asteroids may be a significant contaminant in a search for transients

• We find many more transients (~ 103/Sky) than expected from current models for orphan afterglows.– Most of them are probably QSOs, CVs, flaring stars, and

distant SNe; some may well be afterglows; and some may be new types of phenomena

• The Palomar-Quest survey should provide a major new venue for the exploration of the time domain

The Palomar-Quest Digital Sky Survey• The Palomar-Quest Consortium: Caltech - Yale - JPL• 50% point&shoot (NEAT, M.Brown), 50% drift scan (PQ survey)• PQ survey: a fully digital successor to the historic Palomar sky

surveys; a Caltech-JPL-Yale-NCSA-… collaboration• Data rate ~ 1 TB/month; ~100 TB total; many scientific uses• VO connections and standards built in from the start; a testbed for

many new IT/ VO applications

The PQ Survey: A new, digital, synoptic sky survey using the Quest-2 112-CCD camera

at the Palomar 48” Samuel Oschin telescope

• A collaboration between Caltech, Yale/Indiana U., and NCSA/UIUC; plus collaborations with other groups (INAOE, LBL, JPL…); started in summer of 2003

• About 50% of the telescope time, driftscan mode• Now next-day (or month… processing real-time• NVO connections and standards built in from start• Repeated observations, time baselines minutes to years

• A science and technology precursor/testbed for the LSST and other major synoptic sky surveys in the future

PQ Survey Sky Coverage• Range -25°< < +30°, excluding the Galactic plane• Ultimately cover ~ 14,000 - 15,000 deg2

• Rate ~ 500 deg2/night in 4 bands• As of Jan’05, covered ~ 13,000 deg2 in UBRI, of which

~ 11,000 deg2 at least twice, and ~ 4,700 deg2 at least 4 times; and ~ 14,100 deg2 in rizz, of which ~ 11,600 deg2

at least twice, and ~ 4,200 deg2 at least 4 times

The PQ Survey Science• Large QSO and gravitational lens survey

– Tests of the concordance cosmology– Dark mattter distribution– AGN physics and evolution

• High-redshift (z ~ 4 - 6.5) QSO survey– Probes of reionization and early structure formation

• Exploring the time domain– Supernovae, GRBs, AGN, var. stars, optical transients– Surprizes and new phenomena?– Time baselines ranging from minutes (inter-CCD) to days,

months, years (repeat scans) to decades (cross-match to DPOSS, SDSS, etc.)

• Galactic structure and stellar astrophysics• Etc. etc. - think what it can do for you

Palomar-Quest Data FlowImage

ArchiveCatalogs Archive

Master ArchivePipeline

Data Proc.

Target Selection

CIT

Yale

NCSA

JPL

LBL SNF

SDSC

P48

CITData

BrokerP200

Follow-up

PQ Survey: Recent and Upcoming Developments

• Completely redoing the DRP at Caltech– Much (much!) better data cleaning– Image remapping to a standard pixel grid, to enable

image coaddition and subtraction• Using the HyperAtlas technology for this

• Using some TeraGrid resources

– Yet to come: better object detection, classification

• Next: A real-time DRP for discovery of transents and moving objects (collaborate w. JPL, M. Brown)

Improved Cleaning

of PQ Data

Improved Depth in PQ Image Coadds

PQ SDSS

A Search for Low-z Supernovae

• Calibration of the SN Ia Hubble diagram• New standard candles from SN II• Endpoints of massive star evolution

C. Baltay, R. Ellis, A. Gal-Yam, S.R. Kulkarni, and the LBL SNF

(Using the image subtraction technique)

Optical Transients and Asteroids

(Exploratory work; A. Mahabal, with P. Kollipara, a Caltech undergrad)

CITData

Broker

JPLNEAT

Archive

VariablesArchive

MasterArchive

ImageArchive

Yale

AlertDecisionEngine

SourceClassification

Engine

KnownVariablesChecker

AsteroidSeparator

Engine

CITFast

Pipeline

CIT Next-Day Pipeline

NCSA

Other?LBL SNF

Website

P48

BroadcastAlert

Off-site Archives

NVO / Multi-Off-site Archives

Palomar-Quest Real-Time TransientsDiscovery System Data Flow

ComparisonEngine:

Vars./Trans.Detector

Some Challenges Ahead• Automated, reliable, adaptive data cleaning

• High volume data generators lots of glitches

• Cutting-edge systems poor stability

• High completeness / Low contamination• Must work with “solar system people” - moving

objects are the major contaminant for extra-solar-system variables and transients (and vice versa?)

• Automated, reliable event classification and alert decisions (need Machine Learning methods)– Sparse data from the event originator; folding in

heterogeneous external data; VO connections; etc.