1. Exploration of the Time Domain S. G. Djorgovski VOEvent Workshop, CACR, Caltech, April 2005

2. 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

3. Exploration of the Time Domain Things that go bang! in the night… New and old. Megaflares on normal starsOptical transients Synoptic sky surveys will require a real-time mining of massive data streams and multi-PB archives

4. Exploration of the Time Domain Things that move… Sedna Quauar M. Brown et al. NEAT Tunguska

5. 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 t exp > t burst, then Effective Area = Useful Area  N passes  N filters For DPOSS: ~ 15,000 deg 2  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 yrs Typical limiting mags r ~ 20, using 3 bandpasses (JFN  gri)

6. Plate Flaw Transient Variable Source Types of Detections

7. Preliminary Spectroscopic Follow-Up (34 sources)

8. Spectroscopic Source Identifications: 35% QSOs (1/2 radio loud) 18% CVs 18% M dwarfs 6% Earlier type stars 23% Unidentified (likely BL Lacs?)

9. Examples of DPOSS Transients

10. flaw asteroid

11. 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 (~ 10 3 /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

12. 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

13. 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

14. PQ Survey Sky Coverage Range -25°<  < +30°, excluding the Galactic plane Ultimately cover ~ 14,000 - 15,000 deg 2 Rate ~ 500 deg 2 /night in 4 bands As of Jan’05, covered ~ 13,000 deg 2 in UBRI, of which ~ 11,000 deg 2 at least twice, and ~ 4,700 deg 2 at least 4 times; and ~ 14,100 deg 2 in rizz, of which ~ 11,600 deg 2 at least twice, and ~ 4,200 deg 2 at least 4 times

15. 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

16. Palomar-Quest Data Flow Image Archive Catalogs Archive Master Archive Pipeline Data Proc. Target Selection CIT Yale NCSA JPL LBL SNF SDSC P48 CIT Data Broker P200 Follow-up

17. 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)

18. Improved Cleaning of PQ Data

19. Improved Depth in PQ Image Coadds PQSDSS

22. 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)

23. Optical Transients and Asteroids (Exploratory work; A. Mahabal, with P. Kollipara, a Caltech undergrad)

24. CIT Data Broker JPL NEAT Archive Variables Archive Master Archive Image Archive Yale Alert Decision Engine Source Classification Engine Known Variables Checker Asteroid Separator Engine CIT Fast Pipeline CIT Next-Day Pipeline NCSA Other?LBL SNF Website P48 Broadcast Alert Off-site Archives NVO / Multi- Off-site Archives Palomar-Quest Real-Time Transients Discovery System Data Flow Comparison Engine: Vars./Trans. Detector

25. 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.