1. The Catalina Sky Survey Eric J. Christensen A.Boattini, A. R. Gibbs, A. D. Grauer, R. E. Hill, J. A. Johnson, R. A. Kowalski, S. M. Larson, F. C. Shelly Hot-wiring the Transient Universe III, Santa Fe NM, November 13-15, 2013 1) Facilities and Operations 2) Keys to CSS Success

2. Catalina Sky Survey Overview of facilities, operations, upgrades, pending proposals Keys to CSS success 1)Real-time processing and reporting 2)Human-assisted validation and operation 3)Integrated follow-up capability 4)Narrowly defined goals 5)Constant incremental improvements

3. Current Facilities Mt. Bigelow, AZ 0.7-m Schmidt 8.2 sq. deg. FOV 2.5 arcsec/pixel V lim ~ 19.5 1998 - present Mt. Lemmon, AZ 1.5-m reflector 1.2 sq. deg. FOV 1.0 arcsec/pixel V lim ~ 21.3 2004 - present

4. Retired Facilities Siding Spring Observatory, Australia 0.5-m Uppsala Schmidt 4.2 sq. deg. FOV 1.8 arcsec/pixel V lim ~ 19.0 2004 – 2013 Was the only full-time NEO survey located in the Southern Hemisphere

5. Upcoming Facilities Mt. Lemmon, AZ 1.0-m reflector 0.3 sq. deg. FOV 1.0 arcsec/pixel Currently being commissioned Queue-scheduled, robotic operation Will be primarily used for confirmation and follow-up of newly- discovered NEOs Will have standard UVBRI filters

6. Upgrades in progress – coming 2014 New 10k x 10k cameras will increase the FOV of both survey telescopes by factors of 4x and 2.4x 0.7-m Schmidt will conduct a nightly ~1000 sq. deg. survey around opposition

7. Proposed Facilities CLASS: the Catalina LCOGT Asteroid Southern Survey 3 x 1.0-m telescopes, 25 sq. deg FOV equivalent Flexible operation – can survey separately or together 5800 sq. deg. coverage per night to V~20.4, or 2000 sq. deg. to V~21.6 (assuming 4 visits) To be located on Cerro Tololo, Chile, will preferentially survey south of Declination -15

8. Operations summary Nominal cadence: 4 x 30s over ~30 minutes Typical revisits ~5 days near ecliptic, ~14 days off-ecliptic Observing programs created on the fly Data are processed in real-time, validated by human observers NEO candidates are reported and followed up during the night

9. Data Processing Pipeline Bias correction (from overscan) and flat-fielding (from library) Source Extraction (Sextractor) Astrometry (SCAMP) Global magnitude calibration – unfiltered to V Moving object detection (con4) Known object identification, likely main-belt asteroids flagged Presentation of candidate moving objects to observer Total processing time ~1 minute per image, ~1 minute for moving object detection + known asteroid ID

10. Keys to CSS success 1)Real-time processing and reporting 2)Human-assisted validation and operation 3)Integrated follow-up capability 4)Narrowly defined goals 5)Constant incremental improvements

11. 1) Real-time processing and reporting NEOs have a short shelf life – even a delay of ~12 hours between imaging and reporting can result in lost objects Follow-up observations can be triggered within minutes if desired. Positional uncertainties are kept small with regular follow-up, which minimizes telescope time required Real-time reporting shifts some fraction of the follow-up burden to other sites

12. 2) Human-assisted validation and operation Human-assisted validation:  Humans are good at quickly characterizing visual information  Allows detection software to output a “dirty” transient stream, with many false positives (10:1 false to real for unknown asteroids)  Detection threshold is pushed down to ~1 sigma. Transient stream would be much cleaner at 5 sigma, but efficiency would plummet

13. 2) Human-assisted validation and operation Human-assisted operation: – Real-time scheduling of survey + follow-up  flexibility – Real-time data quality control  better data – Able to troubleshoot complex problems  less downtime

14. At CSS, one observer can handle the data rate from one telescope. Expanding CSS operations (larger FOV cameras) is driving additional investment in processing and GUI software to allow one person to keep up Scaling to larger surveys, humans can still play a valuable role, but including them in the pipeline may require novel techniques (i.e. crowdsourcing) and investment in GUI development 2) Human-assisted validation and operation

15. 3) Integrated follow-up capability Survey instruments can be re-tasked to perform follow-up – Not ideal, as it takes away from primary mission. CSS typically spends 10-15% of survey time on follow-up Commissioning dedicated 1.0-m telescope Collaboration with LCOGT Other professional and amateur sites Current follow-up capability is well-matched to discovery output NEO survey has the advantage that follow-up is much like discovery, and doesn’t require specialized equipment, i.e. spectrographs The more powerful the survey, the greater the follow-up requirements – integrated follow-up becomes a much larger (and perhaps untenable) task

16. 4) Narrow focus: “Do one thing and do it well” For NEOs, unfiltered photometry is acceptable for discovery; gains ~0.7 mag. in sensitivity over R filter 1-sigma vs. 5-sigma gains another ~1.3 mag. Cadence optimized for NEO detection Large pixel scale (1”+) minimizes trailing loss; degraded astrometric accuracy is acceptable for discovery. – Large pixel scale is a consequence of using monolithic detectors instead of FPAs; added benefit is no gaps between detectors which degrades NEO discovery efficiency by (fill factor %)^(required visits) Multi-purpose surveys must compromise cadence, survey design, scheduling flexibility, etc. CSS fortunately has no constraints beyond the primary mission of NEO discovery.

17. 5) Constant incremental improvements Many small improvements in on-sky time, throughput, data quality, false detection control add up to big gains Recent upgrades include: – 10% increase in acquisition throughput – Rewritten detection software: increased sensitivity to both faster and fainter objects (10-15% more discoveries) – Ranked candidate lists, dynamic false detection control – Better automatic flat selection – More accurate and robust astrometry routines – Better data quality analysis tools

18. 5) Constant incremental improvements Pending upgrades include: – Optimization of focus models – Support for binning – Queue scheduling software – Analysis of cadence and survey strategy – Automatic flat generation and characterization for a variety of conditions

19. Summary Keys to CSS success 1)Real-time processing and reporting 2)Human-assisted validation and operation 3)Integrated follow-up capability 4)Narrowly defined goals 5)Constant incremental improvements Contact: Eric Christensen eric@LPL.arizona.edu