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Image Subtraction or. . Peter Nugent(LBNL/UCB) Image Subtraction or. . Peter Nugent(LBNL/UCB)

If I Could Redo Everything Again for PTF, This Is What I Would Do. If I Could Redo Everything Again for PTF, This Is What I Would Do. . . Peter Nugent(LBNL/UCB)

Things to Know § Understand the instrument and changes to it - de-trending is Things to Know § Understand the instrument and changes to it - de-trending is key to getting off to a good start: talk to the instrument scientists! § NEVER be happy with what you have: § Speed/turn-around § Types of db queries § References § Catalogs (stars, galaxies, etc. ) You do not need to visit the observatory! I have processed ~1 PB of data (20 M ccd chips) between Palomar -QUEST and PTF. I did not have to go to the mountain, the mountain came to me. . . § Know what science the collaboration would like to achieve: § Try to accommodate everything from start § Be flexible enough to adapt mid-way § Always look for new scientific opportunities § Learn their science § Do not mix image subtraction with other parts of pipeline i. PTF Summer School

PTF Pipeline 50 -100 GBs/night i. PTF Summer School PTF Pipeline 50 -100 GBs/night i. PTF Summer School

Image Subtraction There are two types of image subtraction and they should not be Image Subtraction There are two types of image subtraction and they should not be confused – ever: § Real-Time § Goal is to identify transients § Photometry should be good, but does not have to be perfect – in principle it can not be § Final Photometry § Good enough to write a paper on cosmology § Strives for perfection § Major advantage: You know where the object is. . . zoom in, pick your calibration stars, make perfect references, etc. i. PTF Summer School

What is out there hotpants – by Andy Becker High Order Transform of PSF What is out there hotpants – by Andy Becker High Order Transform of PSF ANd Template Subtraction http: //www. astro. washington. edu/users/becker/v 2. 0/hotpants. html There a few variants (and you will hear more about one tomorrow) but they all have the same form: § § Make a reference image Align and convolve with a new image Perform a subtraction Identify the candidates i. PTF Summer School

hotpants -inim ${new} –hki -n i -c t -tmplim ${refremap} -outim ${sub} -tu ${template_saturation} hotpants -inim ${new} –hki -n i -c t -tmplim ${refremap} -outim ${sub} -tu ${template_saturation} -iu ${new_sturation} -tl ${template_lower} -il ${input_lower} -r ${2. 5*seeing} -rss ${6. 0*seeing} -tni ${refremapnoise} -ini ${newnoise} -imi ${submask} -nsx ${nsx} -nsy ${nsy} hki : verbose output -c t : convolve to template -n i : normalize image nsx & nsy : size of regions within image (128 X 128 pixels ~ 2. 5’) submasks: are key to getting things right (bad pixels kill) I used the standard 3 gaussian & 6 degree polynomial for the kernel. No need to do more or less. i. PTF Summer School

Reference Ideally the reference comes from one image, contributes no noise in the subtraction, Reference Ideally the reference comes from one image, contributes no noise in the subtraction, and is of comparable seeing. Nothing is ideal: § PTF had a dead chip. § Pointing was atrocious, became ~1’ after improvements § Took ~3 months to obtain images from each field that could make up a good reference § Photometric calibration was USNO B 1 catalog! § Constantly made an effort to make better reference images during the survey Settled on ~7 images, best seeing (but not undersampled) to make reference on a PTF field/chip basis: depth, area & bad pix. i. PTF Summer School

New Don’t settle for having the survey forced down your throat, complain when things New Don’t settle for having the survey forced down your throat, complain when things are going wrong! § Demand that fits header keywords are right, say for example the FILTER: this separates you from them § Know what the pointing/survey strategy is ahead of time (hitting M 31 30 times in one night causes problems if you are not prepared for it) § Don’t bother with subtractions when they are not needed (|galactic latitude| < 10) Everything is relative, treat the references as gold for photometric and astrometric calibration. Work out differences with the universe later (HST guide stars, absolute photometric calibration, etc. ) i. PTF Summer School

New- Ref = Sub Reference Image Subtraction moon New Image This will always be New- Ref = Sub Reference Image Subtraction moon New Image This will always be a needle in a haystack problem. i. PTF Summer School

New- Ref = Sub Per image we would have ~250 5 -σ detections. We New- Ref = Sub Per image we would have ~250 5 -σ detections. We would require 2 independent detections. Up to 300 images taken per night ~ 1000 sq. deg. i. PTF Summer School Use Machine Learning to get rid of the crap. . . Do not attempt to make the perfect subtraction!

PTF Sky Coverage References were made for ~20000 sq. deg. in R-band (minimum 7 PTF Sky Coverage References were made for ~20000 sq. deg. in R-band (minimum 7 minutes w/ seeing < 3. 0” and limiting magnitude > 19. 9). i. PTF Summer School

NERSC • • • Access though general DOEEdison (N 7): Cray XC 30 Intel NERSC • • • Access though general DOEEdison (N 7): Cray XC 30 Intel Ivy Bridge w/ 133, 824 cores HEP call for Cori (N 8) will be one of the first large Intel KNL systems compute time at and will have unique data capabilities. 9, 300 single-socket NERSC. nodes with 60 cores per node and burst buffer (NVRAM) 3 B cpu hrs / year for the entire memory footprint. Hopper (N 6): Cray XE 6 Opteron w/ 153, 216 cores • NERSC has a Global Filesystem which is viewable from all compute systems (40 GB/s). Very high-speed local scratch space on each of the big-irons (168 GB/s) • 240 PB tape archive • Data Transfer nodes using ESnet • Science Gateway and Database nodes for access outside NERSC i. PTF Summer School

Why NERSC • Why buy the cow, when you get the milk for free? Why NERSC • Why buy the cow, when you get the milk for free? • You always want ~10 X the compute you need to run a single night on hand at any time to catch up (network, shutdowns, new refs, etc. ) • The subtractions are the source of all complaints, whether they are justified or not. – Where are my fields from last night? – How come it is taking so long to see the subs? – What is my SN/CV/GRB doing now? Thus you don’t want computing to be one of them. NERSC operates 24/7 with staff on-call for issues that come up round the clock. As PTF was special, 100 khrs/yr but real-time, we were granted special privileges. Special queues, db’s, global disk space, etc. On average there are 3 -4 shutdowns per year: all moved to full moon since 2009. i. PTF Summer School

Observatory Pipeline Processing/db Data Transfer Nodes Science Gateway Node 2 Subtractions Carver NERSC GLOBAL Observatory Pipeline Processing/db Data Transfer Nodes Science Gateway Node 2 Subtractions Carver NERSC GLOBAL FILESYSTEM 250 TB (170 TB used) i. PTF Summer School PTF Collaboration via Web Science Gateway Node 1

PTF db • Chose a Postgres db with q 3 c for spatial queries PTF db • Chose a Postgres db with q 3 c for spatial queries • Based on studies comparing Oracle, mysql and postgres • Runs at NERSC on their scidb nodes: 32 -core nodes on a ZFS filesystem • This currently houses the i. PTF database which has over ~3 M images and ~1. 5 B detections which are queried in realtime 24/7. ZFS is a combined file system and logical volume manager designed by Sun Microsystems. The features of ZFS include protection against data corruption, support for high storage capacities, efficient data compression, integration of the concepts of filesystem and volume management, snapshots and copy-on-write clones, continuous integrity checking and automatic repair, RAID-Z and native NFSv 4 ACLs. i. PTF Summer School

q 3 c Q 3 C is the plugin for Postgre. SQL database, designed q 3 c Q 3 C is the plugin for Postgre. SQL database, designed for working with large astronomical catalogs or any catalogs of objects on the sphere. Q 3 C allows you to perform fast circular, elliptical or polygonal searches on the sphere as well as perform fast positional cross-matches and nearest neighbor queries. Similar to htm (Hierarchical Triangular Mesh). The ideas behind Q 3 C are described in Koposov et al. (2006) i. PTF Summer School

PTF Database R-band g-band images 1. 82 M 305 k subtractions 1. 52 M PTF Database R-band g-band images 1. 82 M 305 k subtractions 1. 52 M 146 k references 29. 2 k 6. 3 k Candidates 890 M 197 M Transients 42945 3120 All in 851 nights. An image is an individual chip (~0. 7 sq. deg. ) The database reached 1 TB. i. PTF Summer School

Turn-around 2012 -07 -06 What does “real-time” subtractions really mean? In the last 2 Turn-around 2012 -07 -06 What does “real-time” subtractions really mean? In the last 2 years of PTF, for 95% of the nights all images are processed, subtractions are run, candidates are put into the database and the local universe script is run in < 1 hr after observation. Median turn-around is 30 m. i. PTF Summer School

Palomar 48” Telescope 100 TBs of Reference Imaging HPWREN Microwave Relay Computing – I/O Palomar 48” Telescope 100 TBs of Reference Imaging HPWREN Microwave Relay Computing – I/O Astrometric Solution SDSC to ESNET Reference Image Creation NERSC Data Transfer Node Image Processing / Detrending Image Subtraction Nightly Image Stacking Networking Data Transfer Star/Asteroid Rejection Transient Candidate Real-Bogus ML Screening 500 GB/night Publish to Web Scanning Page Web UI Outside Database for Triggers Marshal i. PTF Summer School Wake Me Up – Real Time Trigger Real-Time Trigger 40 Minutes Heavy DB Access 1. 5 B objects in D Outside Telescope Follow-up

Future Surveys Telescope i. PTF/PTF 8. 7 DES 11. 7 ZTF 42. 6 LSST Future Surveys Telescope i. PTF/PTF 8. 7 DES 11. 7 ZTF 42. 6 LSST ZTF (46 deg. 2) AΩ 82. 2 i. PTF (7. 2 deg. 2) ZTF image processing will be more challenging as the goal will be to do everything even faster and it is 12 times more data. i. PTF Summer School

Parallel Processing/Subtractions n n n All computers will have many cores, and the same Parallel Processing/Subtractions n n n All computers will have many cores, and the same amount of memory, 2+ years from now (10 -100). Current pipelines work at the level of one ccd chip per core – this will fail in the future. Need to parallelize all aspects of the pipeline where possible. Threading is easy for most of this, keeping things in memory where possible is ideal: n n n Astrometric catalogs matching Bad pixel masks, CR’s Flats, biases, masks, etc. Asteroid rejection (verification) Comparison with historical transients i. PTF Summer School

brightness Bottlenecks…crude vs. real 5 - data in db time i. PTF Summer School brightness Bottlenecks…crude vs. real 5 - data in db time i. PTF Summer School

Conclusions - Future LSST - 15 TB data/night Only one 30 -m telescope i. Conclusions - Future LSST - 15 TB data/night Only one 30 -m telescope i. PTF Summer School