Notes on Schema Version 9


Proposed Image Products and PSPS Tables and Views

IMAGE PRODUCTS 

Survey  | Description               |  Image Products Saved

3pi     | 3pi                       | 3pi warps, 3pi stacks
CNP     | Celestial North Pole (3pi)| CNP warps, stacks
SS      | ecliptic w band data      |   w warps, w stack
MDD     | Medium Deep Deep          |  MD warps, deep stacks 
MDT     | Medium Deep Template      |            template stacks
MDN     | Medium Deep Nightly Stack |            nightly stacks 
MDN     | Medium Deep Off Year      |            off year template stacks 
M31     | M31                       | M31 warps, M31 stacks, template
STS     | STS                       | STS warps, STS template - best iq stack 


Survey  | Description               |  Image Products NOT Saved, but analyzed for difference detections and discarded 

3pi     | difference                |3pi warps - stack 
CNP     | difference                |CNP warps - stack 
SS      | difference image          |  w warps - stack 
MDD     | difference image          | MD warps - off year template stacks  
MDO     | difference image          | MDN stacks - off year template stacks  
M31     | difference image          | M31 warps - stack 
STS     | difference image          | STS warps - template 



PSPS TABLES                                                 


Fundamental IPP Data Products  Origin 

Detection                      detections found in detrended images   
ObjectThin                     positonal and flag data, no fluxes   
MeanObject                     extracted Mean properties as determined from individual Detections.   
StackObject                    Stack Detections in all filters, with forced upperlimits, separate rows for primary, secondary, best stack detections (80% time only primary)  
StackApFlx                     all filters in one table, unconvolved and convolved apertures (up to  6 , 8, 10(?) pixels  in all bands) 
StackModelFit                  all filters in one table. Models of high signal to noise objects in low density regions.     
DiffDet                        difference detections found in difference image = image - template 
DiffObject                     associations of Difference Detections
ForcedWarpMeasure              forced measurements on each warp in all filters at the position of an object found in a stack. Includes StackApFlx-like table   
ForcedMeanObject               like MeanObject, but using the Forced Mean Measurements to form the Mean. 

w band                         TBD 


Derived Data Product Tables 

pcsPhotoZ                       populated from PCS
pcsPhotoZProbability            populated from PCS
pcsStarGalQSOSep                populated from PCS
pcsStellarParams                populated from PCS
mopsMovingObject                populated from MOPS database
ppmObj                          proper motion and parallax populated from IPP  
tcsClassification               populated from transient server database
galExtinction                   populated from Dust team
stargal                         populated from various algorithms, combos of ipp parameters, fits to population as function of magnitude, or Support Vector Machine
GoodObject                      selected and populated post-facto  
StarGalaxySep                   selected and populated post-facto 


Observational Metadata and System MetaData Tables

CameraConfig
DetectionFlags
DiffMeta
Filter
FitModel
ForcedWarpMeta
FrameMeta
ImageFlags
ImageMeta                       add psf 3nd and 4th  moments
Mask
MovingObjectToDet
MovingObjectToDiffDet
MovingObjToObject
ObjectFilterFlags               flag info not in current schema but in PSI interface...
ObjectInfoFlags
ObjectQualityFlags
PhotoCal
ProjectionCell
ProcessingVersion
SkyCell 
StackDetectionFlags
StackMeta                       
StackToImage
StackType
Survey
TelescopeConfig
fitModel
pcsGalaxyModel
pcsPhotoZRecipe
pcsSGSepRecipe
pcsStarModel
pcsStellarParamsRecipe



PSPS VIEWS - examples  

ObjectMean        = ObjectThin join MeanObject 
ObjectStack       = ObjectThin join StackObject
ObjectApFlx       = ObjectThin join StackApFlx  
ObjectModel       = ObjectThin join StackModelFit   
ObjectStackAll    = ObjectThin join StackObject and StackAp and StackModelFit 
ObjectForcedMean  = ObjectThin join ForcedMeanObject
ObjectPrimary     = ObjectThin join StackObject join StackApFlx where primaryDection is true
ObjectBest        = ObjectThin join StackObject join StackApFlx where bestDetection is true


PSPS Indicies 

DetectionID
ObjectID
BatchID
StackTypeID
MovingObjID
DiffObjID 
RandomID           between 1 and 2^16 = 4,294,967,296 so you pick how many random objects you want ,
                   i.e 10,000 pick randomid < 10,000 and if indexed, this will be very fast. 
RandomStackID      between 1 and 2^16 = 4,294,967,296 so you pick how many random objects you want ,
                   i.e 10,000 pick randomid < 10,000 and if indexed, this will be very fast. 





