Using HSC RC2 to test algorithms on tract-scale
34464TL;DR: Scroll down to Steps

Sometimes you need a tract for sufficient number counts of sources (e.g. colors, calibration systematics) or to find rare failures.  The “RC2” HSC dataset is an pre-packaged, continuously maintained repo with on lsst-dev.  Its 5 sq. deg. with ~150 visits across 5 bands. This doc provides visit lists and how to use. If you have questions ask in #dm-hsc-reprocessing.

Note that another good resource is Hsin-Fang’s full recipe in https://github.com/lsst-dm/gen2-hsc-rc2  as of w_2019_38 when she transferred ownership over to Monika. 

HSC Subaru Strategic Program Background Info



Internal external Data Release mapping:
External Name
Internal Name
Notes
Status on lsst-dev:
PDR1
Ref: Bosch+14 


raws: /datasets/hsc/repo 
latest rerun: DM-13666
PDR2
Ref: Aihara+19
S18A
Processed at NAOJ:
Spring 2018   
 w_2018_18 w/ backports. 
Paul scheduled to copy over
by Aug 31st 2019.

S19A
Processed at NAOJ 
Now 
w_2019_16  
Only diff is jointcal, not much more data. 


We have all of the raw Public Data Release 1 (PDR1) data  in /datasets/hsc/repo/  and calibrations are at: /datasets/hsc/repo/CALIB/
 

RC2

Lauren chose three tracts to comprise the RC2 data set. The rationale is described in:  https://jira.lsstcorp.org/browse/DM-11345
GAMA
9615
VVDS
9697
COSMOS
9813

Every month, Hsin-Fang runs:


In addition to a reprocessing of all of PDR1, every month, Hsin-Fang runs a data release production on the above 3 tracts
The last one was /datasets/hsc/repo/rerun/RC/w_2019_22/DM-19244

The following visit lists contain the 5 filter X 3 tract = 15 combinations. It’s actually 16, because there’s a narrow band filter for tract 9813:  NB0921

export VVDS_G=6320^34338^34342^34362^34366^34382^34384^34400^34402^34412^34414^34422^34424^34448^34450^34464^34468^34478^34480^34482^34484^34486
export VVDS_R=7138^34640^34644^34648^34652^34664^34670^34672^34674^34676^34686^34688^34690^34698^34706^34708^34712^34714^34734^34758^34760^34772
export VVDS_I=35870^35890^35892^35906^35936^35950^35974^36114^36118^36140^36144^36148^36158^36160^36170^36172^36180^36182^36190^36192^36202^36204^36212^36214^36216^36218^36234^36236^36238^36240^36258^36260^36262
export VVDS_Z=36404^36408^36412^36416^36424^36426^36428^36430^36432^36434^36438^36442^36444^36446^36448^36456^36458^36460^36466^36474^36476^36480^36488^36490^36492^36494^36498^36504^36506^36508^38938^38944^38950
export VVDS_Y=34874^34942^34944^34946^36726^36730^36738^36750^36754^36756^36758^36762^36768^36772^36774^36776^36778^36788^36790^36792^36794^36800^36802^36808^36810^36812^36818^36820^36828^36830^36834^36836^36838

export GAMA_G=26024^26028^26032^26036^26044^26046^26048^26050^26058^26060^26062^26070^26072^26074^26080^26084^26094
export GAMA_R=23864^23868^23872^23876^23884^23886^23888^23890^23898^23900^23902^23910^23912^23914^23920^23924^28976
export GAMA_I=1258^1262^1270^1274^1278^1280^1282^1286^1288^1290^1294^1300^1302^1306^1308^1310^1314^1316^1324^1326^1330^24494^24504^24522^24536^24538
export GAMA_Z=23212^23216^23224^23226^23228^23232^23234^23242^23250^23256^23258^27090^27094^27106^27108^27116^27118^27120^27126^27128^27130^27134^27136^27146^27148^27156