DIASource Association CHRIS MORRISON, UNIVERSITY OF WASHINGTON - - PowerPoint PPT Presentation

DIASource Association

CHRIS MORRISON, UNIVERSITY OF WASHINGTON

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ap_association Overview

New DIASources DIAObjects Association Updated/New DIAObjects Associated DIASources DIASource History

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ap_association Overview

ap_association’s job:

• Associate previously existing

DIAObjects with newly observed DIASources

• Update DIAObject summary statistics
• r create new DIAObjects
• Send updated/new DIAObjects and

associated DIASources off to storage.

Needs from PPDB interface:

• Retrieve or be given previous

DIAObjects in a sky region.

• Retrieve or be given the DIASource

history of previous DIAObjects

• Format conversion to/from native DB

types from/to pipelines objects. DPDD- ification of DIASources from ip_diffim.

One of the few pipelines that needs tight DAX integration during execution.

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What Tasks exist

AssociationTask:

• Requests/Stores 


DIAObjects/Sources by calling one of the two DB subtasks

• Simple nearest neighbor

“Handshake” match

• Computes DIAObject:
• mean positions
• mean/scatter/err fluxes

AssociationDBSqliteTask:

• Wrapper for Python’s

sqlite3 module

• Load/Stores DIAObjects/

Sources in sqlite DB

• Very simplistic database

structure

• Does its own DPDD-

ification and
 afwTable<->DB conversion AssociationL1DBProtoTask:

• Wrapper of Andy S.’s

l1dbproto package.

• Can be swapped in as a

subtask of AssociationTask.

• Still needs to perform

DPDD-ification on the DIASources output by ip_diffim

• Doesn’t currently use all of

1dbproto’s features.

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Current AP data flow

DIAObject Table DIASource Table DIAObject Table DIASource Table ip_diffim New DIASources DIAObjects Associate List of DIAObjects To Update Associated DIASources DIASource History Updated/ New DIAObjects Update/Create DIAObjects

Science Pipelines Databases

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The pipeline is running.

• Integrated as part of ap_pipe
• Successfully running on

DECam-HiTS data

• More datasets to come
• Next steps (outside of data

interface)

• Time series features
• ForcedPhotometry at

DIAObject locations

• Fancier association

algorithm.

credit: M. Rawls

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• A task for DIASource association exists
• Can use a simple sqlite3 DB or Andy’s

l1dbproto.

• Successfully runs on real data and

produces “light curves”.

• Other summary statistics coming.
• Needs a proper interface to DAX.
• Currently both DB wrappers spend

most of their time DPDD-ing and converting afw<->DB types.

Summary

credit: M. Rawls