1 KFPA Critical Design Review – Fri., Jan. 30, 2009 KFPA Data Pipeline Bob Garwood- NRAO-CV

2 KFPA Critical Design Review – Fri., Jan. 30, 2009 History ● Science and Data Pipeline Workshop – November Initial pipeline sketch. ● Conceptual Design Review – February Initial design. ● KFPA Data Analysis Meeting – June ● Memo describing possible KFPA observing modes. Pisano, August 2008.

3 KFPA Critical Design Review – Fri., Jan. 30, 2009 Changes since Conceptual Design Review ● Basic design essentially unchanged ● Out-of-scope items (deferred) – continuum – cross-correlation (polarization) – complicated calibration schemes (“basketweaving”) ● baseline fitting added as an explicit step

4 KFPA Critical Design Review – Fri., Jan. 30, 2009

5 Existing GBT Data Analysis Software ● sdfits tool produces SDFITS file – associates raw data from a backend with meta data describing the observations. DCR, SP, Spectrometer. (data capture) ● GBTIDL – recommended spectral line analysis tool. Focused on single spectra processing and analysis, not on imaging. Used to prepare the data to be imaged elsewhere. (calibration, editing) ● AIPS is used to produce images.

6 KFPA Critical Design Review – Fri., Jan. 30, 2009 We can reduce k-band data now ● K-band spectrometer data calibrated and imaged using existing tools.

7 KFPA Critical Design Review – Fri., Jan. 30, 2009 Missing components ● None of the steps to an image are automated. ● Uses lab-measured Tcal values. ● Uses a scalar Tcal without regard to any structure in Tcal across the bandpass. ● Cross-correlation (polarization) data is not supported after the sdfits step. ● Poor support for continuum data.

8 KFPA Critical Design Review – Fri., Jan. 30, 2009 Missing Components continued ● Only have prototype tool for visually interacting with large amounts of data (e.g. visual flagging). ● Only prototype tools for statistically flagging or editing the data (e.g. RFI rejection).

9 KFPA Critical Design Review – Fri., Jan. 30, 2009 Goals of the Prototype Pipeline ● Support KFPA commissioning ● Explore new processing tools/techniques not yet widely available in GB (vector calibration, statistical data flagging and editing, visualization, parallel processing). ● Prototype an automated pipeline – add necessary meta data to capture user intent ● Prototype tools necessary to support larger focal plane array (e.g. parallel computing)

10 KFPA Critical Design Review – Fri., Jan. 30, 2009 Goals continued ● Based on prototyped tools, estimate cost associated with delivering a pipeline and necessary computing hardware to handle the expected data rates for a larger focal plane array. ● Develop these tools and pipeline infrastructure for use with data from other backends.

11 KFPA Critical Design Review – Fri., Jan. 30, 2009 Pipelines ● Crude pipeline can be assembled from existing components for quick-look images. – Small modification to sdfits (data capture) to properly capture individual feed offsets from pointing position. – Some additional meta data to capture default image parameters and associated “off” information.

12 KFPA Critical Design Review – Fri., Jan. 30, 2009 Pipelines ● Imperative for large focal plane array. – large data rates and volume ● Necessary for even a modest 7 element array. ● Useful for data from other GBT backends – Users often end up creating partial pipelines – The NRAO archive needs this to be able to provide more than just the raw GBT data. – Other telescopes routinely provide roughly- calibrated data to their users – most institutions consider this the starting point of a data pipeline.

13 KFPA Critical Design Review – Fri., Jan. 30, 2009 Pipelines ● Requires using a standard observing mode. – Sufficient meta data needs to be captured to drive the pipeline (e.g. groups of scans that should be processed together, associated “off” information, etc). ● Individual components can be used outside of the pipeline – often with additional options.

14 KFPA Critical Design Review – Fri., Jan. 30, 2009 Pipeline ● None of those steps is unique to the KFPA – KFPA-specific steps are likely as part of the statistical flagging and editing component as well as in data capture. ● Components are being developed independently. – no dependencies between components ● Some components are likely to be useful interactively – especially flagging and editing.

15 KFPA Critical Design Review – Fri., Jan. 30, 2009 Pipeline Design continued ● Eventually - Continuum data will be extract from the spectral line data at the appropriate point in the pipeline. This work is out-of- scope for the initial pipeline. ● Language – python – Experience with python in Green Bank – Same language used in the ALMA pipeline and in casa.

16 KFPA Critical Design Review – Fri., Jan. 30, 2009 Pipeline design, continued ● Data formats – SDFITS up to imaging step. ● Currently produced by data capture (sdfits) ● Tools already exist to interact with this data. ● May be necessary to split data into multiple SDFITS files for parallel computing needs. – Alternatives used as necessary – for speed or take advantage of existing tools – e.g. AIPS

17 KFPA Critical Design Review – Fri., Jan. 30, 2009 Parallel Computing ● Most of these steps are “embarrassingly parallel” - data from individual feeds can be processed independently – exceptions: some statistical flagging and editing and cross-correlation data – these are out of scope for the initial pipeline. ● Parallel processing will be explored during KFPA pipeline development.

18 KFPA Critical Design Review – Fri., Jan. 30, 2009 Development Priorities ● Calibration – Complete GBTIDL vector Tcal and initial calibration database work. – Design pipeline calibration database. ● Data Capture – This is the current bottleneck. Work is underway to improve the processing speed. A new raw data format may be necessary.

19 KFPA Critical Design Review – Fri., Jan. 30, 2009 Priorities continued ● Data capture (continued) – ensure that feed offsets are used properly with pointing direction to get individual feed pointings – put default calibration values into calibration database (GBTIDL model first, pipeline model when design completed). – Add appropriate meta information as necessary to automate data flow through the pipeline.

20 KFPA Critical Design Review – Fri., Jan. 30, 2009 Priorities, continued ● Pipeline design and implementation – Automate flow of data between existing compontents. – Initially this will be a simple script triggered off of the standard observing modes using default values and available meta information. – It will be possible to re-run the pipeline using alternative parameters (e.g. baseline fits, additional statistical flags, interactive flagging and editing, etc).

21 KFPA Critical Design Review – Fri., Jan. 30, 2009 Priorities, continued ● Data Visualization – Evaluate existing tools for viewing with and interacting with GBT data in sdfits form. ● Data quality throughout the pipeline ● Interactive flagging ● Summer student project – 2008 – prototype data viewer. Can do interactive flagging, not sufficiently general.

22 KFPA Critical Design Review – Fri., Jan. 30, 2009 Priorities, continued ● Investigate simple parallel processing options – start with existing code (sdfits) – take advantage of independence of data from each feed – keep things simple

23 KFPA Critical Design Review – Fri., Jan. 30, 2009 Priorities, continued ● Statistical data flagging – Borrow from code developed by GBTIDL users – Borrow from aips++/casa autoflagger – Develop “basketweaving” equivalent for KFPA array. ● Use (near) crossing points on sky (same feed; multiple feeds) to adjust data. ● out of scope for initial pipeline development

24 KFPA Critical Design Review – Fri., Jan. 30, 2009 Priorities, continued ● Algorithm development (calibration, continuum data handling, etc). Roberto Ricci, U. Calgary.

25 KFPA Critical Design Review – Fri., Jan. 30, 2009 Resources ● Bob Garwood, NRAO – 1 FTE, component design and development ● Robert Ricci, U. Calgary – algorithm development