1 lites csac sot #17 meeting, naoj, april. 2006 solar-b fpp the ncar/hao community...

1LitesCSAC

SOT #17 Meeting, NAOJ,April. 2006

Solar-BFPP

The NCAR/HAO Community Spectro-Polarimetric Analysis Center (CSAC)

Bruce W. Lites

303 497 1517

[email protected]

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Solar-BFPP What is CSAC?

• Repository for analysis software for spectro-polarimetric data

• Community involvement/community access

• Full range of analysis:

– Calibration

– Inversions

– Ambiguity resolution

– Data visualization

• Goals:

– Tested, transportable, documented code

– Conformation to modern software standards

– Computational efficiency

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Solar-BFPP Why CSAC?

•Spectro-Polarimetric (SP) data has the highest information content:

•Allows comprehensive, quantitative measures of B

•Permits extraction of line-of-sight gradients

•BUT:

•SP Data is intrinsically more difficult to reduce

•Higher information content means more detailed analysis

•The problem of the past:

Analysis codes have been cumbersome, opaque, not easily ported to other systems, and not particularly well documented

•CSAC aims to remedy these problems

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Solar-BFPP Why CSAC?

Widespread interest in spectro-polarimetry: many polarimeters are new and under-development.

Ground-Based Full Stokes Polarimeters Type Developers Year Advanced Stokes Polarimeter (ASP) Spectrograph HAO, NSO 1992 Imaging Vector Magnetograph (IVM) F-P Filtergraph U. Hawaii 1992 Zürich Imaging Stokes Polarimeter (ZIMPOL II) Flexible ETH Zürich 1996 THEMIS Spectrograph France, Italy 1997 La Palma Stokes Polarimeter (LPSP) Spectrograph IAC, Spain 1998 Tenerife Infrared Polarimeter (TIP) Spectrograph IAC, Spain 1998 Polarimetric Littrow Spectrograph (POLIS) Spectrograph KIS (Germany), HAO 2002 Diffraction-Limited Spectro-Polarimeter (DLSP) Spectrograph NSO, HAO 2003 SOLIS- VSM Spectrograph NSO 2003 SPINOR (Visible/IR replacement of ASP) Spectrograph HAO,NSO 2004 Swedish 1-m Solar Telescope Spectrograph ROYAC, HAO 2005 ATST Visible/Near IR Polarimeter Spectrograph NSO, HAO 2012 Space-Based Polarimeters Type Developers Year Solar-B Spectrograph Japan/US 2006 Solar Dynamics Observatory HMI (SDO) Michelson Filter Stanford, Lockheed 2007 Sunrise (High Altitude Antarctic Balloon) Spectrograph, Filter Germany/US/Spain 2009 Solar Orbiter F-P Filtergraph ESA 2012

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Solar-BFPP Some CSAC Priorities

1. Data Reduction Routines: Develop software for flat-fielding, polarization calibration, merging, rectification, fringe removal, etc. to prepare data sets for subsequent inversion.

2. Milne-Eddington Inversion: This is the workhorse of analysis of SP data to extract the magnetic field vector (and other associated properties of the magnetized atmosphere).

3. LILIA Inversion: Develop standardized, portable software based upon the SIR (Stokes Inversion by Response functions) procedure. This technique allows for variation of parameters along the line-of-sight.

4. Rapid Inversion Techniques: New techniques such as principal components analysis, neural networks, support vector machines offer meaningful inversions at a very large increase in speed.

5. Ambiguity Resolution: CSAC will serve codes for resolution of the 180º azimuth ambiguity.

6. Data Visualization: The AZAM utility, as well as other methods for visualization, will be maintained by CSAC.

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Solar-BFPP

A More Comprehensive List of CSAC Inversion Methods

INVERSION CODE TECHNIQUE APPLICABILITY ATTRIBUTES REFERENCE, STATUS

Candidate Zeeman Effect Codes Ready for Inclusion in CSAC GIMME (Grid Inversion Method by Milne-Eddington)

Standard Milne-Eddington model, least-squares fit by Marquardt algorithm

Single or multi-line Stokes profile observations of photospheric lines, blends

Robust, slow, limited to ME model assumptions

Skumanich & Lites 1987; widespread use

LILIA (LTE based on Lorien Inversion Algorithm)

Least-squares fitting, LTE 1-D atmosphere in HSE (comparable to IAC SIR)

Retrieves detailed depth variation of magnetic field and model atmosphere

Slow, not as robust as ME inversion

Socas-Navarro 2001; Operational, being improved

DIANNE (Direct Inversion using Artifical Neural Networks)

Neural Network technique for direct inversion of observed profiles

Must “train” network with observed or theoretical profiles

Extroardinarily fast, quantitative accuracy being evaluated

Socas-Navarro (2003); refinement underway

MISMA (Micro-Structured Magnetic Atmospheres)

Least-squares, HSE, thin flux tube approximation

Optically thin structures, reproduces asymmetries

One physical model may not apply in all situations

Sánchez Almeida 1997; working inversion code

Methods Under Development NICOLE (Non-LTE Inversion Code based on Lorien Engine)

Least-squares fitting, 1-D atmosphere, chromospheric non-LTE line formation

Information on L.O.S. structure, chromosphere. Zeeman effect only.

Computationally intensive; robustness issues

Under development

PROZHAIC (PROminence Zeeman Hanle effect Inversion Code)

PCA, database of theoretically generated profiles

Encompases Hanle and Zeeman effects in optically thin case

Applied to He I D3, 10830 prominence observations

López Ariste & Casini et al. 2002; development continues

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Solar-BFPP CSAC Data Reduction Routines

•Most SP data has a lot in common:

•Dual beam polarimeters require merging

•Flat-field determination requires care because of spectral lines

•Spectral skew, curvature are common attributes

•Polarimetric calibration variations over the spectral field-of-view

•CSAC has developed codes for data reduction for several instruments (DLSP, Swedish SP, and now Solar-B)

•Procedures both in IDL and FORTRAN

•FORTRAN routines much faster, allow for real-time processing and calibration

•Data-specific parameters external to the code

•Simplification of the calling process relieves the user of complex data processing sequences

•Commonality among instruments of processed data structure

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Solar-BFPP Milne-Eddington Inversion Code

•Designed for parallel processing via GRID architecture

•Unlike many problems, the inversion of Stokes data consists of many separate but identical computational tasks that require no interaction among them

•This is known as a “scatter-gather” computing problem, amenable to GRID computation

•The GRID consists of a heterogeneous array of loosely interconnected individual nodes sharing common resources

•In our case the interconnect is via a network (LAN, or WWW)

•Code is named Grid Inversion Method by Milne Eddington (GIMME)

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Solar-BFPP Inversion Components

GIMME serves as a model for subsequent, more sophisticated CSAC inversion codes. It consists of 4 components:

1. The Inversion Kernel (IK): A set of libraries of codes (mainly C, C++) to perform the actual inversion computations

2. The Grid Inversion Server (GIS): An executable, run on the separate nodes of the Grid, that listens for incoming commands from the Client to run inversions, then run the IK libraries for such commands

3. The Grid data server (GDS): An executable that accesses requests for slices of a data cube, then serve them to the GIS. If accessed locally, it is a UNIX library. If remote, uses an OPEnDAP server.

4. The Grid client (GC): A web server application that allows the user to initiate inversions of either local or remote data sets.

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Solar-BFPP Stokes Data Inversion

Typical SP data is 4-dimensional:

•Spatial slit scan direction (x)

•Spatial dimension along slit (y)

•Wavelength (λ)

•Polarization (I,Q,U,V)

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Solar-BFPP Data Passed by GDS to GIS

•The individual unit of inversion is the set of Stokes spectra (at right)

•One or more of these may be passed to the GIS

•The GDS accesses the entire data volume, and selects the requested slice, then passes it to the various GIS•Typically, each GIS receives data from one slit position (one x-position, all y-positions)

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Solar-BFPP Grid Topology

ADDRESS Protocol CONNECTION POINT

DUTY

hao.ucar.edu Unix socket /invert/sockets/hao_socket inversion yoda.ucar.edu Unix socket /invert/sockets/yoda_socket inversion abc.llnl.gov TCP/IP Port 2000 inversion dfg.llnl.gov TCP/IP Port 2000 inversion data.hao.ucar.edu NFS /CSAC/data/2003_run data data.llnl.gov TCP/IP Port 3501 data

Example:

•2 local GIS running under Unix sockets

•2 remote GIS running under TCP/IP

•1 local GDS running under NFS

•1 remote GDS running under TCP/IP

•If data sits at llnl.gov: all 4 GIS will be used•If data sits at ucar.edu: only 2 local GIS may be used to access these data

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Solar-BFPP Example of Grid Operations

The Client is a web server. For highest efficiency it resides in proximity to the Grid Inversion Servers so that it may communicate rapidly and receive results.

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Solar-BFPP

Standards for the CSAC Library of Analysis Tools

•Codes are highly transportable (written in C, C++), callable from IDL•Supported under Linux, Solaris•Efficient coding, appropriate for parallel architecture•Well documented, commented, and tested•Flexibility to accommodate data from a wide variety of instruments •Standardized input/output•Standards for presentation in solar coordinates•Filters provided to convert input data from major instruments (Solar-B,

DLSP, SOLIS, etc.)•Codes maintained at a HAO/NCAR•Examples of input/output data provided•Open source for user modification, experimentation, and community

input•Online access to all analysis tools•User forum for suggested modifications, additions

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Solar-BFPP The AZAM Utility

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Solar-BFPP The AZAM Utility

•Interactive, manual resolution of the 180º azimuth ambiguity

•Flexible display of inversion parameters

•Color images

•Arrows

•Blinking images against one another

•Contour plots

•Interactive display of data: images, spectral profiles,

•And much more………………

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Solar-BFPP Advanced Inversion Methods

Measured electric current density at heights 200, 650, and 1600 km – Socas-Navarro 2005, ApJ 633, L57

Advanced inversion techniques allow extraction of the field vector from the photosphere into the chromosphere from simultaneous measurements of photospheric lines and the Ca II IRT lines

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Solar-BFPP CSAC Outlook for the Coming Year

1. Finish GIMME Milne-Eddington Inversion, prepare for analysis of Solar-B data

2. Implement Artificial Neural Network initialization for GIMME

3. Implement LILIA detailed inversion

4. Generalize AZAM to accept data from GIMME (i.e., from any data source)

5. Implement the simulated annealing azimuth ambiguity resolution for automatic processing

6. Host a community workshop to address community needs

7. Sponsor graduate student visits

1 lites csac sot #17 meeting, naoj, april. 2006 solar-b fpp the ncar/hao community...

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