Amda topcat use case
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AMDA-TOPCAT use case. Magnetospheric regions automatic identification V. Génot – October 2012 – V2 [email protected] Special thanks to the CDPP team, M. Taylor (TOPCAT) & K. Meziane. Science use case. Based on Jelinek et al., JGR 2012 (paper1) Uses AMDA for the analysis

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AMDA-TOPCAT use case

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Amda topcat use case

AMDA-TOPCAT use case

Magnetospheric regions automatic identification

V. Génot – October 2012 – V2

[email protected]

Special thanks to the CDPP team,

M. Taylor (TOPCAT) & K. Meziane


Science use case

Science use case

  • Based on Jelinek et al., JGR 2012 (paper1)

  • Uses AMDA for the analysis

  • Uses TOPCAT for visualisations

  • Uses IVOA SAMP to exchange data between AMDA and TOPCAT

    Goal :

  • Reproduce two paper1’s results in a few steps

    • Identify solar wind / magnetosheah / magnetosphere

    • Identify bow shock and magnetopause

  • Explore AMDA/TOPCAT enhanced functionalities

    • AMDA conditional parameters

    • TOPCAT weighted density maps

  • Propose new research perspectives


Magnetospheric region identification in paper1

Magnetospheric region identification in Paper1

All THEMIS data 2007/03/01-2009/10/01

magnetosphere

With ACE data shifted to THEMIS A position

magnetosheath

solar wind


Themis a magnetospheric sampling over 3 years

THEMIS A magnetospheric sampling over ~3 years

3dview.cesr.fr

THEMIS A orbits from 2007/03/01 to 2009/10/01


Magnetospheric regions only themis a

Magnetospheric regions-- only THEMIS A

AMDA – TOPCAT analysis

In both cases, the bin/contour represents the number of events

Jelinek et al., JGR 2012


Amda topcat use case

Magnetospheric regions

rB

magnetosphere

rB=10rn

magnetosheath

rB=4-rn

solar wind

= rn

  • rB>4-rn

  • rB<10rn

Condition from the plot above :

Magnetosheath region


Amda topcat use case

Bow shock and magnetopause identification

AMDA – TOPCAT analysis

In both case, each bin represents the probability (<1) for this location to be in the magnetosheath

In TOPCAT this is automatically computed from the flag_msh values

Jelinek et al., JGR 2012


Amda topcat use case

Step by step AMDA–TOPCAT analysis

Magnetospheric region identification

  • define rB and rn in AMDA (create new parameters, see slide for exact definition)

    • time delay between ACE and THEMIS A is taken constant

      • for instance : shift(param,4000) shifts ACE data from 4000s forward

      • here : param=BACE or nACE

      • a better approach would use (see plot) : T=|XACE-XTHEMIS_A|/VSW

      • a much better approach would use an iterative algorithm to compute T

      • for instance see http://cdpp-amda.cesr.fr/DDHTML/HELP/delay.html

  • launch TOPCAT ; it automatically opens a SAMP hub

  • in AMDA : click the « interoperability » and open a SAMP connection

  • download rB and rn on 2007/03/01 – 2009/10/01 at 60s resolution (all in one file)

  • in AMDA : in the « Download Results » window choose « Send to TOPCAT »

  • the table is automatically loaded in TOPCAT

  • choose « density map » (2D histogram) : rB function of rn

  • adjust binning and plotting range as necessary (0-8 for rn, 0-22 for rB)

  • do not worry about NaN values !


Amda topcat use case

Step by step AMDA–TOPCAT analysis

Bow shock and magnetopause identification

Use of AMDA conditional parameters

  • define the solar wind ram pressure pSW shifted to THEMIS A

    • time delay may be taken as 4000s as before

    • pSW=1.67e-6nACEVACE^2

  • produce a time table T1 when the pSW values are in a restricted band (ex: pSW<4)

  • define a new (conditional) parameter : flag_msh

    • flag_msh=1 if rB>4-rn and rB<10rn (see plot), else 0 (for solar wind and magnetosphere)

  • download XTHA, sqrt(YTHA^2+ZTHA^2), flag_msh at 3600 s resolution (all in one file) for the above T1 time table

Transfer via SAMP (same procedure as before)

  • the table is loaded into TOPCAT

  • choose « density map » (2D histogram) :

    • sqrt(Y^2+Z^2) function of X weighted by flag_msh

  • adjust binning as necessary


Amda topcat use case

Parameter definition in AMDA

r_n = n_i_tha/shiftT_(sw(0),4000)

r_b = bs_tha(3)/shiftT_(imf(3),4000)

p_sw = shiftT_(sw(0)*sw(1)*sw(1),4000)*1.67e-6

flag_msh = (n_i_tha/shiftT_(sw(0),4000)+bs_tha(3)/shiftT_(imf(3),4000) > 4.) & (bs_tha(3)/shiftT_(imf(3),4000) - 10.*n_i_tha/shiftT_(sw(0),4000) <0.)

flag_msh value is either 1 (THEMIS A is in the magnetosheath) or 0


Amda topcat use case

Time delay between ACE and THEMIS A

3dview.cesr.fr

It is computed along the XGSE direction : T=|XACE-XTHEMIS_A|/VSW


Amda topcat use case

Time delay

T=|XACE-XTHEMIS_A|/VSW

Here the delay T is almost constant (2400) so r_b is bs_tha(3)/shiftT_(imf(3),2400)

r_b

T=2400

r_b


Amda topcat use case

Time delay

T=|XACE-XTHEMIS_A|/VSW

Here the delay T varies much more, a « banded » delay could be adopted (not implemented here) with conditional parameters :

bs_tha(3)/shiftT_(imf(3),2400)

or

bs_tha(3)/shiftT_(imf(3),3200)

or

bs_tha(3)/shiftT_(imf(3),4000)

r_b

r_b

In AMDA a conditional parameter P is such that P=1 if P is true

Ex: C=A*(T<3600)+B*(T>3600) is equal to either A or B depending on the value of T

T=3200

T=4000

T=2400


Amda topcat use case

  • Perspectives

  • Refine analysis with smaller ram pressure domains

  • Extend to larger time intervals, and other S/C (all THEMIS, CLUSTER, …)

  • Extend to magnetosphere and solar wind region determinations

  • Use this procedure to deduce bow shock and magnetopause models

  • Tool enhancements

  • TOPCAT

    • Bin value on mouse over

    • Over plot of contours and user defined lines on density maps

  • AMDA

    • Delay procedure (continuous instead of constant or « banded »)


Amda topcat use case

Using the binning TOPCAT functionality for density map :


Amda topcat use case

Using the binning TOPCAT functionality for density map :


Amda topcat use case

Using the binning TOPCAT functionality for density map :


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