Magenta : D-term model

Jongho Park (ASIAA)
in collaboration with Do-Young Byun, Youngjoo Yun (KASI),
Cheng-Yu Kuo (NSYSU), Ivan Marti-Vidal (Univ. Valencia)

Polarimetry with VLBI
Moscibrodzka+ (2017)

Theory & Simulation

Narayan & Quataert (2005)

Jet Launching Mechanism

Magnetic Field

Black Hole Accretion

High Resolution

VLBI Polarimetry

Polarimetry with VLBI
Moscibrodzka+ (2017)

Theory & Simulation

Narayan & Quataert (2005)

Observations

Jet Launching Mechanism

Black Hole Accretion

Park+ (2019)
Asada+ (2002)

B field geometryAccretion Dynamics & ratePolarimetry with VLBI : (known to be) not easy

Observed Voltages

Polarimetry with VLBI : (known to be) not easy

Observed Voltages‘Correct’ source signal

Polarimetry with VLBI : (known to be) not easy

Observed Voltages‘Correct’ source signal‘Instrumental’ polarization (often called “D-Terms”): a fraction of the signal from the opposite polarization is ‘leaked’→ needs to be properly calibrated.

Polarimetry with VLBI : not easy

Polarimetry with VLBI : not easy
Cross-hand visibilities

Polarimetry with VLBI : not easy
Cross-hand visibilities

Source-intrinsic signal

Polarimetry with VLBI : not easy
Cross-hand visibilities

Source-intrinsic signal

D-Terms(that we want to calibrate!)

Polarimetry with VLBI : not easy

𝜑𝜑 ∶ Parallactic angles

Sinusoidal variation over parallactic angles(Or “circular rotation” on the complex plane)

AIPS task LPCAL : a conventional calibration tool

Source Polarization‘Model’ visibility
for total intensity

D-Terms

2nd order terms

What AIPS LPCAL does is...

Assume a ‘constant fractional polarization’ for a source

IgnoreSome situations where LPCAL does not show a good performance...

LPCAL can fit the model to ‘a single source’ visibility data
Source 1
Source 2

Ant1
Ant2
Ant3
Ant4Ant5Time
1. There are often not many scans for ‘long baseline antennas’.
→ D-term estimation accuracy is limited for those antennas.

Some situations where LPCAL does not show a good performance...

LPCAL can fit the model to ‘a single source’ visibility data

2. When we have a small number of antennas (such as the KVN).
→ difficult to determine which source is ‘the best calibrator’?

Some situations where LPCAL does not show a good performance...

LPCAL ignores the 2nd order terms.

Ignore3. When the D-terms are large and source polarization is large→ cannot ignore the 2nd order terms.

Some situations where LPCAL does not show a good performance...

EHT 2017 M87

ALMA baselines highlighted

4. When antennas have quite
different sensitivities (e.g.,
ALMA + EHT, GBT + VLBA)→ We must properly takeantenna weights into account(not possible for LPCAL).

How to improve?

Let’s develop a new algorithm which fits the model to ‘multiple sources’visibilities simultaneously.
Possible advantages are(i) increase in ‘effective’ signal-to-noise ratio (we have more data points).
(ii) improvement of the D-term accuracy thanks to the 2nd order terms included.
(iii) less efforts and time required (no need to figure out which calibrator is the best)(iv) controlling weights for different antennas and sources are flexible (which might beimportant for the EHT).

How does it work?

Extract the visibilities and weights into ascii filesParselTongue

Obtain the best-fit D-Term model

Python code

Obtain the D-Term calibrated UVfits file

ParselTongue

All these processes can be done by running a single script.
It takes a few minutes to less than 15 minutes depending on the data size

Does it work well?

VLBA
Brewster – Fort Davis

Circles : LHS (data)
Magenta : RHS (model)

Does it work well?

VLBA
Los Alamos – Pie Town

Circles : LHS (data)
Magenta : RHS (model)

Does it work well?

VLBA
Mauna Kea – Saint Croix

Circles : LHS (data)
Magenta : RHS (model)

Does it work well?

HSA
Effelsberg – Los Alamos

Circles : LHS (data)
Magenta : RHS (model)

Does it work well?

KaVA (5 Stations)
Mizusawa - Iriki

Circles : LHS (data)
Magenta : RHS (model)

Does it work well?

KaVA (5 Stations)Iriki - Ulsan

Circles : LHS (data)
Magenta : RHS (model)

Does it work well?

KVN
Tamna – Yonsei

Circles : LHS (data)
Magenta : RHS (model)

Probing the linear polarization of AGN jets at mm wavelengths with the KVN

KVN 86 – 142 GHz

VLBA 43 GHz

VLBA (BU) 43 GHz

Probing the linear polarization of AGN jets at mm wavelengths with the KVN

VLBA 43 GHz
VLBA (BU) 43 GHz
KVN 86 – 142 GHz

Probing the linear polarization of AGN jets at mm wavelengths with the KVN

KVN 86 – 142 GHz

VLBA 43 GHz
VLBA (BU) 43 GHz

Probing the linear polarization of AGN jets at mm wavelengths with the KVN

KVN 86 – 142 GHz

VLBA (BU) 43 GHz

Summary― A new algorithm for polarization calibration of VLBI data has been developed.
Which advantages does it have?
(i) It increases effective signal to noise ratio and improves the calibration accuracy.
→ powerful for studying low-polarization sources (such as M87!).
(ii) It will solve the problem of global VLBI arrays that there is not much common
sky for a single source.
→ will be important for on-going and future global VLBI studies by using
GMVA + ALMA, EHT + ALMA, and satellite missions.
(iii) It is easy to use (you can just run a single script).
→ Don’t spend much time to figure out which calibrator is the best. You canuse all!
(iv) It will be very effective for future KVN polarization observations where wehave a small number of baselines but usually have many different sources
observed.
→ You can do a unique science with the KVN (linear polarization and Rotation
Measure analysis at 2 ~ 3 mm).

SummaryPlease use my code for your studies!

AIPS task LPCAL : a conventional calibration toolSource Polarization‘Model’ visibility
for total intensity

D-Terms

2nd order terms

What AIPS LPCAL does is...

Assume a ‘constant fractional polarization’ for a sourceIgnore

There is almost no astronomical object having a constant polarization across its structure.

Assume a constant fractionalpolarization for the CLEANcomponents in each ‘box’

AIPS task LPCAL : a conventional calibration toolD-Terms

Source Polarization‘Model’ visibilityfor each source
sub-components

2nd order terms

What AIPS LPCAL does is...

IgnoreAssume a ‘constant fractional polarization’for each source sub-components

Does it work well?
Filled : Code, Open : LPCAL
VLBAWhen ignoring the 2nd order terms and comparing with the LPCAL result,they are consistent within ~10−4 levels.

Does it work well?
Filled : Code, Open : LPCALHSAWhen ignoring the 2nd order terms and comparing with the LPCAL result,they are consistent within ~10−4 levels.

Does it work well?

OJ 287 + OQ 208

OJ 287

Does it work well?

OJ 287 + OQ 208

OQ 208Does it work well?

OJ 287 + OQ 208

OQ 208

If the multi-source fitting code works well, then the results must be consistent withthe single-source fitting results (because OJ 287 and OQ 208 are good calibrators).
→ This is the case!