The twisted jets of Circinus X-1 · The twisted jets of Circinus X-1 Mickael Coriat Rob Fender, Cyril Tasse, Oleg Smirnov, Tasso Tzioumis, Jess Broderick Journées SF2A 2018

The twisted jets of Circinus X-1

Mickael Coriat

Rob Fender, Cyril Tasse, Oleg Smirnov, Tasso Tzioumis, Jess Broderick

Journées SF2A 2018

Circinus X-1

• Neutron star X-ray binary

• Orbital period ~ 16.5 days

• High eccentricity e~0.45

• Distance ~ 9 kpc

• Powerful jet emitter

• Complex accretion/ejection activity

Armstrong et al. 2013

40 years radio and X-ray light curves

~ 8 arcmin

The youngest X-ray binary

Heinz et al. 2013, 2015

tSNR ~ 3000 years

Jet structure and orientation?

Calvelo et al. 2012

Miller-Jones et al. 2012

VLBI

• Variable jet axis

• Problems with intrinsic variability

• Precession?

• Jets/nebula interaction?

• Sparse coverage

Multi-frequency ATCA observations

• 3 consecutive days in “quiescent” phase

• 2GHz, 5GHz, 9GHz, 35GHz (7mm)

• 6 antennas East-West array in extended config.

observations

8mm dataset

Standard self-calibration

5GHz dataset

• Standard calibration methods failed… • Source likely variable… • Started working with Oleg Smirnov.

Oleg Smirnov

Differential gain calibration

Vpq = GpXpqGH

q

Sky model visibilities

Global gains (direction independent effects)

Measured visibilities

Differential gains

Vpq = Gp

��EX0 �EH +Xrest

�GH

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CirX-1 Rest of the sky

Jets

Core

Calibration using differential gains algorithm (MeqTrees)

5GHz dataset

Recovered “intrinsic” variability of the core

2GHz dataset

~1º

Standard (direction independent) self-calibration

Cyril TasseOleg Smirnov

Direction dependent calibration using Wirtinger Jacobian

(Tasse et al. 2014, Smirnov & Tasse 2015)

Direction dependent faceting imager

killMS DDFacet(Tasse et al. 2018)

Divide and Conquer

• Divide the sky in 7 regions • Compute antenna-time-

frequency dependent gain corrections for each region

• Apply corrections • Image using sub-space

deconvolution and genetic algorithm

• Improve sky model and iterate again

Direction dependent self-calibration

2GHz dataset

Direction independent self-calibration

2GHz dataset

Direction dependent self-calibration

Combined datasets

Precessing jet model

Model parameters:

• 3 angles • Precession period and phase • Velocity • Distance • 3 “signs”

Compute proper motions of ejecta over a range of time

Precessing jet modeling

S = ⌥(�, i) +X

i

"(�↵i ��↵modi)

2

�2↵i

+(��i ���modi)

2

�2�i

#

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Objective function:

+ Global optimisation algorithm

Sanity check

Precessing jet modelBackward sanity check

Miller-Jones et al. 2012

1.5 yrs prior to our observations:

- VLBI observations at mas scale

- Model predicts jet angle of 105.2°

- Observed jet angle: 112 +/- 1.5°Predicted jet angle

Jet angle at the time we observed

Conclusions

The bunny is still alive

Thank you

Fν ∝ ν-0.99

Core spectrum