Äîêóìåíò âçÿò èç êýøà ïîèñêîâîé ìàøèíû. Àäðåñ îðèãèíàëüíîãî äîêóìåíòà : http://www.mrao.cam.ac.uk/wp-content/uploads/2014/04/HCegla.pdf Äàòà èçìåíåíèÿ: Wed Apr 16 17:16:50 2014 Äàòà èíäåêñèðîâàíèÿ: Sun Apr 10 13:22:58 2016 Êîäèðîâêà:

A Pathway to Earth-like Worlds:
Overcoming Astrophysical Noise due to Convection

Dr. Heather Cegla!
!

Dr. Chris Watson, Dr. Sergiy Shelyag, Prof. Mihalis Mathioudakis


Astrophysical Noise

· · · ·

Starspots, Plages Stellar Oscillations Granulation Variable Gravitational
(Cegla et al 2012)

!

Redshift


Astrophysical Noise

· · · ·

Starspots, Plages Stellar Oscillations Granulation Variable Gravitational
(Cegla et al 2012)

!

Redshift



Sun-as-a-Star Model Observations


Parameterisation
Continuum Intensity

· ·

1.4

Separate based on:

· · · · · ·

1.2

Continuum Intensity Magnetic Field
Magnetic Field

1.0

0.8

Four Components Granules Non-Magnetic ! Intergranular Lanes Magnetic Intergranular Lanes MBPs

1500

1000

500

0


4·10

14

Four Average Granulation Components (0°)

3·10

14

Flux

2·10

14

1·10

14

Granules Non-Magnetic Lanes Magnetic Lanes MBPs 0 6302.2 6302.3 6302.4 6302.5 6302.6 Wavelength (Angstroms) 6302.7


200 G Reconstruction
4·10
14

Best (0°)
Original Reconstruct

Worst (0°)

3·10

14

Flux

2·10

14

1·10

14

1.0000 0.9988 0.9975

1.0794 1.0454 1.0113

0 6302.19

Avg Rel Err: 0.00048 6302.34 6302.49 6302.64 Wavelength (Angstroms)

Avg Rel Err: 0.022 6302.34 6302.49 6302.64 Wavelength (Angstroms) 6302.79


The Astrophysical Journal, 763:95 (8pp), 2013 February 1
C

Granulation RVs from MHD Simulations

2013.

60 The American 40 20 -20

doi:10.1088/0004-637X/763/2/95

Astronomical Society. All rights reserved. Printed in the U.S.A.

RV (m s-1)

S 0 TELLAR

SURFACE MAGNETO-CONVECTION AS A SOURCE OF ASTROPHYSICAL NOISE. I. MULTI-COMPONENT PARAMETERIZATION OF ABSORPTION LINE PROFILES

-40 H. M. Cegla1 ,2 ,S. Shelyag1 , C. A. Watson1 , and M. Mathioudakis1 1 Astrophysics Research Centre, School of Mathematics & Physics, Queen's University, University Road, Belfast BT7 1NN, UK; hcegla01@qub.ac.uk -60 2 Department of Physics & Astronomy, Vanderbilt University, Nashville, TN 37235, USA 0 20 40 60
Received 2012 October 16; accepted 2012 December 1; published 2013 January 14

We outline our tecRecovered terize photospheric gRVs ion as anParameterization. A fourhniques to charac Granulation ranulat from astrophysical noise source 60component parameterization of granulation is developed that can be used to reconstruct stellar line asymmetries 40and radial velocity shifts due to photospheric convective motions. The four components are made up of absorption 20line profiles calculated for granules, magnetic intergranular lanes, non-magnetic intergranular lanes, and magnetic 0bright points at disk center. These components are constructed by averaging Fe i 6302 å magnetically sensitive absorption line profiles output from detailed radiative transport calculations of the solar photosphere. Each of the -20 four categories adopted is based on magnetic field and continuum intensity limits determined from examining -40three-dimensional magnetohydrodynamic simulations with an average magnetic flux of 200 G. Using these four-60component line profiles we accurately reconstruct granulation profiles, produced from modeling 12 â 12 Mm2 0 20 40 60 areas on the solar surface, to within ±20 cm s-1 on a 100 m s-1 granulation signal. We have also successfully reconstructed granulation profiles from a 50 G simulation using the parameterized line profiles from the 200 G average magnetic field simulation. This test demonstrates applicability of the characterization to a range of magnetic stellar activity levels.
0.4Key words: line: profiles ­ planets and satellites: detection ­ stars: activity ­ stars: low-mass ­ Sun:

ABSTRACT

RV (m s-1)

Residuals

RV (m s-1)

granulation ­ techniques: radial velocities 0.2Online-only material: color figures
0.0 -0.2

estimate the perturbation due to convection. They successfully demonstrate that they can reproduce variations well below the -0.4 m 401 level. However, as low-mass planets around solar-like s- One of 0 consequences of plasma motions in the outer layers the 20 60 -1 (Minutes) of low-mass stars with convective envelopes is radial velocity Time stars require cm s precision, there is still an urgent need to further characterize and remove the RV variations of surface (RV) shifts due to variable stellar line profile asymmetries, convection. known as astrophysical noise (or stellar "jitter"). This can pose a 1. INTRODUCTION



300 Original Reconstructed Oscillation

200

Velocity (m s-1)

100

0

0

20

40 Inclination (°)

60

80



Initial Results


Initial Results


Initial Results

strength of strength of these these correlation strength of t are likely to beareely elybe are lik liktothe among to cordingly, high cordingly, si cordingly, high precision h targeting brighttargeting bri targeting bright stars, could spectroscopic observations spectroscopic ob spectroscopi

7.10

Noise 7.10 No 7.10 Noise Reductio

In this section this this sectio In we demonstr In section w nostic toolsnostic toolsin th discussed tools nostic disc definition was varied toasw definition w ma definition v was used towaswase/reduce remov usedrem used to to the uncorrecteduncorrected the the uncorrec disc-integra 20.4 cm s 120.4 cm the .robu . Next, s 1 s Ne 20.4 cm 1 then subtractedthenof the R then o subtrac subtracted RV was then V RV was and R measured then was then me All of theseAll of these valu values, of these a All as well techniques is shown in isabl techniques T sh techniques i

Table 7.1: Granulation Table 7.1:7 Table


corrected disc-integrated line profile RVs was calculated; this was found to be m s 1 . Next, the robust bisquares linear fit between the diagnostic and RV was ubtracted o of the RV measurements. The standard deviation of the corrected as then measured and, finally, the percentage of noise reduction was calculated. these values, as well as the correlation coe cients for each of the noise reduction iques is shown in Table 7.1. able 7.1: Granulation Noise Reduction Success for the Various Diagnostics Diagnostic V (cm s 1 ) Fractional Reduction (%) Pearson's R ­ 20.4 ­ ­ BIS 37.8 -85 -0.48 C 13.3 35 -0.84 Vb 15.5 24 0.80 Ab 16.2 21 -0.78 bi-Gauss 46.1 -126 -0.40 Va sy 9.0 56 0.91 FWHM 77.0 -277 0.26 Line Depth 13.0 36 -0.84 EW 17.4 15 -0.76 Brightness 10.5 49 -0.89

Initial Results

strength of strength of these these correlation strength of t are likely to beareely elybe are lik liktothe among to cordingly, high cordingly, si cordingly, high precision h targeting brighttargeting bri targeting bright stars, could spectroscopic observations spectroscopic ob spectroscopi

7.10

Noise 7.10 No 7.10 Noise Reductio

s expected, those diagnostics with the strongest correlation with RV reduced the lation noise by the largest fraction. We also found that those diagnostics with poor lations with RV actually made the scatter within the RVs worse when attempting rect for the granulation noise. By far the largest reduction of noise was obtained using the Va sy (56% noise reduction), followed then by the brightness or intearea under the line profile (49%). A marginal noise reduction was also achieved the line depth (36% reduction) and bisector curvature (35% reduction). Va sy has shown to reduce the standard deviation of the disc-integrated line profile RVs to 152

In this section this this sectio In we demonstr In section w nostic toolsnostic toolsin th discussed tools nostic disc definition was varied toasw definition w ma definition v was used towaswase/reduce remov usedrem used to to the uncorrecteduncorrected the the uncorrec disc-integra 20.4 cm s 120.4 cm the .robu . Next, s 1 s Ne 20.4 cm 1 then subtractedthenof the R then o subtrac subtracted RV was then V RV was and R measured then was then me All of theseAll of these valu values, of these a All as well techniques is shown in isabl techniques T sh techniques i

Table 7.1: Granulation Table 7.1:7 Table


Next Steps...

·

Continue to make model observations more realistic:!

·

Instrumental profile, photon noise, finite exposures,

additional noise sources, various magnetic fields,

· · · · ·

injecting planets Solar data, highest RV precision targets Formation heights, absorption strengths,!

Test observationally! Expand to a suite of stellar lines with varying:!

excitation and ionisation potentials Expand to other spectral types