Noise characterization for LISA Julien Sylvestre Massimo Tinto - - PowerPoint PPT Presentation

1 of 8

Noise characterization for LISA

Julien Sylvestre Massimo Tinto Caltech/JPL GWDAW 2003

2 of 8

Noise characterization with one IFO

• Measured signals are the sum of (possibly continuous)

gravitational wave signals and of instrumental noise

• The instrumental noise must be estimated in the presence of the

gravitational wave “noise”

• Look for TDI measurements that are insensitive to GW but that are

still affected by the instrumental noises

3 of 8

Review of TDI

• The LISA array forms 12 phase measurements

››6 one-way measurements along the arms ››6 inter-bench measurements, 2 per spacecraft

• Form linear combinations of delayed phase measurements to

cancel overwhelming laser and optical bench noises

• Many possible TDI combinations; all can be generated by only four

combinations: α, β, γ, ζ

4 of 8

ζ as a GW shield

• The fully symmetric Sagnac TDI combination ζ is free of laser and
• ptical bench noise, and is much less sensitive to GW than the
• ther TDI combinations

Tinto, Armstrong and Estabrook, Phys. Rev. D 63, 021101 (2001)

5 of 8

Noise measurements with ζ

• Can form four GW-free spectra: Sζζ, Sαζ, Sβζ, Sγζ

››Definition: Sab = E[a b*]

• How well can an arbitrary spectrum be approximated as a linear

combination of these four spectra? Sαα = a Sαζ + b Sβζ + c Sγζ + d Sζζ

• Well-defined linear approximation problem. Solution is a

least-squares fit

• J. S. and M. Tinto, PRD 68, 102002 (2003)

6 of 8

Example: Sαα

10

−4

10

−3

10

−2

10

−43

10

−42

10

−41

10

−40

f (Hz) |spectrum| (Hz−1) 10

−4

10

−3

10

−2

−0.3 −0.2 −0.1 0.1 0.2 f (Hz) spectrum error

Real Estimated

7 of 8

Application: stochastic background

10

−4

10

−3

10

−2

10

−24

10

−23

10

−22

10

−21

10

−20

f (Hz) strain

SXX 1yr, SNR = 5 1σ upper limit

• n SXX

Confusion noise from galactic binaries1

• 1. Bender and Hils, Class. Quantum Grav. 14, 1439 (1997)

8 of 8

Conclusion

• Below ~5 mHz, the spectrum of the instrumental noise can be esti-

mated using ζ with a relative error of ~20%

• This leads to a negligible loss in SNR (~0.1%) when doing

matched filtering

• Directly translates into a ~20+% sensitivity loss when searching for

a stochastic background using excess noise

• It seems possible to use ζ to construct estimators of the

instrumental noise that are good enough for the data analysis, both for signal detection and for signal estimation