applications of the excesspower data analysis pipeline to gravitational wave … · 2018. 7....

Applications of the ExcessPower Data Analysis Pipeline to Gravitational Wave Detection Efforts

Sydney J. Chamberlin1

T h e L e o n a r d E . P a r k e rCenter for Gravitation, Cosmology & Astrophysics

at the University of Wisconsin–Milwaukee

with

Chris Pankow1, Jess McIver2, Laura Nuttall1, Duncan Macleod3 and

Jolien Creighton1

1UW-Milwaukee, 2UMass-Amherst, 3Louisiana State University

October 26, 2013 23rd Midwest Relativity Meeting

Outline

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• Review of gravitational wave bursts

• Gravitational wave data analysis techniques for burst searches

• The ExcessPower pipeline

• Characterizing noise transients with ExcessPower

• Status and future work


Gravitational Wave Bursts

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• Gravitational wave (GW) bursts are transient signals - with durations much shorter than the observational timescale

- identifiable by a distinct arrival time

Image: A. Stuver/LIGO using data from C. Ott, D. Burrows, et al.

Cosmic String Cusp


Gravitational Wave Bursts

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• Gravitational wave (GW) bursts are transient signals - with durations much shorter than the observational timescale

- identifiable by a distinct arrival time

Image: A. Stuver/LIGO using data from C. Ott, D. Burrows, et al.

• Sources in detectable interferometer band include compact binary coalescence, gravitational collapse and possibly cosmic string cusps

GWsGWs

Cosmic String Cusp


Data analysis techniques for GW bursts

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• Many different methods used to search for bursts of GWs in data; method used depends on how well the signal can be modeled



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• Example 1: Inspiral and merger of a binary black hole system

signals well-modeled with post-Newtonian expansion/numerical relativity

Images: Thomas W. Baumgarte and Stuart L. Shapiro in Physics Today




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use matched filtering

Find the buried signal by correlating the template to the data.

• Example 1: Inspiral and merger of a binary black hole system

signals well-modeled with post-Newtonian expansion/numerical relativity

Images: Thomas W. Baumgarte and Stuart L. Shapiro in Physics Today


October 26, 2013 23rd Midwest Relativity Meeting5

• Example 2: Core-collapse supernovae


a lot of physics needed:

signals difficult to model numerically )

Particle physics

Gravitational physics (GR)Element nucleosynthesis

...Hydrodynamics

Neutrino transport

Image: Scientific American

+ computational challenges



• Example 2: Core-collapse supernovae


a lot of physics needed:

signals difficult to model numerically )

how can we search for these sources?

• Many sources fall into category of “poorly modeled” or “unmodeled”.

Particle physics

Gravitational physics (GR)Element nucleosynthesis

...Hydrodynamics

Neutrino transport

Image: Scientific American

+ computational challenges



An excess power method for unmodeled searches

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• With some knowledge of signal, can still do searches for unmodeled/poorly modeled signals

Excess Power MethodScan detectors’ outputs for transients that

are statistically significant relative to background noise

- frequency band of radiation

- timescale of radiation



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time series of data

LIGO Hanford

LIGO Livingston



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get a time series for each frequency channel; sum squared samples in each channel to obtain energy in corresponding frequency band

time series of data

LIGO Hanford

LIGO LivingstonFFT, apply digital filters



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construct time-frequency tiles from time summed/frequency bandwidth


time series of data

LIGO Hanford




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apply a threshold to time-frequency tiles to select important tiles: events

time series of data

LIGO Hanford




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apply a threshold to time-frequency tiles to select important tiles: events

threshold based on probability of getting power in tile from Gaussian noise alone

what does “important” mean?

time series of data

LIGO Hanford




hrss (root sum squared strain)

hrss =

sZ(|h+(t)|2 + |h⇥(t)|2) dt

Final quantity recorded:




hrss =

sZ(|h+(t)|2 + |h⇥(t)|2) dt

Final quantity recorded: Last step:apply coincidence test to results.

Want excess power that is coincident between detectors.



- Constructed using gstlal — a gstreamer based set of library functions for low-latency gravitational wave data analysis — by Chris Pankow et al.

• all of this encoded in the ExcessPower Pipeline (gstlal_excesspower)

- Developed at UWM by Patrick Brady, Kipp Cannon et al. based on original paper describing the analysis by Anderson et al., Physical Review D 63, 042003 (2000)


hrss =

sZ(|h+(t)|2 + |h⇥(t)|2) dt



- Runs realtime (online) or offline on archived data.



- Constructed using gstlal — a gstreamer based set of library functions for low-latency gravitational wave data analysis — by Chris Pankow et al.

• all of this encoded in the ExcessPower Pipeline (gstlal_excesspower)

- Developed at UWM by Patrick Brady, Kipp Cannon et al. based on original paper describing the analysis by Anderson et al., Physical Review D 63, 042003 (2000)


hrss =

sZ(|h+(t)|2 + |h⇥(t)|2) dt



• Other methods for generic transient searches of GWs have also been developed and used in LIGO searches:

coherent WaveBurst — CQG 25, 114029 (2008)

X-Pipeline — NJP 12, 053034 (2010)

Omega — CQG 27, 194017 (2010)

- Runs realtime (online) or offline on archived data.



Image credit: LIGO Magazine

In the meantime:

But detector isn’t currently turned on!

Use ExcessPower to do detector characterization work


Using ExcessPower to characterize noise transients• Distinguishing transient GW signals from transient noise (glitches) is a

major challenge in interferometric GW searches

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- seismic noise, nearby trains/trucks/aircraft or traffic, fluctuating magnetic fields around equipment, bad weather, ...




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• We are not talking about Gaussian noise here...




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... we are talking about generic transients (bursts) of noise that stand out relative to background noise




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... we are talking about generic transients (bursts) of noise that stand out relative to background noise

• First line of defense: demand coincidence between detectors


Detector 1

Freq

uenc

y

Using ExcessPower to characterize noise transients

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triggers: a clump of tiles with enough local power to distinguish from Gaussian noise

TimeTime

Tile

Ene

rgy

Tile

Ene

rgy

Detector 2

Freq

uenc

y

• Does this always work?


Detector 1

Freq

uenc

y


10


TimeTime

Tile

Ene

rgy

Tile

Ene

rgy

Detector 2

Freq

uenc

y

Trigger map — Example 1:


Low rate of noise transients


Detector 1

Freq

uenc

y


10


TimeTime

Tile

Ene

rgy

Tile

Ene

rgy

Detector 2

Freq

uenc

y



High rate of noise transients


Detector 1

Freq

uenc

y


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Even after demanding coincidence, frequent transients are a problem.


TimeTime

Tile

Ene

rgy

Tile

Ene

rgy

Detector 2

Freq

uenc

y



High rate of noise transients


Using ExcessPower to characterize noise transients• But recall: glitches are transient bursts of noise that stand out relative to

background noise...


Using ExcessPower to characterize noise transients• But recall: glitches are transient bursts of noise that stand out relative to

background noise...

ExcessPower an ideal method to better understand noise transients and glitching behavior in detector)

how do we do this?

... and they can be described by their frequency band and duration!



• Use auxiliary channels: data from sensors monitoring environmental and instrumental variables (not sensitive to GWs)

• But recall: glitches are transient bursts of noise that stand out relative to background noise...


how do we do this?

Image credit: C. Hardham, Stanford

- seismic motion

- mirror suspension and control

- laser beam alignment

For example:




• Use auxiliary channels: data from sensors monitoring environmental and instrumental variables (not sensitive to GWs)

• But recall: glitches are transient bursts of noise that stand out relative to background noise...


how do we do this?

Image credit: C. Hardham, Stanford

Our focus:

Seismic isolation and suspension subsystems

0.1 Hz . f . 10 Hz




• Objectives:- Monitor SEI/SUS channels and identify sources/couplings of noise transients when possible




- Construct data quality flags (cut out “bad” stretches of data)


- Determine low-frequency injection efficiency and detectability








Construct waveforms with typical SEI/SUS noise transient parameters

SEI/SUS channel dataExcessPower

(gstlal_excesspower)inject






TimeHsL

Band & Time Limited WNBWaveform

TimeHsL

Sine Gaussian Waveform

Easy to construct many different signals with these basic forms




Two waveform families:





• Specifics for injections:

20000 injections total, spaced evenly in time

0.1-10 Hz central frequency range

0.75 - 5 Hz bandwidth range for WNBs

parameters chosen to match noise transients observed in auxiliary

channels

10k sine Gaussians, 10k WNBs







Status and future work

• ExcessPower is a low-latency pipeline designed to search for unmodeled signals by finding excess power above background noise

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• Transients of noise pose a significant problem in searches for GW bursts with interferometers — but ExcessPower can help




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- we’re using ExcessPower to help characterize/identify these noise transients and develop strategies for mitigating their impact on GW burst searches




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- we have already implemented process of generating injection waveforms and injecting into SEI/SUS data




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- later on — extend work to other auxiliary channels




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Thank you!


- later on — extend work to other auxiliary channels

applications of the excesspower data analysis pipeline to gravitational wave … · 2018. 7....

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