QCS2017 @ YITP (2017.02.20~22)

Current Efforts to the Observation of a Gravitational

Wave Signal from a Neutron Star Young-Min Kim (Pusan Nat’l Univ.)

Collaborators: C.-H.Lee (PNU), J.J. Oh, S.H.Oh, E.J. Son, H. Kim (NIMS), and DetChar people in LSC

and KAGRA

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QCS2017 @ YITP (2017.02.20~22)

First detection of Gravitational-Wave

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PhysRevLett.116.061102

GW150914

QCS2017 @ YITP (2017.02.20~22)

Black Hole Mass Chart

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https://www.ligo.caltech.eduimage/ligo20160615e

62

36

2921

148

35

23

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QCS2017 @ YITP (2017.02.20~22)

Neutron Star of Known Mass

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GW Obs. :1.4M⦿ ? 2.0M⦿ ?

NS-NS ?NS-BH ?

J. Lattimer, Annu.Rev.Nucl.Part.Sci.62,485(2012) and https://stellarcollapse.org by C. Ott

QCS2017 @ YITP (2017.02.20~22)

How to contribute to search for GWs

Modeling (Sources, Waveforms)

Instruments and Commissioning

Data Analysis

Signal Search

Detector Characterization

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QCS2017 @ YITP (2017.02.20~22)

Matched Filtering

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https://losc.ligo.org/s/events/GW150914/LOSC_Event_tutorial_GW150914.html

QCS2017 @ YITP (2017.02.20~22)

Matched Filtering

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https://losc.ligo.org/s/events/GW150914/LOSC_Event_tutorial_GW150914.html

PhysRevD.85.122006(2012)

QCS2017 @ YITP (2017.02.20~22)

Online search & EM followups

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CBC:pyCBCgstLALMBTA

Burst:CWBLIB

GraceDB

RRT

EM partners

Offline searches + PE Final Confirmation

DetChar&Commissioners

far < 1/month

QCS2017 @ YITP (2017.02.20~22)

Update on O2A of Advanced LIGO1. Period: Nov. 30, 2016 ~ Mar. ?(1st week), 2017

Holiday break: Dec. 22, 2016 ~ Jan. 4, 2017

2. Coincident observing data of LHO-LLO : ~12days (as of Jan. 23, 2017)

3. Averaged range

• 1.4 M⦿ - 1.4 M⦿ : ~70 Mpc

• 10 M⦿ - 10 M⦿ : ~ 300 Mpc

• 30 M⦿ - 30 M⦿ : ~700 Mpc

4. Event candidates : 2 ( FAR < 1/Month) by on-line analysis

• Shared with EM partners

• cf. FAR(GW150914) < 1/203000yr by off-line analysis.

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QCS2017 @ YITP (2017.02.20~22)

Update on O2A of Advanced LIGO1. Period: Nov. 30, 2016 ~ Mar. ?(1st week), 2017

Holiday break: Dec. 22, 2016 ~ Jan. 4, 2017

2. Coincident observing data of LHO-LLO : ~12days (as of Jan. 23, 2017)

3. Averaged range

• 1.4 M⦿ - 1.4 M⦿ : ~70 Mpc

• 10 M⦿ - 10 M⦿ : ~ 300 Mpc

• 30 M⦿ - 30 M⦿ : ~700 Mpc

4. Event candidates : 2 ( FAR < 1/Month) by on-line analysis

• Shared with EM partners

• cf. FAR(GW150914) < 1/203000yr by off-line analysis.

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PhysRevX.6.041015

N>2 N>10 N>40

QCS2017 @ YITP (2017.02.20~22)

Prospects of the Observing Runs

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⇤ = R < V T >R: Astrophysical Rate of CBC<VT>: Searched Volume and Time

Living Rev. Relativity, 19, 1 (2016)

QCS2017 @ YITP (2017.02.20~22)

How to contribute to search for GWs

Modeling (Sources, Waveforms)

Instruments and Commissioning

Data Analysis

Signal Search

Detector Characterization

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To increase <VT>

R: Astrophysical rate of CBC

QCS2017 @ YITP (2017.02.20~22)

Lock Acquisition

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Under Control

LIGO-G1500620

QCS2017 @ YITP (2017.02.20~22)

hVeto

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ClassQuantGrav.28.235005(2011)

n : the number of coincidencesT_win : full width of coincidence time windowT_tot : a given total analysis time

S = �log10⌃1k=n[

µke�µ

k!])

µ =N

main tot

Naux tot

Twin

Ttot

QCS2017 @ YITP (2017.02.20~22)

MLA application to DetChar (1)1. Ordered Veto List (OVL) + 3 Machine Learning Algorithms

- application to hundreds of channels among 200,000 auxiliary channels

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Phys. Rev. D 88, 062003 (2013)

Low Latency DQ pipeline in

GraceDB

QCS2017 @ YITP (2017.02.20~22)

MLA application to DetChar (2)1. Deep Learning on spectrograms of glitches : GravitySpy

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arxiv:1611.04596

QCS2017 @ YITP (2017.02.20~22)

MLA application to DetChar (2)1. Deep Learning on spectrograms of glitches : GravitySpy

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arxiv:1611.04596

www.gravityspy.org

Everyone can contribute to GW observation

QCS2017 @ YITP (2017.02.20~22)

Summary and Future works1. aLIGO O2 is running.

- 2 event candidates showed up.

- Detcharians and Commissioners are working hard for detector status and data quality

2. We need more participants in GravitySpy project.

3. KGWG DetChar Activity

- Development of Event Trigger Generator: Etagen

- A monitoring tools for Non-linear correlation: CAGmon

- A veto Algorithm based on Artificial Neural Network: vANN

- Safety channel study with DetChar in KAGRA

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Thank you for your attention.

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