irene%di%palma ,%heinzbernd% eggenstein …...irene%di%palma*,%heinzbernd% eggenstein*,...
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Irene Di Palma*, Heinz-‐Bernd Eggenstein*, David Keitel*, Maria Alessandra Papa*#,
Sinéad Walsh#
*Max Planck InsDtute for GravitaDonal Physics (Albert Einstein InsDtute) and Leibniz Universität Hannover
# University of Wisconsin – Milwaukee LIGO Doc # G1500557
• Brief search overview • The deep follow-‐up • Outlook
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 2
• Data from LIGO L1 and H1 detectors
• Spans 255 days (with gaps) of S6
• ~ 1017 different waveforms searched for. Parameters are: – Sky posiDon: all sky
– GW emission frequency:
– First order spindown:
• CompuDng Resource: Einstein@Home – distributed volunteer compuDng project – compuDng power: order of 1Peta FLOPS
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 3
�2.6⇥ 10�9 Hz/s < f < 3.1⇥ 10�10 Hz/s
50 Hz < f < 510 Hz
• Detector disturbances • Outliers compeDng with “interesDng” outliers for compuDng Dme
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 4
DetecDon
staD
sDc
f
Cartoon, not actual data
• Principle #1: Do not allow outliers from detector disturbances to limit our sensiDvity
• Principle #2: Study the recovery of signals at the border of detectability and opDmize the search accordingly
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 5
• Preprocessing: Replacing known lines in detector data with syntheDc Gaussian Noise
Before
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 6
DetecDon
staD
sDc
f
Cartoon, not actual data
• Preprocessing: Replacing known lines in detector data with syntheDc Gaussian Noise
Ajer:
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 7
DetecDon
staD
sDc
f
Cartoon, not actual data
• Line Robust DetecDon StaDsDc [Keitel,Prix,Papa,Leaci,Siddiqi, Phys. Rev. D 89, 064023 (2014)]
• Published & implemented ajer E@H all-‐sky run was over • Re-‐applied to returned results
Before:
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 8
DetecDon
staD
sDc
f
Cartoon, not actual data
• Line Robust DetecDon StaDsDc [Keitel,Prix,Papa,Leaci,Siddiqi, Phys. Rev. D 89, 064023 (2014)]
• Published & implemented ajer E@H all-‐sky run was over • Re-‐applied to returned results
Ajer:
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 9
DetecDon
staD
sDc
f
Cartoon, not actual data
• Bulk classificaDon of disturbed regions of parameter space: by visual inspecDon
• Exclude or reserve for special analysis
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 10
DetecDon
staD
sDc
f
Cartoon, not actual data
Threshold for “deep” follow-‐up
• Even weak signals more likely to produce clusters of candidates than Gaussian noise
• è use clustering to find such clusters (in 4-‐D parameter space)
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 11
• è Dealing with many ( e.g. 10s of millions) potenDally weak signal candidates
• To claim detecDon: increase SNR • SoluDon: Hierarchical Searches Successively improve SNR and narrow down search volume: – finer grids (sky, f, fdot ,fddot,…), and/or – longer Tcoh and/or – longer Tobs (but: sensiDvity evoluDon of detectors, long gaps between science runs.)
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 12
• Given a set of candidates from stage i this is what one needs to figure out to stage i+1: – Set CompuDng budget for stage i+1 – Determine selecDon criteria for “look-‐further” candidates from stage i
– Any candidates lej? (else done) • Determine necessary follow-‐up volume around candidate nominal parameters
• Determine search set-‐up: template density, method, Tcoh, Tobs,…
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 13
Determined b
y
previous sta
ge
Depends on search volume and budget per
candidate
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 14
2F
• CompuDng Budget: For the first Dme, Einstein@Home used also for follow up searches
• OpDmizing search setups: – Mostly driven by Monte Carlo experiments • On a set of weak fake test signals represenDng
candidates at the edge of detectability
• On noise (we take real data as representaDve of noise)
• CompuDng Budget: For the first Dme, Einstein@Home used also for follow up searches
• OpDmizing search setups: – Mostly driven by Monte Carlo experiments • On a set of weak fake test signals represenDng
candidates at the edge of detectability
• On noise (we take real data as representaDve of noise)
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 15
2F
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 16
E@H all sky search
E@H Follow-‐Up Run #1
E@H Follow-‐Up Run #2
Follow-‐Up Run #3
Apply Line Robust StaDsDc
Apply threshold
Clustering
Visual InspecDon
Apply threshold
Apply threshold
Known line removal
Threshold on cluster
occupancy
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 17
E@H all sky search
E@H Follow-‐Up Run #1
E@H Follow-‐Up Run #2
Follow-‐Up Run #3
Apply Line Robust StaDsDc
Apply threshold
Clustering
Visual InspecDon
Apply threshold
Apply threshold
Known line removal
Threshold on cluster
occupancy
Candidates from disturbed bands. Separate ad-‐hoc
analysis
Candidates from undisturbed bands. This
analysis.
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 18
E@H all sky search
E@H Follow-‐Up Run #1
E@H Follow-‐Up Run #2
Follow-‐Up Run #3
Apply Line Robust StaDsDc
Apply threshold
Clustering
Visual InspecDon
Apply threshold
Apply threshold
Known line removal
Threshold on cluster
occupancy
Directed search for each candidate: Finer sky grids, unchanged Tcoh
Ca 16 Mio candidates at this point
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 19
E@H all sky search
E@H Follow-‐Up Run #1
E@H Follow-‐Up Run #2
Follow-‐Up Run #3
Apply Line Robust StaDsDc
Apply threshold
Clustering
Visual InspecDon
Apply threshold
Apply threshold
Known line removal
Threshold on cluster
occupancy Directed search for each candidate:
longer Tcoh (140 h)
Ca 6 Mio candidates at this point
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 20
E@H all sky search
E@H Follow-‐Up Run #1
E@H Follow-‐Up Run #2
Follow-‐Up Run #3
Apply Line Robust StaDsDc
Apply threshold
Clustering
Visual InspecDon
Apply threshold
Apply threshold
Known line removal
Threshold on cluster
occupancy
Finer grid spacings
Ca 500k candidates at this point
• We have not yet performed this step but we expect: – No survivors or a handful – For these survivors a longer coherent Dme-‐baseline search (FU4), even just fully coherent would be trivial
– A candidate surviving FU4 would be something to get seriously excited about
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 21
• E@H S6 all-‐sky post-‐processing to be finished this year • Focus on signal detecDon rather than ease of Upper Limits
determinaDon • Ways to improve E@H searches in the future:
– AdvLIGO data – Beter theoreDcal understanding of parameter-‐space mismatch behaviour (metric, see also à poster by Karl Wete)
– Even more robust detecDon staDsDc (including transient disturbance & signal hypotheses) à see poster by David Keitel
– InformaDve astrophysical priors (help, please!) – Run setup as decision theory problem (“where to best invest our money”) à poster by Jing Ming
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 22
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 23
• Bruce Allen • David Anderson • Stuart Anderson • Carsten Aulbert • Oliver Bock • Christophe Choquet • Jim Cordes • Teviet Creighton • Julia Deneva • Heinz-‐Bernd
Eggenstein • Henning Fehrmann • Akos Fekete • Joachim Fritzsch
• Steffen Grunewald • Lucas Guillemot • David Hammer • Mike Hewson • Yousuke Itoh • David Keitel • Gaurav Khanna • Benjamin Knispel • Badri Krishnan • Paola Leaci • Bernd Machenschalk • Kathryn Marks • Chris Messenger
• Eric Myers
• Irene Di Palma • Maria Alessandra Papa • Ornella Piccinni • Holger Pletsch • Reinhard Prix • Gary Roberts • Miroslav Shaltev • Peter Shawhan • Xavier Siemens • Sinéad Walsh • Rom Walton • Graham Woan • > 400k volunteers so far
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 25
Client sojware Server sojware
E@H server, UWM & AEI Hannover
• Provided by general public • Ca 50k PCs, notebooks & smartphones
acDvely parDcipaDng currently • MS Windows, OSX, Linux, Android
CompuDng tasks (on request)
• MulDple searches • GW • Radio-‐pulsar search • Gamma-‐ray pulsar search
• Each task send to two different parDcipants
Results sent back
Task generaDon, result validaDon, archival, post-‐ processing & follow up task generaDon
science project-‐ specific back-‐end
Search app. download
Internet
• From [LVC (J. Aasi et al.) , Phys.Rev. D87 (2013) 042001] • Upper Uupper Limits
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 26
Upper limits (at 90% confidence level)
• Some representaDve examples (not from actual S6 data)
• Axes: x: , y: , z & colour: (detecDon stat.)
GWPAW 2015-‐06-‐19 LIGO Doc # G1500557 27
f f 2F
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