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Bruce Allen, U. Wisconsin - Milwaukee and AEI

Einstein@Home

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Overview• Status of Einstein@Home

- Users, credits, size

- Officially funded as a project by NSF!

• Status of the “old style” S3 analysis- Done and reviewed!

• Status of the S4 analysis- Postprocessing underway since mid-summer, still ongoing

- First careful estimation of search sensitivity (for practice, not for publication!)

• Status of the S5 analysis (16 446 454 workunits x 5 CPU hours x 2)- Processing 49% complete

- About 100 more days of processing to go

- Einstein@Home server/project up and operational with no glitches for 143 days!

• Status of the upcoming hierarchial S5 analysis- First CW analysis that integrates long (> 30 min) coherent and an incoherent

methods

- Gives us the optimal sensitivity for our CPU power

- No large data transfers to or from the host machines.

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How big is Einstein@Home?

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http://www.boincsynergy.com/stats/

User/Credit History

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Users/Hosts History

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Current performance

Einstein@Home is currently getting 84 Tflops

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Status of S3 Analysis

• Finished:- Final S3 analysis and writeup have been reviewed and

approved by the CW Review Committee and the LSC Executive Committee.

- Results are posted on the Einstein@Home web site.- We didn’t find any CW sources

LIGO-G060553-00-Z

Overview of S4 analysis• Coherently analyzed 30-hour data stretches (10 LHO, 7 LLO) 540 hours total. Spanned

times vary, but all < 40 hours.

• Searched 50 Hz - 1500 Hz in 6,731,410 work units from 24-12-2005 to 20-7-2006.

• Near optimal grid (within ~2) on the sky and in frequency and df/dt• Explicit search over spindowns (df/dt) corresponding to pulsars older than a few thousand

years. Previous searches had |df/dt|<1/(integration time)2.• Each host searches the entire sky and fdot range and a variable-sized region of

frequency df ~ f-3 and one stretch of data. It then returns list of ‘top 13,000 events’.

• Range of frequency that decreases with increasing frequency as f^-3

• f<300 Hz: mismatch 0.2, spin-down ages > 1,000 years

• f300 Hz: mismatch 0.5, spin-down ages > 10,000 years• Each workunit produces a compressed data file that is about 150kB in size.• The total data volume to post-process is 1 TB (compressed) or 4 TB (uncompressed).

Many hardware improvements “behind the scenes” to handle this data volume and permit postprocessing on Nemo cluster.

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S4 post-processingcoincidence strategy

• Search for signals that appear in each of the 17 different data stretches, with consistent parameters

• Steps:- Shift candidate frequencies to a fixed fiducial time- ‘Bin’ candidates in four dimensions (alpha, delta, f, fdot)- Search for bins which have candidates from many of the 17 data stretches

• Span entire sky, entire frequency band, entire fdot band. • Bins are chosen to be as small as possible, consistent with:

- Sky bin size > largest grid separations (use Gaussian model in delta)- Frequency bin size > frequency resolution + (grid spacing in fdot) x T- Fdot bin size > Fdot grid spacing

• Bins are also shifted by 1/2 the bin width in all 2^4 combinations, so as not to miss any candidates on opposite sides of cell faces.

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How many events to keep?• Goal: constant false alarm probability per data stretch per

coincidence cell.• Why: in the coincidence analysis, this makes it easier to

interpret the results and to predict false alarm probability.• How: in a given frequency band, keep the same number of

events from each data stretch. • How many events to keep: to get a false alarm probability of

0.1% to find 7 or more coincidences (out of 17) in random noise in a 1/2 Hz band.

• Example: band 123.0-123.5 Hz:- Data stretch 1 (10 workunits): keep top 1000 events per workunit- Data stretch 2 (5 workunits): keep top 2000 events per workunit- Data stretch 3 (8 workunits): keep top 1250 events per workunit

…- Data stretch 17 (20 workunits): keep top 500 events per workunit

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Coincidence Analysis Grids

• Typical sky grid has points separated more broadly near the equator.

• Each of the 17 data segments has a different grid

• In doing the coincidence analysis we use a Gaussian fit to the declination differences to ensure that we don’t miss correlated events.

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Sample results (120-130 Hz)

• Number of events:54,841 x 17 per 1/2 Hz

• Number of coincidence cells per 1/2 Hz: 5,398,250

• Max coincidences found: 5

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Sample results (260-270 Hz)

• Number of events: 166,033 x 17 per 1/2 Hz

• Number of coincidence cells per 1/2 Hz: 19,747,000

• Max coincidences found: 7(Line: “Yousuke’s 8 Hz comb”, onlyin L1)

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Sample results (100-110 Hz)

• Number of events: 40,364 x 17 per 1/2 Hz

• Number of coincidence cells per 1/2 Hz: 5,398,250

• Max coincidences found: 11This is fake pulsar #3. Injection was off for 5 of the 17 data stretches.

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Complete S4 results (50-1500 Hz)

• More work still needed:- Postprocessing being repeated to fix some mistakes made the first

time (wrong counting of coincidence cells led to wrong false alarm rate).

- After removal of hardware injections, need to follow up outliers

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Determining S4 sensitivity• Holger Pletsch has developed methods to estimate the sensitivity and has

tested this using software injections.• For a given signal, did analytic estimate the 2F values in each of the 17 data

segments to determine how many times it falls in the top list of candidates.• This method correctly predicts how many coincidences would be observed.• Repeat for 500 randomly placed and oriented simulated signals per 0.5 Hz band• Note: v1 calibration

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Current S5 run

• Very similar to previous S4 run, but more data(22 x 30 hours) which is also more sensitive

• Postprocessing not even started yet• Search has been underway for about 140 days• About 100 days of work left

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Next S5 Search

• The CW group is planning to start running the first true Einstein@Home hierarchical search in about 3 months!

• All-sky, TBD: f < ~900 Hz, spindown ages > 10000 years• A new search code (union of multi-detector Fstat and Hough). A

stack-slide incoherent option is also “in the works”. • This will use approximately 96 x 20 hours of coincident H1/L1

data (adding strains to gain factor of sqrt(2) in strain sensitivity)• Combines coherent Fstat method with incoherent Hough

method (48 25-hour stacks)• Many CW group members are working very hard on this:

Krishnan, Prix, Machenschalk, Siemens, Hammer, Mendell, Sintes, Papa, and others.

• Should permit a search that extends hundreds of pc into the Galaxy

• This should become the most sensitive blind CW search possible with current knowledge and technology

• As soon as we can: a coherent follow-up integration stage.

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Next S5 search: data selection

Tdata = Nstack Tcoherent and hmin ~ Tcoherent-1/2 Nstack

-1/4

• Constraints and tradeoffs- Practical: smaller data Tdata volume is good for Einstein@Home users;

larger data volume gives more sensitivity & confidence

- Sensitivity: larger Tcoherent gives more sensitivity but less data. Coincident data increases sensitivity by sqrt(2) but yields less data

- CPU cost: larger Tcoherent increases cost of coherent stage relative to incoherent Hough stage

• Study carried out by Siemens, Prix, and Krishnan- Determine much data would be available as a function of Tcoherent with and

without 2-detector coincidence- Extrapolate to January 2007, and estimate

– sensitivity hmin

– total data volume Tdata

– computational cost = sum of the two different search stages

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Next S5 search: data selection Tdata Tstack Nstack hmin CPU_inc CPU_coh CPU data_volume 12h 16h 535 1.00 1.00 1.00 1.00 1.00 15h 20h 401 1.04 1.71 2.86 1.72 0.94 20h 25h 226 1.04 1.66 6.56 1.67 0.70

…LHO

LLO …

…

TdataTstack

LHO

LLO

Tdata Tstack Nstack hmin CPU_inc CPU_coh CPU data_volume 12h 16h 320 1.05 0.09 0.60 0.09 0.60 15h 20h 236 1.08 0.15 1.68 0.15 0.55 20h 25h 96 1.00 0.07 2.79 0.08 0.30

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Coherent + incoherent “merge”

FFTFFT

…FFT

FFT

Keep 1/5 ofpeaks (lots

of data!)Make Hough maps

Fstat Fstat

…Keep 1/4 of peaksToo much data tostore or transmit

Make Hough maps

For Einstein@Home, the multi-IFO Fstat (Code 1) and Hough (Code 2) had to be merged into a single stand-alone executable that could pass the needed data between the two stages. This has been done by Krishnan. Also planned: integrate the Stack-Slide incoherent step as an alternative to Hough.

Current Hough analysis

Integrated code for Einstein@Home analysisCode 1 Code 2

DetectorFrequency

SourceFrequency

Tim

eT

ime

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Conclusions

• Einstein@Home is healthy and growing, and providing 10x more CPU cycles than other LSC resources.

• S4 analysis postprocessing is making good progess. First reliable estimates of the sensitivity.

• Similar S5 analysis should be finished in 3 - 6 months.

• Ambitions plans to run the first true hierarchical search under Einstein@Home on S5 data in just a few months.