Gravitational Wave Astronomy s Next Frontier in Computation

Transcription

1 Gravitational Wave Astronomy s Next Frontier in Computation Chad Hanna - Penn State University Penn State Physics Astronomy & Astrophysics

2 Outline 1. Motivation 2. Gravitational waves. 3. Birth of gravitational wave astronomy and astrophysics. 4. Searching for compact binary mergers 5. Case study: GW Real-time GW detection and parameter estimation. 7. Capitalizing on the emerging, worldwide, gravitational wave detector network. a. Real-time full inference b. AI / machine learning for noise rejection 2

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4 Dynamic curvature - gravitational waves 4 By I, Dennis Nilsson, CC BY 3.0, 4 4

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6 How we detect GWs 6

7 The current GW detectors Virgo 7

8 GW

9 The breakthrough: GW By I, Dennis Nilsson, CC BY 3.0, 9 9

10 GW is not typical GW was so loud that it clearly stood out in the data. We will not typically be that lucky. 10

11 Searching for compact binaries Binary black holes parameterized by mass and spin. Each distinguishable binary parameter set forms a template Observation time: Heavy black holes: milliseconds Longer, lower amplitude as you go to lower mass Light black holes: minutes By I, Dennis Nilsson, CC BY 3.0, Detection templates only include masses and z-components of spin (along orbital angular momentum) 11 11

12 Filtering for compact binaries We construct an output for each GW template by filtering the data d with template h i The output is a sample-by-sample SNR time series for each template. We find the max over 1s intervals for each template independently. 12

13 Signal Parameters with Bayesian Inference Where θ is model parameters and H is a particular hypothesis. Assumes gaussian noise. θ = {m 1, m 2, s 1x, s 1y, s 1z, s 2x, s 2y, s 2z, RA, DEC, } Used to make statistical statements, e.g., > 99% Confidence excludes neutron star 13

14 GW151226: a case study 14

15 What did we observe? Credit: SXS 15

16 GW Was NOT identified by generic search algorithms By I, Dennis Nilsson, CC BY 3.0,

17 GW discovery pipeline Real-time data multicast to caltech compute cluster Data taken and calibrated within ~10s [GOAL: ~1s] Rapid sky localization < 2 minutes 250,000 models searched in parallel within ~1 minute [Now 600,00 models In 10s : GOAL : 10 million models in 1s] Candidates sent to database [Now ~few seconds] Full binary parameter estimation CPU hours. Not highly parallel 17

18 Dimmer (GW) sources may contain matter!: Binaries with a neutron star NASA/Swift/Dana Berry 18

19 Binaries with neutron stars are exciting because we may also observe them electromagnetically. Metzger & Berger 2012 This might be so rare that we only get a few chances in a lifetime to make a smoking gun connection. The frontier is to ensure that no opportunity is lost. 19

20 Mass 2 Mass 2 The problems with the current paradigm Detection: Relies on gridded search Currently of parameter uses 4D space parameter space Parameter estimation: Markov Currently uses 15+D chain monte carlo exploration parameter space Mass 1 Pros: Parallel, simple, effective. Cons: Poor scaling to higher dimensional spaces Step 1 xxxx xxxxx xxxxxx xxxxxx xxxxx xxxx xxx xxx xx xx x x x Mass 1 Pros: Efficient in high dimensions, coherent between detectors. Full exploitation of known physics. Cons: Serial, expensive Step 2 Our current two step process is susceptible to selection bias and lost opportunity. We are not doing the most sensitive search at step one. We need to merge the steps, which could increase detection rate over standard paradigm by more than a factor of 2. 20

21 Mass 2 Mass 2 Computational considerations Mass 1 Thousands of CPUs, continuous processing of data one second at a time xxxx xxxxx xxxxxx xxxxxx xxxxx xxxx xxx xxx xx xx x x x Mass CPU hours per event, mostly serial, hours to weeks runtime. Naive merging would be O(100) CPU hours per second of data requiring O(1M) CPUs in steady state. (Realistically, algorithm improvements could probably make this O(100K) ). That *will* be within our compute budget by 2025 without any trouble. Algorithm work still needed to parallelize computations for latency sake to bring O(hours+) to O(seconds) in delay. By 2025 we will have more than enough compute resources to do a substantially better job of detection and ultimately multi-messenger astrophysics, but we have to start developing algorithms and infrastructure now to be ready. 21

22 A place for machine learning and artificial intelligence. Currently We We want are to data presently have quality data building information quality classifiers information is mostly that at use available the auxiliary same after time data detection as from data each observatory 22

23 Conclusion Gravitational wave astronomy and astrophysics relies on substantial computation for discovery. Although our present techniques are sophisticated and effective, we cannot be complacent and must plan to fully leverage computational resources by Many events will be rare and we need to ensure every opportunity, especially with multimessenger astrophysics. We look forward to collaboration with other ICS members! 23

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25 External Alerts 36:45:54 25