PATTERN RECOGNITION STRATEGIES DETECTION OF FAST TRANSIENTS AS “DATA TRIAGE” Jet Propulsion Laboratory California Institute of Technology David Thompson,

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Presentation transcript:

PATTERN RECOGNITION STRATEGIES DETECTION OF FAST TRANSIENTS AS “DATA TRIAGE” Jet Propulsion Laboratory California Institute of Technology David Thompson, Kiri Wagstaff 6/13/ Jet Propulsion Laboratory / California Institute of Technology Research described in this presentation was carried out at the Jet Propulsion Laboratory under a JPL Research & Technology Development Grant. Images are provided courtesy NASA / Caltech JPL / Swinburne University / Curtin University. Copyright 2011 California Institute of Technology. All Rights Reserved. US Government Support Acknowledged.

Example 1: Detection without De-dispersion 2 DM search can find typical dispersed pulses To find exotics, relax assumptions Parkes Multibeam Survey [Edwards et al. 2001] – 13 beams, 1.4 GHz (96 chan.), 125 μs sample time – 1-bit data (on/off) J (“bright”) J (“faint”) Mystery bursts (“peryton”)

Computer vision to analyze the “time/frequency image” 6/13/ Jet Propulsion Laboratory / California Institute of Technology Time Frequency Entropy of local windows Region-growing result Pulsar J Parkes Beam 5 [Edwards et al. 2001] Segmentation identifies contiguous regions that deviate from statistical properties of white noise

PCA Novelty Detection 6/13/2016Jet Propulsion Laboratory / California Institute of Technology 4 Learn a compressed representation of the data stream (including antenna noise and RFI) Flag time segments that do not compress well – i.e., deviate from historical patterns Adapt the compression model with each new time step No explicit dedispersion, RFI excision, or channel flagging

Example 2: Multi-station detection 6/13/ Jet Propulsion Laboratory / California Institute of Technology Transient detection using VLBI instruments can exploit statistical independence of local RFI De-disperse normally, but consider all independent stations in the detection decision Data-driven methods can learn each station’s noise properties, outperform incoherent summation [Thompson et al. 2011] Pulses from Pulsar B as observed by 9 VLBA antennas

Pulsar B observation 6/13/2016Jet Propulsion Laboratory / California Institute of Technology 6 classical summation, RFI excision via comparison of event lists Multi-station detection Better performance Thompson et al., 2011