Engineering topics

Madsen, E. C.

Publications and source records attributed to Madsen, E. C..

3 records

DOE OSTI2016

The repeating fast radio burst FRB 121102: multi-wavelength observations and additional bursts

We report on radio and X-ray observations of the only known repeating Fast Radio Burst (FRB) source, FRB 121102. We have detected six additional radio bursts from this source: five with the Green Bank Telescope at 2 GHz, and one at 1.4 GHz with the Arecibo Observatory, for a total of 17 bursts from this source. All have dispersion measures consistent with a single value (∼559 pc cm{sup −3}) that is three times the predicted maximum Galactic contribution. The 2 GHz bursts have highly variable spectra like those at 1.4 GHz, indicating that the frequency structure seen across the individual 1.4 and 2 GHz bandpasses is part of a wideband process. X-ray observations of the FRB 121102 field with the Swift and Chandra observatories show at least one possible counterpart; however, the probability of chance superposition is high. A radio imaging observation of the field with the Jansky Very Large Array at 1.6 GHz yields a 5σ upper limit of 0.3 mJy on any point-source continuum emission. This upper limit, combined with archival Wide-field Infrared Survey Explorer 22 μm and IPHAS Hα surveys, rules out the presence of an intervening Galactic H ii region. We update our estimate of the FRB detection rate in the PALFA survey to be 1.1{sub −1.0}{sup +3.7}×10{sup 4} FRBs sky{sup −1} day{sup −1} (95% confidence) for peak flux density at 1.4 GHz above 300 mJy. We find that the intrinsic widths of the 12 FRB 121102 bursts from Arecibo are, on average, significantly longer than the intrinsic widths of the 13 single-component FRBs detected with the Parkes telescope.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

Discovery and follow-up of rotating radio transients with the Green Bank and LOFAR telescopes

We have discovered 21 Rotating Radio Transients (RRATs) in data from the Green Bank Telescope (GBT) 350 MHz Drift-scan and the Green Bank North Celestial Cap pulsar surveys using a new candidate sifting algorithm. RRATs are pulsars with sporadic emission that are detected through their bright single pulses rather than Fourier domain searches. We have developed RRATtrap, a single-pulse sifting algorithm that can be integrated into pulsar survey data analysis pipelines in order to find RRATs and Fast Radio Bursts. We have conducted follow-up observations of our newly discovered sources at several radio frequencies using the GBT and Low Frequency Array, yielding improved positions and measurements of their periods, dispersion measures (DMs), and burst rates, as well as phase-coherent timing solutions for four of them. The new RRATs have DMs ranging from 15 to 97 pc cm{sup −3}, periods of 240 ms to 3.4 s, and estimated burst rates of 20 to 400 pulses hr{sup −1} at 350 MHz. We use this new sample of RRATs to perform statistical comparisons between RRATs and canonical pulsars in order to shed light on the relationship between the two populations. We find that the DM and spatial distributions of the RRATs agree with those of the pulsars found in the same survey. We find evidence that slower pulsars (i.e., P>200 ms) are preferentially more likely to emit bright single pulses than are faster pulsars (P<200 ms), although this conclusion is tentative. Our results are consistent with the proposed link between RRATs, transient pulsars, and canonical pulsars as sources in various parts of the pulse activity spectrum.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Searching for pulsars using image pattern recognition

In the modern era of big data, many fields of astronomy are generating huge volumes of data, the analysis of which can sometimes be the limiting factor in research. Fortunately, computer scientists have developed powerful data-mining techniques that can be applied to various fields. In this paper, we present a novel artificial intelligence (AI) program that identifies pulsars from recent surveys by using image pattern recognition with deep neural nets—the PICS (Pulsar Image-based Classification System) AI. The AI mimics human experts and distinguishes pulsars from noise and interference by looking for patterns from candidate plots. Different from other pulsar selection programs that search for expected patterns, the PICS AI is taught the salient features of different pulsars from a set of human-labeled candidates through machine learning. The training candidates are collected from the Pulsar Arecibo L-band Feed Array (PALFA) survey. The information from each pulsar candidate is synthesized in four diagnostic plots, which consist of image data with up to thousands of pixels. The AI takes these data from each candidate as its input and uses thousands of such candidates to train its ∼9000 neurons. The deep neural networks in this AI system grant it superior ability to recognize various types of pulsars as well as their harmonic signals. The trained AI's performance has been validated with a large set of candidates from a different pulsar survey, the Green Bank North Celestial Cap survey. In this completely independent test, the PICS ranked 264 out of 277 pulsar-related candidates, including all 56 previously known pulsars and 208 of their harmonics, in the top 961 (1%) of 90,008 test candidates, missing only 13 harmonics. The first non-pulsar candidate appears at rank 187, following 45 pulsars and 141 harmonics. In other words, 100% of the pulsars were ranked in the top 1% of all candidates, while 80% were ranked higher than any noise or interference. The performance of this system can be improved over time as more training data are accumulated. This AI system has been integrated into the PALFA survey pipeline and has discovered six new pulsars to date.

79 ASTRONOMY AND ASTROPHYSICS↗