Initial results from a realtime FRB search with the GBT

We present the data analysis pipeline, commissioning observations, and initial results from the GREENBURST fast radio burst (FRB) detection system on the Robert C. Byrd Green Bank Telescope (GBT) previously described by Surnis et al., which uses the 21-cm receiver observing commensally with other projects. The pipeline makes use of a state-of-the-art deep learning classifier to winnow down the very large number of false-positive single-pulse candidates that mostly result from radio frequency interference. In our observations, totalling 156.5 d so far, we have detected individual pulses from 20 known radio pulsars that provide an excellent verification of the system performance. We also demonstrate, through blind injection analyses, that our pipeline is complete down to a signal-to-noise threshold of 12. Depending on the observing mode, this translates into peak flux sensitivities in the range 0.14–0.89 Jy. Although no FRBs have been detected to date, we have used our results to update the analysis of Lawrence et al. to constrain the FRB all-sky rate to be 1150+200−180 per day above a peak flux density of 1 Jy. We also constrain the source count index α = 0.84 ± 0.06, which indicates that the source count distribution is substantially flatter than expected from a Euclidean distribution of standard candles (where α = 1.5). We discuss this result in the context of the FRB redshift and luminosity distributions. Finally, we make predictions for detection rates with GREENBURST, as well as other ongoing and planned FRB experiments

ALFABURST: a commensal search for fast radio bursts with Arecibo

ALFABURST has been searching for fast radio bursts (FRBs) commensally with other projects using the Arecibo L-band Feed Array receiver at the Arecibo Observatory since 2015 July. We describe the observing system and report on the non-detection of any FRBs from that time until 2017 August for a total observing time of 518 h. With current FRB rate models, along with measurements of telescope sensitivity and beam size, we estimate that this survey probed redshifts out to about 3.4 with an effective survey volume of around 600 000 Mpc 3 . Based on this, we would expect, at the 99 per cent confidence level, to see at most two FRBs. We discuss the implications of this non-detection in the context of results from other telescopes and the limitation of our search pipeline. During the survey, single pulses from 17 known pulsars were detected. We also report the discovery of a Galactic radio transient with a pulse width of 3 ms and dispersion measure of 281 pc cm -3 , which was detected while the telescope was slewing between fields

Monitoring the Morphology of M87* in 2009-2017 with the Event Horizon Telescope

The Event Horizon Telescope (EHT) has recently delivered the first resolved images of M87*, the supermassive black hole in the center of the M87 galaxy. These images were produced using 230 GHz observations performed in 2017 April. Additional observations are required to investigate the persistence of the primary image feature- A ring with azimuthal brightness asymmetry- A nd to quantify the image variability on event horizon scales. To address this need, we analyze M87* data collected with prototype EHT arrays in 2009, 2011, 2012, and 2013. While these observations do not contain enough information to produce images, they are sufficient to constrain simple geometric models. We develop a modeling approach based on the framework utilized for the 2017 EHT data analysis and validate our procedures using synthetic data. Applying the same approach to the observational data sets, we find the M87* morphology in 2009-2017 to be consistent with a persistent asymmetric ring of ∼40 μas diameter. The position angle of the peak intensity varies in time. In particular, we find a significant difference between the position angle measured in 2013 and 2017. These variations are in broad agreement with predictions of a subset of general relativistic magnetohydrodynamic simulations. We show that quantifying the variability across multiple observational epochs has the potential to constrain the physical properties of the source, such as the accretion state or the black hole spin

Initial results from a realtime FRB search with the GBT

We present the data analysis pipeline, commissioning observations, and initial results from the GREENBURST fast radio burst (FRB) detection system on the Robert C. Byrd Green Bank Telescope (GBT) previously described by Surnis et al., which uses the 21-cm receiver observing commensally with other projects. The pipeline makes use of a state-of-the-art deep learning classifier to winnow down the very large number of false-positive single-pulse candidates that mostly result from radio frequency interference. In our observations, totalling 156.5 d so far, we have detected individual pulses from 20 known radio pulsars that provide an excellent verification of the system performance. We also demonstrate, through blind injection analyses, that our pipeline is complete down to a signal-to-noise threshold of 12. Depending on the observing mode, this translates into peak flux sensitivities in the range 0.14–0.89 Jy. Although no FRBs have been detected to date, we have used our results to update the analysis of Lawrence et al. to constrain the FRB all-sky rate to be 1150+200−180 per day above a peak flux density of 1 Jy. We also constrain the source count index α = 0.84 ± 0.06, which indicates that the source count distribution is substantially flatter than expected from a Euclidean distribution of standard candles (where α = 1.5). We discuss this result in the context of the FRB redshift and luminosity distributions. Finally, we make predictions for detection rates with GREENBURST, as well as other ongoing and planned FRB experiments

GREENBURST: A commensal Fast Radio Burst search back-end for the Green Bank Telescope

We describe the design and deployment of GREENBURST, a commensal Fast Radio Burst (FRB) search system at the Green Bank Telescope. GREENBURST uses the dedicated L-band receiver tap to search over the 960$-$1920 MHz frequency range for pulses with dispersion measures out to $10^4$ pc cm$^{-3}$. Due to its unique design, GREENBURST will obtain data even when the L-band receiver is not being used for scheduled observing. This makes it a sensitive single pixel detector capable of reaching deeper in the radio sky. While single pulses from Galactic pulsars and rotating radio transients will be detectable in our observations, and will form part of the database we archive, the primary goal is to detect and study FRBs. Based on recent determinations of the all-sky rate, we predict that the system will detect approximately one FRB for every 2$-$3 months of continuous operation. The high sensitivity of GREENBURST means that it will also be able to probe the slope of the FRB source function, which is currently uncertain in this observing band

GREENBURST: a commensal fast radio burst search back-end for the Green Bank Telescope

We describe the design and deployment of GREENBURST, a commensal Fast Radio Burst (FRB) search system at the Green Bank Telescope. GREENBURST uses the dedicated L-band receiver tap to search over the 960$-$1920 MHz frequency range for pulses with dispersion measures out to $10^4$ pc cm$^{-3}$. Due to its unique design, GREENBURST will obtain data even when the L-band receiver is not being used for scheduled observing. This makes it a sensitive single pixel detector capable of reaching deeper in the radio sky. While single pulses from Galactic pulsars and rotating radio transients will be detectable in our observations, and will form part of the database we archive, the primary goal is to detect and study FRBs. Based on recent determinations of the all-sky rate, we predict that the system will detect approximately one FRB for every 2$-$3 months of continuous operation. The high sensitivity of GREENBURST means that it will also be able to probe the slope of the FRB source function, which is currently uncertain in this observing band

Erratum: PAPER-64 constraints on reionization: The 21 cm power spectrum at z = 8.4 (Astrophysical Journal (2015) 809 (61) DOI: 10.1088/0004-637X/809/1/61)

In this erratum, we retract the upper limits on the 21 cm power spectrum presented in the published article. The published article reported an upper limit on δ 212(k ) of (22.4 mK)2 at z= 8.4 in the range 0.15 < k < 0.5h Mpc-1. This analysis underestimated the level of signal loss, or attenuation of the target cosmological 21 cm signal associated with the chosen power spectrum estimator, and also underestimated the statistical error on those estimates. A revised result, with a new analysis, is presented in M. K. Kolopanis et al. (2018, in preparation). Below, we briefly summarize the errors in the original analysis and how they are corrected. For an indepth analysis and discussion of the errors, we refer the reader to Cheng et al. (2018). Signal loss was expected in the original analysis because the covariance matrices, C, used to weight the un-normalized bandpower estimates, qa, in (Formula Presented) were empirically estimated from a time-averaged finite ensemble of the data, x, such that (Formula Presented). While the true covariance C leads to an inherently unbiased lossless estimator of the power spectrum, using an empirically estimated Ĉ can lead to signal loss. Specifically, weighting data by an empirically estimated covariance carries the risk of overfitting and downweighting EoR fluctuations that are coupled to the data. In Cheng et al. (2018), it is shown that these couplings are especially strong in the fringe-rate filtered PAPER-64 data set. The first and most impactful error relates to the method by which signal loss was estimated. To assess signal loss from the empirically estimated covariance matrix, different realizations of mock cosmological signals e of known amplitudes are added to the original data to form a new data vector, (Formula Presented). New covariance matrices, (Formula Presented) are used to estimate un-normalized bandpowers (Formula Presented), which can be written as (Formula Presented). The normalized power estimate can then be compared to the known injected power in e to estimate signal loss. The key error in the previous analysis was to assume that, since e was statistically independent of x, that the two middle crossterms in Equation (2) would average to zero in an ensemble. However, as shown in Cheng et al. (2018) and Switzer et al. (2015), these cross-terms can contain significant negative power because Ĉr contains information that correlates the two vectors. Ignoring these cross-terms leads to a significant underestimate of signal loss. As a result, we presented negligible signal loss in our original analysis, when in fact approximately 99.99% of the signal was removed (Cheng et al. 2018). Correcting for the actual signal loss is the biggest factor revising the upper limit on 21 D2 . The second mistake made in the original analysis was to underestimate the statistical errors in the reported power spectrum estimates. The original analysis used a bootstrap resampling technique on power spectral measurements over the baseline and time axes. However, fringe-rate filtering introduces significant correlations in the data along the time axis. As is discussed in Cheng et al. (2018), bootstrapping across correlated samples can result in a significant underestimate of the variation in the data if the number of resamplings is not equal to the number of independent samples in the data, as in the case of the original analysis. The error bars associated with this oversampling were underestimated by approximately a factor of 2 (in mK). The revised analysis in M. K. Kolopanis et al. (2018, in preparation) only applies bootstrap resampling across the baseline axis to avoid this problem. The mistake in estimating the statistical errors should have become apparent when comparing results to our theoretical thermal noise sensitivity. Unfortunately, a third miscalculation was made in estimating the thermal noise sensitivity. As detailed in Cheng et al. (2018), this miscalculation stemmed from numerous small mismatches between the idealized analysis pipeline used to estimate sensitivity and the actual analysis applied to the data. As a result, our estimated thermal noise sensitivity was approximately a factor of 3 low (in mK), leading to the mistaken impression that our error bars were consistent with the level of thermal noise. In summary, we retract the power spectrum results shown in Figures 18 and 20 in the published article. Results that relied on the original limits, including those presented in Figure 21, are retracted. Additionally, the companion paper to the original manuscript, Pober et al. (2015), used the original limits to place constraints on the spin temperature of the intergalactic medium (IGM) at z=8.4. Our revised limits do not place significant constraints on the IGM temperature, and the results of Figure 4 from Pober et al. (2015) should be disregarded. However, we note that their analysis would still be relevant should a future experiment place constraints on the 21 cm signal similar to those claimed in the published article. An updated analysis of this same data set is presented in M. K. Kolopanis et al. (2018, in preparation), where these revised results are put into context with measurements at other redshifts