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The redshifted 21 cm line of neutral hydrogen (H i), potentially observable at low radio frequencies (∼50–200 MHz), is a promising probe of the physical conditions of the intergalactic medium during Cosmic Dawn and the Epoch of Reionization (EoR). The sky-averaged H i signal is expected to be extremely weak (∼100 mK) in comparison to the Galactic foreground emission (∼104 K). Moreover, the sky-averaged spectra measured by ground-based instruments are affected by chromatic propagation effects (∼tens of kelvin) originating in the ionosphere. We analyze data collected with the upgraded Broadband Instrument for Global Hydrogen Reionization Signal system deployed at the Murchison Radio-astronomy Observatory to assess the significance of ionospheric effects on the detection of the global EoR signal. The ionospheric effects identified in these data are, particularly during nighttime, dominated by absorption and emission. We measure some properties of the ionosphere, such as the electron temperature (Te ≈ 470 K at nighttime), magnitude, and variability of optical depth (τ100 MHz ≈ 0.01 and δτ ≈ 0.005 at nighttime). According to the results of a statistical test applied on a large data sample, very long integrations (∼100 hr collected over approximately 2 months) lead to increased signal-to-noise ratio even in the presence of ionospheric variability. This is further supported by the structure of the power spectrum of the sky temperature fluctuations, which has flicker noise characteristics at frequencies ≳10−5 Hz, but becomes flat below ≈10−5 Hz. Hence, we conclude that the stochastic error introduced by the chromatic ionospheric effects tends to zero in an average. Therefore, the ionospheric effects and fluctuations are not fundamental impediments preventing ground-based instruments from integrating down to the precision required by global EoR experiments, provided that the ionospheric contribution is properly accounted for in the data analysis. [ABSTRACT FROM AUTHOR]

The V-FASTR experiment on the Very Long Baseline Array was designed to detect dispersed pulses of milliseconds in duration, such as fast radio bursts (FRBs). We use all V-FASTR data through 2015 February to report V-FASTR’s upper limits on the rates of FRBs, and compare these with rederived rates from Parkes FRB detection experiments. V-FASTR’s operation at allows direct comparison with the 20 cm Parkes rate, and we derive a power-law limit of (95% confidence limit) on the index of FRB source counts, . Using the previously measured FRB rate and the unprecedented amount of survey time spent searching for FRBs at a large range of wavelengths ( cm), we also place frequency-dependent limits on the spectral distribution of FRBs. The most constraining frequencies place two-point spectral index limits of and , where fluence if we assume that the burst rate reported by Champion et al. of is accurate (for bursts of ∼3 ms duration). This upper limit on α suggests that if FRBs are extragalactic but noncosmological, on average they are not experiencing excessive free–free absorption due to a medium with high optical depth (assuming temperature ∼8000 K), which excessively inverts their low-frequency spectrum. This in turn implies that the dispersion of FRBs arises in either or both of the intergalactic medium or the host galaxy, rather than from the source itself. [ABSTRACT FROM AUTHOR]

Time domain radio astronomy observing campaigns frequently generate large volumes of data. Our goal is to develop automated methods that can identify events of interest buried within the larger data stream. The V-FASTR fast transient system was designed to detect rare fast radio bursts within data collected by the Very Long Baseline Array. The resulting event candidates constitute a significant burden in terms of subsequent human reviewing time. We have trained and deployed a machine learning classifier that marks each candidate detection as a pulse from a known pulsar, an artifact due to radio frequency interference, or a potential new discovery. The classifier maintains high reliability by restricting its predictions to those with at least 90% confidence. We have also implemented several efficiency and usability improvements to the V-FASTR web-based candidate review system. Overall, we found that time spent reviewing decreased and the fraction of interesting candidates increased. The classifier now classifies (and therefore filters) 80%–90% of the candidates, with an accuracy greater than 98%, leaving only the 10%–20% most promising candidates to be reviewed by humans. [ABSTRACT FROM AUTHOR]