On the use of variability time-scales as an early classifier of radio transients and variables

Abstract

We have shown previously that a broad correlation between the peak radio luminosity and the variability time-scales, approximately L ∝ τ 5 , exists for variable synchrotron emitting sources and that different classes of astrophysical source occupy different regions of luminosity and time-scale space. Based on those results, we investigate whether the most basic information available for a newly discovered radio variable or transient -their rise and/or decline rate -can be used to set initial constraints on the class of events from which they originate. We have analysed a sample of ≈ 800 synchrotron flares, selected from light-curves of ≈ 90 sources observed at 5-8 GHz, representing a wide range of astrophysical phenomena, from flare stars to supermassive black holes. Selection of outbursts from the noisy radio light-curves has been done automatically in order to ensure reproducibility of results. The distribution of rise/decline rates for the selected flares is modelled as a Gaussian probability distribution for each class of object, and further convolved with estimated areal density of that class in order to correct for the strong bias in our sample. We show in this way that comparing the measured variability time-scale of a radio transient/variable of unknown origin can provide an early, albeit approximate, classification of the object, and could form part of a suite of measurements used to provide early categorisation of such events. Finally, we also discuss the effect scintillating sources will have on our ability to classify events based on their variability time-scales.

Discovery of a radio transient in M81

“…V404 Cyg, V6461 Sgr and H1743-322; Han & Hjellming 1992;Hjellming et al 2000;McClintock et al 2009) or periods of stable radio flux (GRS 1915+105;, which are more consistent with the timescale of VTC J095517.5+690813. However, typical LMXB minimum energies are also at least an order of magnitude lower than our estimate for VTC J095517.5+690813 (see Table 6), with few reaching a similar jet power (Pietka et al 2017;Fender et al 1999;Brocksopp et al 2007;Curran et al 2014;Tetarenko et al 2017;Dunn et al 2010). Another important difference to LMBXs is the lack of short-term radio variability displayed by VTC J095517.5+690813 within the four 1 hour epochs at which VTC J095517.5+690813 was detected.…”

“…The VLA sampling of M81 did not resolve the initial rise in emission so we cannot adequately constrain its risetime. However, recent work by Pietka et al (2015Pietka et al ( , 2017 has demonstrated that a broad correlation exists between the peak radio luminosity and the variability timescales (rise/decline rates) of a transient. Using this relation, the measured peak radio luminosity of VTC J095517.5+690813 (νL ν = 8.7 × 10 33 erg s −1 ) is brighter than most XRB flares, with the correlation suggesting the transient should have had an exponential rise timescale (rise-time or e-folding time) of τ ∼ 2.6 d (see figure 7 of Pietka et al 2017, but note the scatter on the rise-time for XRBs, which is 10 −2 τ 10 3 days).…”

“…However, recent work by Pietka et al (2015Pietka et al ( , 2017 has demonstrated that a broad correlation exists between the peak radio luminosity and the variability timescales (rise/decline rates) of a transient. Using this relation, the measured peak radio luminosity of VTC J095517.5+690813 (νL ν = 8.7 × 10 33 erg s −1 ) is brighter than most XRB flares, with the correlation suggesting the transient should have had an exponential rise timescale (rise-time or e-folding time) of τ ∼ 2.6 d (see figure 7 of Pietka et al 2017, but note the scatter on the rise-time for XRBs, which is 10 −2 τ 10 3 days). If we assume a non-relativisitic expansion velocity, then for a maximum possible rise-time of 112 d, the source size (ct) could be as large as 1.9×10 4 AU by the first detection on 2015 Jan 2.…”

Giant burst of methanol maser in S255IR-NIRS3

“…In order to characterize the light curve for each individual spectral channel, we followed the thresholding approach of Pietka et al (2017). The background flux density level, b, and the signal variation due to noise, σ, were estimated from the preburst part of the light curve, that is, before MJD ∼56716.…”

Classification of Multiwavelength Transients with Machine Learning

“…The radio transient data used were collected by Pietka et al (2017) except for the kilonova light curve, which was collected by Dobie et al (2018).…”