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Abstract We present the discovery of a second radio flare from the tidal disruption event (TDE) AT2020vwl via long-term monitoring radio observations. Late-time radio flares from TDEs are being discovered more commonly, with many TDEs showing radio emission thousands of days after the stellar disruption, but the mechanism that powers these late-time flares is uncertain. Here, we present radio spectral observations of the first and second radio flares observed from the TDE AT2020vwl. Through detailed radio spectral monitoring, we find evidence for two distinct outflow ejection episodes or a period of renewed energy injection into the preexisting outflow. We deduce that the second radio flare is powered by an outflow that is initially slower than the first flare but carries more energy and shows tentative indication of accelerating over time. Through modelling the long-term optical and UV emission from the TDE as arising from an accretion disk, we infer that the second radio outflow launch or energy injection episode occurred approximately at the time of the peak accretion rate. The fast decay of the second flare precludes environmental changes as an explanation, while the velocity of the outflow is at all times too low to be explained by an off-axis relativistic jet. Future observations that search for any link between the accretion disk properties and late-time radio flares from TDEs will aid understanding of what powers the radio outflows in TDEs and confirm if multiple outflow ejections or energy injection episodes are common. 

Abstract We present results from an extensive follow-up campaign of the tidal disruption event (TDE) ASASSN-15oi spanningδt ∼ 10–3000 days, offering an unprecedented window into the multiwavelength properties of a TDE during its first ≈8 yr of evolution. ASASSN-15oi is one of the few TDEs with strong detections at X-ray, optical/UV, and radio wavelengths and it also featured two delayed radio flares atδt ∼ 180 days andδt ∼ 1400 days. Our observations atδt > 1400 days reveal an absence of thermal X-rays, a late-time variability in the nonthermal X-ray emission, and sharp declines in the nonthermal X-ray and radio emission atδt ∼ 2800 days and ∼3000 days, respectively. The UV emission shows no significant evolution atδt > 400 days and remains above the pre-TDE level. We show that a cooling envelope model can explain the thermal emission consistently across all epochs. We also find that a scenario involving episodic ejection of material due to stream–stream collisions can possibly explain the first radio flare. Given the peculiar spectral and temporal evolution of the late-time emission, however, constraining the origins of the second radio flare and the nonthermal X-rays remains challenging. Our study underscores the critical role of long-term, multiwavelength follow-up to fully characterize the extended evolutionary phases of a TDE. 

ABSTRACT The All-Sky Automated Survey for Supernovae (ASAS-SN) is the first optical survey to monitor the entire sky, currently with a cadence of ≲ 24 h down to g ≲ 18.5 mag. ASAS-SN has routinely operated since 2013, collecting ∼ 2 000 to over 7 500 epochs of V- and g-band observations per field to date. This work illustrates the first analysis of ASAS-SN’s newer, deeper, and higher cadence g-band data. From an input source list of ∼55 million isolated sources with g < 18 mag, we identified 1.5 × 106 variable star candidates using a random forest (RF) classifier trained on features derived from Gaia, 2MASS, and AllWISE. Using ASAS-SN g-band light curves, and an updated RF classifier augmented with data from Citizen ASAS-SN, we classified the candidate variables into eight broad variability types. We present a catalogue of ∼116 000 new variable stars with high-classification probabilities, including ∼111 000 periodic variables and ∼5 000 irregular variables. We also recovered ∼263 000 known variable stars. 

Abstract We present the first results from Citizen ASAS-SN, a citizen science project for the All-Sky Automated Survey for Supernovae (ASAS-SN) hosted on the Zooniverse platform. Citizen ASAS-SN utilizes the newer, deeper, higher cadence ASAS-SN g -band data and tasks volunteers to classify periodic variable star candidates based on their phased light curves. We started from 40,640 new variable candidates from an input list of ∼7.4 million stars with δ < −60° and the volunteers identified 10,420 new discoveries which they classified as 4234 pulsating variables, 3132 rotational variables, 2923 eclipsing binaries, and 131 variables flagged as Unknown. They classified known variable stars with an accuracy of 89% for pulsating variables, 81% for eclipsing binaries, and 49% for rotational variables. We examine user performance, agreement between users, and compare the citizen science classifications with our machine learning classifier updated for the g -band light curves. In general, user activity correlates with higher classification accuracy and higher user agreement. We used the user’s “Junk” classifications to develop an effective machine learning classifier to separate real from false variables, and there is a clear path for using this “Junk” training set to significantly improve our primary machine learning classifier. We also illustrate the value of Citizen ASAS-SN for identifying unusual variables with several examples.