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Prajna Nair

Publications and source records attributed to Prajna Nair.

2 recordsLinked to original sources

A Suppressed Volumetric Rate of High-Luminosity Mid-Infrared Selected Tidal Disruption Events

Tidal Disruption Events (TDEs) serve as direct probes of the population of supermassive black holes in the center of galaxies and are nowadays regularly detected in optical wide-field time-domain sky surveys. Recent studies have demonstrated that a large fraction of TDEs can be uniquely identified in the infrared (IR) waveband, but these studies have to date been limited to relatively nearby events. In this work, we searched for highly luminous IR-bright TDEs that are rare and thus missed by searches in the local universe. We performed a systematic search of the NEOWISE archive and developed a new selection criterion based on the evolution of the W1-W2 color to select TDE candidates. We identified 10 IR bright TDEs with peak luminosities above $L_{\rm peak\, W2} \simeq 3 \times 10^{43}$ erg s$^{-1}$ and estimated an event rate of $1.2^{+0.5}_{-0.4}\times10^{-10}$ Mpc$^{-3}$year$^{-1}$ for the luminosity range of our sample. Compared to the existing local luminosity function of lower luminosity events, we detect a suppressed rate for these highly luminous events. This turn-over in the luminosity function can be naturally explained by the suppressed amount of TDEs taking place in systems with larger black hole masses, thereby confirming the TDE nature of our sources.

astro-ph.HE

Predicting the Age of Astronomical Transients from Real-Time Multivariate Time Series

Astronomical transients, such as supernovae and other rare stellar explosions, have been instrumental in some of the most significant discoveries in astronomy. New astronomical sky surveys will soon record unprecedented numbers of transients as sparsely and irregularly sampled multivariate time series. To improve our understanding of the physical mechanisms of transients and their progenitor systems, early-time measurements are necessary. Prioritizing the follow-up of transients based on their age along with their class is crucial for new surveys. To meet this demand, we present the first method of predicting the age of transients in real-time from multi-wavelength time-series observations. We build a Bayesian probabilistic recurrent neural network. Our method can accurately predict the age of a transient with robust uncertainties as soon as it is initially triggered by a survey telescope. This work will be essential for the advancement of our understanding of the numerous young transients being detected by ongoing and upcoming astronomical surveys.

astro-ph.IM