Hintze Centre for Astrophysical Surveys

Abstract:

A tidal disruption event (TDE) occurs when a star travels too close to a supermassive black hole. In some cases, accretion of the disrupted material onto the black hole launches a relativistic jet. In this paper, we present a long-term observing campaign to study the radio and submillimeter emission associated with the fifth jetted/relativistic TDE: AT 2022cmc. Our campaign reveals a long-lived counterpart. We fit three different models to our data: a nonthermal jet, a spherical outflow consisting of both thermal and nonthermal electrons, and a jet with thermal and nonthermal electrons. We find that the data are best described by a relativistic spherical outflow propagating into an environment with a density profile following R−1.8. Comparison of AT 2022cmc to other TDEs finds agreement in the density profile of the environment but also that AT 2022cmc is twice as energetic as the other well-studied relativistic TDE, Swift J1644. Our observations of AT 2022cmc allow a thermal electron population to be inferred for the first time in a jetted transient, providing new insights into the microphysics of relativistic transients jets.

Abstract:

The Time Domain Extragalactic Survey (TiDES) conducted on the 4 m Multi-Object Spectroscopic Telescope will perform spectroscopic follow-up of extragalactic transients discovered in the era of the NSF-DOE Vera C. Rubin Observatory. TiDES will conduct a 5 yr survey, covering >14, 000squaredegrees , and use around 250,000 fibre hours to address three main science goals: (i) spectroscopic observations of >30,000 live transients, (ii) comprehensive follow-up of >200,000 host galaxies to obtain redshift measurements, and (iii) repeat spectroscopic observations of active galactic nuclei to enable reverberation mapping studies. The live spectra from TiDES will be used to reveal the diversity and astrophysics of both normal and exotic supernovae across the luminosity-timescale plane. The extensive host-galaxy redshift campaign will allow exploitation of the larger sample of supernovae and improve photometric classification, providing the largest-ever sample of SNe Ia, capable of a sub-2% measurement of the equation-of-state of dark energy. Finally, the TiDES reverberation mapping experiment of 700–1000 AGN will complement the SN Ia sample and extend the Hubble diagram to z ∼ 2.5.

Abstract:

A key science goal of large sky surveys such as those conducted by the Vera C. Rubin Observatory and precursors to the Square Kilometre Array is the identification of variable and transient objects. One approach is analyzing time series of the changing brightness of sources, namely, light curves. However, finding adequate statistical representations of light curves is challenging because of the sparsity of observations, irregular sampling, and nuisance factors inherent in astronomical data collection. The wide diversity of objects that a large-scale survey will observe also means that making parametric assumptions about the shape of light curves is problematic. We present a Gaussian process (GP) regression approach for characterizing light-curve variability that addresses these challenges. Our approach makes no assumptions about the shape of a light curve and, therefore, is general enough to detect a range of variable and transient source types. In particular, we propose using the joint distribution of GP amplitude hyperparameters to distinguish variable and transient candidates from nominally stable ones and apply this approach to 6394 radio light curves from the ThunderKAT survey. We compare our results with two variability metrics commonly used in radio astronomy, namely ην and Vν, and show that our approach has better discriminatory power and interpretability. Finally, we conduct a rudimentary search for transient sources in the ThunderKAT data set to demonstrate how our approach might be used as an initial screening tool. Computational notebooks in Python and R are available to help deploy this framework to other surveys.

Abstract:

Modern astronomical surveys deliver immense volumes of transient detections, yet distinguishing real astrophysical signals (for example, explosive events) from bogus imaging artefacts remains a challenge. Convolutional neural networks are effectively used for real versus bogus classification; however, their reliance on opaque latent representations hinders interpretability. Here we show that large language models (LLMs) can approach the performance level of a convolutional neural network on three optical transient survey datasets (Pan-STARRS, MeerLICHT and ATLAS) while simultaneously producing direct, human-readable descriptions for every candidate. Using only 15 examples and concise instructions, Google’s LLM, Gemini, achieves a 93% average accuracy across datasets that span a range of resolution and pixel scales. We also show that a second LLM can assess the coherence of the output of the first model, enabling iterative refinement by identifying problematic cases. This framework allows users to define the desired classification behaviour through natural language and examples, bypassing traditional training pipelines. Furthermore, by generating textual descriptions of observed features, LLMs enable users to query classifications as if navigating an annotated catalogue, rather than deciphering abstract latent spaces. As next-generation telescopes and surveys further increase the amount of data available, LLM-based classification could help bridge the gap between automated detection and transparent, human-level understanding.