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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:

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.

Abstract:

We present multiband ATLAS and ZTF photometry for SN 2019tsf, a Type Ib stripped-envelope supernova (SESN). The slow spectral evolution could be associated with an uncommon explosion mechanism specific to this SN. Possible explanations include fallback accretion onto a compact remnant or a long-lived central engine, both of which could provide extended energy injection responsible for the late-time rebrightening and unusual spectral features. The rebrightening observations represent the latest photometric measurements of a multipeaked Type Ib SN. As late-time photometry and spectroscopy suggest no hydrogen, the potential circumstellar material (CSM) must be H-poor. The absence of a nebular phase and the lack of narrow emission lines in the late-time spectra (>142 days) of the SNe suggest that any CSM interaction is likely asymmetric and enveloped by the SN ejecta. However, an extended CSM structure is evident through a follow-up radio campaign with the Karl G. Jansky Very Large Array (VLA), indicating a source of bright optically thick radio emission at late times, which is highly unusual among H-poor SESNe. We attribute this phenomenology to an interaction of the supernova ejecta with asymmetric CSM, potentially disk-like, and we present several models that may explain the origin of this rare Type Ib supernova. We propose a warped disk model in which a tertiary companion—commonly present around massive stars—perturbs the progenitor’s CSM, producing density enhancements that may explain the observed multipeaked SN 2019tsf light curve. This SN 2019tsf is a unique SN Type Ib among the recently discovered class of SNe that undergo mass transfer at the moment of explosion.

Abstract:

We present the Virtual Research Assistant (VRA) of the ATLAS sky survey, which performs preliminary eyeballing on our clean transient data stream. The VRA uses histogram-based gradient-boosted decision tree classifiers trained on real data to score incoming alerts on two axes: “Real” and “Galactic.” The alerts are then ranked using a geometric distance such that the most “real” and “extragalactic” receive high scores; the scores are updated when new lightcurve data is obtained on subsequent visits. To assess the quality of the training we use the recall at rank K, which is more informative to our science goal than general metrics (e.g., accuracy, F1-scores). We also establish benchmarks for our metric based on the pre-VRA eyeballing strategy, to ensure our models provide notable improvements before being added to the ATLAS pipeline. Then, policies are defined on the ranked list to select the most promising alerts for humans to eyeball and to automatically remove bogus alerts. In production the VRA method has resulted in a reduction in eyeballing workload by 85% with a loss of follow-up opportunity <0.08%. It also allows us to automatically trigger follow-up observations with the Lesedi telescope, paving the way toward automated methods that will be required in the era of LSST. Finally, this is a demonstration that feature-based methods remain extremely relevant in our field, being trainable on only a few thousand samples and highly interpretable; they also offer a direct way to inject expertise into models through feature engineering.