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ABSTRACT Fulfilling the rich promise of rapid advances in time-domain astronomy is only possible through confronting our observations with physical models and extracting the parameters that best describe what we see. Here, we introduce redback; a Bayesian inference software package for electromagnetic transients. redback provides an object-orientated python interface to over 12 different samplers and over 100 different models for kilonovae, supernovae, gamma-ray burst afterglows, tidal disruption events, engine-driven transients among other explosive transients. The models range in complexity from simple analytical and semi-analytical models to surrogates built upon numerical simulations accelerated via machine learning. redback also provides a simple interface for downloading and processing data from various catalogues such as Swift and FINK. The software can also serve as an engine to simulate transients for telescopes such as the Zwicky Transient Facility and Vera Rubin with realistic cadences, limiting magnitudes, and sky coverage or a hypothetical user-constructed survey or a generic transient for target-of-opportunity observations with different telescopes. As a demonstration of its capabilities, we show how redback can be used to jointly fit the spectrum and photometry of a kilonova, enabling a more powerful, holistic probe into the properties of a transient. We also showcase general examples of how redback can be used as a tool to simulate transients for realistic surveys, fit models to real, simulated, or private data, multimessenger inference with gravitational waves, and serve as an end-to-end software toolkit for parameter estimation and interpreting the nature of electromagnetic transients. 

Stars with zero-age main sequence masses between 140 and 260 M_⊙are thought to explode as pair-instability supernovae (PISNe). During their thermonuclear runaway, PISNe can produce up to several tens of solar masses of radioactive nickel, resulting in luminous transients similar to some superluminous supernovae (SLSNe). Yet, no unambiguous PISN has been discovered so far. SN 2018ibb is a hydrogen-poor SLSN atz = 0.166 that evolves extremely slowly compared to the hundreds of known SLSNe. Between mid 2018 and early 2022, we monitored its photometric and spectroscopic evolution from the UV to the near-infrared (NIR) with 2–10 m class telescopes. SN 2018ibb radiated > 3 × 10⁵¹ erg during its evolution, and its bolometric light curve reached > 2 × 10⁴⁴ erg s⁻¹at its peak. The long-lasting rise of > 93 rest-frame days implies a long diffusion time, which requires a very high total ejected mass. The PISN mechanism naturally provides both the energy source (⁵⁶Ni) and the long diffusion time. Theoretical models of PISNe make clear predictions as to their photometric and spectroscopic properties. SN 2018ibb complies with most tests on the light curves, nebular spectra and host galaxy, and potentially all tests with the interpretation we propose. Both the light curve and the spectra require 25–44M_⊙of freshly nucleosynthesised⁵⁶Ni, pointing to the explosion of a metal-poor star with a helium core mass of 120–130M_⊙at the time of death. This interpretation is also supported by the tentative detection of [Co II]λ1.025 μm, which has never been observed in any other PISN candidate or SLSN before. We observe a significant excess in the blue part of the optical spectrum during the nebular phase, which is in tension with predictions of existing PISN models. However, we have compelling observational evidence for an eruptive mass-loss episode of the progenitor of SN 2018ibb shortly before the explosion, and our dataset reveals that the interaction of the SN ejecta with this oxygen-rich circumstellar material contributed to the observed emission. That may explain this specific discrepancy with PISN models. Powering by a central engine, such as a magnetar or a black hole, can be excluded with high confidence. This makes SN 2018ibb by far the best candidate for being a PISN, to date.