Hannah Wichern | Searcharxiv

arXiv2026

Aarmed with Data: Bumps, Outflows, and Disk-like Emission in TDE 2025aarm

The origin of the optical emission in tidal disruption events (TDEs) remains one of the major outstanding questions in the field, in part due to the limited number of nearby events with high-cadence monitoring to track their evolving photometric and spectroscopic properties. We present multi-wavelength observations of the nearby ($z=0.01368$) TDE\,2025aarm, including near-daily spectroscopic coverage prior to the optical peak. Its proximity makes it one of the brightest TDEs discovered, reaching a peak magnitude of $m_r\sim15.5$ ($M_r\sim-18$). The light curve deviates from a smooth evolution, exhibiting multiple rebrightening episodes visible in both the individual filter light curves and the bolometric luminosity. Blackbody modelling reveals that these rebrightenings are associated with an increase in temperature of $> 5,000-10,000$\,K, while the inferred photospheric radius remains approximately constant. Simultaneously, the H$\alpha$ line not only increases in blueshift but also broadens, suggesting a link between the continuum rebrightenings to changes in the kinematics of the line-forming gas. We identify a persistent absorption component at $\sim-3900$\,km\,s$^{-1}$ in multiple Balmer lines, providing further evidence for outflowing material. The H$\alpha$ profile also exhibits excess flux compared to a Gaussian on both sides of the line, inconsistent with simple scattering-dominated outflow models. Disk-profile modelling provides evidence for the emergence of a disk-like component least $\sim20$ days after peak, with substantial changes in the disk properties between $\sim50$ and 60 days. These observations highlight the complexity of TDE emission processes and demonstrate how dense multi-wavelength monitoring can disentangle the roles of accretion, reprocessing, and outflows in shaping TDE emission.

astro-ph.HE↗

arXiv2025

Efficient Bayesian analysis of kilonovae and gamma ray burst afterglows with fiesta

Gamma-ray burst (GRB) afterglows and kilonovae (KNe) are electromagnetic transients that can accompany binary neutron star (BNS) mergers. Therefore, studying their emission processes is of general interest for constraining cosmological parameters or the behavior of ultra-dense matter. One common method to analyze electromagnetic data from BNS mergers is to sample a Bayesian posterior over the parameters of a physical model for the transient. However, sampling the posterior is computationally costly and because of the many likelihood evaluations required in this process, detailed models are too expensive to be used directly in Bayesian inference. In this paper, we address the problem by introducing fiesta, a python package to train machine learning (ML) surrogates for GRB afterglow and kilonova models that have the capacity to accelerate likelihood evaluations. Specifically, we introduce extensive ML surrogates for the state-of-the-art GRB afterglow models afterglowpy and pyblastafterglow, along with a new surrogate for KN emission based on the possis code. Our surrogates enable evaluation of the light-curve posterior within minutes. We also provide built-in posterior sampling capabilities in fiesta that rely on the flowMC package, which efficiently scale to higher dimensions when adding up to tens of nuisance sampling parameters. Because of its use of the JAX framework, fiesta also allows for GPU acceleration during both surrogate training and posterior sampling. We applied our framework to reanalyze AT2017gfo/GRB170817A and GRB211211A with our surrogates, thus employing the new pyblastafterglow model for the first time in Bayesian inference.

astro-ph.HE↗