Fitting transients with discs ( <scp>fitted</scp> ): a public light curve and spectral fitting package based on evolving relativistic discs
ABSTRACT We present fitted, a public light curve and spectral fitting python-package based on evolving relativistic discs. At its heart, this package uses the solutions of the time dependent general relativistic disc equations, assuming a newly formed accretion flow, to compute multiband light curves and spectra. This package can be used to study transient events in galaxies with previously dormant supermassive black holes. In its current form, it cannot be used in systems with large scale pre-existing accretion-discs, such as active galactic nuclei. All relevant relativistic optics effects (Doppler and gravitational energy shifting, and gravitational lensing) are included. Additional, non-disc light curve and spectral components can be included to (for example) model the early time rise and decay of tidal disruption event light curves in optical-to-UV bands. Monte Carlo Markov Chain fitting procedures are included which return posterior distributions of black hole and disc parameters, allowing for the future automated processing of the large populations of transient sources discovered by (e.g.) the Vera Rubin Observatory. As an explicit example, in this paper, we model the multiwavelength light curves of the tidal disruption event AT2019dsg, finding a good fit to the data, a black hole mass consistent with galactic scaling relationships, and a late-time disc Eddington ratio consistent with the observed launching of an outflow observed in radio bands.
Authors
- Adam Ingram (ORCID: https://orcid.org/0000-0002-5311-9078)
- Andrew Mummery (ORCID: https://orcid.org/0009-0006-4590-8583)
- Edward Nathan (ORCID: https://orcid.org/0000-0002-9633-9193)
- Martin Gardner
Institutions
- California Institute of Technology (US)
- Newcastle University (GB)
Publication Details
- Journal
- Monthly Notices of the Royal Astronomical Society
- Published
- 2025-09-19
- DOI
- https://doi.org/10.1093/mnras/staf1565
- Citations
- 5
- Primary Topic
- Statistical and numerical algorithms
- Type
- article
- Field-Weighted Citation Impact
- 18.64