4 papers
Recovering Sharp Conductivity Features in the Finite-Data Calderón Problem with Physics-Informed Neural Networks
Ali AlHadi Kalout, Pablo Tejerina-Pérez, Konstantin Karchev +5
Physics-informed neural networks (PINNs) have recently emerged as a promising framework for addressing the Calderón inverse problem from limited boundary data. In this work, we re…
The colour variability of low-z SNe Ia is entirely explained by dust
Marco Giunta, Konstantin Karchev, Roberto Trotta
The relative importance of intrinsic colour variability of supernovae type Ia (SN Ia) versus dust-induced reddening remains an open question with important ramifications for unders…
CIGaRS I: Combined simulation-based inference from type Ia supernovae and host photometry
Konstantin Karchev, Roberto Trotta, Raul Jimenez
Using type Ia supernovae as cosmological probes requires empirical corrections that are correlated with their host environment. Here we present a unified Bayesian hierarchical mode…
One never walks alone: the effect of the perturber population on subhalo measurements in strong gravitational lenses
Adam Coogan, Noemi Anau Montel, Konstantin Karchev +3
Analyses of extended arcs in strong gravitational lensing images to date have constrained the properties of dark matter by measuring the parameters of one or two individual subhalo…