No rungs attached: A distance-ladder-free determination of the Hubble constant through type II supernova spectral modelling

Context. The ongoing discrepancy among Hubble constant (H0) estimates obtained through local distance ladder methods and early Universe observations poses a significant challenge to the ΛCDM model, suggesting potential new physics. Type II supernovae (SNe II) offer a promising technique for determining H0 in the Local Universe independently of the traditional distance ladder approach, opening up a complimentary path for testing this discrepancy. Aims. We aim to provide the first H0 estimate using the tailored expanding photosphere method (EPM) applied to SNe II, made possible by recent advancements in spectral modelling that enhance its precision and efficiency. Methods. Our tailored EPM measurement utilises a spectral emulator to interpolate between radiative transfer models calculated with <ns0:sc>TARDIS</ns0:sc>, allowing us to fit SN spectra efficiently and derive self-consistent values for luminosity-related parameters. We applied the method to a set of public data for ten SNe II at redshifts between 0.01 and 0.04. Results. Our analysis demonstrates that the tailored EPM allows us to obtain H0 measurements with a precision comparable to the most competitive established techniques, even when applied to literature data that are not designed for cosmological applications. We find an independent H0 value of 74.9 ± 1.9 (stat) km s−1 Mpc−1, which is consistent with most current local measurements. Considering dominant sources of systematic effects, we conclude that our systematic uncertainty is comparable to (or less than) the current statistical uncertainty. Conclusions. This proof-of-principle study highlights the potential of the tailored EPM as a robust and precise tool for investigating the Hubble tension independently of the local distance ladder. Observations of SNe II tailored to H0 estimations could make this an even more powerful tool by improving the precision and allowing us to improve our understanding of the systematic uncertainties and how to control them.