Richard Stiskalek

DPhil candidate in Astrophysics at the University of Oxford, Balliol College

Research

My overarching goal is to infer the initial conditions of the local Universe – the primordial fluctuations from which all nearby structure grew – and to develop new field-level tests of galaxy formation and cosmology along the way.

For a full list of publications, see my CV or ADS library.

Digital twins and the initial conditions of the local Universe

Digital twins of the local Universe are simulations whose initial conditions are inferred to reproduce the specific structures – galaxies, clusters, voids, filaments – that we observe in our cosmic neighbourhood, rather than a random patch of the Universe. I am interested in exploiting these to learn about cosmology and galaxy formation: mapping large-scale flows, precision measurements of the expansion rate, semi-analytical modelling and object-by-object tests of galaxy formation models, and ultimately field-level inference of the initial conditions and cosmological parameters.

  • R. Stiskalek, H. Desmond, J. Devriendt, A. Slyz, G. Lavaux, M. Hudson, D. Bartlett, H. Courtois (2025). The Velocity Field Olympics. MNRAS 545. arXiv:2502.00121
  • S. McAlpine, J. Jasche, M. Ata, G. Lavaux, R. Stiskalek, C. S. Frenk, A. Jenkins (2025). The Manticore Project I: a digital twin of our cosmic neighbourhood. MNRAS 540:716. arXiv:2505.10682

Distance-ladder cosmology

I develop new statistical frameworks for distance-ladder cosmology based on rigorous Bayesian forward modelling. A particular focus is exploiting relatively small samples of distance indicators, such as Cepheids, tip of the red giant branch (TRGB) stars, and masers, that are otherwise dominated by cosmic variance, using digital twins of the local Universe to suppress it and deliver precision measurements of the Hubble constant. Upcoming goals include delivering a forward model of the entire distance ladder – from Milky Way stars to distant supernovae – and developing a novel scalable framework for the LSST era.

  • R. Stiskalek, H. Desmond, E. Tsaprazi, A. Heavens, G. Lavaux, S. McAlpine, J. Jasche (2025). 1.8 per cent measurement of H₀ from Cepheids alone. MNRAS. arXiv:2509.09665
  • H. Desmond, R. Stiskalek, J. A. Najera, I. Banik (2025). The subtle statistics of the distance ladder: On the distance prior and selection effects. Submitted. arXiv:2511.03394

Peculiar velocities

Peculiar velocities – deviations from the smooth Hubble flow – can be inferred by comparing a galaxy’s observed redshift with an independent estimate of its distance from scaling relations such as Tully-Fisher, fundamental plane, or supernova standardisation. I use these surveys to constrain cosmological parameters such as fσ₈, test the cosmological principle through searches for anisotropies in the local expansion rate, and ultimately to build the next generation of digital twins of the local Universe.

  • R. Stiskalek (2025). S₈ from Tully-Fisher, fundamental plane, and supernova distances agree with Planck. Submitted. arXiv:2509.20235
  • R. Stiskalek, H. Desmond, G. Lavaux (2025). No evidence for local H₀ anisotropy from Tully-Fisher or supernova distances. MNRAS 546. arXiv:2509.14997

Galaxy–halo connection

I am interested in all aspects of the galaxy–halo connection, from empirical to semi-analytic models. On the empirical side, I extend traditional models such as abundance matching and test them against diverse observational samples, including optical and HI-selected populations. I also use semi-analytic models in conjunction with digital twins to predict properties of nearby structures on an object-by-object basis.

  • R. Stiskalek, H. Desmond, T. Holvey, M. G. Jones (2021). The dependence of subhalo abundance matching on galaxy photometry and selection criteria. MNRAS 506:3205. arXiv:2101.02765

Machine learning methods

I am interested in graph-based methods and geometric deep learning to capture physical structure in cosmological datasets, and simulation-based inference (SBI) to perform implicit likelihood inference where traditional likelihoods are intractable – including applications to JWST data to infer the ionising photon contributions of high-redshift galaxies. I commonly employ normalising flows, Gaussian processes, neural networks, and tree-based models to study, for example, the scatter in the galaxy–halo connection and to quantify uncertainties in astrophysical models. I also work extensively with Hamiltonian Monte Carlo and gradient-based samplers for scalable Bayesian inference.

  • R. Stiskalek, D. J. Bartlett, H. Desmond, D. Anbajagane (2022). The scatter in the galaxy-halo connection: a machine learning analysis. MNRAS 514:4026. arXiv:2202.14006
  • N. Choustikov, R. Stiskalek, A. Saxena, H. Katz, J. Devriendt, A. Slyz (2025). Inferring the ionizing photon contributions of high-redshift galaxies to reionization with JWST NIRCam photometry. MNRAS 537:2273. arXiv:2405.09720
  • N. Huang, R. Stiskalek, J.-Y. Lee, A. E. Bayer, C. C. Margossian, C. K. Jespersen, L. A. Perez, L. K. Saul, F. Villaescusa-Navarro (2025). CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning. NeurIPS 2025. arXiv:2507.03707

Gravitational-wave astronomy

During my master’s degree, I worked on gravitational-wave data analysis and strong-field lensing of gravitational waves. This included testing the isotropy of binary black hole mergers with LIGO/Virgo data, exploring transdimensional parameter estimation, and studying frequency- and polarisation-dependent lensing effects in the gravitational spin Hall effect.

  • R. Stiskalek, J. Veitch, C. Messenger (2021). Are stellar-mass binary black hole mergers isotropically distributed? MNRAS 501:970. arXiv:2003.02919
  • M. A. Oancea, R. Stiskalek, M. Zumalacárregui (2024). Frequency- and polarisation-dependent lensing of gravitational waves in strong gravitational fields. Phys. Rev. D 109, 124045. arXiv:2209.06459