289 citations · 1.5k across the 66 of their papers we have counts for
69 papers
A Global Overview of Starobinsky Inflation beyond slow-roll with CMB-BAO data
Tarun, Tanmoy Modak, Björn Malte Schäfer
We perform a global analysis of the Starobinsky inflation using most recent CMB data from ACT and SPT together with DESI BAO measurements. By directly solving the equations of moti…
Microcanonical Hamiltonian Monte Carlo and the Helmholtz Theorem
Heinrich von Campe, Bjoern Malte Schaefer
The recently proposed Microcanonical Hamiltonian Monte Carlo algorithm has not yet been studied in detail from a thermodynamic point of view; this work aims to fill that gap. We de…
The Latent Information Geometry of Jet Classification
Rebecca Maria Kuntz, Tilman Plehn, Björn Malte Schäfer +2
Latent representations are an important theme in modern machine learning. Any network training with the notion of locality introduces a latent geometry which we can analyze with th…
Starobinsky in Stereo: SKA-CMB Synergy in SBI
Benedikt Schosser, Caroline Heneka, Björn Malte Schäfer
Modern machine learning techniques can unlock the vast cosmological information encoded in forthcoming Square Kilometre Array (SKA) observations. We show that tomographic 21 cm dat…
Rényi-Induced Information Geometry and Hartigan's Prior Family
Rebecca Maria Kuntz, Heinrich von Campe, Björn Malte Schäfer
We derive the information geometry induced by the statistical Rényi divergence, namely its metric tensor, its dual parametrized connections, as well as its dual Laplacians. Based o…
A novel inversion algorithm for weak gravitational lensing using quasi-conformal geometry
Jan Jakob, Björn Malte Schäfer
The challenge in weak gravitational lensing caused by galaxies and clusters is to infer the projected mass density distribution from gravitational lensing measurements, known as th…
Approximating non-Gaussian Bayesian partitions with normalising flows: statistics, inference and application to cosmology
Tobias Röspel, Adrian Schlosser, Björn Malte Schäfer
Subject of this paper is the simplification of Markov chain Monte Carlo sampling as used in Bayesian statistical inference by means of normalising flows, a machine learning method…
Partition function approach to non-Gaussian likelihoods: information theory and state variables for Bayesian inference
Rebecca Maria Kuntz, Heinrich von Campe, Tobias Röspel +2
The significance of statistical physics concepts such as entropy extends far beyond classical thermodynamics. We interpret the similarity between partitions in statistical mechanic…
Markov Walk Exploration of Model Spaces: Bayesian Selection of Dark Energy Models with Supernovae
Benedikt Schosser, Tobias Röspel, Bjoern Malte Schaefer
Central to model selection is a trade-off between performing a good fit and low model complexity: A model of higher complexity should only be favoured over a simpler model if it pr…
PINNferring the Hubble Function with Uncertainties
Lennart Röver, Björn Malte Schäfer, Tilman Plehn
The Hubble function characterizes a given Friedmann-Robertson-Walker spacetime and can be related to the densities of the cosmological fluids and their equations of state. We show…
A unified linear intrinsic alignment model for elliptical and disc galaxies and the resulting ellipticity spectra
Basundhara Ghosh, Kai Nussbaumer, Eileen Sophie Giesel +1
Alignments of spiral galaxies were thought to result from tidal torquing, where tidal field of the cosmic large-scale structure exert torquing moments onto dark matter haloes, dete…
Partition function approach to non-Gaussian likelihoods: macrocanonical partitions and replicating Markov-chains
Maximilian Philipp Herzog, Heinrich von Campe, Rebecca Maria Kuntz +2
Monte-Carlo techniques are standard numerical tools for exploring non-Gaussian and multivariate likelihoods. Many variants of the original Metropolis-Hastings algorithm have been p…
Partition function approach to non-Gaussian likelihoods: partitions for the inference of functions and the Fisher-functional
Rebecca Maria Kuntz, Maximilian Philipp Herzog, Heinrich von Campe +2
Motivated by constraints on the dark energy equation of state from supernova-data, we propose a formalism for the Bayesian inference of functions: Starting at a functional variant…
Partition function approach to non-Gaussian likelihoods: physically motivated convergence criteria for Markov-chains
Lennart Röver, Heinrich von Campe, Maximilian Philipp Herzog +2
Non-Gaussian distributions in cosmology are commonly evaluated with Monte Carlo Markov-chain methods, as the Fisher-matrix formalism is restricted to the Gaussian case. The Metropo…
A weak lensing perspective on nonlinear structure formation with fuzzy dark matter
Alexander Kunkel, Tzihong Chiueh, Björn Malte Schäfer
We investigate nonlinear structure formation in the fuzzy dark matter (FDM) model in comparison to cold dark matter (CDM) models from a weak lensing perspective using perturbative…
Cornering Extended Starobinsky Inflation with CMB and SKA
Tanmoy Modak, Lennart Röver, Björn Malte Schäfer +2
Starobinsky inflation is an attractive, fundamental model to explain the Planck measurements, and its higher-order extension may allow us to probe quantum gravity effects. We show…
Partition function approach to non-Gaussian likelihoods: Formalism and expansions for weakly non-Gaussian cosmological inference
Lennart Röver, Lea Carlotta Bartels, Björn Malte Schäfer
Non-Gaussian likelihoods, ubiquitous throughout cosmology, are a direct consequence of nonlinearities in the physical model. Their treatment requires Monte-Carlo Markov-chain or mo…
Etherington duality breaking: gravitational lensing in non-metric spacetimes versus intrinsic alignments
Eileen Sophie Giesel, Basundhara Ghosh, Björn Malte Schäfer
The Etherington distance duality relation is well-established for metric theories of gravity, and confirms the duality between the luminosity distance and the angular diameter dist…
On the Cosmic Web Elongation in Fuzzy Dark Matter Cosmologies: Effects on Density Profiles, Shapes and Alignments of Halos
Tibor Dome, Anastasia Fialkov, Philip Mocz +3
The fuzzy dark matter (FDM) scenario has received increased attention in recent years due to the small-scale challenges of the vanilla Lambda cold dark matter (CDM) cosmological…
Probing the Inflaton Potential with SKA
Tanmoy Modak, Tilman Plehn, Lennart Röver +1
SKA will be a major step forward not only in astrophysics, but also in precision cosmology. We show how the neutral hydrogen intensity map can be combined with the Planck measureme…
Testing modified (Horndeski) gravity by combining intrinsic galaxyalignments with cosmic shear
Robert Reischke, Victor Bosca, Tim Tugendhat +1
We study the impact of modified gravity of the Horndeski class, on intrinsic shape correlations in cosmic shear surveys. As intrinsic shape correlations (IAs) are caused by tidal g…
Intrinsic Alignments in IllustrisTNG and their implications for weak lensing: Tidal shearing and tidal torquing mechanisms put to the test
Jolanta Zjupa, Björn Malte Schäfer, Oliver Hahn
Accurate measurements of the cosmic shear signal require a separation of the true weak gravitational lensing signal from intrinsic shape correlations of galaxies. These `intrinsic…
Information entropy in cosmological inference problems
Ana Marta Pinho, Robert Reischke, Marie Teich +1
The subject of this paper is a quantification of the information content of cosmological probes of the large-scale structures, specifically of temperature and polarisation anisotro…
Intrinsic and extrinsic correlations of galaxy shapes and sizes in weak lensing data
Basundhara Ghosh, Ruth Durrer, Bjoern Malte Schaefer
The subject of this paper is to build a physical model describing shape and size correlations of galaxies due to weak gravitational lensing and due to direct tidal interaction of e…