arXiv subjects

Tobias Dieselhorst

Publications and source records attributed to Tobias Dieselhorst.

3 recordsLinked to original sources

arXiv2026

Information on hidden birth events restores identifiability in phylodynamic inference

The parameters of many classes of birth-death processes cannot be inferred uniquely from phylogenetic trees: infinitely many parameter combinations yield the same distribution of phylogenetic trees. Here, we show that parameter identifiability can be recovered even for the most general cases of time-dependent rates when additional information on hidden birth events along branches of the reconstructed tree is available. This holds both for models in which individuals are sampled at a single point in time or through time at a time-dependent rate. Moreover, we prove that when mutations occur at birth - assuming two different models for the accumulation of mutations at a birth event - then information about hidden birth events is available in the sequences and thus all parameters of time-dependent birth-death models become identifiable. Thus, phylodynamic inference is identifiable whenever evolutionary models with mutation accumulation at birth (such as at speciation, transmission, or cell division) are plausible.

q-bio.PE↗

arXiv2025

Branch length statistics in phylogenetic trees under constant-rate birth-death dynamics

Phylogenetic trees represent the evolutionary relationships between extant lineages, where extinct or non-sampled lineages are omitted. Extending the work of Stadler and collaborators, this paper focuses on the branch lengths in phylogenetic trees arising under a constant-rate birth-death model. We derive branch length distributions of phylogenetic branches with and without random sampling of individuals of the extant population under two distinct statistical scenarios: a fixed age of the birth-death process and a fixed number of individuals at the time of observation. We find that branches connected to the tree leaves (pendant branches) and branches in the interior of the tree behave very differently under sampling; pendant branches grow longer without limit as the sampling probability is decreased, whereas the interior branch lengths quickly reach an asymptotic distribution that does not depend on the sampling probability.

q-bio.PE↗

arXiv2021

Machine Learning for Conservative-to-Primitive in Relativistic Hydrodynamics

The numerical solution of relativistic hydrodynamics equations in conservative form requires root-finding algorithms that invert the conservative-to-primitive variables map. These algorithms employ the equation of state of the fluid and can be computationally demanding for applications involving sophisticated microphysics models, such as those required to calculate accurate gravitational wave signals in numerical relativity simulations of binary neutron stars. This work explores the use of machine learning methods to speed up the recovery of primitives in relativistic hydrodynamics. Artificial neural networks are trained to replace either the interpolations of a tabulated equation of state or directly the conservative-to-primitive map. The application of these neural networks to simple benchmark problems shows that both approaches improve over traditional root finders with tabular equation-of-state and multi-dimensional interpolations. In particular, the neural networks for the conservative-to-primitive map accelerate the variable recovery by more than an order of magnitude over standard methods while maintaining accuracy. Neural networks are thus an interesting option to improve the speed and robustness of relativistic hydrodynamics algorithms.

astro-ph.IM↗