Inferring phylogenetic trees under the general Markov model via a minimum spanning tree backbone

Phylogenetic trees are models of the evolutionary relationships among species, with species typically placed at the leaves of trees. We address the following problems regarding the calculation of phylogenetic trees. (1) Leaf-labeled phylogenetic trees may not be appropriate models of evolutionary relationships among rapidly evolving pathogens which may contain ancestor-descendant pairs. (2) The models of gene evolution that are widely used unrealistically assume that the base composition of DNA sequences does not evolve. Regarding problem (1) we present a method for inferring generally labeled phylogenetic trees that allow sampled species to be placed at non-leaf nodes of the tree. Regarding problem (2), we present a structural expectation maximization method (SEM-GM) for inferring leaf-labeled phylogenetic trees under the general Markov model (GM) which is the most complex model of DNA substitution that allows the evolution of base composition. In order to improve the scalability of SEM-GM we present a minimum spanning tree (MST) framework called MST-backbone. MST-backbone scales linearly with the number of leaves. However, the unrealistic location of the root as inferred on empirical data suggests that the GM model may be overtrained. MST-backbone was inspired by the topological relationship between MSTs and phylogenetic trees that was introduced by Choi et al. (2011). We discovered that the topological relationship does not necessarily hold if there is no unique MST. We propose so-called vertex-order based MSTs (VMSTs) that guarantee a topological relationship with phylogenetic trees.Phylogenetische Bäume modellieren evolutionäre Beziehungen zwischen Spezies, wobei die Spezies typischerweise an den Blättern der Bäume sitzen. Wir befassen uns mit den folgenden Problemen bei der Berechnung von phylogenetischen Bäumen. (1) Blattmarkierte phylogenetische Bäume sind möglicherweise keine geeigneten Modelle der evolutionären Beziehungen zwischen sich schnell entwickelnden Krankheitserregern, die Vorfahren-Nachfahren-Paare enthalten können. (2) Die weit verbreiteten Modelle der Genevolution gehen unrealistischerweise davon aus, dass sich die Basenzusammensetzung von DNA-Sequenzen nicht ändert. Bezüglich Problem (1) stellen wir eine Methode zur Ableitung von allgemein markierten phylogenetischen Bäumen vor, die es erlaubt, Spezies, für die Proben vorliegen, an inneren des Baumes zu platzieren. Bezüglich Problem (2) stellen wir eine strukturelle Expectation-Maximization-Methode (SEM-GM) zur Ableitung von blattmarkierten phylogenetischen Bäumen unter dem allgemeinen Markov-Modell (GM) vor, das das komplexeste Modell von DNA-Substitution ist und das die Evolution von Basenzusammensetzung erlaubt. Um die Skalierbarkeit von SEM-GM zu verbessern, stellen wir ein Minimale Spannbaum (MST)-Methode vor, die als MST-Backbone bezeichnet wird. MST-Backbone skaliert linear mit der Anzahl der Blätter. Die Tatsache, dass die Lage der Wurzel aus empirischen Daten nicht immer realistisch abgeleitet warden kann, legt jedoch nahe, dass das GM-Modell möglicherweise übertrainiert ist. MST-backbone wurde von einer topologischen Beziehung zwischen minimalen Spannbäumen und phylogenetischen Bäumen inspiriert, die von Choi et al. 2011 eingeführt wurde. Wir entdeckten, dass die topologische Beziehung nicht unbedingt Bestand hat, wenn es keinen eindeutigen minimalen Spannbaum gibt. Wir schlagen so genannte vertex-order-based MSTs (VMSTs) vor, die eine topologische Beziehung zu phylogenetischen Bäumen garantieren

Investigating the effect of in-plane spin directions for Precessing BBH systems

Morphology of coalescing BBH waveforms are affected by its spins. Waveform models built for inference of source parameters have several in-built approximations. In current precessing IMRPhenom and SEOBNR waveform models, systems with the same spin magnitude but varying orientation of spins projected on the orbital plane are effectively mapped to the same system (bar an overall phase change) and the asymmetry due to precession between the $+m$ and $-m$ modes is not modelled. In this study, we investigate the validity of these approximations by generating numerical relativity (NR) simulations of single-spin NR systems with varying in-plane spin directions (including several superkick configurations) and provide an estimate of the SNR at which the effect of varying in-plane spin directions would be measurable. This is done computing the match between these waveforms and using these match values to estimate the distinguishability SNR. We also use NR waveforms with different spin magnitudes to compare the measurability of spin magnitude vs. in-plane spin direction. We find that the in-plane spin direction could be measurable at SNRs accessible by current generation detectors, with the distinguishability SNR of varying in-plane spins comparable to or lower than varying the in-plane spin magnitude. We then remove the mode-asymmetry content from the waveforms and find that, i) removing mode-asymmetry increases the SNR at which in-plane spin direction can be measured and ii) not modelling mode-asymmetry will lead to measurement biases. The SNRs that we see at which the in-plane spins would be measurable and at which mode-asymmetric content impacts the measurements are the SNRs at which precession would be measurable, and we therefore conclude that modelling in-plane spin direction and mode-asymmetry effects is necessary for unbiassed measurements of precession.Comment: 13 pages, 8 figure

Selecting Optimal Minimum Spanning Trees that Share a Topological Correspondence with Phylogenetic Trees

Choi et. al (2011) introduced a minimum spanning tree (MST)-based method called CLGrouping, for constructing tree-structured probabilistic graphical models, a statistical framework that is commonly used for inferring phylogenetic trees. While CLGrouping works correctly if there is a unique MST, we observe an indeterminacy in the method in the case that there are multiple MSTs. In this work we remove this indeterminacy by introducing so-called vertex-ranked MSTs. We note that the effectiveness of CLGrouping is inversely related to the number of leaves in the MST. This motivates the problem of finding a vertex-ranked MST with the minimum number of leaves (MLVRMST). We provide a polynomial time algorithm for the MLVRMST problem, and prove its correctness for graphs whose edges are weighted with tree-additive distances

Parameter Estimation with a spinning multi-mode waveform model: IMRPhenomHM

Gravitational waves from compact binary coalescence sources can be decomposed into spherical-harmonic multipoles, the dominant being the quadrupole ($\ell=2, m=\pm2$) modes. The contribution of sub-dominant modes towards total signal power increases with increasing binary mass ratio and source inclination to the detector. It is well-known that in these cases neglecting higher modes could lead to measurement biases, but these have not yet been quantified with a higher-mode model that includes spin effects. In this study, we use the multi-mode aligned-spin phenomenological waveform model IMRPhenomHM to investigate the effects of including multi-mode content in estimating source parameters and contrast the results with using a quadrupole-only model (IMRPhenomD). We use as sources IMRPhenomHM and hybrid EOB-NR waveforms over a range of mass-ratio and inclination combinations, and recover the parameters with IMRPhenomHM and IMRPhenomD. These allow us to quantify the accuracy of parameter measurements using a multi-mode model, the biases incurred when using a quadrupole-only model to recover full (multi-mode) signals, and the systematic errors in the IMRPhenomHM model. We see that the parameters recovered by multi-mode templates are more precise for all non-zero inclinations as compared to quadrupole templates. For multi-mode injections, IMRPhenomD recovers biased parameters for non-zero inclinations with lower likelihood while IMRPhenomHM recovered parameters are accurate for most cases, and if a bias exists, it can be explained as a combined effect of observational priors and (in the case of hybrid-NR signals) waveform inaccuracies. For cases where IMRPhenomHM recovers biased parameters, the bias is always smaller than the corresponding IMRPhenomD recovery, and we conclude that IMRPhenomHM will be sufficiently accurate to allow unbiased measurements for most GW observations.Comment: 14 pages, 7 figure

First higher-multipole model of gravitational waves from spinning and coalescing black-hole binaries

Gravitational-wave observations of binary black holes currently rely on theoretical models that predict the dominant multipoles (l,m) of the radiation during inspiral, merger and ringdown. We introduce a simple method to include the subdominant multipoles to binary black hole gravitational waveforms, given a frequency-domain model for the dominant multipoles. The amplitude and phase of the original model are appropriately stretched and rescaled using post-Newtonian results (for the inspiral), perturbation theory (for the ringdown), and a smooth transition between the two. No additional tuning to numerical-relativity simulations is required. We apply a variant of this method to the non-precessing PhenomD model. The result, PhenomHM, constitutes the first higher-multipole model of spinning black-hole binaries, and currently includes the (l,m) = (2,2), (3,3), (4,4), (2,1), (3,2), (4,3) radiative moments. Comparisons with numerical-relativity waveforms demonstrate that PhenomHM is more accurate than dominant-multipole-only models for all binary configurations, and typically improves the measurement of binary properties.Comment: 4 pages, 4 figure

Modelling and studying gravitational waves from black-hole-binary mergers

The source parameters of the first direct detection (GW150914 [3]) of gravitational waves (GW) from a binary black hole (BBH) system were determined by using approximate models of the BBH coalescence, the errors on which could be driven by the noise (statistical errors) or the approximate nature of the model (systematic errors). To determine the systematic errors, a set of numerical relativity (NR) waveforms with similar parameters as of GW150914 were injected over a range of inclination and polarisation values and recovered with IMRPhenomPv2. The main result of this study was that the systematic errors induced due to waveform model inaccuracies were much smaller than corresponding statistical errors, and hence, the statistical errors dominate the systematic for the inferred parameters of GW150914. For current precessing waveform models, the six dimensional spin space is mapped to a two dimensional space of effective spin parameters. We investigate the effects of changing the in-plane spin direction on the GW signal and determine whether these effects are strong enough to be measured by current ground based GW detectors. We also study the effect of disregarding the mode-asymmetry content present in the signals and attempt to answer whether mode-asymmetries need to be included in future waveform models. GW signals, when decomposed in the spin weighted spherical harmonic basis, are made of its different modes (hlms), with the quadrupole mode being dominant. The waveform model IMRPhenomHM models a few of the sub-dominant modes with the quadrupole mode for aligned-spin binaries. We wanted to investigate the effects of using a multimode (IMRPhenomHM) and quadrupole only (IMRPhenomD) waveform model to recover source parameters from multimode signals (IMRPhenomHM signals) and real physical signals (NR waveform signals) across a range of physical parameters and inclination values