arXiv subjects

Jakob Macke

Publications and source records attributed to Jakob Macke.

4 recordsLinked to original sources

arXiv2026

Eccentricity constraints disfavor single-single capture in nuclear star clusters as the origin of all LIGO-Virgo-KAGRA binary black holes

Multiple formation pathways have been proposed for the origin of binary black holes (BBHs). These include isolated binary evolution and dynamical assembly in dense stellar environments such as nuclear or globular star clusters. Yet, the fraction of BBHs originating from each channel remains uncertain. One way to constrain this fraction is by investigating the orbital eccentricities of the BH coalescences detected by the LIGO-Virgo-KAGRA (LVK) Collaboration. We analyze 84 BBHs from the first part of the fourth LVK observing run (O4a) using a multipolar, eccentric, aligned-spin effective-one-body waveform model. We perform parameter inference with neural posterior estimation and nested sampling. After incorporating astrophysical prior odds and comparing to the quasicircular precessing-spin hypothesis, we find that no candidates reach a high enough significance to claim a confident detection of eccentricity. We use these upper limits to explore a model, in which all O4a BBHs originate from single-single gravitational wave (GW) captures. We perform hierarchical inference on the velocity dispersion of the host environment of the BBHs and find $\sigma$ < 19.7 km/s (95% credible upper bound). This disfavors single-single capture in nuclear star clusters (approximately 20-200 km/s) as the dominant source of all observed BBH mergers. Our analysis also jointly infers the mass, spin and redshift distributions and takes into account selection effects due to using quasi-circular templates for BBH detection. Our results place improved constraints on the number of eccentric BBHs and highlight the importance of eccentricity measurements in disentangling compact-binary formation channels in current and future GW detectors.

astro-ph.HE↗

arXiv2024

Evidence for eccentricity in the population of binary black holes observed by LIGO-Virgo-KAGRA

Binary black holes (BBHs) in eccentric orbits produce distinct modulations in gravitational waves (GWs); measuring orbital eccentricity provides evidence for dynamical binary formation channels. We analyze 57 GW events from the LIGO-Virgo-KAGRA (LVK) O1-O3 observing runs using a multipolar aligned-spin inspiral-merger-ringdown waveform with two eccentric parameters: eccentricity and relativistic anomaly (assuming a quasi-circular merger-ringdown), made computationally feasible by the machine-learning code \texttt{DINGO}, which accelerates inference by 2-3 orders of magnitude. First, with a uniform eccentricity prior, eccentric vs. quasi-circular aligned-spin $\log_{10}$ Bayes factors are 1.84-4.75 (depending on glitch mitigation) for GW200129, 3.0 for GW190701 and 1.77 for GW200208_22. We infer $e_{\text{gw, 10Hz}}$ $(e_{\text{gw, 20Hz}})$ to be $0.27_{-0.12}^{+0.10}$ ($0.16_{-0.05}^{+0.04}$) to $0.17_{-0.13}^{+0.14}$ ($0.1_{-0.04}^{+0.05}$) for GW200129, $0.54_{-0.30}^{+0.12}$ ($0.31_{-0.13}^{+0.12}$) for GW190701 and $0.39_{-0.23}^{+0.23}$ ($0.21_{-0.08}^{+0.08}$) for GW200208_22. Second, eccentric aligned-spin vs. quasi-circular precessing-spin $\log_{10}$ Bayes factors are 1.43-4.92 for GW200129, 2.61 for GW190701 and 1.23 for GW200208_22. Third, GW190521 shows no evidence for eccentricity ($\log_{10}$ Bayes factor 0.04). Fourth, neglecting spin-precession with an astrophysically-motivated prior on the eccentric BBH rate, the probability of one of the 57 events being eccentric exceeds 99.5\% or $(100-8.4\times10^{-4})$\% (depending on glitch mitigation). Fifth, we study parameter estimation impacts of neglecting eccentricity in quasi-circular models or higher modes in eccentric models. These results underscore the inclusion of eccentric parameters in BBH characterization for upcoming LVK runs and future ground- and space-based detectors probing more diverse BBH populations.

gr-qc↗

arXiv2022

Simulation Intelligence: Towards a New Generation of Scientific Methods

The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence operating system stack (SI-stack) and the motifs therein: (1) Multi-physics and multi-scale modeling; (2) Surrogate modeling and emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; (9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science.

cs.AI↗

arXiv2022

Efficient identification of informative features in simulation-based inference

Simulation-based Bayesian inference (SBI) can be used to estimate the parameters of complex mechanistic models given observed model outputs without requiring access to explicit likelihood evaluations. A prime example for the application of SBI in neuroscience involves estimating the parameters governing the response dynamics of Hodgkin-Huxley (HH) models from electrophysiological measurements, by inferring a posterior over the parameters that is consistent with a set of observations. To this end, many SBI methods employ a set of summary statistics or scientifically interpretable features to estimate a surrogate likelihood or posterior. However, currently, there is no way to identify how much each summary statistic or feature contributes to reducing posterior uncertainty. To address this challenge, one could simply compare the posteriors with and without a given feature included in the inference process. However, for large or nested feature sets, this would necessitate repeatedly estimating the posterior, which is computationally expensive or even prohibitive. Here, we provide a more efficient approach based on the SBI method neural likelihood estimation (NLE): We show that one can marginalize the trained surrogate likelihood post-hoc before inferring the posterior to assess the contribution of a feature. We demonstrate the usefulness of our method by identifying the most important features for inferring parameters of an example HH neuron model. Beyond neuroscience, our method is generally applicable to SBI workflows that rely on data features for inference used in other scientific fields.

cs.LG↗