Clémence Allietta | Search arXiv

arXiv2026

Untangling Extreme Mass Ratio Inspirals and Massive Black Hole Binaries in LISA Data

The Laser Interferometer Space Antenna (LISA) will observe sources in the millihertz regime, such as Galactic Binaries (GBs), Massive Black Hole Binaries (MBHBs), and Extreme Mass Ratio Inspirals (EMRIs), so its data stream will be a superposition of an unknown number of gravitational-wave sources. We consider the joint inference of various representative combinations of MBHB and EMRI signals in LISA data (with assumed instrumental noise and stochastic GB foreground). Our Bayesian analyses confirm the expectation of weak inter-source coupling between EMRIs, and between EMRIs and MBHBs, despite strong intra-source parameter correlations. Weak EMRI-MBHB coupling enables a hierarchically sequential approach to EMRI and MBHB inference, rather than fully joint inference. We further show that imperfect MBHB subtraction, arising from missing modes or overlapping MBHBs, has only a weak impact on EMRI inference. For multiple MBHBs, we mitigate inference errors through a joint MBHB fit. We demonstrate this strategy on simulated LISA data containing noise, three MBHBs, and three EMRIs, jointly fitting and subtracting the MBHBs before sequentially inferring the EMRIs - ultimately observing little to no bias on all recovered astrophysical parameters. The notions described throughout our work are highly relevant to informing optimal strategies for the global-fit-based pipelines that are currently being developed for LISA.

gr-qc↗

arXiv2023

HLoOP -- Hyperbolic 2-space Local Outlier Probabilities

Hyperbolic geometry has recently garnered considerable attention in machine learning due to its capacity to embed hierarchical graph structures with low distortions for further downstream processing. This paper introduces a simple framework to detect local outliers for datasets grounded in hyperbolic 2-space referred to as HLoOP (Hyperbolic Local Outlier Probability). Within a Euclidean space, well-known techniques for local outlier detection are based on the Local Outlier Factor (LOF) and its variant, the LoOP (Local Outlier Probability), which incorporates probabilistic concepts to model the outlier level of a data vector. The developed HLoOP combines the idea of finding nearest neighbors, density-based outlier scoring with a probabilistic, statistically oriented approach. Therefore, the method consists in computing the Riemmanian distance of a data point to its nearest neighbors following a Gaussian probability density function expressed in a hyperbolic space. This is achieved by defining a Gaussian cumulative distribution in this space. The HLoOP algorithm is tested on the WordNet dataset yielding promising results. Code and data will be made available on request for reproductibility.

stat.ML↗