Galaxy formation and evolution

Abstract:

Eccentric binary black holes (BBHs) formed through dynamical interactions can significantly contribute to gravitational wave (GW) detections. In this work, we present a simulated catalog of dynamically formed, stellar-mass BBHs in the local universe, incorporating contributions from the Galactic field (flyby interactions), Galactic nucleus (eccentric Kozai-Lidov evolution), and globular clusters (N-body interactions). Our results predict a wide, highly eccentric BBH population in the Milky Way, with source counts of ∼25, 9, 3, 1, and <1 for a 4-year LISA mission (∼36, 13, 5, 2, and 1 for a 10-year extended mission), for SNR>1, 3, 8, 20, and 50, respectively. Extending this model to cosmological populations, we show that different dynamical channels can produce distinct eccentricity distributions in the LVK band and can contribute hundreds of additional low-SNR mHz sources. Specifically, our model yields a merger rate of Γ∼9 Gpc-3 yr-1 and ∼150 extragalactic mHz BBHs with SNR>1. However, due to the lower mass and weaker GW signals of stellar-mass BBHs, this number declines sharply at higher detection thresholds (e.g., ∼1 for SNR>8). We further highlight the impact of eccentric BBH signals on the LISA global fit, showing that their individual harmonics can be independently detected in the Milky Way, and may mimic circular binaries with systematically biased chirp masses. Lastly, we show that post-Newtonian waveforms converge reliably for eccentric BBHs with masses of ≲103M⊙ in the mHz band. Overall, eccentric BBHs represent a prevalent and promising target for future space-based GW observatories.

Abstract:

Abstract In this second paper of the E-INSPIRE series, we train a machine-learning-based regression on ~430 nearby (z < 0.5) ultra-compact massive galaxies (UCMGs) with spectroscopically inferred kinematics, stellar population parameters and a measured “degree of relicness” (DoR). Our goal is to investigate how robustly the spectroscopically inferred DoR can be statistically reconstructed from observable galaxy properties, and to explore the potential applicability of this framework to future wide-area surveys. We test several regression algorithms finding that Support Vector Regression (SVR) provides the best performance. We explore multiple input feature configurations, from a minimal set including only age and metallicity to more comprehensive ones incorporating stellar population parameters, kinematics, structural properties, and the associated uncertainties. All tested models achieve similarly high performance on the training set (R2 ≥ 0.81), except for the minimal configuration (R2 ~ 0.78). When evaluated on an independent INSPIRE sample of 52 UCMGs, the predictive power remains robust, although with increased model-to-model variation. The DoR distribution shows three regimes, with low (DoR<0.3) and high (DoR>0.6) values sparsely populated, leading to mild regression shrinkage toward intermediate values. However, this behaviour enables a conservative selection strategy: galaxies with predicted DoR≥0.6 are strongly biased toward genuine extreme relics, making them prime targets for follow-up observations. This proof-of-concept confirms that the spectroscopically inferred DoR is robustly connected to observable stellar population and kinematical properties, and provides a first step toward future relic-candidate selection strategies in large photometric and spectroscopic surveys.