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Context. Gravitational waves (GWs) provide a unique probe of the explosion mechanism of massive stars and the evolution of nascent proto-neutron stars (PNSs). Magnetorotational explosions are one of the promising noncanonical core-collapse supernova scenarios, and they might be linked to magnetar formation and energetic supernova explosions. However, the GW signatures of such events currently remain incompletely understood. Aims. We investigate the origin and nature of GW polarization arising from a magnetorotational core-collapse model and examine its potential detectability by current GW observatories. Methods. We performed a three-dimensional simulation of general-relativistic magnetohydrodynamics of a rapidly rotating, strongly magnetized 20 M<SUB>⊙</SUB> progenitor, including multi-energy neutrino transport. The GW signals were extracted using the standard quadrupole formalism, and their polarization states were analyzed with Stokes parameters. Results. Strong circular polarization emerges along the rotation axis during the early post-bounce phase (≲230 ms after core bounce). The characteristic GW spectrum peaks at ∼90 Hz, consistent with the emission at twice the local angular velocity (∼45 Hz) around the PNS surface at cylindrical radii of ∼50 km. These features are attributed to the low-T/|W| instabilities and nonaxisymmetric motions near the PNS and not to the magnetohydrodynamic jets themselves. The polarization signals lie within the sensitivity bands of current detectors such as Advanced LIGO, Advanced Virgo, and KAGRA. Conclusions. Our study demonstrates that models in which magnetorotationally driven jets are launched can produce circularly polarized GW signals originating from the inner PNS region. This provides an observational signature that complements previous findings from nonmagnetized rotating models. Thus, our novel findings establish that the GW polarization is a promising diagnostic of noncanonical core-collapse supernovae. Future third-generation detectors will be crucial to fully exploit this potential.

We develop an accurate simulation-based inference framework for high-mass (<inline-formula><mml:math><mml:mrow><mml:mo>≳</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mn>7</mml:mn></mml:mrow></mml:msup><mml:msub><mml:mrow><mml:mi>M</mml:mi></mml:mrow><mml:mrow><mml:mo>⊙</mml:mo></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>) black-hole binaries observable by LISA. The method is implemented within the DINGO gravitational-wave parameter-estimation code, extending its application from ground-based detectors to the LISA band. We train a normalizing-flow model using aligned-spin higher-mode waveform models and a low-frequency approximation of the detector response at fixed reference time. After sampling, we importance sample to the true posterior based on the underlying likelihood and prior. We validate performance on simulated signals spanning the signal-to-noise regimes relevant for LISA observations and benchmark our new DINGO implementation against standard methods. We report robust agreement in the inferred posterior distributions up to signal-to-noise ratios of <inline-formula><mml:math><mml:mo>∼</mml:mo><mml:mn>500</mml:mn></mml:math></inline-formula>. At higher signal-to-noise ratios of <inline-formula><mml:math><mml:mo>∼</mml:mo><mml:mn>1000</mml:mn></mml:math></inline-formula>, we observe a reduction in sampling efficiency, while still yielding unbiased and tightly localized posteriors that can be used as a starting point for follow-up with traditional methods. The trained flow can generate 20 thousand posterior samples in less than a minute, establishing DINGO as a promising neural inference framework for rapid full-parameter estimation of massive black-hole binaries in the LISA band. The proposed approach allows for straightforward generalizations, including a time-dependent detector response, nonstationary noise artifacts such as gaps and glitches, and low-latency parameter estimations.