A list of the previously discussed papers can be found here .
Gravitational waves (GW) emitted by binary systems allow us to perform precision tests of general relativity in the strong field regime. Ringdown signals allow for probing black hole mass and spin with high precision in GW astronomy. With improvements in current and next-generation GW detectors, developing likelihood-free parameter inference methods is crucial. This is especially important when facing challenges such as non-standard noise, partial data, or incomplete signal models that prevent the use of analytical likelihood functions. In this work, we propose an amortized simulation-based inference strategy to estimate ringdown parameters directly. Specifically, our method is based on amortized neural posterior estimation, which trains a neural density estimator of the posterior for all data segments within the prior range. The results show that our trained amortized network achieves statistically consistent parameter estimates with valid confidence coverage compared to established Markov-chain methods, while offering inference speeds that are orders of magnitude faster. Furthermore, we evaluate the robustness of the method against transient noise contamination. Our analysis reveals that the timing of glitch injection has a decisive impact on estimation bias, particularly during the tail of a signal with sparse information. Glitch strength is positively correlated with estimation error, but has limited effect at low signal-to-noise ratios. Mass and spin parameters are most sensitive to noise. This study not only provides an efficient and accurate inference framework for ringdown analysis but also lays a foundation for developing robust data-processing pipelines for future GW astronomy in realistic noise environments.
The X-ray emission of active galactic nuclei (AGN) is generally attributed to inverse Compton scattering of accretion-disk photons by hot electrons in a compact corona. In local AGN, directly constraining coronal properties is challenging because the high-energy cutoff often lies beyond the NuSTAR bandpass. High-redshift, luminous quasars enable systematic constraints on the high-energy cutoff, as cosmological redshift shifts the spectal cutoff into the observable hard X-ray band. We present first results from the ``Probing the AGN Coronae with High-redshift AGN'' (PACHA) project, based on quasi-simultaneous NuSTAR and XMM-Newton observations of 13 radio-quiet AGN at $z>1$. We constrain the high-energy cutoff and coronal temperature at 90\% confidence level for 10 and 9 sources, respectively. The sample exhibits a mean cutoff energy of $E_{\rm cut}=80.8\pm8.1$ keV and a mean coronal temperature of $kT_{\rm e}=18.4\pm1.6$ keV, both significantly lower than those measured in local {\it Swift}-BAT AGN, while the mean optical depth ($\tau=4.8\pm0.3$) is significantly higher. The uncertainties are at 1~$\sigma$. Combining our high-redshift sample with local AGN, we find a potential anti-correlation between cutoff energy and both X-ray luminosity and black hole mass, with no significant dependence on Eddington ratio. Within a hybrid coronal framework, the inferred temperatures lie well below the pair-production limits for purely thermal coronae, indicating a substantial efficient Compton cooling and/or non-thermal electron component. The detection of low coronal temperatures in high-luminosity AGN is broadly consistent with predictions from recent radiation MHD simulations that consider purely thermal electron populations, implying that non-thermal electrons may not be the primary drivers of the observed coronal properties in these systems.