Gravitational-wave echoes in the post-merger ringdown phase are under intense scrutiny as probes of near-horizon quantum structures and as signatures of exotic states of matter in ultracompact stars. We present an analytical template that describes the ringdown and the echo signal for nonspinning objects in terms of two physical parameters: the reflectivity and the redshift at the surface of the object. We characterize the properties of the template and adopt it in a preliminary parameter estimation with current (aLIGO) and future (Cosmic Explorer, Einstein Telescope, LISA) gravitational-wave detectors. For fixed signal-to-noise ratio in the post-merger phase, the constraints on the model parameters depend only mildly on the details of the detector sensitivity curve, but depend strongly on the reflectivity. Our analysis suggests that it might be possible to detect or rule out Planckian corrections at the horizon scale for perfectly-reflecting ultracompact objects at 5σ confidence level with Advanced LIGO/Virgo. On the other hand, signal-to-noise ratios in the ringdown phase equal to ≈ 100 (as achievable with future interferometers) might allow us to probe near-horizon quantum structures with reflectivity 30% ( 85%) at 2σ (3σ) level.

Gravitational-wave echoes in the post-merger signal of a binary coalescence are predicted in various scenarios, including near-horizon quantum structures, exotic states of matter in ultracompact stars, and certain deviations from general relativity. The amplitude and frequency of each echo is modulated by the photon-sphere barrier of the remnant, which acts as a spin-and frequencydependent high-pass filter, decreasing the frequency content of each subsequent echo. Furthermore, a major fraction of the energy of the echo signal is contained in low-frequency resonances corresponding to the quasi-normal modes of the remnant. Motivated by these features, in this work we provide an analytical gravitational-wave template in the low-frequency approximation describing the postmerger ringdown and the echo signal of a spinning ultracompact object. Besides the standard ringdown parameters, the template is parametrized in terms of only two physical quantities: the reflectivity coefficient and the compactness of the remnant. We discuss novel effects related to the spin and to the complex reflectivity, such as a more involved modulation of subsequent echoes, the mixing of two polarizations, and the ergoregion instability in case of perfectly-reflecting spinning remnants. Finally, we compute the errors in the estimation of the template parameters with current and future gravitational-wave detectors using a Fisher matrix framework. Our analysis suggests that models with almost perfect reflectivity can be excluded/detected with current instruments, whereas probing values of the reflectivity smaller than 80% at 3σ confidence level requires future detectors (Einstein Telescope, Cosmic Explorer, LISA). The template developed in this work can be easily implemented to perform a matched-filter based search for echoes and to constrain models of exotic compact objects.

In general relativity, predictions for observable quantities can be expressed in a coordinate independent way. Nonetheless it may be inconvenient to do so. Using a particular frame may be the easiest way to connect theoretical predictions to measurable quantities. For the cosmological curvature bispectrum such frame is described by the conformal Fermi coordinates. In single field inflation it was shown that going to this frame cancels the squeezed limit of the density perturbation bispectrum calculated in global coordinates. We explore this issue in quasisingle field inflation when the curvaton mass and the curvatoninflaton mixing are small. In this case, the contribution to the bispectrum from the coordinate transformation to conformal Fermi coordinates is of the same order as that from the inflaton-curvaton interaction term but does not cancel it.

Funding Acknowledgements Type of funding sources: None. Background/Introduction Despite mounting evidence, the impact of the interplay between weather and pollution features on the risk of acute cardiac and cerebrovascular events has not been entirely appraised. Purpose We aimed to perform a comprehensive cluster analysis of weather and pollution features in a large metropolitan area, and their association with acute cardiac and cerebrovascular events. Methods Anonymized data on acute myocardial infarction (AMI) and acute cerebrovascular events were obtained from 3 tertiary care center from a single large metropolitan area. Weather and pollution data were obtained averaging measurements from several city measurement stations managed by the competent regional agency for enviromental protection, and from the Metereologic Center of Italian Military Aviation. Unsupervised machine learning was performed with hierarchical clustering to identify specific days with distinct weather and pollution features. Clusters were then compared for rate of acute cardiac and cerebrovascular events with Poisson models. Results As expected, significant pairwise correlations were found between weather and pollution features. Building upon these correlations, hierarchical clustering, from a total of 1169 days, generated 4 separate clusters: mostly winter days with low temperatures and high ozone concentrations (cluster 1, n=60, 5.1%), days with moderately high temperatures and low pollutants concentrations (cluster 2, n=419, 35.8%), mostly summer and spring days with high temperatures and high ozone concentrations (cluster 3, n=673, 57.6%), and mostly winter days with low temperatures and low ozone concentrations (cluster 4, n=17, 1.5%). Overall cluster-wise comparisons showed significant overall differences in adverse cardiac and cerebrovascular events (p<0.001), as well as in cerebrovascular events (p<0.001) and strokes (p=0.001). Between-cluster comparisons showed that Cluster 1 was associated with an increased risk of any event, cerebrovascular events, and strokes in comparison to Cluster 2, Cluster 3 and Cluster 4 (all p<0.05), as well as AMI in comparison to Cluster 3 (p=0.047). In addition, Cluster 2 was associated with a higher risk of strokes in comparison to Cluster 4 (p=0.030). Analysis adjusting for season confirmed the increased risk of any event, cerebrovascular events and strokes for Cluster 1 and Cluster 2. Conclusions Unsupervised machine learning can be leveraged to identify specific days with a unique clustering of adverse weather and pollution features which are associated with an increases risk of acute cardiovascular events, especially cerebrovascular events. These findings may improve collective and individual risk prediction and prevention.