Related papers: Normalizing flows as an avenue to study overlappin…
Overlapping signals represent one of the major data analysis challenges in next-generation gravitational wave detectors. We leverage Transformers and Normalizing Flows, state-of-the-art machine learning algorithms, to address the parameter…
In the coming years, third-generation detectors such as Einstein Telescope and Cosmic Explorer will enter the network of ground-based gravitational-wave detectors. Their current design predicts a significantly improved sensitivity band with…
Third-generation gravitational wave detectors such as Einstein Telescope and Cosmic Explorer will have significantly better sensitivities than current detectors, as well as a wider frequency bandwidth. This will increase the number and…
Glitches represent a category of non-Gaussian and transient noise that frequently intersects with gravitational wave (GW) signals, exerting a notable impact on the processing of GW data. The inference of GW parameters, crucial for GW…
The speed-up of parameter estimation is an active field of research in gravitational-wave data analysis. In this paper we present GP15, a deep-learning method that merges residual networks and normalizing flows into a general-purpose,…
We introduce the use of autoregressive normalizing flows for rapid likelihood-free inference of binary black hole system parameters from gravitational-wave data with deep neural networks. A normalizing flow is an invertible mapping on a…
Gravitational wave (GW) detection is of paramount importance in fundamental physics and GW astronomy, yet it presents formidable challenges. One significant challenge is the removal of noise transient artifacts known as glitches, which…
Detecting the coalescences of massive black hole binaries (MBHBs) is one of the primary targets for space-based gravitational wave observatories such as LISA, Taiji, and Tianqin. The fast and accurate parameter estimation of merging MBHBs…
Understanding and dealing with inference biases in gravitational-wave (GW) parameter estimation when a plethora of signals are present in the data is one of the key challenges for the analysis of data from future GW detectors. Working…
The growing number of gravitational-wave detections from binary black holes enables increasingly precise measurements of their population properties. The observed population is most likely drawn from multiple formation channels.…
Binary population synthesis simulations allow detailed modelling of gravitational-wave sources from a variety of formation channels. These population models can be compared to the observed catalogue of merging binaries to infer the…
A yet undetected class of GW signals is represented by the close encounters between compact objects in highly-eccentric e~1 orbits, that can occur in binary systems formed in dense environments such as globular clusters. The expected…
We present a machine learning approach using normalising flows for inferring cosmological parameters from gravitational wave events. Our methodology is general to any type of compact binary coalescence event and cosmological model and…
Third generation and future upgrades of current gravitational-wave detectors will present exquisite sensitivities which will allow to detect a plethora of gravitational wave signals. Hence, a new problem to be solved arises: the detection…
In future gravitational-wave (GW) detections, a large number of overlapping GW signals will appear in the data stream of detectors. When extracting information from one signal, the presence of other signals can cause large parameter…
Gravitational wave detection has opened up new avenues for exploring and understanding some of the fundamental principles of the universe. The optimal method for detecting modelled gravitational-wave events involves template-based matched…
In the past few years, the detection of gravitational waves from compact binary coalescences with the Advanced LIGO and Advanced Virgo detectors has become routine. Future observatories will detect even larger numbers of gravitational-wave…
Gravitational wave detection requires an in-depth understanding of the physical properties of gravitational wave signals, and the noise from which they are extracted. Understanding the statistical properties of noise is a complex endeavor,…
Third-generation gravitational wave detectors, such as the Einstein Telescope and Cosmic Explorer, will detect a bunch of gravitational-wave (GW) signals originating from the coalescence of binary neutron star (BNS) and binary black hole…
Once a gravitational wave signal is detected, the measurement of its source parameters is important to achieve various scientific goals. This is done through Bayesian inference, where the analysis cost increases with the model complexity…