Related papers: Kilonova Spectral Inverse Modelling with Simulatio…
Identifying the parameters of a non-linear model that best explain observed data is a core task across scientific fields. When such models rely on complex simulators, evaluating the likelihood is typically intractable, making traditional…
In the era of multi-messenger astronomy, early classification of photometric alerts from wide-field and high-cadence surveys is a necessity to trigger spectroscopic follow-ups. These classifications are expected to play a key role in…
In recent years, there has been a remarkable development of simulation-based inference (SBI) algorithms, and they have now been applied across a wide range of astrophysical and cosmological analyses. There are a number of key advantages to…
The standard approach to inference from cosmic large-scale structure data employs summary statistics that are compared to analytic models in a Gaussian likelihood with pre-computed covariance. To overcome the idealising assumptions about…
Kilonova spectra provide us with information of r-process nucleosynthesis in neutron star mergers. However, it is still challenging to identify individual elements in the spectra mainly due to lack of experimentally accurate atomic data for…
Type Ia supernovae (SNae Ia), standardisable candles that allow tracing the expansion history of the Universe, are instrumental in constraining cosmological parameters, particularly dark energy. State-of-the-art likelihood-based analyses…
Kilonovae (KNe) are one of the fastest types of optical transients known, cooling rapidly in the first few days following their neutron-star merger origin. We show here that KN spectral features go through rapid recombination transitions,…
X-ray spectral fitting in high-energy astrophysics can be reliably accelerated using Machine Learning. In particular, Simulation-based Inference (SBI) produces accurate posterior distributions in the Gaussian and Poisson regime for…
Context: New spectroscopic surveys will increase the number of astronomical objects requiring characterization by over tenfold.. Machine learning tools are required to address this data deluge in a fast and accurate fashion. Most machine…
We predict linear polarization for a radioactively-powered kilonova following the merger of a black hole and a neutron star. Specifically, we perform 3-D Monte Carlo radiative transfer simulations for two different models, both featuring a…
Kilonovae are a rare class of astrophysical transients powered by the radioactive decay of nuclei heavier than iron, synthesized in the merger of two compact objects. Over the first few days, the kilonova evolution is dominated by a large…
Generative models and those with computationally intractable likelihoods are widely used to describe complex systems in the natural sciences, social sciences, and engineering. Fitting these models to data requires likelihood-free inference…
The kilonova (KN) associated with the binary neutron star (BNS) merger GW170817 is the only known electromagnetic counterpart to a gravitational wave source. Here we produce a sequence of radiative transfer models (using $\textsc{tardis}$)…
We describe the fast transient classification algorithm in the center of the kilonova (KN) science module currently implemented in the Fink broker and report classification results based on simulated catalogs and real data from the ZTF…
The coalescence of double neutron star (NS-NS) and black hole (BH)-NS binaries are prime sources of gravitational waves (GW) for Advanced LIGO/Virgo and future ground-based detectors. Neutron-rich matter released from such events undergo…
We examine the use of a novel variant of Physics-Informed Neural Networks to predict cosmological parameters from recent supernovae and baryon acoustic oscillations (BAO) datasets. Our machine learning framework generates uncertainty…
With the epochal first detection of gravitational waves from a binary neutron star (NS) merger with the GW170817 event, and its direct confirmation that NS-NS mergers are significant sources of the of the r-process nucleosynthesis of heavy…
The large number of strong lenses discoverable in future astronomical surveys will likely enhance the value of strong gravitational lensing as a cosmic probe of dark energy and dark matter. However, leveraging the increased statistical…
Simulation-based inference (SBI) offers a flexible and general approach to performing Bayesian inference: In SBI, a neural network is trained on synthetic data simulated from a model and used to rapidly infer posterior distributions for…
We develop the framework of Linear Simulation-based Inference (LSBI), an application of simulation-based inference where the likelihood is approximated by a Gaussian linear function of its parameters. We obtain analytical expressions for…