Related papers: Kilonova Spectral Inverse Modelling with Simulatio…
A major challenge in extracting information from current and upcoming surveys of cosmological Large-Scale Structure (LSS) is the limited availability of computationally expensive high-fidelity simulations. We introduce calibrated Neural…
Recent analysis of the kilonova, AT2017gfo, has indicated that this event was highly spherical. This may challenge hydrodynamics simulations of binary neutron star mergers, which usually predict a range of asymmetries, and radiative…
We present a cadence optimization strategy to unveil a large population of kilonovae using optical imaging alone. These transients are generated during binary neutron star and potentially neutron star-black hole mergers and are…
Current and future optical and near-infrared wide-field surveys have the potential of finding kilonovae, the optical and infrared counterparts to neutron star mergers, independently of gravitational-wave or high-energy gamma-ray burst…
The measurement of non-zero polarization can be used to infer the presence of departures from spherical symmetry in supernovae (SNe). The origin of the majority of the intrinsic polarization observed in SNe is in electron scattering, which…
Retrieving the physical parameters from spectroscopic observations of exoplanets is key to understanding their atmospheric properties. Exoplanetary atmospheric retrievals are usually based on approximate Bayesian inference and rely on…
The Laser Interferometer Space Antenna (LISA) data stream will inevitably contain gaps due to maintenance and environmental disturbances, introducing nonstationarities and spectral leakage that compromise standard frequency-domain…
On the 17th of August, 2017 came the simultaneous detections of GW170817, a gravitational wave that originated from the coalescence of two neutron stars, along with the gamma-ray burst GRB170817A, and the kilonova counterpart AT2017gfo.…
The first binary neutron star merger, GW170817, was accompanied by a radioactivity-powered optical/infrared transient called a kilonova. To date, no compelling kilonova has been found during optical surveys of the sky, independent of…
Kilonovae are optical transients following the merger of neutron star binaries, which are powered by the r-process heating of merger ejecta. However, if a merger remnant is a long-lived supramassive neutron star supported by its uniform…
Neural networks are being extensively used for modelling data, especially in the case where no likelihood can be formulated. Although in the case of X-ray spectral fitting, the likelihood is known, we aim to investigate the neural networks…
Simulation-Based Inference (SBI) is a promising Bayesian inference framework that alleviates the need for analytic likelihoods to estimate posterior distributions. Recent advances using neural density estimators in SBI algorithms have…
Prediction algorithms, such as deep neural networks (DNNs), are used in many domain sciences to directly estimate internal parameters of interest in simulator-based models, especially in settings where the observations include images or…
The ejecta from binary neutron star mergers, which powers its associated kilonova, can inform us about source properties, merger dynamics, and the dense nuclear matter equation of state. While now in the era of multi-messenger astronomy, we…
In binary neutron star mergers, lanthanide-rich dynamical ejecta and lanthanide-poor post-merger ejecta have been often linked to the red and blue kilonova emission, respectively. However, analytic light curve modeling of kilonova often…
Compact binary mergers detectable in gravitational waves can be accompanied by a kilonova, an electromagnetic transient powered by radioactive decay of newly synthesised r-process elements. A few kilonova candidates have been observed…
We present simulated observations to assess the ability of LSST and the WFD survey to detect and characterize kilonovae - the optical emission associated with binary neutron star (and possibly black hole - neutron star) mergers. We expand…
Simulation-Based Inference (SBI) is a common name for an emerging family of approaches that infer the model parameters when the likelihood is intractable. Existing SBI methods either approximate the likelihood, such as Approximate Bayesian…
Despite the promise of Neural Posterior Estimation (NPE) methods in astronomy, the adaptation of NPE into the routine inference workflow has been slow. We identify three critical issues: the need for custom featurizer networks tailored to…
Accurately estimating parameters of physiological models is essential to achieving reliable digital twins. For Type 1 Diabetes, this is particularly challenging due to the complexity of glucose-insulin interactions. Traditional methods…