Related papers: Kilonova Light Curve Parameter Estimation Using Li…
The observed spectra and light curves of the kilonova produced by the GW170817 binary neutron star merger provide complementary insights, but modeling both the spectral- and time-domain has proven challenging. Here, we model the…
Many statistical models in cosmology can be simulated forwards but have intractable likelihood functions. Likelihood-free inference methods allow us to perform Bayesian inference from these models using only forward simulations, free from…
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract extra information from the simulator.…
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…
Detecting the thermal emission from double neutron star merger events is a challenging task because of the quick fading of the observed flux. In order to create an efficient observing strategy for their observing method it is crucial to…
Photometric data-driven classification of supernovae becomes a challenge due to the appearance of real-time processing of big data in astronomy. Recent studies have demonstrated the superior quality of solutions based on various machine…
Gravitational waves from compact binaries measured by the LIGO and Virgo detectors are routinely analyzed using Markov Chain Monte Carlo sampling algorithms. Because the evaluation of the likelihood function requires evaluating millions of…
We present an inference method utilizing artificial neural networks for parameter estimation of a quantum probe monitored through a single continuous measurement. Unlike existing approaches focusing on the diffusive signals generated by…
This paper introduces an algorithm to select demonstration examples for in-context learning of a query set. Given a set of $n$ examples, how can we quickly select $k$ out of $n$ to best serve as the conditioning for downstream inference?…
We introduce SuperNNova, an open source supernova photometric classification framework which leverages recent advances in deep neural networks. Our core algorithm is a recurrent neural network (RNN) that is trained to classify light-curves…
L\'evy processes are widely used in financial modeling due to their ability to capture discontinuities and heavy tails, which are common in high-frequency asset return data. However, parameter estimation remains a challenge when associated…
This paper addresses the task of estimating a covariance matrix under a patternless sparsity assumption. In contrast to existing approaches based on thresholding or shrinkage penalties, we propose a likelihood-based method that regularizes…
Substantial effort has been devoted to the characterization of transient phenomena from photometric information. Automated approaches to this problem have taken advantage of complete phase-coverage of an event, limiting their use for…
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…
We present an improved version of the 3D Monte Carlo radiative transfer code POSSIS to model kilonovae from neutron star mergers, wherein nuclear heating rates, thermalization efficiencies and wavelength-dependent opacities depend on local…
Latent position models are widely used for the analysis of networks in a variety of research fields. In fact, these models possess a number of desirable theoretical properties, and are particularly easy to interpret. However, statistical…
Vast amounts of astronomical photometric data are generated from various projects, requiring significant effort to identify variable stars and other object classes. In light of this, a general, widely applicable classification framework…
Multi-sensor state space models underpin fusion applications in networks of sensors. Estimation of latent parameters in these models has the potential to provide highly desirable capabilities such as network self-calibration. Conventional…
With the enhancement of sensitivity of Gravitational Wave (GW) detectors and capabilities of large survey facilities, such as Vera Rubin Observatory Legacy Survey of Space and Time (LSST) and 2.5-m Wide Field Survey Telescope (WFST), we now…
Event reconstruction is a central step in many particle physics experiments, turning detector observables into parameter estimates; for example estimating the energy of an interaction given the sensor readout of a detector. A corresponding…