Related papers: A Statistical Approach to Quantifying Uncertainty …
We investigate a uniform sample of 113 light curves (LCs) of meteors collected at the Wise Observatory in November 2002 while observing the Leonid meteor shower. We use previously defined descriptors such as the skewness F and a recently…
Context. The sizes of many asteroids, especially slowly rotating, low-amplitude targets, remain poorly constrained due to selection effects. These biases limit the availability of high-quality data, leaving size estimates reliant on…
We use a generalised procedure for the combined likelihood analysis of different cosmological probes, the `Hyper-Parameters' method, that allows freedom in the relative weights of the raw measurements. We perform a joint analysis of the…
In this paper, we investigate the applicability of the astrometric method to the determination of the lens parameters for gravitational microlensing events toward both the LMC and the Galactic bulge. For this analysis, we investigate the…
The seven known main belt comets (MBCs) have orbital characteristics of main belt asteroids yet exhibit dust ejection like comets. In order to constrain their physical and orbital properties we searched the Thousand Asteroid Light Curve…
Light from `point sources' such as supernovae is observed with a beam width of order of the sources' size - typically less than 1 AU. Such a beam probes matter and curvature distributions that are very different from coarse-grained…
The Earth is impacted by 35-40 metre-scale objects every year. These meteoroids are the low mass end of impactors that can do damage on the ground. Despite this they are very poorly surveyed and characterised, too infrequent for ground…
Analyzing multi-featured time series data is critical for space missions making efficient event detection, potentially onboard, essential for automatic analysis. However, limited onboard computational resources and data downlink constraints…
New supernova surveys such as the Dark Energy Survey, Pan-STARRS and the LSST will produce an unprecedented number of photometric supernova candidates, most with no spectroscopic data. Avoiding biases in cosmological parameters due to the…
Cosmological probes pose an inverse problem where the measurement result is obtained through observations, and the objective is to infer values of model parameters which characterize the underlying physical system -- our Universe. Modern…
Reliable quantification of uncertainty in Mobile Laser Scanning (MLS) point clouds is essential for ensuring the accuracy and credibility of downstream applications such as 3D mapping, modeling, and change analysis. Traditional backward…
Uncertain partially observable Markov decision processes (uPOMDPs) allow the probabilistic transition and observation functions of standard POMDPs to belong to a so-called uncertainty set. Such uncertainty, referred to as epistemic…
We report on an eight year survey of simultaneous optical and radar meteor detections with the goal of isolating the fraction of meteors missed by specular radars. A total of 10,503 Electron Multiplied Charge Couple Device (EMCCD) meteors…
Probabilistic models analyze data by relying on a set of assumptions. Data that exhibit deviations from these assumptions can undermine inference and prediction quality. Robust models offer protection against mismatch between a model's…
Probabilistic collision detection (PCD) is essential in motion planning for robots operating in unstructured environments, where considering sensing uncertainty helps prevent damage. Existing PCD methods mainly used simplified geometric…
By measuring the fundamental constants in astrophysical objects one can test basic physical principles as space-time invariance of physical laws along with probing the applicability limits of the standard model of particle physics. The…
The estimation of cosmological parameters from precision observables is an important industry with crucial ramifications for particle physics. This article discusses the statistical methods presently used in cosmological data analysis,…
The original formulation of BEAMS - Bayesian Estimation Applied to Multiple Species - showed how to use a dataset contaminated by points of multiple underlying types to perform unbiased parameter estimation. An example is cosmological…
Estimating and disentangling epistemic uncertainty, uncertainty that is reducible with more training data, and aleatoric uncertainty, uncertainty that is inherent to the task at hand, is critically important when applying machine learning…
Photometric redshifts play an important role as a measure of distance for various cosmological topics. Spectroscopic redshifts are only available for a very limited number of objects but can be used for creating statistical models. A broad…