Related papers: Accurate uncertainty estimation in crowded fields:…
Given the basic parameters of a cosmic shear weak lensing survey, how well can systematic errors due to anisotropy in the point spread function (PSF) be corrected? The largest source of error in this correction to date has been the…
The point spread function (PSF) is fundamental to any type of microscopy, most importantly so for single-molecule localization techniques, where the exact PSF shape is crucial for precise molecule localization at the nanoscale. However,…
Current cosmological analyses which use Type Ia supernova (SN Ia) observations combine SN samples to expand the redshift range beyond that of a single sample and increase the overall sample size. The inhomogeneous photometric calibration…
We describe a method for deriving the position and flux of point and compact sources observed by a scanning survey mission. Results from data simulated to test our method are presented, which demonstrate that at least a 10-fold improvement…
Handling big data has largely been a major bottleneck in traditional statistical models. Consequently, when accurate point prediction is the primary target, machine learning models are often preferred over their statistical counterparts for…
We present a machine learning method to assign stellar parameters (temperature, surface gravity, metallicity) to the photometric data of large photometric surveys such as SDSS and SKYMAPPER. The method makes use of our previous effort in…
Planned wide-field weak lensing surveys are expected to reduce the statistical errors on the shear field to unprecedented levels. In contrast, systematic errors like those induced by the convolution with the point spread function (PSF) will…
One of the most important techniques for astrophysics with adaptive optics is the ability to do spectroscopy at diffraction limited scales. The extreme difficulty of positioning a faint target accurately on a very narrow slit can be avoided…
Detecting or placing upper limits on spacetime variations of fundamental constants requires quantifying every potential source of uncertainty. We continue our previous study into the impact of continuum variations on measurements of the…
High-fidelity simulated astronomical images are an important tool in developing and measuring the performance of image-processing algorithms, particularly for high precision measurements of cosmic shear -- correlated distortions of images…
The amount, size, and complexity of astronomical data-sets and databases are growing rapidly in the last decades, due to new technologies and dedicated survey telescopes. Besides dealing with poly-structured and complex data, sparse data…
In safety-critical applications like medical diagnosis, certainty associated with a model's prediction is just as important as its accuracy. Consequently, uncertainty estimation and reduction play a crucial role. Uncertainty in predictions…
One of the most useful techniques in astronomical instrumentation is image slicing. It enables a spectrograph to have a more compact angular slit, whilst retaining throughput and increasing resolving power. Astrophotonic components like the…
We describe several projects addressing the growth of galaxies and massive black holes, for which adaptive optics is mandatory to reach high spatial resolution but is also a challenge due to the lack of guide stars and long integrations. In…
Accurate redshift estimates are a vital component in understanding galaxy evolution and precision cosmology. In this paper, we explore approaches to increase the applicability of machine learning models for photometric redshift estimation…
Uncertainty quantification for Particle Image Velocimetry (PIV) is critical for comparing flow fields with Computational Fluid Dynamics (CFD) results, and model design and validation. However, PIV features a complex measurement chain with…
We present a robust and fast algorithm for performing astrometry and source cross-identification on two dimensional point lists, such as between a catalogue and an astronomical image, or between two images. The method is based on minimal…
A method is developed for fitting theoretically predicted astronomical spectra to an observed spectrum. Using a hierarchical Bayesian principle, the method takes both systematic and statistical measurement errors into account, which has not…
We investigate the impact of point spread function (PSF) fitting errors on cosmic shear measurements using the concepts of complexity and sparsity. Complexity, introduced in a previous paper, characterizes the number of degrees of freedom…
The observed stellar kinematics of dispersion-supported galaxies are often used to measure dynamical masses. Recently, several analytical relationships between the stellar line-of-sight velocity dispersion, the projected (2D) or deprojected…