MAVIS: system modelling and performance prediction

Towards Realistic Modeling of the Astrometric Capabilities of MCAO Systems: Detecting an Intermediate Mass Black Hole with MAVIS

Accurate astrometry is a key deliverable for the next generation of multi-conjugate adaptive optics (MCAO) systems. The MCAO Visible Imager and Spectrograph (MAVIS) is being designed for the Very Large Telescope Adaptive Optics Facility and must achieve 150 $μ$as astrometric precision (50 $μ$as goal). To test this before going on-sky, we have created MAVISIM, a tool to simulate MAVIS images. MAVISIM accounts for three major sources of astrometric error, high- and low-order point spread function (PSF) spatial variability, tip-tilt residual error and static field distortion. When exploring the i…

Linear prediction of atmospheric wave-fronts for tomographic Adaptive Optics systems: modelling and robustness assessment

We use a theoretical frame-work to analytically assess temporal prediction error functions on von-Karman turbulence when a zonal representation of wave-fronts is assumed. Linear prediction models analysed include auto-regressive of order up to three, bilinear interpolation functions and a minimum mean square error predictor. This is an extension of the authors' previously published work (see ref. 2) in which the efficacy of various temporal prediction models was established. Here we examine the tolerance of these algorithms to specific forms of model errors, thus defining the expected change i…

多信使能谱建模对blazar-中微子关联的影响

Impact of multi-messenger spectral modelling on blazar-neutrino associations

研究多信使能谱建模对blazar-中微子关联的影响，发现p-gamma能谱在高能事件关联中表现更优。

Modelling stars with Gaussian Process Regression: Augmenting Stellar Model Grid

Grid-based modelling is widely used for estimating stellar parameters. However, stellar model grid is sparse because of the computational cost. This paper demonstrates an application of a machine-learning algorithm using the Gaussian Process (GP) Regression that turns a sparse model grid onto a continuous function. We train GP models to map five fundamental inputs (mass, equivalent evolutionary phase, initial metallicity, initial helium fraction, and the mixing-length parameter) to observable outputs (effective temperature, surface gravity, radius, surface metallicity, and stellar age). We tes…