Related papers: MAGIC: Microlensing Analysis Guided by Intelligent…
The Dark Matter present in the Large-Scale Structure of the Universe is invisible, but its presence can be inferred through the small gravitational lensing effect it has on the images of far away galaxies. By measuring this lensing effect…
We present a new method to estimate time delays from light curves of lensed quasars. The method is based on chi^2 minimization between the data and a numerical model light curve. A linear variation can be included in order to correct for…
A central area of research in nonlinear science is the study of instabilities that drive the emergence of extreme events. Unfortunately, experimental techniques for measuring such phenomena often provide only partial characterization. For…
We report the result of the analysis of the light curve of a caustic-crossing binary-lens microlensing event OGLE-2009-BLG-023/MOA-2009-BLG-028. Even though the event was observed solely by survey experiments, we could uniquely determine…
Laser machining is a highly flexible non-contact manufacturing technique that has been employed widely across academia and industry. Due to nonlinear interactions between light and matter, simulation methods are extremely crucial, as they…
Pixel microlensing, i.e. gravitational microlensing of unresolved stars, can be used to explore distant stellar systems, and as a bonus may be able to detect extragalactic planets. In these studies, binary-lens events with multiple…
A common remark at conferences and meetings has been: "One can fit any light curve with binary lens models because of the large number of parameters!" It is especially tempting to claim that some of the possible binary events in the…
The MACHO collaboration has been carrying out Difference Image Analysis (DIA) since 1996 with the aim of increasing the sensitivity to the detection of gravitational microlensing. This is a preliminary report on the application of DIA to…
The IMAGINE Consortium aims to bring modeling of the magnetic field of the Milky Way to a next level, by using Bayesian inference. IMAGINE includes an open-source modular software pipeline that optimizes parameters in a user-defined…
Bayesian methods for graphical log-linear marginal models have not been developed in the same extent as traditional frequentist approaches. In this work, we introduce a novel Bayesian approach for quantitative learning for such models.…
Image classification is an essential part of computer vision which assigns a given input image to a specific category based on the similarity evaluation within given criteria. While promising classifiers can be obtained through deep…
Exploring signals from the outer space has become an observational science under fast expansion. On the basis of its advanced technology the MAGIC telescope is the natural building block for the first large scale ground based high energy…
(abridged) Using the particularly long gravitational microlensing event OGLE-2014-BLG-1186 with a time-scale $t_\mathrm{E}$ ~ 300 d, we present a methodology for identifying the nature of localised deviations from single-lens point-source…
Checklists have been widely recognized as effective tools for completing complex tasks in a systematic manner. Although originally intended for use in procedural tasks, their interpretability and ease of use have led to their adoption for…
This paper proposes the use of causal modeling to detect and mitigate algorithmic bias. We provide a brief description of causal modeling and a general overview of our approach. We then use the Adult dataset, which is available for download…
We introduce a model of hadronization based on invertible neural networks that faithfully reproduces a simplified version of the Lund string model for meson hadronization. Additionally, we introduce a new training method for normalizing…
The optimal instant of observation of astrophysical phenomena for objects that vary on human time-sales is an important problem, as it bears on the cost-effective use of usually scarce observational facilities. In this paper we address this…
The MACHO Project monitors millions of stars in the Large Magellanic Cloud, the Small Magellanic Cloud and the bulge of the Milky Way searching for the gravitational microlensing signature of baryonic dark matter. This Project has yielded…
While deep neural networks have become the go-to approach in computer vision, the vast majority of these models fail to properly capture the uncertainty inherent in their predictions. Estimating this predictive uncertainty can be crucial,…
We introduce a novel methodology for addressing systematic uncertainties in unbinned inclusive cross-section measurements and related collider-based inference problems. Our approach incorporates known analytic dependencies on parameters of…