arXiv subjects

Nicholas Chandler

Publications and source records attributed to Nicholas Chandler.

3 recordsLinked to original sources

arXiv2026

CURED: Creating, Understanding, and Repairing Errors Demonstrator

Detecting and cleaning errors in tabular data is a prerequisite for data intense software applications. Recent research at the intersection of Machine Learning (ML) and Database Management Systems (DBMS) highlights the potential of statistical learning algorithms for error detection and cleaning. This paper combines our recent work on ML-based data cleaning and error models in a unified demonstrator. The web application allows users to upload tabular data, perturb the data with realistic data dependent errors and use modern ML methods to clean and understand error mechanisms in data. Our demonstrator helps to bridge the gap between theoretical advancements and intuitive practical insights in the context of error models and data cleaning algorithms for tabular data. The demonstrator is available at https://cured.demo.calgo-lab.de/

cs.LG↗

arXiv2026

Probabilistic neural network approach to determining parameters of eclipsing binaries

Eclipsing binaries provide one of the most direct mechanisms for measuring stellar properties such as mass and radius, but historically, determining these properties has been non-trivial and computationally prohibitive. As such, only a small fraction of all eclipsing binaries for which data have been available have been fully characterized. To improve computational efficiency, we construct an uncertainty-aware neural network which can ingest phase-folded light curves in any of 50 commonly used passbands, combined with phase-folded radial velocity measurements for both primary and secondary, as well as fluxes across the spectral energy distribution to predict stellar and orbital parameters of eclipsing binaries. The model was trained to be agnostic to the presence of third light, spots (both cool and hot), and incomplete data. As the model is operating in a probabilistic framework, it is also capable of outputting uncertainties in all of the parameters. The model was trained on synthetic data, and applied to a set of $\sim$200 previously solved real eclipsing binaries to demonstrate its performance. The model is capable of determining masses and radii of eclipsing binaries with precision of $\lesssim$20\% and $T_{\rm eff}$ with precision of $\sim$500 K in only a fraction of the time it takes the more traditional solvers. Although the resulting uncertainties are larger than what is possible to produce using more boutique analysis of individual stars, in the era of large photometric surveys, this approach allows to identify the most interesting systems, and it provides a starting point of the distributions in all of the parameters that these solvers could improve upon.

astro-ph.SR↗

arXiv2025

MechDetect: Detecting Data-Dependent Errors

Data quality monitoring is a core challenge in modern information processing systems. While many approaches to detect data errors or shifts have been proposed, few studies investigate the mechanisms governing error generation. We argue that knowing how errors were generated can be key to tracing and fixing them. In this study, we build on existing work in the statistics literature on missing values and propose MechDetect, a simple algorithm to investigate error generation mechanisms. Given a tabular data set and a corresponding error mask, the algorithm estimates whether or not the errors depend on the data using machine learning models. Our work extends established approaches to detect mechanisms underlying missing values and can be readily applied to other error types, provided that an error mask is available. We demonstrate the effectiveness of MechDetect in experiments on established benchmark datasets.

cs.LG↗