E. Reynolds | Searcharxiv

arXiv2025

reLAISS: A Python Package for Flexible Similarity Searches of Supernovae and Their Host Galaxies

Discovery rates of supernovae are expected to surpass one million events annually with the Vera C. Rubin Observatory. With unprecedented sample sizes of both common and rare transient types, photometric classification alone will be insufficient for finding one-in-a-million events and prioritizing the 1% of events for spectroscopic follow-up observations. Here, we present reLAISS, a modified framework for similarity searches of supernovae using extracted features of ZTF light curves and Pan-STARRS host galaxy photometry and built on the original LAISS framework. Unlike its predecessor, reLAISS couples interpretable light curve morphology features with extinction-corrected host-galaxy colors to probe both explosion physics and associated stellar populations simultaneously. The library allows users to customize the number of neighbors retrieved, the weight of host and light curve features, and the use of Monte Carlo simulations to ensure relevant matches when features are poorly constrained. We release reLAISS as a pip-installable package with an accompanying reference set of 20,000 features, and a set of tutorials that demonstrate the code's expanded functionality. All source code can be found at https://github.com/evan-reynolds/re-laiss .

astro-ph.IM↗

arXiv2022

Non-Parametric Data-Driven Background Modelling using Conditional Probabilities

Background modelling is one of the main challenges in particle physics data analysis. Commonly employed strategies include the use of simulated events of the background processes, and the fitting of parametric background models to the observed data. However, reliable simulations are not always available or may be extremely costly to produce. As a result, in many cases, uncertainties associated with the accuracy or sample size of the simulation are the limiting factor in the analysis sensitivity. At the same time, parametric models are limited by the a priori unknown functional form and parameter values of the background distribution. These issues become ever more pressing when large datasets become available, as it is already the case at the CERN Large Hadron Collider, and when studying exclusive signatures involving hadronic backgrounds. Two novel and widely applicable non-parametric data-driven background modelling techniques are presented, which address these issues for a broad class of searches and measurements. The first, relying on ancestral sampling, uses data from a relaxed event selection to estimate a graph of conditional probability density functions of the variables used in the analysis, accounting for significant correlations. A background model is then generated by sampling events from this graph, before the full event selection is applied. In the second, a generative adversarial network is trained to estimate the joint probability density function of the variables used in the analysis. The training is performed on a relaxed event selection which excludes the signal region, and the network is conditioned on a blinding variable. Subsequently, the conditional probability density function is interpolated into the signal region to model the background. The application of each method on a benchmark analysis is presented in detail, and the performance is discussed.

hep-ex↗