Antonio Pagliaro | Searcharxiv

arXiv2026

Machine Learning for Event Reconstruction in Imaging Atmospheric Cherenkov Telescopes

Imaging Atmospheric Cherenkov Telescopes (IACTs) are the leading instruments for very-high-energy (VHE) gamma-ray astronomy, covering the range from hundreds of GeV to hundreds of TeV. This chapter reviews the critical role of machine learning in reconstructing the physical properties of particles detected by IACTs. We introduce the IACT technique and its central challenge: distinguishing rare gamma-ray showers from the overwhelming cosmic-ray background. We detail the standard reconstruction pipeline, from image cleaning and Hillas parameterization to stereoscopic observation, and frame event reconstruction as a supervised learning problem encompassing particle classification and energy/direction regression. Two frontiers of innovation are then explored: the exploitation of the temporal dimension of shower images through novel timing-based features that enhance background rejection at low energies, and the application of advanced ensemble methods (gradient boosting, stacking) that surpass baseline Random Forests, notably in mitigating systematic energy bias. Finally, we discuss performance metrics and provide an outlook on next-generation approaches dominated by deep learning, including Convolutional and Graph Neural Networks.

astro-ph.IM↗

arXiv2026

Time Series Analysis in Machine Learning

Time series analysis is a fundamental component of machine learning, especially in astrophysics and cosmology where temporal data abound. This chapter provides a pedagogical review of time series analysis techniques from a machine learning perspective. We cover the basic concepts of time series (stationarity, autocorrelation, seasonality), classical statistical models (autoregressive, moving average, ARIMA, exponential smoothing, state-space models), and modern machine learning approaches. In particular, we discuss how traditional statistical methods lay the groundwork, and then explore machine learning methods for time series, including feature-based regression, tree-based ensemble methods, hidden Markov models, Gaussian processes, and deep learning models (recurrent neural networks, convolutional networks, transformers). Throughout, we illustrate with examples drawn from multiple domains (e.g. astronomy, weather forecasting, finance) to emphasize common principles. The goal is to equip readers with both the theoretical understanding and practical context to apply machine learning techniques for time series analysis in their research.

astro-ph.IM↗