Chinmoy Biswas | Searcharxiv

arXiv2026

Density-gradient effect in high-harmonic generation in gases

High-harmonic generation (HHG) in gaseous targets is the most widespread method to produce coherent extreme-ultraviolet (XUV) pulses with sub-femtosecond duration. However, this process has intrinsically low efficiency, and substantial research and development is devoted worldwide to increase the achievable photon flux through this highly nonlinear light--matter interaction process. In this work, we show the strong interplay of phase matching and absorption in gas-pressure gradients, substantially affecting macroscopic HHG efficiency. Through detailed experimental analysis and supporting numerical studies, we highlight their significance, particularly at the boundaries of the interaction volume. The concluded results have implications in the massively expanding application possibilities of HHG sources requiring high photon flux, for example in the semiconductor industry, in nanoscale imaging of biological and industrial samples, or in nonlinear optics in the XUV regime.

physics.optics↗

arXiv2025

Load Forecasting on A Highly Sparse Electrical Load Dataset Using Gaussian Interpolation

Sparsity, defined as the presence of missing or zero values in a dataset, often poses a major challenge while operating on real-life datasets. Sparsity in features or target data of the training dataset can be handled using various interpolation methods, such as linear or polynomial interpolation, spline, moving average, or can be simply imputed. Interpolation methods usually perform well with Strict Sense Stationary (SSS) data. In this study, we show that an approximately 62\% sparse dataset with hourly load data of a power plant can be utilized for load forecasting assuming the data is Wide Sense Stationary (WSS), if augmented with Gaussian interpolation. More specifically, we perform statistical analysis on the data, and train multiple machine learning and deep learning models on the dataset. By comparing the performance of these models, we empirically demonstrate that Gaussian interpolation is a suitable option for dealing with load forecasting problems. Additionally, we demonstrate that Long Short-term Memory (LSTM)-based neural network model offers the best performance among a diverse set of classical and neural network-based models.

cs.LG↗