arXiv subjects

Abhimanyu Borthakur

Publications and source records attributed to Abhimanyu Borthakur.

7 recordsLinked to original sources

arXiv2026

Ultrafast Pulse Retrieval from Partial FROG Traces Using Implicit Diffusion Models

Ultrashort laser pulses enable attosecond-scale measurements and drive breakthroughs across science and technology, but their routine use hinges on reliable pulse characterization. Frequency-Resolved Optical Gating (FROG) is a leading solution, forming a spectrogram by scanning the delay between two pulse replicas and recording the nonlinear signal spectrum. In online settings, however, dense delay-frequency scans are costly or impractical-especially for long pulses, wavelength regimes with limited spectrometer coverage (e.g., UV), or hardware with coarse resolution, yielding severely undersampled FROG traces. Existing reconstruction methods struggle in this regime-iterative algorithms are computationally heavy, convolutional networks blur fine structure, and sequence models are unstable when inputs are discontinuous or sparse. We present a generative diffusion framework tailored to recover ultrafast pulse intensity and phase from incomplete FROG measurements. Our model infers missing spectro-temporal content with high fidelity, enabling accurate retrieval from aggressively downsampled inputs. On a simulated benchmark of FROG-pulse pairs, the diffusion approach surpasses strong CNN and Seq2Seq baselines in accuracy and stability while remaining efficient enough for near real-time deployment.

physics.optics↗

arXiv2025

Structured Light at the Extreme: Harnessing Spatiotemporal Control for High-Field Laser-Matter Interactions

This review charts the emerging paradigm of intelligent structured light for high-field laser-matter interactions, where the precise spatiotemporal and vectorial control of light is a critical degree of freedom. We outline a transformative framework built upon three synergistic pillars. First, we survey the advanced electromagnetic toolkit, moving beyond conventional spatial light modulators to include robust static optics and the promising frontier of plasma light modulators. Second, we detail the optimization engine for this high-dimensional design space, focusing on physics-informed digital twins and AI-driven inverse design to automate the discovery of optimal light structures. Finally, we explore the groundbreaking applications enabled by this integrated approach, including programmable electron beams, orbital-angular-momentum-carrying γ-rays, compact THz accelerators, and robust communications. The path forward necessitates overcoming grand challenges in material science, real-time adaptive control at MHz rates, and the extension of these principles to the quantum realm. This review serves as a call to action for a coordinated, interdisciplinary effort to command, rather than merely observe, light-matter interactions at the extreme.

physics.optics↗

arXiv2025

Deep Learning Assisted Modeling for $χ^{(2)}$ Nonlinear Optics

Modeling second-order ($χ^{(2)}$) nonlinear optical processes remains computationally expensive due to the need to resolve fast field oscillations and simulate wave propagation using methods like the split-step Fourier method (SSFM). This can become a bottleneck in real-time applications, such as high-repetition-rate laser systems requiring rapid feedback and control. We present an LSTM-based surrogate model trained on SSFM simulations generated from a start-to-end model of the photocathode drive laser at SLAC National Accelerator Laboratory's Linac Coherent Light Source II. The model achieves over 250x speedup while maintaining high fidelity, enabling future real-time optimization and laying the foundation for data-integrated modeling frameworks and digital twins of laser systems.

physics.optics↗

arXiv2024

Gamma Ray AGNs: Estimating Redshifts and Blazar Classification using traditional Neural Networks with smart initialization and self-supervised learning

Redshift estimation and the classification of gamma-ray AGNs represent crucial challenges in the field of gamma-ray astronomy. Recent efforts have been made to tackle these problems using traditional machine learning methods. However, the simplicity of existing algorithms, combined with their basic implementations, underscores an opportunity and a need for further advancement in this area. Our approach begins by implementing a Bayesian model for redshift estimation, which can account for uncertainty while providing predictions with the desired confidence level. Subsequently, we address the classification problem by leveraging intelligent initialization techniques and employing soft voting. Additionally, we explore several potential self-supervised algorithms in their conventional form. Lastly, in addition to generating predictions for data with missing outputs, we ensure that the theoretical assertions put forth by both algorithms mutually reinforce each other.

astro-ph.HE↗

arXiv2024

Gamma-ray Blazar Classification using Machine Learning with Advanced Weight Initialization and Self-Supervised Learning Techniques

Machine learning has emerged as a powerful tool in the field of gamma-ray astrophysics. The algorithms can distinguish between different source types, such as blazars and pulsars, and help uncover new insights into the high-energy universe. The Large Area Telescope (LAT) on-board the Fermi Gamma-ray telescope has significantly advanced our understanding of the Universe. The instrument has detected a large number of gamma-ray emitting sources, among which a significant number of objects have been identified as active galactic nuclei (AGN). The sample is primarily composed of blazars; however, more than one-third of these sources are either of an unknown class or lack a definite association with a low-energy counterpart. In this work, we employ multiple machine learning algorithms to classify the sources based on their other physical properties. In particular, we utilized smart initialisation techniques and self-supervised learning for classifying blazars into BL Lacertae objects (BL Lac) and flat spectrum radio quasars (FSRQ). The core advantage of the algorithm is its simplicity, usage of minimum number of features and easy deployment due to lesser number of parameters without compromising on the performance. The model predicts that out of the 1115 sources of uncertain type in the 4FGL-DR3 catalog, 820 can be classified as BL Lacs, and 295 can be classified as FSRQs.

astro-ph.HE↗

arXiv2023

Estimation of redshift and associated uncertainty of Fermi/LAT extra-galactic sources with Deep Learning

With the advancement of technology, machine learning-based analytical methods have pervaded nearly every discipline in modern studies. Particularly, a number of methods have been employed to estimate the redshift of gamma-ray loud active galactic nuclei (AGN), which are a class of supermassive black hole systems known for their intense multi-wavelength emissions and violent variability. Determining the redshifts of AGNs is essential for understanding their distances, which, in turn, sheds light on our current understanding of the structure of the nearby universe. However, the task involves a number of challenges such as the need for meticulous follow-up observations across multiple wavelengths and astronomical facilities. In this study, we employ a simple yet effective deep learning model with a single hidden layer having $64$ neurons and a dropout of 0.25 in the hidden layer, on a sample of AGNs with known redshifts from the latest AGN catalog, 4LAC-DR3, obtained from Fermi-LAT. We utilized their spectral, spatial, and temporal properties to robustly predict the redshifts of AGNs as well quantify their associated uncertainties, by modifying the model using two different variational inference methods. We achieve a correlation coefficient of 0.784 on the test set from the frequentist model and 0.777 and 0.778 from both the variants of variational inference, and, when used to make predictions on the samples with unknown redshifts, we achieve mean predictions of 0.421, 0.415 and 0.393, with standard deviations of 0.258, 0.246 and 0.207 from the models, respectively.

astro-ph.HE↗

arXiv2023

An Image Processing approach to identify solar plages observed at 393.37 nm by the Kodaikanal Solar Observatory

Solar plages, which are bright regions on the Sun's surface, are an important indicator of solar activity. In this study, we propose an automated algorithm for identifying solar plages in Ca K wavelength solar data obtained from the Kodaikanal Solar Observatory. The algorithm successfully annotates all visually identifiable plages in an image and outputs the corresponding calculated plage index. We perform a time series analysis of the plage index (rolling mean) across multiple solar cycles to test the algorithm's reliability and robustness. The results show a strong correlation between the calculated plage index and those reported in a previous study. The correlation coefficients obtained for all the solar cycles are higher than 0.90, indicating the reliability of the model. We also suggest that adjusting the hyperparameters appropriately for a specific image using our web-based app can increase the model's efficiency. The algorithm has been deployed on the Streamlit Community Cloud platform, where users can upload images and customize the hyperparameters for desired results. The input data used in this study is freely available from the KSO data archive, and the code and the generated data are publicly available on our GitHub repository. Our proposed algorithm provides an efficient and reliable method for identifying solar plages, which can aid the study of solar activity and its impact on the Earth's climate, technology, and space weather.

astro-ph.SR↗