arXiv subjects

Ilya Agapov

Publications and source records attributed to Ilya Agapov.

8 recordsLinked to original sources

arXiv2026

Laser-Plasma Accelerator Beams in Light Sources: Femtosecond High-Brightness Radiation through Chirped Pulse Injection

We propose a chirped-pulse injection scheme into a hard x-ray low-emittance synchrotron light source such as PETRA IV from a laser-plasma electron injector with active energy compression. The scheme enables delivering kA-scale short pulses with several tens of hertz repetition rate to any synchrotron beamline in the ring and allows producing femtosecond temporally coherent radiation pulses at target beamlines.

physics.acc-ph↗

arXiv2025

Beam Intensity Limitations in Future Multi-Bend Achromat Light Sources

We show that emittance of fourth-generation 6 GeV machines such as PETRA IV is close to what is theoretically achievable due to beam intensity limitations from space charge and intra-beam scattering. Investigating these limitations, in particular their scaling with the bare lattice emittance and the beam energy, we argue that achieving further significant emittance reduction and increase in radiation brightness is only possible by increasing the beam energy. We outline the design and technological challenges on the way to such improvement.

physics.acc-ph↗

arXiv2024

Optics tuning simulations for FCC-ee using Python Accelerator Toolbox

The development of ultra-low emittance storage rings, such as the e+/e- Future Circular Collider (FCC-ee) with a circumference of about 90 km, aims to achieve unprecedented luminosity and beam size. One significant challenge is correcting the optics, which becomes increasingly difficult as we target lower emittances. In this paper, we investigate optics correction methods to address these challenges. We examined the impact of arc region magnet alignment errors in the baseline optics for the FCC-ee lattice at Z energy. To establish realistic alignment tolerances, we developed a sequence of correction steps using the Python Accelerator Toolbox (PyAT) to correct the lattice optics, achieve the nominal emittance, Dynamic Aperture (DA), and in the end, the design luminosity. The correction scheme has been recently optimized and better machine performance demonstrated. A comparison was conducted between two optics correction approaches: Linear Optics from Closed Orbits (LOCO) with phase advance + $η_x$ and coupling Resonance Driving Terms (RDTs) + $η_y$. The latter method demonstrated better performance in achieving the target emittance and enhancing the DA.

physics.acc-ph↗

arXiv2024

Neural Networks for ID Gap Orbit Distortion Compensation in PETRA III

Undulators are used in storage rings to produce extremely brilliant synchrotron radiation. In the ideal case, a perfectly tuned undulator always has a first and second field integrals equal to zero. But, in practice, field integral changes during gap movements can never be avoided for real-life devices. As they significantly impact the circulating electron beam, there is the need to routinely compensate such effects. Deep Neural Networks can be used to predict the distortion in the closed orbit induced by the undulator gap variations on the circulating electron beam. In this contribution several current state-of-the-art deep learning algorithms were trained on measurements from PETRA~III. The different architecture performances are then compared to identify the best model for the gap-induced distortion compensation.

physics.acc-ph↗

arXiv2023

Wakefield assisted bunch compression in storage rings

Equilibrium bunch lengths typical to modern day light sources of tens of picoseconds limit the spectral reach of superradiant approaches for exploring materials and biological samples. In particular, generation of terahertz radiation seems appealing for pump-probe experiments. Taking advantage of self-wakes generated by carefully chosen structure one can control the bunch length and shape the bunch profile to increase significantly the high-frequency spectral components. Numerical analysis indicates the presence of self-consistent steady states that can be obtained with passive corrugated or dielectric structures. This approach has potential applications for generating superradiant terahertz radiation and pulse shaping in circular accelerators.

physics.acc-ph↗

arXiv2020

Physics-Based Deep Neural Networks for Beam Dynamics in Charged Particle Accelerators

This paper presents a novel approach for constructing neural networks which model charged particle beam dynamics. In our approach, the Taylor maps arising in the representation of dynamics are mapped onto the weights of a polynomial neural network. The resulting network approximates the dynamical system with perfect accuracy prior to training and provides a possibility to tune the network weights on additional experimental data. We propose a symplectic regularization approach for such polynomial neural networks that always restricts the trained model to Hamiltonian systems and significantly improves the training procedure. The proposed networks can be used for beam dynamics simulations or for fine-tuning of beam optics models with experimental data. The structure of the network allows for the modeling of large accelerators with a large number of magnets. We demonstrate our approach on the examples of the existing PETRA III and the planned PETRA IV storage rings at DESY.

cs.NE↗

arXiv2018

Opportunities in Machine Learning for Particle Accelerators

Machine learning (ML) is a subfield of artificial intelligence. The term applies broadly to a collection of computational algorithms and techniques that train systems from raw data rather than a priori models. ML techniques are now technologically mature enough to be applied to particle accelerators, and we expect that ML will become an increasingly valuable tool to meet new demands for beam energy, brightness, and stability. The intent of this white paper is to provide a high-level introduction to problems in accelerator science and operation where incorporating ML-based approaches may provide significant benefit. We review ML techniques currently being investigated at particle accelerator facilities, and we place specific emphasis on active research efforts and promising exploratory results. We also identify new applications and discuss their feasibility, along with the required data and infrastructure strategies. We conclude with a set of guidelines and recommendations for laboratory managers and administrators, emphasizing the logistical and technological requirements for successfully adopting this technology. This white paper also serves as a summary of the discussion from a recent workshop held at SLAC on ML for particle accelerators.

physics.acc-ph↗

arXiv2014

The Full Potential of the Baseline SASE Undulators of the European XFEL

The output SASE characteristics of the baseline European XFEL, recently used in the TDRs of scientific instruments and X-ray optics, have been previously optimized assuming uniform undulators without considering the potential of undulator tapering in the SASE regime. Here we demonstrate that the performance of European XFEL sources can be significantly improved without additional hardware. The procedure simply consists in the optimization of the undulator gap configuration for each X-ray beamline. Here we provide a comprehensive description of the soft X-ray photon beam properties as a function of wavelength and bunch charge. Based on nominal parameters for the electron beam, we demonstrate that undulator tapering allows one to achieve up to a tenfold increase in peak power and photon spectral density in the conventional SASE regime. We illustrate this fact for the SASE3 beamline. The FEL code Genesis has been extensively used for these studies. Based on these findings we suggest that the requirements for the SASE3 instrument (SCS, SQS) and for the SASE3 beam transport system be updated.

physics.acc-ph↗