Interregional Air Pollutant Transport: The Linearity Question

This report contains extended abstracts from an international meeting held in Budapest, Hungary. Its main subject is the question of proportionality and linearity between emissions and deposition/airborne concentration of air pollutants including sulfur, nitrogen, oxidants, and acidity. Session topics (which serve here as section headings) included analysis of measurements, ammonia and its implications for linearity, modeling with emphasis on chemistry, simplified approaches to the linearity issue, and results from long-range transport models. Linearity was found to be strongly dependent on the distance between emitters and receptors, the averaging time of pollutants, and the form of deposition

Application of Nanostructures and Metamaterials in Accelerator Physics

Carbon-based nanostructures and metamaterials offer extraordinary mechanical and opto-electrical properties, which make them suitable for applications in diverse fields, including, for example, bioscience, energy technology and quantum computing. In the latest years, important R&D efforts have been made to investigate the potential use of graphene and carbon-nanotube (CNT) based structures to manipulate and accelerate particle beams. In the same way, the special interaction of graphene and CNTs with charged particles and electromagnetic radiation might open interesting possibilities for the design of compact coherent radiation sources, and novel beam diagnostics techniques as well. This paper gives an overview of novel concepts based on nanostructures and metamaterials with potential application in the field of accelerator physics. Several examples are shown and future prospects discussed

Phenomenological description of the gamma* p cross section at low Q2

Low Q2 photon-proton cross sections are analysed using a simple, QCD-motivated parametrisation $\sigma_{\gamma^\star p}\propto 1/(Q^2+Q_0^2)$, which gives a good description of the data. The Q2 dependence of the gamma* p cross section is discussed in terms of the partonic transverse momenta of the hadronic state the photon fluctuates into.Comment: 14 pages, revtex, epsfig, 2 figure

Machine learning-based analysis of experimental electron beams and gamma energy distributions

The photon flux resulting from high-energy electron beam interactions with high field systems, such as in the upcoming FACET-II experiments at SLAC National Accelerator Laboratory, may give deep insight into the electron beam's underlying dynamics at the interaction point. Extraction of this information is an intricate process, however. To demonstrate how to approach this challenge with modern methods, this paper utilizes data from simulated plasma wakefield acceleration-derived betatron radiation experiments and high-field laser-electron-based radiation production to determine reliable methods of reconstructing key beam and interaction properties. For these measurements, recovering the emitted 200 keV to 10 GeV photon energy spectra from two advanced spectrometers now being commissioned requires testing multiple methods to finalize a pipeline from their responses to incident electron beam information. In each case, we compare the performance of: neural networks, which detect patterns between data sets through repeated training; maximum likelihood estimation (MLE), a statistical technique used to determine unknown parameters from the distribution of observed data; and a hybrid approach combining the two. Further, in the case of photons with energies above 30 MeV, we also examine the efficacy of QR decomposition, a matrix decomposition method. The betatron radiation and the high-energy photon cases demonstrate the effectiveness of a hybrid ML-MLE approach, while the high-field electrodynamics interaction and the low-energy photon cases showcased the machine learning (ML) model's efficiency in the presence of noise. As such, while there is utility in all the methods, the ML-MLE hybrid approach proves to be the most generalizable.Comment: 23 pages, 30 figure

IMPROVEMENTS ON THE MODIFIED NOMARSKI INTERFEROMETER FOR MEASUREMENTS OF SUPERSONIC GAS JET DENSITY PROFILES

For supersonic gas jet based beam profile monitors such as that developed for the High Luminosity Large Hadron Collider (HL-LHC) upgrade, density profile is a key characteristic. Due to this, non-invasive diagnostics to study the jet's behaviour have been designed. A Nomarski interferometer was constructed to image jets 30 µm to 1 mm in diameter and study changes in their density. A microscope lens has been integrated into the original interferometer system to capture phase changes on a much smaller scale than previous experiments have achieved. This contribution presents the optimisation and results gained from this interferometer

DEVELOPMENT OF A FAST MICRON-RESOLUTION BEAM POSITION MONITOR SIGNAL PROCESSOR FOR LINEAR COLLIDER BEAMBASED FEEDBACK SYSTEMS

We present the design of a prototype fast beam position monitor (BPM) signal processor for use in inter-bunch beam-based feedbacks for linear colliders and electron linacs. We describe the FONT4 intra-train beam-based digital position feedback system prototype deployed at the Accelerator test facility (ATF) extraction line at KEK, Japan. The system incorporates a fast analogue beam position monitor front-end signal processor, a digital feedback board, and a fast kicker-driver amplifier. The total feedback system latency is less than 150ns, of which less than 10ns is used for the BPM processor. We report preliminary results of beam tests using electron bunches separated by c. 150ns. Position resolution of order 1 micron was obtained