No Arabic abstract
We have developed a framework for the Monte-Carlo simulation of the X-Ray Telescopes (XRT) and the X-ray Imaging Spectrometers (XIS) onboard Suzaku, mainly for the scientific analysis of spatially and spectroscopically complex celestial sources. A photon-by-photon instrumental simulator is built on the ANL platform, which has been successfully used in ASCA data analysis. The simulator has a modular structure, in which the XRT simulation is based on a ray-tracing library, while the XIS simulation utilizes a spectral Redistribution Matrix File (RMF), generated separately by other tools. Instrumental characteristics and calibration results, e.g., XRT geometry, reflectivity, mutual alignments, thermal shield transmission, build-up of the contamination on the XIS optical blocking filters (OBF), are incorporated as completely as possible. Most of this information is available in the form of the FITS (Flexible Image Transport System) files in the standard calibration database (CALDB). This simulator can also be utilized to generate an Ancillary Response File (ARF), which describes the XRT response and the amount of OBF contamination. The ARF is dependent on the spatial distribution of the celestial target and the photon accumulation region on the detector, as well as observing conditions such as the observation date and satellite attitude. We describe principles of the simulator and the ARF generator, and demonstrate their performance in comparison with in-flight data.
CASCADE is a full hadron level Monte Carlo event generator for ep, gamma p and pbar{p} and pp processes, which uses the CCFM evolution equation for the initial state cascade in a backward evolution approach supplemented with off - shell matrix elements for the hard scattering. A detailed program description is given, with emphasis on parameters the user wants to change and variables which completely specify the generated events.
We present a new strategy using artificial intelligence (AI) to build the first AI-based Monte Carlo event generator (MCEG) capable of faithfully generating final state particle phase space in lepton-hadron scattering. We show a blueprint for integrating machine learning strategies with calibrated detector simulations to build a vertex-level, AI-based MCEG, free of theoretical assumptions about femtometer scale physics. As the first steps towards this goal, we present a case study for inclusive electron-proton scattering using synthetic data from the PYTHIA MCEG for testing and validation purposes. Our quantitative results validate our proof of concept and demonstrate the predictive power of the trained models. The work suggests new venues for data preservation to enable future QCD studies of hadrons structure, and the developed technology can boost the science output of physics programs at facilities such as Jefferson Lab and the future Electron-Ion Collider.
The new monte-carlo generator of heavy ion collisions, DCM-SMM, based on Dubna Cascade Model (DCM-QGSM) and Statistical Multifragmentation Model (SMM) is described. The model aimed to generate particle--nucleus and nucleus--nucleus collisions at a wide range of energy was created to provide the computer simulation support to new experimental facilities BMN and MPD at the accelerator complex NICA. It can simulate the production of both light particles and nuclear fragments and hyperfragments on the event by event basis.
A charge injection technique is applied to the X-ray CCD camera, XIS (X-ray Imaging Spectrometer) onboard Suzaku. The charge transfer inefficiency (CTI) in each CCD column (vertical transfer channel) is measured by the injection of charge packets into a transfer channel and subsequent readout. This paper reports the performances of the charge injection capability based on the ground experiments using a radiation damaged device, and in-orbit measurements of the XIS. The ground experiments show that charges are stably injected with the dispersion of 91eV in FWHM in a specific column for the charges equivalent to the X-ray energy of 5.1keV. This dispersion width is significantly smaller than that of the X-ray events of 113eV (FWHM) at approximately the same energy. The amount of charge loss during transfer in a specific column, which is measured with the charge injection capability, is consistent with that measured with the calibration source. These results indicate that the charge injection technique can accurately measure column-dependent charge losses rather than the calibration sources. The column-to-column CTI correction to the calibration source spectra significantly reduces the line widths compared to those with a column-averaged CTI correction (from 193eV to 173eV in FWHM on an average at the time of one year after the launch). In addition, this method significantly reduces the low energy tail in the line profile of the calibration source spectrum.
We review the main software and computing challenges for the Monte Carlo physics event generators used by the LHC experiments, in view of the High-Luminosity LHC (HL-LHC) physics programme. This paper has been prepared by the HEP Software Foundation (HSF) Physics Event Generator Working Group as an input to the LHCC review of HL-LHC computing, which has started in May 2020.