Monte Carlo Data Sets Research Articles

Heuristic absorption calculation in bilayered media from a white Monte Carlo dataset

Heuristic absorption calculation in bilayered media from a white Monte Carlo dataset

Fast and accurate algorithm for calculating long-baseline neutrino oscillation probabilities with matter effects

Neutrino oscillation experiments will be entering the precision era in the next decade with the advent of high statistics experiments like DUNE, HK, and JUNO. Correctly estimating the confidence intervals from data for the oscillation parameters requires very large Monte Carlo datasets involving calculating the oscillation probabilities in matter many, many times. In this paper, we leverage past work to present a new, fast, precise technique for calculating neutrino oscillation probabilities in matter optimized for long-baseline neutrino oscillations in the Earth’s crust including both accelerator and reactor experiments. For ease of use by theorists and experimentalists, we provide fast ++ and codes . Published by the American Physical Society 2024

Noise-reduction techniques for 1H-FID-MRSI at 14.1 T: Monte Carlo validation and in vivo application.

Proton magnetic resonance spectroscopic imaging (1H-MRSI) is a powerful tool that enables the multidimensional non-invasive mapping of the neurochemical profile at high resolution over the entire brain. The constant demand for higher spatial resolution in 1H-MRSI has led to increased interest in post-processing-based denoising methods aimed at reducing noise variance. The aim of the present study was to implement two noise-reduction techniques, Marchenko-Pastur principal component analysis (MP-PCA) based denoising and low-rank total generalized variation (LR-TGV) reconstruction, and to test their potential with and impact on preclinical 14.1 T fast in vivo 1H-FID-MRSI datasets. Since there is no known ground truth for in vivo metabolite maps, additional evaluations of the performance of both noise-reduction strategies were conducted using Monte Carlo simulations. Results showed that both denoising techniques increased the apparent signal-to-noise ratio (SNR) while preserving noise properties in each spectrum for both in vivo and Monte Carlo datasets. Relative metabolite concentrations were not significantly altered by either method and brain regional differences were preserved in both synthetic and in vivo datasets. Increased precision of metabolite estimates was observed for the two methods, with inconsistencies noted for lower-concentration metabolites. Our study provided a framework for how to evaluate the performance of MP-PCA and LR-TGV methods for preclinical 1H-FID MRSI data at 14.1 T. While gains in apparent SNR and precision were observed, concentration estimations ought to be treated with care, especially for low-concentration metabolites.

Deployment of ML in Changing Environments

The High-Luminosity LHC upgrade of the CMS experiment will utilise a large number of Machine Learning (ML) based algorithms in its hardware-based trigger. These ML algorithms will facilitate the selection of potentially interesting events for storage and offline analysis. Strict latency and resource requirements limit the size and complexity of these models due to their use in a high-speed trigger setting and deployment on FPGA hardware. It is envisaged that these ML models will be trained on large, carefully tuned, Monte Carlo datasets and subsequently deployed in a real-world detector environment. Not only is there a potentially large difference between the MC training data and real-world conditions but these detector conditions could change over time leading to a shift in model output which could degrade trigger performance. The studies presented explore different techniques to reduce the impact of this effect, using the CMS track finding and vertex trigger algorithms as a test case. The studies compare a baseline retraining and redeployment of the model and episodic training of a model as new data arrives in a continual learning context. The results show that a continually learning algorithm outperforms a simple retrained model when degradation in detector performance is applied to the training data and is a viable option for maintaining performance in an evolving environment such as the High-Luminosity LHC.

Radiation exposure assessment in coronary angiography computed tomography scan: Establishing diagnostic reference level for northeast Assam population

Background/Aim: Coronary angiography computed tomography (CT) scans play a pivotal role in diagnosing cardiovascular diseases, providing crucial information for treatment planning. However, concerns regarding radiation exposure have prompted the need for establishing region-specific diagnostic reference levels (DRLs) to ensure patient safety. This study aimed to assess radiation exposure during coronary angiography CT scans in the northeast Assam population and establish DRLs tailored to this demographic. Methods: A total of 380 patients were referred to the Primus Diagnostic Centre and Heath City Hospital, Guwahati Assam with coronary artery disturbances. Data on the technical parameters used in CT procedures were taken in 2021-2022. Organ and surface dose to specific radiosensitive organs (chest) estimation was done using software imPACT 1.0.4 from the National Radiological Protection Board (NRPB) SR250 Monte Carlo dataset. Results: The study population (n = 380) comprised 190 men and 190 women with an age range from 29 to 75 years. The mean body mass index (BMI) and effective dose (ED) were 22.42 ± 1.06 kg/m 2 and 21.57 ± 4.27 mSv.cm, respectively. The mean the dose-length product (DLP) was 854.67 mSv.cm and the mean ED was 21.57 mSv.cm. The ED for males was 13-27 mSv and 13-29 mSv for females. The DRL for the male population was found to be 24.26 mSv.cm 2 whereas for the female population was 24.69 mSv.cm 2 . Conclusion: This study highlights the necessity of establishing tailored DRLs for coronary angiography CT scans in the northeast Assam population. By doing so, healthcare providers can ensure optimal image quality while minimising radiation exposure, ultimately enhancing patient safety and quality of care. These findings have implications for radiological practice in the region and contribute to the ongoing efforts to standardise radiation doses in medical imaging procedures.

A Monte Carlo dose recalculation pipeline for durable datasets: an I-125 LDR prostate brachytherapy use case

Monte Carlo (MC) dose datasets are valuable for large-scale dosimetric studies. This work aims to build and validate a DICOM-compliant automated MC dose recalculation pipeline with an application to the production of I-125 low dose-rate prostate brachytherapy MC datasets. Built as a self-contained application, the recalculation pipeline ingested clinical DICOM-RT studies, reproduced the treatment into the Monte Carlo simulation, and outputted a traceable and durable dose distribution in the DICOM dose format. MC simulations with TG43-equivalent conditions using both TOPAS and egs_brachy MC codes were compared to TG43 calculations to validate the pipeline. The consistency of the pipeline when generating TG186 simulations was measured by comparing simulations made with both MC codes. Finally, egs_brachy simulations were run on a 240-patient cohort to simulate a large-scale application of the pipeline. Compared to line source TG43 calculations, simulations with both MC codes had more than 90% of voxels with a global difference under ±1%. Differences of 2.1% and less were seen in dosimetric indices when comparing TG186 simulations from both MC codes. The large-scale comparison of egs_brachy simulations with treatment planning system dose calculation seen the same dose overestimation of TG43 calculations showed in previous studies. The MC dose recalculation pipeline built and validated against TG43 calculations in this work efficiently produced durable MC dose datasets. Since the dataset could reproduce previous dosimetric studies within 15 h at a rate of 20 cases per 25 min, the pipeline is a promising tool for future large-scale dosimetric studies.

Application of k-sigma Algorithm Based on Monte Carlo in Outlier Detection

Under the background of intelligent times based on information technology, the analysis and replacement of outliers become particularly critical for databases in the face of massive sequential data streams. In order to improve the effectiveness and practicability of the detection method, this paper determines the abnormal scores of data points in a specific data set by using the K-Sigma algorithm of the Monte Carlo method, adaptively adjusts the k value according to the abnormal scores, and marks the abnormal points.The advantages of the traditional k-sigma algorithm are fast operation speed and theoretical basis, while the disadvantages are that each index dimension is determined independently. However, in industrial production, due to the multi-index dimension of time series data, multiple size indicators of a data set are related to each other. Therefore, it is necessary to comprehensively consider whether an anomaly is abnormal by combining it with the data of other indicators. In addition, when there are no outliers in the data set, the traditional k-sigma algorithm is used to take the mean and variance of the data set as parameters. Due to the limitations of the data set itself, some data points will be mislabeled as outliers. Through the k-sigma algorithm based on the Monte Carlo method, we can effectively solve the above problems.The k-sigma algorithm based on the Monte Carlo method can generate a normal distribution according to the distribution of original data points, and extract a large quantity of data from the distribution to generate Monte Carlo data set. The Mahalanobis distance li from each sample point to the mean in the Monte Carlo data set is calculated and compared with the Mahalanobis distance lj from the samples to be detected to the mean in the original data set. According to the number of sample points satisfying li < lj in the Monte Carlo data set, the value of the parameter kj is determined adaptively, and thus the outliers are determined. We implemented the k-sigma algorithm based on the Monte Carlo method through python, evaluated the effectiveness of the algorithm by accuracy, recall rate, and F1 score, and compared it with some machine learning algorithms. The results verified the feasibility and effectiveness of the algorithm, which can be used for real-time anomaly detection in the energy management database.

Initial Steps towards a Multilevel Functional Principal Components Analysis Model of Dynamical Shape Changes.

In this article, multilevel principal components analysis (mPCA) is used to treat dynamical changes in shape. Results of standard (single-level) PCA are also presented here as a comparison. Monte Carlo (MC) simulation is used to create univariate data (i.e., a single "outcome" variable) that contain two distinct classes of trajectory with time. MC simulation is also used to create multivariate data of sixteen 2D points that (broadly) represent an eye; these data also have two distinct classes of trajectory (an eye blinking and an eye widening in surprise). This is followed by an application of mPCA and single-level PCA to "real" data consisting of twelve 3D landmarks outlining the mouth that are tracked over all phases of a smile. By consideration of eigenvalues, results for the MC datasets find correctly that variation due to differences in groups between the two classes of trajectories are larger than variation within each group. In both cases, differences in standardized component scores between the two groups are observed as expected. Modes of variation are shown to model the univariate MC data correctly, and good model fits are found for both the "blinking" and "surprised" trajectories for the MC "eye" data. Results for the "smile" data show that the smile trajectory is modelled correctly; that is, the corners of the mouth are drawn backwards and wider during a smile. Furthermore, the first mode of variation at level 1 of the mPCA model shows only subtle and minor changes in mouth shape due to sex; whereas the first mode of variation at level 2 of the mPCA model governs whether the mouth is upturned or downturned. These results are all an excellent test of mPCA, showing that mPCA presents a viable method of modeling dynamical changes in shape.

Estimation of Effective Dose in Monte Carlo Simulation Method for CT Coronary Angiography Patients

Computed tomography (CT) scans of Coronary Angiography have significantly aided in the diagnosis of disorders of the coronary artery. In contrast to other radiological examinations, the patient's radiation exposure is notably higher. This study aimed to optimize the radiation dose and estimate the adequate amount in computed tomography (CT) for coronary angiography. A total of 380 patients were referred to the Primus Diagnostic Centre and Heath City Hospital, Guwahati Assam, during the study with coronary artery disturbances. Data on the technical parameters used in CT procedures were taken in 2022. The aim and study's objective was Organ and surface dose to specific radiosensitive organs (Chest) estimation using software imPACT 1.0.4 from National Radiological Protection Board (NRPB) SR250 Monte Carlo dataset. The study population (n = 380) comprised 190 men and 190 women with an average age of 29 to 75 years. The Mean ± SD of BMI and ED are 22.42±1.06 and 21.57±4.27 respectively. The mean DLP is 854.67, and the mean ED is 21.57. The effective doses for males (13- 27) mSv were in females (13-29) mSv. This study was a pioneer in presenting actual amounts of CT examinations in Assam because other countries have already started with more advanced CT procedures, such as dosages for paediatrics, coronary angiography and CT fluoroscopy. With this study, there may be more opportunities to create complex new studies or enhance the data from related studies that may be done in future work. The high precision with minimum risk, the current study can be considered as the need of the hour.

A revised Gaussian pencil beam model for calculation of the in-water dose caused by clinical electron-beam irradiation

Purposes: The dose in water caused by clinical electron-beam irradiation is mainly composed of the doses due to the direct electrons, the indirect electrons, and the contaminant X-rays. In this paper, an analytical method for 3-dimensional (3D) dose calculation in water for the direct electrons and for the direct-plus-indirect electrons is proposed in light of the electron Monte Carlo (eMC) datasets. Methods: The dose calculation was performed for square fields, based on a revised Gaussian pencil beam model, where the parallel beam depth-dose dataset under an infinitely large field was reconstructed from the depth-dose dataset of a fan beam with a finite field. We used a semi-infinite water phantom, setting the beam incident surface on the isocenter plane that is perpendicular to the beam axis. The dose calculation model sets an effective field at each dose calculation depth, where the effective field is larger than the geometrical field that the electron applicator forms on the basis of its divergent spread under the effective source-surface distance (SSDeff). Results and conclusions: By comparing the calculated datasets of depth dose (DD) and off-axis dose (OAD) in water with the eMC datasets for electron beams of E=6, 12, and 18 MeV by Wieslander and Knöös (2006), it has been found that the revised Gaussian pencil beam model is of practical use and has a bright prospect to give almost the same calculation results as the eMC datasets.

Rendered Image Superresolution Reconstruction with Multichannel Feature Network

In the process of film and television production, clear images can give the audience a real sensory experience, but high-resolution images require a massive amount of production time and highly specialized imaging equipment, which is not a cost-effective solution at the moment. To achieve a better cost efficiency during video production, we propose a multichannel featured superresolution network model that utilizes rendered low-resolution images according to their characteristics. This model includes a feature extraction layer, a series of subnetworks, and a reconstruction module. Inside the network model, a series of subnetworks are cascaded to improve the information flow from coarse to fine, which helps to fully extract the depth, normal vector, edge, and texture features from low-resolution rendered images to reconstruct the high-resolution image. Additionally, residual learning is introduced at each stage to further improve the reconstruction performance. We experiment with the model on the classic Disney Monte Carlo datasets and compare it with several related algorithms. The results show that our algorithm is able to reconstruct the image with clearer details and texture. Thus, our research not only helps to maintain the audience’s sensory experience but also increases the efficiency of film and television production, which also brings considerable economic benefits.

A unified formalism for estimating photon absorbed fractions in spherical biovolumes: analytical equations without fitting parameters

A new analytical formalism, previously developed for estimating electron-absorbed fractions, was extended for estimating photon absorbed fractions in soft-tissue spheres, containing uniformly distributed photon-emitter. Analytical equations were formulated for calculating values of photon-absorbed fractions. The method involves a rescaling procedure with transformation of real biological sizes to unitless effective ones, combining information of photon energy, object’s size, and material. Rescaling was applied to large published datasets of photon absorbed fractions in soft-tissue spheres, computed with Monte Carlo codes. A new effect was demonstrated in which the rescaled data formed a single smooth ‘unified curve’ with saturation. The unified curve for photon absorbed fractions was described analytically, using simple equations without fitting parameters. The new method was tested for a wide range of spheres—from 1 mg up to 1000 kg, and wide range of photon energies—from 0.02 up to 5 MeV. For larger spheres, a close agreement between analytical values and Monte Carlo datasets was demonstrated. For small biovolumes, analytical equations predict higher values than available Monte Carlo data. The unified formalism is now available for direct calculating radiation absorbed fractions in soft-tissue spherical organs and organisms without Monte Carlo codes.

Knot probabilities in equilateral random polygons

We consider the probability of knotting in equilateral random polygons in Euclidean three-dimensional space, which model, for instance, random polymers. Results from an extensive Monte Carlo dataset of random polygons indicate a universal scaling formula for the knotting probability with the number of edges. This scaling formula involves an exponential function, independent of knot type, with a power law factor that depends on the number of prime components of the knot. The unknot, appearing as a composite knot with zero components, scales with a small negative power law, contrasting with previous studies that indicated a purely exponential scaling. The methodology incorporates several improvements over previous investigations: our random polygon data set is generated using a fast, unbiased algorithm, and knotting is detected using an optimised set of knot invariants based on the Alexander polynomial.

MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks

In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum. In view of the planned high-luminosity upgrade of the CERN Large Hadron Collider (LHC), it is necessary to revisit existing reconstruction algorithms and ensure that both the physics and computational performance are sufficient in an environment with many simultaneous proton–proton interactions (pileup). Machine learning may offer a prospect for computationally efficient event reconstruction that is well-suited to heterogeneous computing platforms, while significantly improving the reconstruction quality over rule-based algorithms for granular detectors. We introduce MLPF, a novel, end-to-end trainable, machine-learned particle-flow algorithm based on parallelizable, computationally efficient, and scalable graph neural network optimized using a multi-task objective on simulated events. We report the physics and computational performance of the MLPF algorithm on a Monte Carlo dataset of top quark–antiquark pairs produced in proton–proton collisions in conditions similar to those expected for the high-luminosity LHC. The MLPF algorithm improves the physics response with respect to a rule-based benchmark algorithm and demonstrates computationally scalable particle-flow reconstruction in a high-pileup environment.

Using deep learning to enhance event geometry reconstruction for the telescope array surface detector

The extremely low flux of ultra-high energy cosmic rays (UHECR) makes their direct observation by orbital experiments practically impossible. For this reason all current and planned UHECR experiments detect cosmic rays indirectly by observing the extensive air showers (EAS) initiated by cosmic ray particles in the atmosphere. The world largest statistics of the ultra-high energy EAS events is recorded by the networks of surface stations. In this paper we consider a novel approach for reconstruction of the arrival direction of the primary particle based on the deep convolutional neural network. The latter is using raw time-resolved signals of the set of the adjacent trigger stations as an input. The Telescope Array (TA) Surface Detector (SD) is an array of 507 stations, each containing two layers plastic scintillator with an area of 3 m2. The training of the model is performed with the Monte-Carlo dataset. It is shown that within the Monte-Carlo simulations, the new approach yields better resolution than the traditional reconstruction method based on the fitting of the EAS front. The details of the network architecture and its optimization for this particular task are discussed.

Comparing loss functions and interval estimates for survival data

We compare parameter point and interval estimates based on the symmetric bounded loss function, as used in the Add-my-Pet collection on animal energetics, with the maximum likelihood method for number of surviving individuals as function of time. The aging module of Dynamic Energy Budget theory is used to generate Monte Carlo data sets. The simulations show that estimates based on the symmetric loss function give almost the same results in terms of point as well as interval estimates, compared to maximum likelihood estimation, while this loss function avoids the need to model the stochastic component of data sets. For most data types on energetics, we don’t have such stochastic models, so maximum likelihood methods cannot be used. Our findings support the view that model plasticity dominates interval estimates, rather than the detailed structure of the stochastic component.

Using Deep Learning in Ultra-High Energy Cosmic Ray Experiments

The extremely low flux of ultra-high energy cosmic rays (UHECR) makes their direct observation by orbital experiments practically impossible. For this reason all current and planned UHECR experiments detect cosmic rays indirectly by observing the extensive air showers (EAS) initiated by cosmic ray particles in the atmosphere. Various types of shower observables are analyzed in the modern UHECR experiments including a secondary radio signal and fluorescent light from the excited nitrogen molecules. Most of the data is collected by the network of surface stations which allows to measure the lateral EAS profile. The raw observables in this case are the time-resolved signals for the set of adjacent triggered stations. The Monte Carlo shower simulation is performed in order to recover the primary particle properties. In traditional techniques the MC simulation is used to fit some synthetic observables such as the shower rise time, the shower front curvature and the particle density normalized to a given distance from the core. In this talk we’ll consider an alternative approach based on the deep convolutional neural network trained on a large Monte-Carlo dataset, using the detector signal time series as an input. The above approach has proven its efficiency with the Monte-Carlo simulations of the Telescope Array Observatory surface detector. We’ll discuss in detail how the network architecture is optimized for this particular task.

A DNA damage multiscale model for NTCP in proton and hadron therapy.

To develop a first principle and multiscale model for normal tissue complication probability (NTCP) as a function of dose and LET for proton and in general for particle therapy with a goal of incorporating nanoscale radio-chemical to macroscale cell biological pathways, spanning from initial DNA damage to tissue late effects. The method is a combination of analytical and multiscale computational steps including (a) derivation of functional dependencies of NTCP on DNA-driven cell lethality in nanometer and mapping to dose and LET in millimeter, and (b) three-dimensional-surface fitting to Monte Carlo data set generated based on postradiation image change and gathered for a cohort of 14 pediatric patients treated by scanning beam of protons for ependymoma. We categorize voxel-based dose and LET associated with development of necrosis in NTCP. Our model fits well the clinical data, generated for postradiation tissue toxicity and necrosis. The fitting procedure results in extraction of in vivo radio-biological α-β indices and their numerical values. The NTCP model, explored in this work, allows to correlate the tissue toxicities to DNA initial damage, cell lethality and the properties and qualities of radiation, dose, and LET.

A response-matrix-centred approach to presenting cross-section measurements

The current canonical approach to publishing cross-section data is to unfold the reconstructed distributions. Detector effects like efficiency and smearing are undone mathematically, yielding distributions in true event properties. This is an ill-posed problem, as even small statistical variations in the reconstructed data can lead to large changes in the unfolded spectra. This work presents an alternative or complementary approach: the response-matrix-centred forward-folding approach. It offers a convenient way to forward-fold model expectations in truth space to reconstructed quantities. These can then be compared to the data directly, similar to what is usually done with full detector simulations within the experimental collaborations. For this, the detector response (efficiency and smearing) is parametrised as a matrix. The effects of the detector on the measurement of a given model is simulated by simply multiplying the binned truth expectation values by this response matrix. Systematic uncertainties in the detector response are handled by providing a set of matrices according to the prior distribution of the detector properties and marginalising over them. Background events can be included in the likelihood calculation by giving background events their own bins in truth space. To facilitate a straight-forward use of response matrices, a new software framework has been developed: the Response Matrix Utilities (\\ReMU). \\ReMU is a Python package distributed via the Python Package Index. It only uses widely available, standard scientific Python libraries and does not depend on any custom experiment-specific software. It offers all methods needed to build response matrices from Monte Carlo data sets, use the response matrix to forward-fold truth-level model predictions, and compare the predictions to real data using Bayesian or frequentist statistical inference.

Simulating the LHCb hadron calorimeter with generative adversarial networks

Generative adversarial networks are known as a tool for fast simulation of data. Our aim is to research and develop a physical application of these tools by simulating LHCb hadron calorimeter (HCAL) in order to speed up the Monte Carlo datasets production.