Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed

Accurate simulation of physical processes is crucial for the success of modern particle physics. However, simulating the development and interaction of particle showers with calorimeter detectors is a time consuming process and drives the computing needs of large experiments at the LHC and future colliders. Recently, generative machine learning models based on deep neural networks have shown promise in speeding up this task by several orders of magnitude. We investigate the use of a new architecture -- the Bounded Information Bottleneck Autoencoder -- for modelling electromagnetic showers in the central region of the Silicon-Tungsten calorimeter of the proposed International Large Detector. Combined with a novel second post-processing network, this approach achieves an accurate simulation of differential distributions including for the first time the shape of the minimum-ionizing-particle peak compared to a full GEANT4 simulation for a high-granularity calorimeter with 27k simulated channels. The results are validated by comparing to established architectures. Our results further strengthen the case of using generative networks for fast simulation and demonstrate that physically relevant differential distributions can be described with high accuracy.Comment: 17 pages, 12 figure

New Angles on Fast Calorimeter Shower Simulation

The demands placed on computational resources by the simulation requirements of high energy physics experiments motivate the development of novel simulation tools. Machine learning based generative models offer a solution that is both fast and accurate. In this work we extend the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture, designed for the simulation of particle showers in highly granular calorimeters, in two key directions. First, we generalise the model to a multi-parameter conditioning scenario, while retaining a high degree of physics fidelity. In a second step, we perform a detailed study of the effect of applying a state-of-the-art particle flow-based reconstruction procedure to the generated showers. We demonstrate that the performance of the model remains high after reconstruction. These results are an important step towards creating a more general simulation tool, where maintaining physics performance after reconstruction is the ultimate target.Comment: 26 pages, 19 figure

Migration-related emotional distress among Vietnamese psychiatric patients in Germany: An interdisciplinary, mixed methods study

Culture and socialization influence how individuals perceive and express emotional distress. Research therefore, must consider the context to capture individual experiences. However, the majority of studies on factors associated with emotional distress among migrants use quantitative approaches, limiting an in-depth understanding. This study investigates emic themes of emotional distress among Vietnamese migrants by integrating anthropological and psychiatric approaches. The mixed methods study first quantified differences in reported themes of distress between Vietnamese (n = 104) and German (n = 104) patients, who utilized two psychiatric outpatient clinics in Berlin, Germany. Based on these differences, ethnographic interviews were conducted with 20 Vietnamese patients. In the quantitative part, differences in frequency of reported distress between Vietnamese and German patients indicate cultural and migration-related issues among Vietnamese migrants, such as the upbringing of children in a transcultural context. In the qualitative part, interviews with Vietnamese patients elicited contextualizing information and additional themes of distress. Besides commonly expressed socioeconomic themes, such as work and finances, we identified affectively charged themes concerning roles toward partnership and children. A central emic theme is expressed as “moments of speechlessness,” which go beyond a lack of language proficiency and challenge patients in different spheres of life. Migration entails complex affective dynamics, determined by a specific migratory and post-migratory context. Within this context, norms and values determine which themes of distress patients articulate openly. Therefore, an interdisciplinary, mixed-methods approach can yield a contextualized understanding of emotional distress and the complex nature of migration

Digital rectal examination skills: first training experiences, the motives and attitudes of standardized patients

Background: Physical clinical examination is a core clinical competence of medical doctors. In this regard, digital rectal examination (DRE) plays a central role in the detection of abnormalities of the anus and rectum. However, studies in undergraduate medical students as well as newly graduated doctors show that they are insufficiently prepared for performing DRE. Training units with Standardized Patients (SP) represent one method to deliver DRE skills. As yet, however, it is little known about SPs’ attitudes. Methods: This is a qualitative study using a grounded theory approach. Interviews were conducted with 4 standardized patients about their experiences before, during and after structured SP training to deliver DRE competencies to medical students. The resulting data were subjected to thematic content analysis. Results: Results show that SPs do not have any predominant motives for DRE program participation. They participate in the SP training sessions with relatively little prejudice and do not anticipate feeling highly vulnerable within teaching sessions with undergraduate medical students. Conclusions: The current study examined SPs’ motives, views, expectations and experiences regarding a DRE program during their first SP training experiences. The results enabled us to derive distinct action guidelines for the recruitment, informing and briefing of SPs who are willing to participate in a DRE program

Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network

Given the increasing data collection capabilities and limited computing resources of future collider experiments, interest in using generative neural networks for the fast simulation of collider events is growing. In our previous study, the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture for generating photon showers in a high-granularity calorimeter showed a high accuracy modeling of various global differential shower distributions. In this work, we investigate how the BIB-AE encodes this physics information in its latent space. Our understanding of this encoding allows us to propose methods to optimize the generation performance further, for example, by altering latent space sampling or by suggesting specific changes to hyperparameters. In particular, we improve the modeling of the shower shape along the particle incident axis

Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network

Given the increasing data collection capabilities and limited computing resources of future collider experiments, interest in using generative neural networks for the fast simulation of collider events is growing. In our previous study, the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture for generating photon showers in a high-granularity calorimeter showed a high accuracy modeling of various global differential shower distributions. In this work, we investigate how the BIB-AE encodes this physics information in its latent space. Our understanding of this encoding allows us to propose methods to optimize the generation performance further, for example, by altering latent space sampling or by suggesting specific changes to hyperparameters. In particular, we improve the modeling of the shower shape along the particle incident axis

Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed

Accurate simulation of physical processes is crucial for the success of modern particle physics. However, simulating the development and interaction of particle showers with calorimeter detectors is a time consuming process and drives the computing needs of large experiments at the LHC and future colliders. Recently, generative machine learning models based on deep neural networks have shown promise in speeding up this task by several orders of magnitude. We investigate the use of a new architecture—the Bounded Information Bottleneck Autoencoder—for modelling electromagnetic showers in the central region of the Silicon-Tungsten calorimeter of the proposed International Large Detector. Combined with a novel second post-processing network, this approach achieves an accurate simulation of differential distributions including for the first time the shape of the minimum-ionizing-particle peak compared to a full Geant4 simulation for a high-granularity calorimeter with 27k simulated channels. The results are validated by comparing to established architectures. Our results further strengthen the case of using generative networks for fast simulation and demonstrate that physically relevant differential distributions can be described with high accuracy

New Angles on Fast Calorimeter Shower Simulation

The demands placed on computational resources by the simulation requirements of high energy physics experiments motivate the development of novel simulation tools. Machine learning based generative models offer a solution that is both fast and accurate. In this work we extend the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture, designed for the simulation of particle showers in highly granular calorimeters, in two key directions. First, we generalise the model to a multi-parameter conditioning scenario, while retaining a high degree of physics fidelity. In a second step, we perform a detailed study of the effect of applying a state-of-the-art particle flow-based reconstruction procedure to the generated showers. We demonstrate that the performance of the model remains high after reconstruction. These results are an important step towards creating a more general simulation tool, where maintaining physics performance after reconstruction is the ultimate target

CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation

Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models would enable them to augment traditional simulations and alleviate a major computing constraint. This work achieves a major breakthrough in this task by, for the first time, directly generating a point cloud of a few thousand space points with energy depositions in the detector in 3D space without relying on a fixed-grid structure. This is made possible by two key innovations: i) using recent improvements in generative modeling we apply a diffusion model to generate ii) an initial even higher-resolution point cloud of up to 40,000 so-called Geant4 steps which is subsequently down-sampled to the desired number of up to 6,000 space points. We showcase the performance of this approach using the specific example of simulating photon showers in the planned electromagnetic calorimeter of the International Large Detector (ILD) and achieve overall good modeling of physically relevant distributions.Comment: 25 pages, 11 figure