EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets

With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generative machine learning models enable fast event generation, yet so far these approaches are largely constrained to fixed data structures and rigid detector geometries. In this paper, we introduce EPiC-GAN - equivariant point cloud generative adversarial network - which can produce point clouds of variable multiplicity. This flexible framework is based on deep sets and is well suited for simulating sprays of particles called jets. The generator and discriminator utilize multiple EPiC layers with an interpretable global latent vector. Crucially, the EPiC layers do not rely on pairwise information sharing between particles, which leads to a significant speed-up over graph- and transformer-based approaches with more complex relation diagrams. We demonstrate that EPiC-GAN scales well to large particle multiplicities and achieves high generation fidelity on benchmark jet generation tasks.Comment: 18 pages, 8 figures, 3 tables, code available at: https://github.com/uhh-pd-ml/EPiC-GA

Satisfaction of trauma-affected refugees treated with antidepressants and Cognitive Behavioural Therapy

Purpose: This study seeks to evaluate the satisfaction of trauma-affected refugees after treatment with antidepressants, psycho-education and flexible Cognitive Behavioral Therapy (CBT) including trauma exposure. Material and methods: A treatment satisfaction questionnaire was completed by patients at the end of a randomised controlled trial (RCT) comparing treatment with CBT and antidepressants. A patient satisfaction score was developed based on the questionnaire, and predictors of satisfaction were analysed in regression models. Telephone interviews were conducted with patients dropping out of treatment before the end of the trial. Results: In total, 193 trauma-affected refugees with PTSD were included in the study. Patients were overall satisfied with flexible CBT including exposure treatment in cases where this was part of the treatment. There was no statistically significant association between treatment outcome and satisfaction and satisfaction and treatment efficacy were independent of each other. The results showed that bi-cultural patients who had lived in Denmark for more than a decade were satisfied with the treatment based on a western psychotherapy model. Discussion: Treatment with selective serotonin reuptake inhibitor and flexible CBT, including trauma exposure, is acceptable for trauma-affected refugees. More studies are needed to evaluate patient satisfaction with western psychotherapy models in refugee patients who have recently arrived and to compare satisfaction with alternative treatment models

Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information

We introduce the first generative model trained on the JetClass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizing flow (CNF) trained with the flow matching technique. It is conditioned on the jet type, so that a single model can be used to generate the ten different jet types of JetClass. For the first time, we also introduce a generative model that goes beyond the kinematic features of jet constituents. The JetClass dataset includes more features, such as particle-ID and track impact parameter, and we demonstrate that our CNF can accurately model all of these additional features as well. Our generative model for JetClass expands on the versatility of existing jet generation techniques, enhancing their potential utility in high-energy physics research, and offering a more comprehensive understanding of the generated jets

Follow-up study of the treatment outcomes at a psychiatric trauma clinic for refugees

Purpose: To describe change in mental health after treatment with antidepressants and trauma-focused cognitive behavioral therapy. Methods: Patients receiving treatment at the Psychiatric Trauma Clinic for Refugees in Copenhagen completed self-ratings of level of functioning, quality of life, and symptoms of PTSD, depression, and anxiety before and after treatment. Changes in mental state and predictors of change were evaluated in a sample that all received well-described and comparable treatment. Results: 85 patients with PTSD or depression were included in the analysis. Significant improvement and effect size were observed on all rating scales (p-value <0.01 and Cohen’s d 45-0.68). Correlation analysis showed no association between severity of symptoms at baseline and the observed change. Conclusion: Despite methodological limitations, the finding of a significant improvement on all rating scales is important considering that previous follow-up studies of comparable patient populations have not found significant change in the patients’ condition after treatment

Full phase space resonant anomaly detection

Physics beyond the Standard Model that is resonant in one or more dimensions has been a longstanding focus of countless searches at colliders and beyond. Recently, many new strategies for resonant anomaly detection have been developed, where sideband information can be used in conjunction with modern machine learning, in order to generate synthetic datasets representing the Standard Model background. Until now, this approach was only able to accommodate a relatively small number of dimensions, limiting the breadth of the search sensitivity. Using recent innovations in point cloud generative models, we show that this strategy can also be applied to the full phase space, using all relevant particles for the anomaly detection. As a proof of principle, we show that the signal from the R&D dataset from the LHC Olympics is findable with this method, opening up the door to future studies that explore the interplay between depth and breadth in the representation of the data for anomaly detection

Cognitive behavioral psychotherapeutic treatment at a psychiatric trauma clinic for refugees: description and evaluation

Introduction: Cognitive behavioural therapy (CBT) with trauma focus is the most evidence supported psychotherapeutic treatment of PTSD, but few CBT treatments for traumatized refugees have been described in detail. Purpose: To describe and evaluate a manualized cognitive behavioral therapy for traumatized refugees incorporating exposure therapy, mindfulness and acceptance, and commitment therapy. Material and methods: 85 patients received six months’ treatment at a Copenhagen Trauma Clinic for Refugees and completed self-ratings before and after treatment. The treatment administered to each patient was monitored in detail. The changes in mental state and the treatment components associated with change in state were analyzed statistically. Results: Despite the low level of functioning and high co-morbidity of patients, 42% received highly structured CBT, which was positively associated with all treatment outcomes. The more methods used and the more time each method was used, the better the outcome. The majority of patients were able to make homework assignments and this was associated with better treatment outcome. Correlation analysis showed no association between severity of symptoms at baseline and the observed change. Conclusion: The study suggests that CBT treatment incorporating mindfulness and acceptance and commitment therapy is promising for traumatized refugees and punctures the myth that this group of patients are unable to participate fully in structured CBT. However, treatment methods must be adapted to the special needs of refugees and trauma exposure should be further investigated.&nbsp

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

CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation

Fast simulation of the energy depositions in high-granular detectors is needed for future collider experiments with ever-increasing luminosities. Generative machine learning (ML) models have been shown to speed up and augment the traditional simulation chain in physics analysis. However, the majority of previous efforts were limited to models relying on fixed, regular detector readout geometries. A major advancement is the recently introduced CaloClouds model, a geometry-independent diffusion model, which generates calorimeter showers as point clouds for the electromagnetic calorimeter of the envisioned International Large Detector (ILD). In this work, we introduce CaloClouds II which features a number of key improvements. This includes continuous time score-based modelling, which allows for a 25-step sampling with comparable fidelity to CaloClouds while yielding a $6\times$ speed-up over Geant4 on a single CPU ($5\times$ over CaloClouds). We further distill the diffusion model into a consistency model allowing for accurate sampling in a single step and resulting in a $46\times$ ($37\times$ over CaloClouds) speed-up. This constitutes the first application of consistency distillation for the generation of calorimeter showers.Comment: 30 pages, 7 figures, 3 tables, code available at https://github.com/FLC-QU-hep/CaloClouds-

Shared Data and Algorithms for Deep Learning in Fundamental Physics

We introduce a collection of datasets from fundamental physics research -- including particle physics, astroparticle physics, and hadron- and nuclear physics -- for supervised machine learning studies. These datasets, containing hadronic top quarks, cosmic-ray induced air showers, phase transitions in hadronic matter, and generator-level histories, are made public to simplify future work on cross-disciplinary machine learning and transfer learning in fundamental physics. Based on these data, we present a simple yet flexible graph-based neural network architecture that can easily be applied to a wide range of supervised learning tasks in these domains. We show that our approach reaches performance close to state-of-the-art dedicated methods on all datasets. To simplify adaptation for various problems, we provide easy-to-follow instructions on how graph-based representations of data structures, relevant for fundamental physics, can be constructed and provide code implementations for several of them. Implementations are also provided for our proposed method and all reference algorithms.Comment: 13 pages, 5 figures, 5 table

Alignment of the CMS tracker with LHC and cosmic ray data

© CERN 2014 for the benefit of the CMS collaboration, published under the terms of the Creative Commons Attribution 3.0 License by IOP Publishing Ltd and Sissa Medialab srl. Any further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation and DOI.The central component of the CMS detector is the largest silicon tracker ever built. The precise alignment of this complex device is a formidable challenge, and only achievable with a significant extension of the technologies routinely used for tracking detectors in the past. This article describes the full-scale alignment procedure as it is used during LHC operations. Among the specific features of the method are the simultaneous determination of up to 200 000 alignment parameters with tracks, the measurement of individual sensor curvature parameters, the control of systematic misalignment effects, and the implementation of the whole procedure in a multi-processor environment for high execution speed. Overall, the achieved statistical accuracy on the module alignment is found to be significantly better than 10μm