arXiv subjects

Isabell Melzer-Pellmann

Publications and source records attributed to Isabell Melzer-Pellmann.

5 recordsLinked to original sources

arXiv2026

Charged long-lived particles in the GMSB scenario at the Future Circular Collider (FCC-ee)

Long-lived particles have emerged as a compelling signature of physics beyond the standard model, offering unique discovery opportunities at current and future colliders. We present an analysis of charged long-lived particles leading to signatures with kinked tracks and displaced vertices. Our study is motivated by the gauge mediated supersymmetry breaking model (GMSB) within the minimal supersymmetric standard model scenario, in which the lightest supersymmetric particle is a gravitino and the next-to-lightest supersymmetric particle is the stau, the superpartner of the tau lepton. The stau is predicted to be long-lived due to the small coupling between it and the gravitino. This work presents a phenomenological study of such long-lived staus with lifetimes between 20~cm and 20~m and explores their discovery prospects at the Future Circular Collider (FCC-ee), a proposed $e^{+}e^{-}$ collider. The results highlight the sensitivity to the GMSB model.

hep-ph↗

arXiv2024

Pay Attention To Mean Fields For Point Cloud Generation

Collider data generation with machine learning has become increasingly popular in particle physics due to the high computational cost of conventional Monte Carlo simulations, particularly for future high-luminosity colliders. We propose a generative model for point clouds that employs an attention-based aggregation while preserving a linear computational complexity with respect to the number of points. The model is trained in an adversarial setup, ensuring input permutation equivariance and invariance for the generator and critic, respectively. To stabilize known unstable adversarial training, a feature matching loss is introduced. We evaluate the performance on two different datasets. The former is the top-quark \textsc{JetNet150} dataset, where the model outperforms the current state-of-the-art GAN-based model, despite having significantly fewer parameters. The latter is dataset 2 of the CaloChallenge, which comprises point clouds with up to $30\times$ more points compared to the first dataset. The model and its corresponding code are available at \url{https://github.com/kaechb/MDMA/tree/NeurIPS}.

hep-ex↗

arXiv2023

Attention to Mean-Fields for Particle Cloud Generation

The generation of collider data using machine learning has emerged as a prominent research topic in particle physics due to the increasing computational challenges associated with traditional Monte Carlo simulation methods, particularly for future colliders with higher luminosity. Although generating particle clouds is analogous to generating point clouds, accurately modelling the complex correlations between the particles presents a considerable challenge. Additionally, variable particle cloud sizes further exacerbate these difficulties, necessitating more sophisticated models. In this work, we propose a novel model that utilizes an attention-based aggregation mechanism to address these challenges. The model is trained in an adversarial training paradigm, ensuring that both the generator and critic exhibit permutation equivariance/invariance with respect to their input. A novel feature matching loss in the critic is introduced to stabilize the training. The proposed model performs competitively to the state-of-art whilst having significantly fewer parameters.

hep-ex↗

arXiv2022

JetFlow: Generating Jets with Conditioned and Mass Constrained Normalising Flows

Fast data generation based on Machine Learning has become a major research topic in particle physics. This is mainly because the Monte Carlo simulation approach is computationally challenging for future colliders, which will have a significantly higher luminosity. The generation of collider data is similar to point cloud generation with complex correlations between the points. In this study, the generation of jets with up to 30 constituents with Normalising Flows using Rational Quadratic Spline coupling layers is investigated. Without conditioning on the jet mass, our Normalising Flows are unable to model all correlations in data correctly, which is evident when comparing the invariant jet mass distributions between ground truth and generated data. Using the invariant mass as a condition for the coupling transformation enhances the performance on all tracked metrics. In addition, we demonstrate how to sample the original mass distribution by interpolating the empirical cumulative distribution function. Similarly, the variable number of constituents is taken care of by introducing an additional condition on the number of constituents in the jet. Furthermore, we study the usefulness of including an additional mass constraint in the loss term. On the \texttt{JetNet} dataset, our model shows state-of-the-art performance combined with fast and stable training.

hep-ex↗

arXiv2022

Point Cloud Generation using Transformer Encoders and Normalising Flows

Data generation based on Machine Learning has become a major research topic in particle physics. This is due to the current Monte Carlo simulation approach being computationally challenging for future colliders, which will have a significantly higher luminosity. The generation of collider data is similar to point cloud generation, but arguably more difficult as there are complex correlations between the points which need to be modelled correctly. A refinement model consisting of normalising flows and transformer encoders is presented. The normalising flow output is corrected by a transformer encoder, which is adversarially trained against another transformer encoder discriminator/critic. The model reaches state-of-the-art performance while yielding a stable training.

hep-ex↗