Publications (36)
Learning representations of irregular particle-detector geometry with distance-weighted graph networks
Shah Rukh Qasim, Jan Kieseler, Yutaro Iiyama +1
We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction. Thanks to their representation-learning capabilities, grap…
A method and tool for combining differential or inclusive measurements obtained with simultaneously constrained uncertainties
Jan Kieseler
A method is discussed that allows combining sets of differential or inclusive measurements. It is assumed that at least one measurement was obtained with simultaneously fitting a s…
On the Utility Function of Experiments in Fundamental Science
Tommaso Dorigo, Michele Doro, Max Aehle +7
The majority of experiments in fundamental science today are designed to be multi-purpose: their aim is not simply to measure a single physical quantity or process, but rather to e…
Efficient Forward-Mode Algorithmic Derivatives of Geant4
Max Aehle, Xuan Tung Nguyen, Mihály Novák +5
We have applied an operator-overloading forward-mode algorithmic differentiation tool to the Monte-Carlo particle simulation toolkit Geant4. Our differentiated version of Geant4 al…
End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks
Shah Rukh Qasim, Nadezda Chernyavskaya, Jan Kieseler +4
We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-lum…
Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper
Tommaso Dorigo, Andrea Giammanco, Pietro Vischia +33
The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, given the large dimensi…
End-to-End Detector Optimization with Diffusion models: A Case Study in Sampling Calorimeters
Kylian Schmidt, Nikhil Kota, Jan Kieseler +16
Recent advances in machine learning have opened new avenues for optimizing detector designs in high-energy physics, where the complex interplay of geometry, materials, and physics…
Isothermal annealing of radiation defects in bulk material of diodes from 8" silicon wafers
Jan Kieseler, Pedro Goncalo Dias Almeida, Oliwia Kaluzinska +4
The high luminosity upgrade of the LHC will provide unique physics opportunities, such as the observation of rare processes and precision measurements. However, the accompanying ha…
Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning
Tobias Kortus, Ralf Keidel, Nicolas R. Gauger +1
Reinforcement learning demonstrated immense success in modelling complex physics-driven systems, providing end-to-end trainable solutions by interacting with a simulated or real en…
Running of the top quark mass at NNLO in QCD
Matteo M. Defranchis, Jan Kieseler, Katerina Lipka +1
The running of the top quark mass () is probed at the next-to-next-to-leading order in quantum chromodynamics for the first time. The result is obtained by comparing…
Toward the End-To-End Optimization of the SWGO Array Layout
Tommaso Dorigo, Max Aehle, Cornelia Arcaro +13
In this document we consider the problem of finding the optimal layout for the array of water Cherenkov detectors proposed by the SWGO collaboration to study very-high-energy gamma…
Temperature dependence of the long-term annealing behavior of neutron irradiated diodes from 8-inch p-type silicon wafers
Leena Diehl, Oliwia Kaluzinska, Marie Mühlnikel +7
To face the higher levels of radiation due to the 10-fold increase in integrated luminosity during the High-Luminosity LHC, the CMS detector will replace the current Calorimeter En…
Deep Regression of Muon Energy with a K-Nearest Neighbor Algorithm
T. Dorigo, Sofia Guglielmini, Jan Kieseler +2
Within the context of studies for novel measurement solutions for future particle physics experiments, we developed a performant kNN-based regressor to infer the energy of highly-r…
Fast convolutional neural networks for identifying long-lived particles in a high-granularity calorimeter
Juliette Alimena, Yutaro Iiyama, Jan Kieseler
We present a first proof of concept to directly use neural network based pattern recognition to trigger on distinct calorimeter signatures from displaced particles, such as those t…
Multi-particle reconstruction in the High Granularity Calorimeter using object condensation and graph neural networks
Shah Rukh Qasim, Kenneth Long, Jan Kieseler +2
The high-luminosity upgrade of the LHC will come with unprecedented physics and computing challenges. One of these challenges is the accurate reconstruction of particles in events…
Classifier Surrogates: Sharing AI-based Searches with the World
Sebastian Bieringer, Gregor Kasieczka, Jan Kieseler +1
In recent years, neural network-based classification has been used to improve data analysis at collider experiments. While this strategy proves to be hugely successful, the underly…
Optimising longitudinal and lateral calorimeter granularity for software compensation in hadronic showers using deep neural networks
Coralie Neubüser, Jan Kieseler, Paul Lujan
We investigate the effect of longitudinal and transverse calorimeter segmentation on event-by-event software compensation for hadronic showers. To factorize out sampling and electr…
Jet Flavour Classification Using DeepJet
Emil Bols, Jan Kieseler, Mauro Verzetti +2
Jet flavour classification is of paramount importance for a broad range of applications in modern-day high-energy-physics experiments, particularly at the LHC. In this paper we pro…
Les Houches guide to reusable ML models in LHC analyses
Jack Y. Araz, Andy Buckley, Gregor Kasieczka +10
With the increasing usage of machine-learning in high-energy physics analyses, the publication of the trained models in a reusable form has become a crucial question for analysis p…
Detector-aware target definitions for full-event particle reconstruction
Katharina Schäuble, Alessandro Brusamolino, Dolores Garcia +1
Hit-level ML-based particle reconstruction methods have recently shown promising results. However, the reconstruction models are currently provided with targets that are unaware of…
FastGraph: Optimized GPU-Enabled Algorithms for Fast Graph Building and Message Passing
Aarush Agarwal, Raymond He, Jan Kieseler +2
We introduce FastGraph, a novel GPU-optimized k-nearest neighbor algorithm specifically designed to accelerate graph construction in low-dimensional spaces (2-10 dimensions), criti…
Calibration of the Top-Quark Monte-Carlo Mass
Jan Kieseler, Katerina Lipka, Sven-Olaf Moch
We present a method to establish experimentally the relation between the top-quark mass as implemented in Monte-Carlo generators and the Lagrangian mass parameter …
On the Codesign of Scientific Experiments and Industrial Systems
Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas +84
The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for in…
TomOpt: Differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography
Giles C. Strong, Maxime Lagrange, Aitor Orio +11
We describe a software package, TomOpt, developed to optimise the geometrical layout and specifications of detectors designed for tomography by scattering of cosmic-ray muons. The…
Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data
Jan Kieseler
High-energy physics detectors, images, and point clouds share many similarities in terms of object detection. However, while detecting an unknown number of objects in an image is w…
GNN-based end-to-end reconstruction in the CMS Phase 2 High-Granularity Calorimeter
Saptaparna Bhattacharya, Nadezda Chernyavskaya, Saranya Ghosh +11
We present the current stage of research progress towards a one-pass, completely Machine Learning (ML) based imaging calorimeter reconstruction. The model used is based on Graph Ne…
Detecting long-lived particles trapped in detector material at the LHC
Jan Kieseler, Juliette Alimena, Jasmine Simms +3
We propose to implement a two-stage detection strategy for exotic long-lived particles that could be produced at the CERN LHC, become trapped in detector material, and decay later.…
End-to-End Optimal Detector Design with Mutual Information Surrogates
Kinga Anna Wozniak, Stephen Mulligan, Jan Kieseler +3
We introduce a novel approach for end-to-end black-box optimization of high energy physics (HEP) detectors using local deep learning (DL) surrogates. These surrogates approximate a…
Neuromorphic Readout for Hadron Calorimeters
Enrico Lupi, Abhishek, Max Aehle +17
We simulate hadrons impinging on a homogeneous lead-tungstate (PbWO4) calorimeter to investigate how the resulting light yield and its temporal structure, as detected by an array o…
An Optimal Observable Machine for reinterpretable measurements in high-energy physics
Torben Mohr, Alejandro Quiroga Triviño, Fabian Riemer +6
A machine-learning-based framework for constructing generator-level observables optimized for parameter extraction in particle physics analyses is introduced, referred to as the Op…
Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming
Max Aehle, Lorenzo Arsini, R. Belén Barreiro +27
In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models…
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics
Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta +19
Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering…
Calorimetric Measurement of Multi-TeV Muons via Deep Regression
Jan Kieseler, Giles C. Strong, Filippo Chiandotto +2
The performance demands of future particle-physics experiments investigating the high-energy frontier pose a number of new challenges, forcing us to find improved solutions for the…
Muon Energy Measurement from Radiative Losses in a Calorimeter for a Collider Detector
Tommaso Dorigo, Jan Kieseler, Lukas Layer +1
The performance demands of future particle-physics experiments investigating the high-energy frontier pose a number of new challenges, forcing us to find new solutions for the dete…
Hadron Identification Prospects With Granular Calorimeters
Andrea De Vita, Abhishek, Max Aehle +15
In this work we consider the problem of determining the identity of hadrons at high energies based on the topology of their energy depositions in dense matter, along with the time…
Annealing behaviour of charge collection of neutron irradiated diodes from 8-inch p-type silicon wafers
Oliwia Agnieszka KaÅuziÅska, Leena Diehl, Eva Sicking +6
To face the higher levels of radiation due to the 10-fold increase in integrated luminosity during the High-Luminosity LHC, the CMS detector will replace the current Calorimeter En…