Dr Matthias Komm
Physicist, Big Data, Artificial Intelligence, Supercomputing, Simulation
Like to know more?
Get in touch

Welcome

I'm a postdoctoral researcher specializing in experimental High-Energy Physics (HEP), focusing on the study of fundamental particles of matter and their interactions. With over a decade of experience, I've worked extensively on the CMS experiment at the Large Hadron Collider (LHC) in Geneva, Switzerland.

I'm proficient in software development & advanced computing tools, such as High-Performance Computing (HPC) & Machine Learning (ML), for investigating the big data sets recorded at the LHC.

I enjoy tackling complex problems and solving technical challenges while continuously expanding my skill set.

Highlights

  • BA, MA & PhD in physics (2007–2017)
  • +10 yrs. experience in statistics & data analysis
  • daily programming in C++ & Python
  • experienced in AI/ML & distributed computing
  • collaborating internationally with +1000 scientists
  • leading of research teams (à 5–15 pers.)
  • supervision of PhD & undergraduate students
  • organization of international workshops,
    hackathons & schools (à 20–60 pers.)
  • publication of scientific papers in peer-reviewed journals

Particle physics

The LHC experiments aim to measure the properties of fundamental particles, building blocks of matter, that existed only in the early universe, thereby expanding our understanding of particle physics.

My research has been focused on the following topics:

More details can be found in my publication list.

Data analysis & AI

The LHC experiments are Big Data generators, \(\mathcal{O}\)(Exabytes), requiring sophisticated algorithms and statistical methods for quantitative analysis in particle physics. This process extensively employs machine learning techniques as well. Drawing on my experience, I led the CMS Machine Learning Innovation Group (2020–2022) and since 2024 the Inter-Experimental LHC ML Group at CERN.

Throughout my career I acquired extensive knowledge in various areas:

  • Boosted Decision Trees (BDTs) & Neural Networks (NNs)
  • Numpy, Pandas, Scipy, scikit-learn, TensorFlow, Keras, PyTorch
  • \(\chi^{2}\)/likelihood-based parameter estimation
  • Frequentist/Bayesian inference & hypothesis testing
  • Particle detector simulation

Computing & software

I use C++ & Python daily and am experienced in working with and contributing to large codebases. I am proficient at learning and adapting to new programming languages and concepts independently.

  • Programming: Python, C++, Java
  • DevOps: cmake, git, CI/CD, QA, Docker, etc.
  • Networking, webservices, IoT
  • Distributed computing
  • GUI & data visualization

A detailed list of my software expertise can be found in my CV. Some of my smaller developments can be found on github.

Hardware

Working on particle physics detectors involves a blend of advanced technology, innovative engineering, and a deep understanding of physics, which I apply to the development of the high granularity calorimeter (HGCAL) upgrade of the CMS detector.

I have a keen interest and posses expertise in the following areas:

  • Analog & digital electronics (2 sem. course during BA studies)
  • FPGA programming (1 sem. course during PhD studies)
  • Accelerator physics (1 sem. course during MA studies)
  • Operation of detector components in testbeam setups
  • Real-time computing for hardware operation

Neural network to search for exotic long-lived particles

CMS experiment at the CERN LHC

Professional experience

since 2022

Research fellow at DESY Hamburg, Germany

I am leading efforts in searching for 3 or 4 top quark events via heavy beyond the standard model (BSM) resonances, while simultaneously providing guidance to two PhD students engaged in related analyses and machine learning projects. Additionally, I am actively involved in characterizing HGCAL SiPM-on-tile modules intended for the CMS detector upgrade at the CERN LHC.

2019 – 2022

Senior fellow at CERN, Switzerland

I conducted extensive research on long-lived heavy neutral leptons decaying to leptons and displaced jets, while overseeing the progress of two PhD students throughout the study. Moreover, I introduced a novel methodology to measure the top quark width, utilizing a neural network to infer the b quark charge for distinguishing between on- and off-shell production.

2017 – 2019

Research associate at Imperial College London, UK

I searched for long-lived particles using a novel displaced jet tagger based on state- of-the-art neural network techniques. Furthermore, I exploited neural networks for improving the sensitivity in measurements of B hadron decays and in the FPGA-based event trigger system.

2012 – 2013

Research assistant at RWTH Aachen University, Germany

I steered the development of the VISPA@web application, a graphical development environment for high energy and astroparticle physics analyses.

Skills

Programming

C++
Expert
Python
Expert
Java
Advanced
JavaScript
Advanced
SQL
Advanced
VHDL
Beginner
CUDA
Beginner

Software development tools

Version control
git, mercurial, svn
Build tools
CMake, make, Apache Ant
Container
Docker
Testing
GoogleTest, unittest, pytest
Analysis
igProf, Valgrind, Clang, Pylint, mypy, Gcov, Flake8
Documentation
Doxygen

Software libraries

C++
CERN ROOT, Geant4, Boost, Qt, OpenGL, ZeroMQ
Python
NumPy, SciPy, pandas, Matplotlib, Bokeh, CherryPy
Machine learning
Keras, TensorFlow, PyTorch, scikit-learn, xgboost

Operating systems

Linux
Expert
Windows
Expert
OSX
Advanced

Languages

German
native
English
fluent
French
basic knowledge

Group leader / Convenerships

since 2024

Coordinator of the inter-experimental LHC ML group

In this role, I serve as representative of the CMS collaboration. Together with other representatives we organize monthly meetings to facilitate and foster an exchange of ML ideas between the LHC experiments (ATLAS, CMS, LHCb, ALICE), theory, and accelerator groups. At our annual workshop we regularly connect with experts from industry (eg. from DeepMind, Meta, etc) as well. Further information can be found on our website.

2021 – 2023

CMS Single top quark cross section (tX) group

In this role, I regularly organized meetings to review the methods and progress of approximately 30 analysts engaged in single top quark research, ensuring effective communication and collaboration to facilitate publications.

2020 – 2022

CMS Machine learning innovation group

The group is responsible for identifying and integrating new and advanced machine learning (ML) developments into all areas of the CMS experiment, including data analyses, detector simulations & calibration, data quality monitoring, and the event trigger system. I coordinated topical hackathons (15-25 participants per hackathon) and managed regular seminars (about 20 participants) to stay updated on the latest ML advancements in the field.

2019 – 2020

CMS Standard Model combination group

In this role, I was responsible for overseeing the statistical inference and the treatment of systematic uncertainties in the precise measurements of parameters of the Standard Model of particle physics and of parton distribution functions.

2014 – 2017

CMS Fast Simulation group

In this role, I assumed responsibility for the dedicated track reconstruction used in the CMS FastSimulation package, managing and reviewing the work of a team of five developers.

Awards

2013 – 2017

FRIA grant

Recipient of the FRIA grant (fully-funded doctoral scholarship). Awarded by Fonds National de la Recherche Scientifique (F.R.S.-FNRS), Belgium.

Education

2013 – 2017

Doctor of Science, UCLouvain, Belgium

PhD thesis: "Differential single-top-quark cross sections in t channel at 8 and 13 TeV with the CMS experiment".
Supervisor: Prof. Dr. Andrea Giammanco.

2010 – 2012

Master of Science in Physics, RWTH Aachen University, Germany

Final mark: excellent (100%).
Master thesis: "Measurement of top-quark spin asymmetries in t-channel single-top-quark production at 7 TeV with the CMS experiment".
Supervisor: Prof. Dr. Martin. Erdmann.

2007 – 2010

Bachelor of Science in Physics, RWTH Aachen University, Germany

Final mark: very good (91%).
Bachelor thesis: "Development of the VISPA@web program and measurement of the dijet mass with CMS".
Supervisor: Prof. Dr. Martin Erdmann.

Teaching

2024

Lecture: Introduction to machine learning

DESY summer student programme, Hamburg, Germany.

2024

Tutor: Machine learning

CMS Data Analysis School (DAS), CERN, Switzerland.

2024

Lecture: Introduction to machine learning

Terascale school for undergraduate students, Hamburg, Germany.

2023

Tutor: Machine learning

CMS Data Analysis School (DAS), Hamburg, Germany.

2018 – 2019

Teaching assistant: Senior laboratory courses

Imperial College London, UK.

2016

Tutor: CMS FastSimulation

3rd CMS FastSim workshop, Fermilab, USA.

2010 – 2012

Teaching assistant: Experimental physics

RWTH Aachen University, Germany.

Conference on Computing in High Energy and Nuclear Physics (CHEP'19)

Projects on GitHub

The following is a collection of small pieces of software I developed to solve specific tasks.

HGCAL Testbeam Data Inspector

A web-based bokeh application to inspect data recorded during test beams with CMS HGCAL SiPM-on-tile modules. The application is written in Python and uses uproot to process CERN ROOT files. Since the visualization is within a browser the data on the server-side does not need to be on the same system as the client.

test-beam app

ROOT-TensorFlow Interface

A set of C++ plugins to asynchronous read and process CERN ROOT files directly within the multithreaded TensorFlow (v1) data pipeline for neural network training.

Unfortunately, the input pipeline has been significantly restructured in TensorFlow v2 such that these plugins won't work with newer versions.

ROOT-TF-pipeline

B jet charge tagging

This repository shows the training of a deep neural network multiclass classifier. The b jet charge is inferred from particle jet constituents including charged particle tracks, neutral particles in the calorimeters, muons, and electrons, as well as secondary vertices formed by at least two tracks. The resulting score is charge-symmetric by construction, ie. flipping all charges of the tracks will also flip the predicted class.

B jet charge tagger

LLP particle gun

A simple CMSSW plugin to artificially generate long-lived particles and their isotropic decay. The particle types, lifetime, and decay channels can be easily adapted to emulate various signatures.

This plugin is of great use for generating large samples of artifical particles to improve the generalization of neural network classifiers.

HNL particle gun

End-to-end vertex reconstruction

This project targets the reconstruction of the primary interaction vertex using a multistage neural network. A novel aspect is the use of a custom TensorFlow plugin to generate histograms from tracks with a learnable weight function.

Since the final classifier is ported into FPGAs a high focus on its computational footprint (eg. BRAM, DSP, etc.) and latency is required.

VTXNN-E2E

Dataformat converter

A C++ based CMSSW plugin to convert collections in the CMSSW-internal EDM format into the PXLIO format used by the VISPA project.

EDM2PXLIO

Slim C++ plugin system

A minimal multi-OS (Windows, Linux, OSX) plugin system for C++.

CPX

Slim C++ logging system

A minimal logging system for C++.

C++ logging
template<class T> void ComputeTmpl(
    OpKernelContext* context, 
    unsigned int input_index, 
    const std::vector<unsigned int>& diced_rates,
    unsigned int sum_rates
) const
{
    const Tensor& input_tensor = context->input(input_index);
    auto input_data = input_tensor.flat<T>();
    int batch_size = input_tensor.dim_size(0);
    int batch_length = input_tensor.NumElements()/batch_size;
    
    Tensor* output_tensor = nullptr;
    TensorShape output_shape = input_tensor.shape();
    output_shape.set_dim(0,sum_rates);
    OP_REQUIRES_OK(context, context->allocate_output(input_index-1, output_shape,&output_tensor));
    auto output_data = output_tensor->flat<T>();
    
    unsigned int output_batch_index = 0;
    for (unsigned int ibatch = 0; ibatch<batch_size; ++ibatch)
    {
        for (unsigned int icopy = 0; icopy<diced_rates[ibatch];++icopy)
        {
            for (unsigned int idata = 0; idata<batch_length; ++idata)
            {
                output_data(output_batch_index*batch_length+idata) = input_data(ibatch*batch_length+idata);
            }
            output_batch_index+=1;
        }
    }
}

Custom TensorFlow kernel for data preprocessing

Die shot Intel i9-13900K by Fritzchens Fritz

Publications

Principal

2024

CMS Collaboration, JHEP 03 (2024) 105 Search for long-lived heavy neutral leptons with lepton flavour conserving or violating decays to a jet and a charged lepton

The LHC collision data has been searched for signs of exotic long-lived particles, so-called heavy neutral leptons. Their existence could answer various observed phenomena: Why are the neutrino masses so tiny? What is the nature of dark matter? Why is there much more matter than antimatter in the universe?

Unfortunately, no evidence for such particles have been found, yet the search significantly narrowed their possible parameters (eg. the particle's lifetime \(c\tau_{0}\) as shown in the plot) helping future analyses to focus their efforts.

2020

CMS Collaboration, Mach. Learn.: Sci. Technol. 1 (2020) 035012 A deep neural network to search for new long-lived particles decaying to jets

An advanced deep neural network (DNN) has been developed, designed to tag jets, sprays of collimated particles, that originate from 0.1mm and up to 1m away from the proton-proton collision point in the CMS detector's center. This novel classifier significantly enhances our sensitivity in searching for exotic long-lived particles (LLPs), which can travel macroscopic distances before decaying, as illustrated in the accompanying plot.

A key innovation is the use of domain adaptation. This technique ensures that the performance of the classifier, which is initially trained on simulated data, matches its performance on real data. This on-the-fly calibration ensures that the DNN predictions remain accurate and reliable in practical applications.

2020

CMS Collaboration, Eur. Phys. J. C 80 (2020) 370 Measurement of differential cross sections and charge ratios for \(t\)-channel single top quark production in proton-proton collisions at \(\sqrt{s}\) =13 TeV

The elusive production of single top quarks was first observed in 2009, at the Tevatron collider. Now, for the first time, the LHC enables comprehensive studies of single top quark production. This paper includes also a first measurement of the production ratio of top quark (\(t\)) and its antiparticle (\(\bar{t}\)), providing insights into the quark composition of the proton.

The measured ratio of about \(\frac{2}{3}\), as shown in the figure, confirms that the proton consists of two up quarks and one down quark. This is because interacting up quarks can lead to the production of top quarks, whereas interacting down quarks yield their antiparticles.

2016

CMS Collaboration, JHEP 04 (2016) 073 Measurement of top quark polarisation in \(t\)-channel single top quark production

A distinctive feature of single top quarks is their near 100% polarization. Simply put, this means their spin is almost always aligned with their direction of flight. The accompanying figure illustrates an approximation of the cosine of the polarization angle. The degree of polarization can be derived from the slope of the data points.

The polarization provides valuable insights into the properties and interactions of single top quarks. Such results can also indirectly exclude certain new theories, that postulate new interactions and/or particles. If these would cause a depolarization of top quarks that contradicts the observed data, they can be ruled out.

2016

CMS Collaboration, CMS-PAS-TOP-16-004 Measurement of the differential cross section for \(t\)-channel single-top-quark production at \(\sqrt{s}\) =13 TeV

Previously, the LHC operated at around half its maximum energy, specifically at 7 and 8 TeV. In 2015, it achieved a significant milestone by increasing its energy to 13 TeV for the first time. This paper presents the initial measurement of single top quark production using the very first data collected at this unprecedented energy level.

The figure displays the output score of a boosted decision tree (BDT), which effectively separates single top quark events (shown in red and labeled "\(t\)-channel") from other processes.

Presentations

International conferences

2023

Searches for Heavy Neutral Leptons with the CMS experiment

European Physical Society Conference on High Energy Physics (EPS-HEP), Hamburg, Germany. Proceedings: PoS (EPS-HEP2023) 452

2021

Summary of Working Group 6: Higgs, Top, and interplay between flavour and high-pT physics

11th International Workshop on the CKM Unitarity Triangle (CKM), (virtual). Proceedings: PoS (CKM2021) 024

2021

Heavy Neutral Lepton searches at the LHC

9th Annual Large Hadron Collider Physics Conference (LHCP), (virtual). Proceedings: PoS (LHCP2021) 001

2019

Identification of new long-lived particle states using deep neural networks

24th International Conference on Computing in High Energy and Nuclear Physics (CHEP), Adelaide, Australia. Proceedings: EPJ Web Conf. 245 (2020) 06013

2018

Single top quark production in CMS

39th International Conference on High Energy Physics (ICHEP), Seoul, Korea. Proceedings: PoS (ICHEP2018) 025

2017

Measurements of single top quark cross sections at 13 TeV with the CMS experiment

10th International Workshop on Top Quark Physics (TOP), Braga, Portugal. Proceedings: arXiv:1711.11445 [hep-ex]

2016

Single top quark production with CMS

QCD@LHC, Zurich, Switzerland.

2016

Single top quark cross section and properties measurements in CMS

4th Annual Large Hadron Collider Physics Conference (LHCP), Lund, Sweden. Proceedings: PoS (LHCP2016) 168

2016

Best poster presentation: Measurement of differential cross sections for t-channel single-top-quark production at 13 TeV

9th International Workshop on Top Quark Physics (TOP), Olomouc, Czech Republic. Proceedings: arXiv:1611.04852 [hep-ex]

2015

Single top quark production: t-channel

8th International Workshop on Top Quark Physics (TOP), Ischia, Italy. Proceedings: PoS (TOP2015) 023

2012

Best poster presentation: A server-client-based graphical development environment for physics analyses (VISPA)

19th International Conference on Computing in High Energy and Nuclear Physics (CHEP), New York, USA. Proceedings: J.Phys.Conf.Ser. 396 (2012) 052015

Workshops

2023

HGCAL, SiPM-on-tile developments

CMS Upgrade Days, CERN, Switzerland.

2022

Neural network based primary vertex reconstruction with FPGAs for the upgrade of the CMS level-1 trigger system

Spring Conference of the German Physical Society (DPG), (virtual).

2020

ROOT preprocessing pipeline for machine learning with TensorFlow

PyHEP Workshop, (virtual).

2018

Polarisation studies in top physics

Invited talk at VBS Polarization Workshop, Laboratoire Leprince-Ringuet (LLR), France.

2018

Differential single top quark cross section in the t-channel

5th CMS Single-Top Workshop, Oviedo, Spain.

2017

Single top quark t-channel: inclusive and differential

4th CMS Single-Top Workshop, Karlsruhe, Germany.

2016

Experimental single top quark overview: LHC Run 2

3th CMS Single-Top Workshop, Strasbourg, France.

2015

Top quark polarization in t-channel single top quark production with CMS at 8 TeV

Seminar at University of Hamburg, Germany.

2015

Experimental summary on top quark measurements, anomalous couplings & FCNC

1st CMS Single-Top Workshop, Naples, Italy.

Worldwide LHC Computing Grid for distributed data analyses

Contact

Email Subject Message

Impressum

Dr. Matthias Komm, Deutsches Elektronen-Synchrotron (DESY), Notkestraße 85, 22607 Hamburg, Germany.