6th Inter-experiment Machine Learning Workshop

Contribution List

127. Workshop welcome and introduction

Julian Garcia Pardinas (CERN)

29/01/2024, 14:00

68. Thoughts about Machine Learning

Prof. Juergen Schmidhuber (KAUST and IDSIA)

29/01/2024, 14:05

Keynotes ML Research

I’ll discuss past, present, future, and far future of machine learning and artificial intelligence.

Mini-bio. The New York Times headlined: "When A.I. Matures, It May Call Jürgen Schmidhuber 'Dad'." Since age 15, his main goal has been to build a self-improving A.I. smarter than himself, then retire. His lab's deep learning artificial neural networks based on ideas published in the "Annus...

69. Modeling epistemics with machine learning

Dr Gael Varoquaux (INRIA)

29/01/2024, 15:05

Keynotes ML Research

How do we model and reason with machine learning? Machine learning models typically do not build upon mechanistic assumptions. I will discuss important concepts required to gauge machine-learning model outputs, in particular controling their uncertainty and wether they support causal reasonning. I will also discuss implicit modeling (inductive biases), contrasting tree-based models with neural...

59. Attention to the strengths of physics interactions: Enhanced Deep Learning Event Classification for Particle Physics Experiments

Polina Moskvitina (Nikhef National institute for subatomic physics (NL))

29/01/2024, 16:30

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Contributed talk

Contributed Talks

A major task in particle physics is the measurement of rare signal processes. These measurements are highly dependent on the classification accuracy of these events in relation to the huge background of other Standard Model processes. Reducing the background by a few tens of percent with the same signal efficiency can already increase the sensitivity considerably.

This study demonstrates...

39. Modeling $N_{\mathrm{ch}}$ distributions and $p_{\mathrm{T}}$ spectra in high-energy pp collisions with DNNs

Maria Alejandra Calmon Behling (Goethe University Frankfurt (DE))

29/01/2024, 16:50

3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model

Contributed talk

Contributed Talks

During the data-taking campaigns Run 1 and Run 2 of the Large Hadron Collider (LHC), the ALICE collaboration collected a large amount of proton-proton (pp) collisions across a variety of center-of-mass energies ($\sqrt{s\,}$). This extensive dataset is well suited to study the energy dependence of particle production. Deep neural networks (DNNs) provide a powerful regression tool to capture...

31. The DL Advocate: Playing the devil's advocate with hidden systematic uncertainties

Andrea Mauri (Imperial College (GB))

29/01/2024, 17:10

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Contributed talk

Contributed Talks

We propose a new method based on machine learning to play the devil’s advocate and investigate the impact of unknown systematic effects in a quantitative way. This method proceeds by reversing the measurement process and using the physics results to interpret systematic effects under the Standard Model hypothesis. We explore this idea with two alternative approaches, one relies on a...

58. the Fair Universe project and the HiggsML Uncertainty Challenge

David Rousseau (IJCLab-Orsay)

29/01/2024, 17:30

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Contributed talk

Contributed Talks

The Fair Universe project is building a large-compute-scale AI ecosystem for sharing datasets, training large models and hosting challenges and benchmarks. Furthermore, the project is exploiting this ecosystem for an AI challenge series focused on minimizing the effects of systematic uncertainties in High-Energy Physics (HEP), and on predicting accurate confidence intervals. This talk will...

102. ML and Quantum Physics

Eliska Greplova (TU Delft)

30/01/2024, 10:00

Keynotes Physics + ML

55. Accelerating Graph-Based Tracking with Symbolic Regression

Nathalie Soybelman (Weizmann Institute of Science (IL))

30/01/2024, 11:30

4 Fast ML : Application of Machine Learning to DAQ/Trigger/Real Time Analysis

Contributed talk

Contributed Talks

Tracking, the reconstruction of particle trajectories from hits in the inner detector is a computationally intensive task due to the large combinatorics of detector signals. Recent efforts have proven that ML techniques can be successfully applied to the tracking problem, extending and improving the conventional methods based on feature engineering. However, the inference of complex networks...

10. Reinforcement Learning Algorithms for Charged Particle Tracking with Applications in Proton Computed Tomography

Tobias Kortus

30/01/2024, 11:50

1 ML for object identification and reconstruction

Contributed talk

Contributed Talks

Deep learning, especially graph neural networks, significantly improved tracking performances in modern particle detectors while reducing runtimes compared to previous state of the art approaches. However, training neural networks requires significant amount of labeled data, usually acquired by performing complex particle simulations. We present first studies of leveraging deep reinforcement...

6. Differentiable Vertex Fitting for Jet Flavour Tagging

Rachel Emma Clarke Smith (SLAC National Accelerator Laboratory (US)), Ruben Miguel De Almeida Inacio (LIP - Laboratorio de Instrumentação e Física Experimental de Partículas (PT))

30/01/2024, 12:10

1 ML for object identification and reconstruction

Contributed talk

Contributed Talks

We propose a differentiable vertex fitting algorithm that can be used for secondary vertex fitting, and that can be seamlessly integrated into neural networks for jet flavour tagging. Vertex fitting is formulated as an optimization problem where gradients of the optimized solution vertex are defined through implicit differentiation and can be passed to upstream or downstream neural network...

49. Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC

Simon Akar (University of Cincinnati (US))

30/01/2024, 12:30

4 Fast ML : Application of Machine Learning to DAQ/Trigger/Real Time Analysis

Contributed talk

Contributed Talks

We have been studying the use of deep neural networks (DNNs) to identify and locate primary vertices (PVs) in proton-proton collisions at the LHC. Earlier work focused on finding primary vertices in simulated LHCb data using a hybrid approach that started with kernel density estimators (KDEs) derived from the ensemble of charged track parameters heuristically and predicted “target histogram”...

28. End-to-end Reconstruction Algorithm for Highly Granular Calorimeters

Mr Philipp Zehetner (Ludwig Maximilians Universitat (DE))

30/01/2024, 14:00

1 ML for object identification and reconstruction

Contributed talk

Contributed Talks

We present an end-to-end reconstruction algorithm for highly granular calorimeters that includes track information to aid the reconstruction of charged particles. The algorithm starts from calorimeter hits and reconstructed tracks, and outputs a coordinate transformation in which all shower objects are well separated from each other, and in which clustering becomes trivial. Shower properties...

20. Electron and Proton Classification with AMS ECAL Using Convolutional Vision Transformers and Domain Adaptation

Berk Turk (Middle East Technical University (TR))

30/01/2024, 14:20

7 ML for astroparticle

Contributed talk

Contributed Talks

Alpha Magnetic Spectrometer (AMS-02) is a precision high-energy cosmic-ray experiment on the ISS operating since 2011 and has collected more than 228 billion particles. Among them, positrons are important to understand the particle nature of dark matter. Separating the positrons from cosmic background protons is challenging above 1 TeV. Therefore, we use state-of-the-art convolutional and...

46. Parametrising profiled likelihoods with neural networks

Dr Humberto Reyes-González (University of Genoa)

30/01/2024, 14:40

8 ML for phenomenology and theory

Contributed talk

Contributed Talks

Full statistical models encapsulate the complete information of an experimental result, including the likelihood function given observed data. Since a few years ago ATLAS started publishing statistical models that can be reused via the pyhf framework; a major step towards fully publishing LHC results. In the case of fast Simplified Model Spectra based reinterpretation we are often only...

19. Accelerating the search for mass bumps using the Data-Directed Paradigm

Bruna Pascual (Universite de Montreal (CA))

30/01/2024, 15:00

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Contributed talk

Contributed Talks

The Data-Directed paradigm (DDP) is a search strategy for efficiently probing new physics in a large number of spectra with smoothly-falling SM backgrounds. Unlike the traditional analysis strategy, DDP avoids the need for a simulated or functional-form based background estimate by directly predicting the statistical significance using a convolutional neural network trained to regress the...

21. Training and optimisation of large transformer models at CERN: an ATLAS case study on Kubeflow

Maxence Draguet (University of Oxford (GB))

30/01/2024, 15:50

5 ML infrastructure : Hardware and software for Machine Learning

Contributed talk

Contributed Talks

Heavy flavour jets underpin a large part of the ATLAS physics programme, such as analyses of Higgs boson decays to quarks and super-symmetry searches with b-jets. The algorithms for identifying jets originating from b- and c-quarks are instrumental in these efforts, with the recently introduced GN2 model [1] showing remarkable improvements in tagging efficiency. Given its complexity and data...

43. Masked particle modelling

Samuel Byrne Klein (Universite de Geneve (CH))

30/01/2024, 16:10

1 ML for object identification and reconstruction

Contributed talk

Contributed Talks

The Bert pretraining paradigm has proven to be highly effective in many domains including natural language processing, image processing and biology. To apply the Bert paradigm the data needs to be described as a set of tokens, and each token needs to be labelled. To date the Bert paradigm has not been explored in the context of HEP. The samples that form the data used in HEP can be described...

54. Finetuning Foundation Models for Joint Analysis Optimization

Matthias Vigl (Technische Universitat Munchen (DE))

30/01/2024, 16:30

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Contributed talk

Contributed Talks

Most searches at the LHC employ an analysis pipeline consisting of various discrete components, each individually optimized and later combined to provide relevant features used to discriminate SM background from potential signal. These are typically high-level features constructed from particle four-momenta. However, the combination of individually optimized tasks doesn't guarantee an optimal...

48. Re-simulation-based self-supervision for representation learning

Jeffrey Krupa (Massachusetts Institute of Technology)

30/01/2024, 16:50

1 ML for object identification and reconstruction

Contributed talk

Contributed Talks

Self-Supervised Learning (SSL) is at the core of training modern large ML models, providing a scheme for learning powerful representations in base models that can be used in a variety of downstream tasks. However, SSL training strategies must be adapted to the type of training data, thus driving the question: what are powerful SSL strategies for collider physics data? In the talk, we present a...

89. Improving data-driven model predictions using physics in the CERN accelerator complex

Francesco Maria Velotti (CERN)

31/01/2024, 09:00

Keynotes Physics + ML

Most of limitations to fully exploit the CERN injector complex comes from problems that have a rather known formalism, but usually, due to interplay of many other factors make the analytical or numerical solutions almost impossible. In this context, machine learning is playing a key role allowing us to produce empirical models to estimate the accelerator behaviour to the desired accuracy for...

90. ML and Molecular Physics

Jonas Koehler (Microsoft Research)

31/01/2024, 09:30

Keynotes Physics + ML

91. Problem solving as a translation task

Francois Charton (Facebook AI Research)

31/01/2024, 11:00

CERN Data Science Seminar

Neural architectures designed for machine translation can be used to solve problems of mathematics, by considering that solving amounts to translating the problem, a sentence in some mathematical language, into its solution, another sentence in mathematical language. Presenting examples from symbolic and numerical mathematics, and theoretical physics, I show how such techniques can be applied...

103. Retrieval Augmented Language Models

Casey Fitzpatrick (Contextual AI)

31/01/2024, 14:00

Keynote Industry

30. Machine learning approaches for parameter reweighting in MC samples of top quark production in CMS

Valentina Guglielmi (Deutsches Elektronen-Synchrotron (DE))

31/01/2024, 15:30

3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model

Poster

Poster Session

In particle physics, Monte Carlo (MC) event generators are needed to compare theory to the measured data. Many MC samples have to be generated to account for theoretical systematic uncertainties, at a significant computational cost. Therefore, the MC statistic becomes a limiting factor for most measurements and the significant computational cost of these programs a bottleneck in most physics...

25. ML-assisted reconstruction of hadron-collider events with mini-jets

Josef Modestus Murnauer (Max Planck Society (DE))

31/01/2024, 15:35

1 ML for object identification and reconstruction

Poster

Poster Session

The task of reconstructing physical observables from recorded experimental data in hadron collider events is a common challenge in LHC data analysis. Experimental measurements, such as hits in tracking detectors and signals in calorimeters, are combined into particle-flow objects, such as jets, muons, electrons, and missing transverse energy. However, reconstructing key observables related to...

24. Deep learning for the rare top decay t→sW at the LHC

Jeewon Heo (University of Seoul, Department of Physics (KR))

31/01/2024, 15:40

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster

Poster Session

The Cabibbo Kobayashi Maskawa (CKM) matrix describes the flavor-changing quark interactions. Vts is the matrix element that describes the coupling between the top and strange quark, has not been directly measured. A direct measurement of |Vts| can be performed by identifying the strange jets from top decays. The strange jet tagging problem is challenging due to the similarity of strange jets...

23. Improving the line-segment tracking algorithm with machine learning for the High Luminosity LHC

Jonathan Guiang (Univ. of California San Diego (US))

31/01/2024, 15:45

1 ML for object identification and reconstruction

Poster

Poster Session

In this work, we present a study on how machine learning (ML) can be used to enhance charged particle tracking algorithms. In particular, we focus on the line-segment-based tracking (LST) algorithm that we have designed to be naturally parallelized and vectorized on modern processors. LST has been developed specifically for the Compact Muon Solenoid (CMS) Experiment at the LHC, towards the...

17. A data-driven and model-agnostic approach to solving combinatorial assignment problems in searches for new physics

Anthony Badea (University of Chicago (US)), Javier Montejo Berlingen (The Barcelona Institute of Science and Technology (BIST) (ES))

31/01/2024, 15:50

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster

Poster Session

We present a novel approach to solving combinatorial assignment problems in particle physics without the need to introduce prior knowledge or assumptions about the particles' decay. The correct assignment of decay products to parent particles is achieved in a model-agnostic fashion by introducing a novel neural network architecture, Passwd-ABC, which combines a custom layer based on attention...

16. DeepCore2.0: Convolutional Neural Network for Tracking in the Core of High-Energy Jets

Hichem Bouchamaoui (Princeton University (US))

31/01/2024, 15:55

1 ML for object identification and reconstruction

Poster

Poster Session

Tracking of charged particles in dense environments, especially in the core of high transverse-momentum ($p_T$) jets, presents a growing challenge with increasing LHC luminosity. Despite the CMS phase-1 pixel detector upgrade, and dedicated cluster splitting and pattern recognition algorithms like JetCore, there is still significant room for improvement. Limiting the computation time for track...

14. Topological separation of dielectron signals using machine learning in Pb--Pb collisions with ALICE

Jerome Jung (Goethe University Frankfurt (DE))

31/01/2024, 16:00

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster

Poster Session

Dielectrons are an exceptional tool to study the evolution of the medium created in heavy-ion collisions. In central collisions, the energy densities are sufficient to create a quark-gluon plasma (QGP). At LHC energies, the dominant background process for the measurements of thermal e$^{+}$e$^{-}$ pairs originating from the QGP are correlated HF hadron decays which dominate the dielectron...

13. Model-independent strategy for New Physics seartch at LHC using Anomaly Detection algorithms

Louis Vaslin (High Energy Accelerator Research Organization (JP))

31/01/2024, 16:05

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster

Poster Session

The search for rare New Physics signals is one of the main challenges that are addressed in LHC experiments. Classical search strategies rely on signal hypothesis and simulation to optimize the sensitivity of an analysis. However, relying on hypothesis and simulation has drawbacks. To address this problem, I propose a model independent strategy for heavy resonance searches.
      This...

12. Electron identification with a convolutional neural network

Hoang Dai Nghia Nguyen (Université de Montreal (CA))

31/01/2024, 16:10

1 ML for object identification and reconstruction

Poster

Poster Session

The identification of electrons plays an important role in a large fraction of the physics analyses performed at ATLAS. An improved electron identification algorithm is presented that is based on a convolutional neural network (CNN). The CNN utilizes the images of the deposited energy in the calorimeter cells around the reconstructed electron candidates for each of the electromagnetic and...

11. Particle identification with Machine Learning for ALICE ITS2

Giorgio Alberto Lucia (Universita e INFN Torino (IT))

31/01/2024, 16:15

1 ML for object identification and reconstruction

Poster

Poster Session

Particle identification (PID) plays a pivotal role in numerous measurements performed by the ALICE Collaboration. Various ALICE detectors offer PID information through complementary experimental techniques. The former ALICE Inner Tracking System 1 (ITS1) provided PID information by measuring the specific energy loss of low-momentum charged particles during LHC Run 1 and Run 2. The upgraded ITS...

9. Decorrelation using Optimal Transport

Malte Algren (Universite de Geneve (CH))

31/01/2024, 16:20

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster

Poster Session

Novel decorrelation method using Convex Neural Optimal Transport Solvers (Cnots) that is able to decorrelate a continuous feature space against protected attributes with optimal transport. We demonstrate how well it performs in the context of jet classification in high energy physics, where classifier scores are desired to be decorrelated from the mass of a jet.

8. Particle Transformer for tau lepton pair invariant mass reconstruction for the HH→bbtautau CMS analysis

Valentina Camagni (Universita & INFN, Milano-Bicocca (IT))

31/01/2024, 16:25

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster

Poster Session

One of the most interesting channels to probe theories beyond the Standard Model at LHC, is the production of a new massive particle, that decays into pairs of Higgs Bosons which, in turn, decay into a pair of b-quarks and a pair of tau leptons. A fundamental discriminant variable to separate HH signal from the backgrounds is the invariant mass of the di-tau system, which can be reconstructed...

7. Apples to Apples in Jet Quenching

João A. Gonçalves (LIP - IST)

31/01/2024, 16:30

1 ML for object identification and reconstruction

Poster

Poster Session

Progress in the theoretical understanding of parton branching dynamics that occurs within an expanding QGP relies on detailed and fair comparisons with experimental data for reconstructed jets. Such validation is only meaningful when the computed object, be it analitically or via event generation, accounts for the complexity of experimentally reconstructed jets. The reconstruction of jets in...

5. Uncertainty-aware Machine Learning for Proton Therapy Range Verification with a Digital Tracking Calorimeter

Alexander Schilling (University of Applied Sciences Worms)

31/01/2024, 16:35

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster

Poster Session

Proton therapy is highly sensitive to range uncertainties due to the nature of the dose deposition of charged particles. To ensure treatment quality, range verification methods can be used to verify that the individual spots in a pencil beam scanning treatment fraction match the treatment plan. We employ uncertainty-aware deep neural networks to predict the Bragg peak depth in an...

115. Training and optimisation of large transformer models at CERN: an ATLAS case study on Kubeflow - Poster

Maxence Draguet (University of Oxford (GB))

31/01/2024, 16:40

5 ML infrastructure : Hardware and software for Machine Learning

Poster (from contributed talk)

Poster Session

Heavy flavour jets underpin a large part of the ATLAS physics programme, such as analyses of Higgs boson decays to quarks and super-symmetry searches with b-jets. The algorithms for identifying jets originating from b- and c-quarks are instrumental in these efforts, with the recently introduced GN2 model [1] showing remarkable improvements in tagging efficiency. Given its complexity and data...

113. Reinforcement Learning Algorithms for Charged Particle Tracking with Applications in Proton Computed Tomography - Poster

Tobias Kortus

31/01/2024, 16:45

1 ML for object identification and reconstruction

Poster (from contributed talk)

Poster Session

Deep learning, especially graph neural networks, significantly improved tracking performances in modern particle detectors while reducing runtimes compared to previous state of the art approaches. However, training neural networks requires significant amount of labeled data, usually acquired by performing complex particle simulations. We present first studies of leveraging deep reinforcement...

111. Out-of-Distribution Multi-set Generation with Context Extrapolation for Amortized Simulation and Inverse Problems - Poster

Hosein Hashemi (LMU Munich)

31/01/2024, 16:50

3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model

Poster (from contributed talk)

Poster Session

Addressing the challenge of Out-of-Distribution (OOD) multi-set generation, we introduce YonedaVAE, a novel equivariant deep generative model inspired by Category Theory, motivating the Yoneda-Pooling mechanism. This approach presents a learnable Yoneda Embedding to encode the relationships between objects in a category, providing a dynamic and generalizable representation of complex...

114. Electron and Proton Classification with AMS ECAL Using Convolutional Vision Transformers and Domain Adaptation - Poster

Berk Turk (Middle East Technical University (TR))

31/01/2024, 16:55

7 ML for astroparticle

Poster (from contributed talk)

Poster Session

Alpha Magnetic Spectrometer (AMS-02) is a precision high-energy cosmic-ray experiment on the ISS operating since 2011 and has collected more than 228 billion particles. Among them, positrons are important to understand the particle nature of dark matter. Separating the positrons from cosmic background protons is challenging above 1 TeV. Therefore, we use state-of-the-art convolutional and...

112. Differentiable Vertex Fitting for Jet Flavour Tagging - Poster

Ruben Miguel De Almeida Inacio (LIP - Laboratorio de Instrumentação e Física Experimental de Partículas (PT))

31/01/2024, 17:00

1 ML for object identification and reconstruction

Poster (from contributed talk)

Poster Session

We propose a differentiable vertex fitting algorithm that can be used for secondary vertex fitting, and that can be seamlessly integrated into neural networks for jet flavour tagging. Vertex fitting is formulated as an optimization problem where gradients of the optimized solution vertex are defined through implicit differentiation and can be passed to upstream or downstream neural network...

118. Modeling $N_{\mathrm{ch}}$ distributions and $p_{\mathrm{T}}$ spectra in high-energy pp collisions with DNNs - Poster

Maria Alejandra Calmon Behling (Goethe University Frankfurt (DE))

31/01/2024, 17:05

3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model

Poster (from contributed talk)

Poster Session

During the data-taking campaigns Run 1 and Run 2 of the Large Hadron Collider (LHC), the ALICE collaboration collected a large amount of proton-proton (pp) collisions across a variety of center-of-mass energies ($\sqrt{s\,}$). This extensive dataset is well suited to study the energy dependence of particle production. Deep neural networks (DNNs) provide a powerful regression tool to capture...

110. DeepTreeGANv2: Iterative Pooling of Point Clouds - Poster

Mr Moritz Scham (Deutsches Elektronen-Synchrotron (DE))

31/01/2024, 17:10

3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model

Poster (from contributed talk)

Poster Session

In High Energy Physics, detailed and time-consuming simulations are used for particle interactions with detectors. To bypass these simulations with a generative model, the generation of large point clouds in a short time is required, while the complex dependencies between the particles must be correctly modelled. Particle showers are inherently tree-based processes, as each particle is...

108. Accelerating the search for mass bumps using the Data-Directed Paradigm - Poster

Bruna Pascual (Universite de Montreal (CA))

31/01/2024, 17:15

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster (from contributed talk)

Poster Session

The Data-Directed paradigm (DDP) is a search strategy for efficiently probing new physics in a large number of spectra with smoothly-falling SM backgrounds. Unlike the traditional analysis strategy, DDP avoids the need for a simulated or functional-form based background estimate by directly predicting the statistical significance using a convolutional neural network trained to regress the...

107. A Deep Generative Model for Hadronization - Poster

Jay Chan (Lawrence Berkeley National Laboratory)

31/01/2024, 17:20

3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model

Poster (from contributed talk)

Poster Session

Hadronization is a critical step in the simulation of high-energy particle and nuclear physics experiments. As there is no first principles understanding of this process, physically-inspired hadronization models have a large number of parameters that are fit to data. We propose an alternative approach that uses deep generative models, which are a natural replacement for classical techniques,...

125. Attention to the strengths of physics interactions: Enhanced Deep Learning Event Classification for Particle Physics Experiments - Poster

Polina Moskvitina (Nikhef National institute for subatomic physics (NL))

31/01/2024, 17:25

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster (from contributed talk)

Poster Session

A major task in particle physics is the measurement of rare signal processes. These measurements are highly dependent on the classification accuracy of these events in relation to the huge background of other Standard Model processes. Reducing the background by a few tens of percent with the same signal efficiency can already increase the sensitivity considerably.

This study demonstrates...

106. Longitudinal Beam Diagnostics and Phase Space Reconstruction in the LHC Using ML - Poster

Konstantinos Iliakis (CERN)

31/01/2024, 17:30

3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model

Poster (from contributed talk)

Poster Session

Accurate knowledge of longitudinal beam parameters is essential for optimizing the performance and operational efficiency of particle accelerators like the Large Hadron Collider (LHC). However, conventional methods to determine them, such as fitting techniques and tracking-based longitudinal tomography, are time-consuming and limited to analyzing data from a few bunches only. To address this,...

126. Quantum machine learning

Dr Sofia Vallecorsa (CERN)

01/02/2024, 09:00

Tutorials

104. Thinking like Transformers

Dr Gail Weiss (EPFL)

01/02/2024, 11:00

Tutorials

Transformers - the purely attention based NN architecture - have emerged as a powerful tool in sequence processing. But how does a transformer think? When we discuss the computational power of RNNs, or consider a problem that they have solved, it is easy for us to think in terms of automata and their variants (such as counter machines and pushdown automata). But when it comes to transformers,...

73. Collider Physics and ML

Gregor Kasieczka (Hamburg University (DE))

01/02/2024, 14:00

Keynotes Physics + ML

66. Simultaneous calibration of jet energy and mass using DNN in ATLAS

Pierre Antoine Delsart (LPSC/CNRS (Grenoble, FR))

01/02/2024, 15:30

1 ML for object identification and reconstruction

Poster

Poster Session

The energy and mass measurements of jets are crucial tasks for the Large Hadron Collider experiments. This paper presents a new calibration method to simultaneously calibrate these quantities for large-radius jets measured with the ATLAS detector using a deep neural network (DNN). To address the specificities of the calibration problem, special loss functions and training procedures are...

65. Search for long-lived heavy neutral leptons using a displaced jet tagger

Matthias Komm (Deutsches Elektronen-Synchrotron (DE))

01/02/2024, 15:35

1 ML for object identification and reconstruction

Poster

Poster Session

A search for long-lived heavy neutral leptons (HNLs) is presented, which considers the hadronic final state and coupling scenarios involving all three lepton generations in the 2-20 GeV HNL mass range for the first time. A central feature of the analysis is a novel jet tagger, based on a deep neural network (DNN), that has been developed to identify displaced jets from an HNL decay using...

61. Transformers for Particle Track Reconstruction and Hit Clustering

Nadezhda Dobreva

01/02/2024, 15:40

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster

Poster Session

Track reconstruction is a crucial part of High Energy Physics (HEP) experiments. Traditional methods for the task scale poorly, making machine learning and deep learning appealing alternatives. Following the success of transformers in the field of language processing, we investigate the feasibility of training a Transformer to translate detector signals into track parameters. We study and...

57. Simulation-based inference in the search for CP violation in leptonic WH production

Ricardo Barrué (LIP - Laboratorio de Instrumentacao e Fisica Experimental de Partículas)

01/02/2024, 15:45

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster

Poster Session

Beyond the Standard Model (BSM) sources of CP violation are one of the required ingredients for solving the matter-antimatter puzzle. Simulation-based inference methods hold the promise of allowing the estimation of optimal observables or likelihood ratios without requiring approximations (e.g. of the effect of shower and hadronization), ensuring a high sample efficiency through the use of...

50. Conditional normalizing flows for correcting simulations

Caio Cesar Daumann (Rheinisch Westfaelische Tech. Hoch. (DE))

01/02/2024, 15:50

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster

Poster Session

Simulated events are key ingredients for almost all high-energy physics analyses. However, imperfections in the configuration of the simulation often result in mis-modelling and lead to discrepancies between data and simulation. Such mis-modelling often must be taken into account by correction factors accompanied by systematical uncertainties, that can compromise the sensitivity of...

45. Unsupervised tagging of semivisible jets with energy-based autoencoders in CMS

Florian Eble (ETH Zurich (CH))

01/02/2024, 15:55

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster

Poster Session

A particularly interesting application of autoencoders (AE) for High Energy Physics is their use as anomaly detection (AD) algorithms to perform a signal-agnostic search for new physics. This is achieved by training AEs on standard model physics and tagging potential new physics events as anomalies. The use of an AE as an AD algorithm relies on the assumption that the network better...

44. Energy-based graph autoencoders for semivisible jet tagging in the Lund representation

Christoph Frederik Ribbe (ETH Zurich (CH))

01/02/2024, 16:00

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster

Poster Session

Semivisible jets are are a novel signature arising in Hidden Valley (HV) extensions of the SM with a confining interaction [1]. Originating from a double shower and hadronization process and containing undetectable dark bound states, semivisible jets are expected to have a substantially different radiation pattern compared to SM jets.

Unsupervised...

41. Systematic Effects in Jet Tagging Performance for the ATLAS Detector

Kevin Thomas Greif (University of California Irvine (US))

01/02/2024, 16:05

1 ML for object identification and reconstruction

Poster

Poster Session

Machine learning based jet tagging techniques have greatly enhanced the sensitivity of measurements and searches involving boosted final states at the LHC. However, differences between the Monte-Carlo simulations used for training and data lead to systematic uncertainties on tagger performance. This talk presents the performance of boosted top and W boson taggers when applied on data sets...

38. BSM models and parameter inference via an n-channel 1D-CNN

Yong Sheng Koay

01/02/2024, 16:10

8 ML for phenomenology and theory

Poster

Poster Session

In this study, we focused on inferring BSM models and their parameters from the kinematic distributions of collider signals via an n-channel 1D-Convolutional Neural Network (1D-CNN). As new physics may influence two or more observables, a 2D-CNN approach might not always be the best option. Alternatively, one can use a simple MLP with any number of observables via an event-by-event approach....

32. Generating parton-level events from reconstructed events with Conditional Normalizing Flows

Adrian Antonio Petre (ETH Zurich (CH))

01/02/2024, 16:15

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster

Poster Session

We introduce a new approach using generative machine learning to sample meaningful generator-level events given reconstructed events in the CMS detector. Our method combines Transformers and Normalizing Flows to tackle the challenge of integrating the Matrix Element Method with importance sampling. We propose using a Transformer network to analyze the full reconstructed event and extract...

29. Machine learning for enhanced measurement of Higgs boson production cross section via vector boson fusion in H->WW* with the ATLAS detector

Metea Castilleja Marr (Simon Fraser University (CA))

01/02/2024, 16:20

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster

Poster Session

In this analysis, we apply modern machine learning techniques to the $H\rightarrow WW^* \rightarrow e \nu \mu \nu$ decay channel using data from the ATLAS detector collected during Run-2 of the LHC to precisely measure the total cross sections of both gluon-gluon Fusion (ggF) and Vector Boson Fusion (VBF) Higgs production modes. The detailed results can be found in the 2023 publication...

105. The DL Advocate: Playing the devil's advocate with hidden systematic uncertainties - Poster

Guillermo Hijano Mendizabal (University of Zurich (CH))

01/02/2024, 16:25

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster (from contributed talk)

Poster Session

We propose a new method based on machine learning to play the devil’s advocate and investigate the impact of unknown systematic effects in a quantitative way. This method proceeds by reversing the measurement process and using the physics results to interpret systematic effects under the Standard Model hypothesis. We explore this idea with two alternative approaches, one relies on a...

116. Unweighted event generation with matrix element surrogates - Poster

Timo Janssen

01/02/2024, 16:30

8 ML for phenomenology and theory

Poster (from contributed talk)

Poster Session

We show that employing a sophisticated neural network emulation of QCD multijet matrix elements based on dipole factorisation can lead to a drastic acceleration of unweighted event generation in high-multiplicity LHC production processes. We incorporate these emulations as fast and accurate surrogates in a two-stage rejection sampling algorithm within the SHERPA Monte Carlo that yields...

124. the Fair Universe project and the HiggsML Uncertainty Challenge - Poster

Ragansu Chakkappai (Université Paris-Saclay (FR))

01/02/2024, 16:35

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster (from contributed talk)

Poster Session

The Fair Universe project is building a large-compute-scale AI ecosystem for sharing datasets, training large models and hosting challenges and benchmarks. Furthermore, the project is exploiting this ecosystem for an AI challenge series focused on minimizing the effects of systematic uncertainties in High-Energy Physics (HEP), and on predicting accurate confidence intervals. This talk will...

121. Reinforcement learning for automatic data quality monitoring in HEP experiments - Poster

Olivia Jullian Parra (CERN)

01/02/2024, 16:40

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster (from contributed talk)

Poster Session

The usage of modern ML techniques to automate the search for anomalies in collider physics is a very active and prolific field. Typical cases are the search for signatures of physics beyond the Standard Model and the identification of problems in the detector systems that would lead to bad-quality data, unusable for physics data analysis. We are interested in the second type of task, which can...

122. Re-simulation-based self-supervision for representation learning - Poster

Jeffrey Krupa (Massachusetts Institute of Technology)

01/02/2024, 16:45

1 ML for object identification and reconstruction

Poster (from contributed talk)

Poster Session

Self-Supervised Learning (SSL) is at the core of training modern large ML models, providing a scheme for learning powerful representations in base models that can be used in a variety of downstream tasks. However, SSL training strategies must be adapted to the type of training data, thus driving the question: what are powerful SSL strategies for collider physics data? In the talk, we present a...

120. Parametrising profiled likelihoods with neural networks - Poster

Dr Humberto Reyes-González (University of Genoa)

01/02/2024, 16:50

8 ML for phenomenology and theory

Poster (from contributed talk)

Poster Session

Full statistical models encapsulate the complete information of an experimental result, including the likelihood function given observed data. Since a few years ago ATLAS started publishing statistical models that can be reused via the pyhf framework; a major step towards fully publishing LHC results. In the case of fast Simplified Model Spectra based reinterpretation we are often only...

117. End-to-end Reconstruction Algorithm for Highly Granular Calorimeters - Poster

Mr Philipp Zehetner (Ludwig Maximilians Universitat (DE))

01/02/2024, 16:55

1 ML for object identification and reconstruction

Poster (from contributed talk)

Poster Session

We present an end-to-end reconstruction algorithm for highly granular calorimeters that includes track information to aid the reconstruction of charged particles. The algorithm starts from calorimeter hits and reconstructed tracks, and outputs a coordinate transformation in which all shower objects are well separated from each other, and in which clustering becomes trivial. Shower properties...

119. Masked particle modelling - Poster

Samuel Byrne Klein (Universite de Geneve (CH))

01/02/2024, 17:00

1 ML for object identification and reconstruction

Poster (from contributed talk)

Poster Session

The Bert pretraining paradigm has proven to be highly effective in many domains including natural language processing, image processing and biology. To apply the Bert paradigm the data needs to be described as a set of tokens, and each token needs to be labelled. To date the Bert paradigm has not been explored in the context of HEP. The samples that form the data used in HEP can be described...

123. Finetuning Foundation Models for Joint Analysis Optimization - Poster

Matthias Vigl (Technische Universitat Munchen (DE))

01/02/2024, 17:05

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Poster (from contributed talk)

Poster Session

Most searches at the LHC employ an analysis pipeline consisting of various discrete components, each individually optimized and later combined to provide relevant features used to discriminate SM background from potential signal. These are typically high-level features constructed from particle four-momenta. However, the combination of individually optimized tasks doesn't guarantee an optimal...

109. Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC - Poster

Simon Akar (University of Cincinnati (US))

01/02/2024, 17:10

4 Fast ML : Application of Machine Learning to DAQ/Trigger/Real Time Analysis

Poster (from contributed talk)

Poster Session

We have been studying the use of deep neural networks (DNNs) to identify and locate primary vertices (PVs) in proton-proton collisions at the LHC. Earlier work focused on finding primary vertices in simulated LHCb data using a hybrid approach that started with kernel density estimators (KDEs) derived from the ensemble of charged track parameters heuristically and predicted “target histogram”...

3. DeepTreeGANv2: Iterative Pooling of Point Clouds

Mr Moritz Scham (Deutsches Elektronen-Synchrotron (DE))

02/02/2024, 09:00

3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model

Contributed talk

Contributed Talks

In High Energy Physics, detailed and time-consuming simulations are used for particle interactions with detectors. To bypass these simulations with a generative model, the generation of large point clouds in a short time is required, while the complex dependencies between the particles must be correctly modelled. Particle showers are inherently tree-based processes, as each particle is...

4. Out-of-Distribution Multi-set Generation with Context Extrapolation for Amortized Simulation and Inverse Problems

Hosein Hashemi (LMU Munich)

02/02/2024, 09:20

3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model

Contributed talk

Contributed Talks

Addressing the challenge of Out-of-Distribution (OOD) multi-set generation, we introduce YonedaVAE, a novel equivariant deep generative model inspired by Category Theory, motivating the Yoneda-Pooling mechanism. This approach presents a learnable Yoneda Embedding to encode the relationships between objects in a category, providing a dynamic and generalizable representation of complex...

56. Conditional Set-to-Set Generation for Fast Simulation using Diffusion and Graph-to-Graph Translation

Dmitrii Kobylianskii (Weizmann Institute of Science (IL))

02/02/2024, 09:40

3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model

Contributed talk

Contributed Talks

Simulating particle physics data is a crucial yet computationally expensive aspect of analyzing data at the LHC. Typically, in fast simulation methods, we rely on a surrogate calorimeter model with a subsequent reconstruction algorithm to generate a set of reconstructed objects. This work demonstrates the potential to generate these reconstructed objects in one shot, effectively replacing both...

26. Unweighted event generation with matrix element surrogates

Timo Janssen

02/02/2024, 10:00

8 ML for phenomenology and theory

Contributed talk

Contributed Talks

We show that employing a sophisticated neural network emulation of QCD multijet matrix elements based on dipole factorisation can lead to a drastic acceleration of unweighted event generation in high-multiplicity LHC production processes. We incorporate these emulations as fast and accurate surrogates in a two-stage rejection sampling algorithm within the SHERPA Monte Carlo that yields...

22. A Deep Generative Model for Hadronization

Jay Chan (Lawrence Berkeley National Laboratory)

02/02/2024, 10:50

3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model

Contributed talk

Contributed Talks

Hadronization is a critical step in the simulation of high-energy particle and nuclear physics experiments. As there is no first principles understanding of this process, physically-inspired hadronization models have a large number of parameters that are fit to data. We propose an alternative approach that uses deep generative models, which are a natural replacement for classical techniques,...

47. Reinforcement learning for automatic data quality monitoring in HEP experiments

Olivia Jullian Parra (CERN)

02/02/2024, 11:10

2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection

Contributed talk

Contributed Talks

The usage of modern ML techniques to automate the search for anomalies in collider physics is a very active and prolific field. Typical cases are the search for signatures of physics beyond the Standard Model and the identification of problems in the detector systems that would lead to bad-quality data, unusable for physics data analysis. We are interested in the second type of task, which can...

1. Longitudinal Beam Diagnostics and Phase Space Reconstruction in the LHC Using ML

Konstantinos Iliakis (CERN)

02/02/2024, 11:30

3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model

Contributed talk

Contributed Talks

Accurate knowledge of longitudinal beam parameters is essential for optimizing the performance and operational efficiency of particle accelerators like the Large Hadron Collider (LHC). However, conventional methods to determine them, such as fitting techniques and tracking-based longitudinal tomography, are time-consuming and limited to analyzing data from a few bunches only. To address this,...

92. Closeout

02/02/2024, 11:50