Contributor info: Prof. Tilman Plehn

Theo Heimel, Nathan Huetsch, Ramon Winterhalder, Tilman Plehn, Anja Butter

SciPost Phys. 17, 129 (2024) · published 8 November 2024 | Toggle abstract · pdf

The matrix element method is the LHC inference method of choice for limited statistics. We present a dedicated machine learning framework, based on efficient phase-space integration, a learned acceptance and transfer function. It is based on a choice of INN and diffusion networks, and a transformer to solve jet combinatorics. We showcase this setup for the CP-phase of the top Yukawa coupling in associated Higgs and single-top production.

Anja Butter, Tomáš Ježo, Michael Klasen, Mathias Kuschick, Sofia Palacios Schweitzer, Tilman Plehn

SciPost Phys. Core 7, 064 (2024) · published 16 September 2024 | Toggle abstract · pdf

Off-shell effects in large LHC backgrounds are crucial for precision predictions and, at the same time, challenging to simulate. We present a novel method to transform high-dimensional distributions based on a diffusion neural network and use it to generate a process with off-shell kinematics from the much simpler on-shell one. Applied to a toy example of top pair production at LO we show how our method generates off-shell configurations fast and precisely, while reproducing even challenging on-shell features.

Barry M. Dillon, Luigi Favaro, Friedrich Feiden, Tanmoy Modak, Tilman Plehn

SciPost Phys. Core 7, 056 (2024) · published 16 August 2024 | Toggle abstract · pdf

We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC2021. The AnomalyCLR technique is data-driven and uses augmentations of the background data to mimic non-Standard-Model events in a model-agnostic way. It uses a permutation-invariant Transformer Encoder architecture to map the objects measured in a collider event to the representation space, where the data augmentations define a representation space which is sensitive to potential anomalous features. An AutoEncoder trained on background representations then computes anomaly scores for a variety of signals in the representation space. With AnomalyCLR we find significant improvements on performance metrics for all signals when compared to the raw data baseline.

Theo Heimel, Nathan Huetsch, Fabio Maltoni, Olivier Mattelaer, Tilman Plehn, Ramon Winterhalder

SciPost Phys. 17, 023 (2024) · published 29 July 2024 | Toggle abstract · pdf

In pursuit of precise and fast theory predictions for the LHC, we present an implementation of the MadNIS method in the MadGraph event generator. A series of improvements in MadNIS further enhance its efficiency and speed. We validate this implementation for realistic partonic processes and find significant gains from using modern machine learning in event generators.

Jona Ackerschott, Rahool Kumar Barman, Dorival Gonçalves, Theo Heimel, Tilman Plehn

SciPost Phys. 17, 001 (2024) · published 2 July 2024 | Toggle abstract · pdf

Optimal kinematic observables are often defined in specific frames and then approximated at the reconstruction level. We show how multi-dimensional unfolding methods allow us to reconstruct these observables in their proper rest frame and in a probabilistically faithful way. We illustrate our approach with a measurement of a CP-phase in the top Yukawa coupling. Our method makes use of key advantages of generative unfolding, but as a constructed observable it fits into standard LHC analysis frameworks.

J. M. Campbell, M. Diefenthaler, T. J. Hobbs, S. Höche, J. Isaacson, F. Kling, S. Mrenna, J. Reuter, S. Alioli, J. R. Andersen, C. Andreopoulos, A. M. Ankowski, E. C. Aschenauer, A. Ashkenazi, M. D. Baker, J. L. Barrow, M. van Beekveld, G. Bewick, S. Bhattacharya, C. Bierlich, E. Bothmann, P. Bredt, A. Broggio, A. Buckley, A. Butter, J. M. Butterworth, E. P. Byrne, C. M. Carloni-Calame, S. Chakraborty, X. Chen, M. Chiesa, J. T. Childers, J. Cruz-Martinez, J. Currie, N. Darvishi, M. Dasgupta, A. Denner, F. A. Dreyer, S. Dytman, B. K. El-Menoufi, T. Engel, S. Ferrario Ravasio, D. Figueroa, L. Flower, J. R. Forshaw, R. Frederix, A. Friedland, S. Frixione, H. Gallagher, K. Gallmeister, S. Gardiner, R. Gauld, J. Gaunt, A. Gavardi, T. Gehrmann, A. Gehrmann-De Ridder, L. Gellersen, W. Giele, S. Gieseke, F. Giuli, E. W. N. Glover, M. Grazzini, A. Grohsjean, C. Gütschow, K. Hamilton, T. Han, R. Hatcher, G. Heinrich, I. Helenius, O. Hen, V. Hirschi, M. Höfer, J. Holguin, A. Huss, P. Ilten, S. Jadach, A. Jentsch, S. P. Jones, W. Ju, S. Kallweit, A. Karlberg, T. Katori, M. Kerner, W. Kilian, M. M. Kirchgaeßer, S. Klein, M. Knobbe, C. Krause, F. Krauss, J. Lang, J. -N. Lang, G. Lee, S. W. Li, M. A. Lim, J. M. Lindert, D. Lombardi, L. Lönnblad, M. Löschner, N. Lurkin, Y. Ma, P. Machado, V. Magerya, A. Maier, I. Majer, F. Maltoni, M. Marcoli, G. Marinelli, M. R. Masouminia, P. Mastrolia, O. Mattelaer, J. Mazzitelli, J. McFayden, R. Medves, P. Meinzinger, J. Mo, P. F. Monni, G. Montagna, T. Morgan, U. Mosel, B. Nachman, P. Nadolsky, R. Nagar, Z. Nagy, D. Napoletano, P. Nason, T. Neumann, L. J. Nevay, O. Nicrosini, J. Niehues, K. Niewczas, T. Ohl, G. Ossola, V. Pandey, A. Papadopoulou, A. Papaefstathiou, G. Paz, M. Pellen, G. Pelliccioli, T. Peraro, F. Piccinini, L. Pickering, J. Pires, W. Placzek, S. Plätzer, T. Plehn, S. Pozzorini, S. Prestel, C. T. Preuss, A. C. Price, S. Quackenbush, E. Re, D. Reichelt, L. Reina, C. Reuschle, P. Richardson, M. Rocco, N. Rocco, M. Roda, A. Rodriguez Garcia, S. Roiser, J. Rojo, L. Rottoli, G. P. Salam, M. Schönherr, S. Schuchmann, S. Schumann, R. Schürmann, L. Scyboz, M. H. Seymour, F. Siegert, A. Signer, G. Singh Chahal, A. Siódmok, T. Sjöstrand, P. Skands, J. M. Smillie, J. T. Sobczyk, D. Soldin, D. E. Soper, A. Soto-Ontoso, G. Soyez, G. Stagnitto, J. Tena-Vidal, O. Tomalak, F. Tramontano, S. Trojanowski, Z. Tu, S. Uccirati, T. Ullrich, Y. Ulrich, M. Utheim, A. Valassi, A. Verbytskyi, R. Verheyen, M. Wagman, D. Walker, B. R. Webber, L. Weinstein, O. White, J. Whitehead, M. Wiesemann, C. Wilkinson, C. Williams, R. Winterhalder, C. Wret, K. Xie, T-Z. Yang, E. Yazgan, G. Zanderighi, S. Zanoli, K. Zapp

SciPost Phys. 16, 130 (2024) · published 24 May 2024 | Toggle abstract · pdf

We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments.

Anja Butter, Tilman Plehn, Nathalie Soybelman, Johann Brehmer

SciPost Phys. 16, 037 (2024) · published 29 January 2024 | Toggle abstract · pdf

While neural networks offer an attractive way to numerically encode functions, actual formulas remain the language of theoretical particle physics. We use symbolic regression trained on matrix-element information to extract, for instance, optimal LHC observables. This way we invert the usual simulation paradigm and extract easily interpretable formulas from complex simulated data. We introduce the method using the effect of a dimension-6 coefficient on associated ZH production. We then validate it for the known case of CP-violation in weak-boson-fusion Higgs production, including detector effects.

Ilaria Brivio, Sebastian Bruggisser, Nina Elmer, Emma Geoffray, Michel Luchmann, Tilman Plehn

SciPost Phys. 16, 035 (2024) · published 29 January 2024 | Toggle abstract · pdf

Global SMEFT analyses have become a key interpretation framework for LHC physics, quantifying how well a large set of kinematic measurements agrees with the Standard Model. This agreement is encoded in measured Wilson coefficients and their uncertainties. A technical challenge of global analyses are correlations. We compare, for the first time, results from a profile likelihood and a Bayesian marginalization for a given data set with a comprehensive uncertainty treatment. Using the validated Bayesian framework we analyse a series of new kinematic measurements. For the updated dataset we find and explain differences between the marginalization and profile likelihood treatments.

Ranit Das, Luigi Favaro, Theo Heimel, Claudius Krause, Tilman Plehn, David Shih

SciPost Phys. 16, 031 (2024) · published 25 January 2024 | Toggle abstract · pdf

Well-trained classifiers and their complete weight distributions provide us with a well-motivated and practicable method to test generative networks in particle physics. We illustrate their benefits for distribution-shifted jets, calorimeter showers, and reconstruction-level events. In all cases, the classifier weights make for a powerful test of the generative network, identify potential problems in the density estimation, relate them to the underlying physics, and tie in with a comprehensive precision and uncertainty treatment for generative networks.

Anja Butter, Barry M. Dillon, Tilman Plehn, Lorenz Vogel

SciPost Phys. Core 6, 085 (2023) · published 12 December 2023 | Toggle abstract · pdf

Discriminating quark-like from gluon-like jets is, in many ways, a key challenge for many LHC analyses. First, we use a known difference in PYTHIA and HERWIG simulations to show how decorrelated taggers would break down when the most distinctive feature is aligned with theory uncertainties. We propose conditional training on interpolated samples, combined with a controlled Bayesian network, as a more resilient framework. The interpolation parameter can be used to optimize the training evaluated on a calibration dataset, and to test the stability of this optimization. The interpolated training might also be useful to track generalization errors when training networks on simulations.

Barry M. Dillon, Luigi Favaro, Tilman Plehn, Peter Sorrenson, Michael Krämer

SciPost Phys. Core 6, 074 (2023) · published 3 November 2023 | Toggle abstract · pdf

Autoencoders are an effective analysis tool for the LHC, as they represent one of its main goal of finding physics beyond the Standard Model. The key challenge is that out-of-distribution anomaly searches based on the compressibility of features do not apply to the LHC, while existing density-based searches lack performance. We present the first autoencoder which identifies anomalous jets symmetrically in the directions of higher and lower complexity. The normalized autoencoder combines a standard bottleneck architecture with a well-defined probabilistic description. It works better than all available autoencoders for top vs QCD jets and reliably identifies different dark-jet signals.

Thorsten Buss, Barry M. Dillon, Thorben Finke, Michael Krämer, Alessandro Morandini, Alexander Mück, Ivan Oleksiyuk, Tilman Plehn

SciPost Phys. 15, 168 (2023) · published 17 October 2023 | Toggle abstract · pdf

Searches for anomalies are a significant motivation for the LHC and help define key analysis steps, including triggers. We discuss specific examples how LHC anomalies can be defined through probability density estimates, evaluated in a physics space or in an appropriate neural network latent space, and discuss the model-dependence in choosing an appropriate data parameterisation. We illustrate this for classical k-means clustering, a Dirichlet variational autoencoder, and invertible neural networks. For two especially challenging scenarios of jets from a dark sector we evaluate the strengths and limitations of each method.

Theo Heimel, Ramon Winterhalder, Anja Butter, Joshua Isaacson, Claudius Krause, Fabio Maltoni, Olivier Mattelaer, Tilman Plehn

SciPost Phys. 15, 141 (2023) · published 6 October 2023 | Toggle abstract · pdf

Theory predictions for the LHC require precise numerical phase-space integration and generation of unweighted events. We combine machine-learned multi-channel weights with a normalizing flow for importance sampling, to improve classical methods for numerical integration. We develop an efficient bi-directional setup based on an invertible network, combining online and buffered training for potentially expensive integrands. We illustrate our method for the Drell-Yan process with an additional narrow resonance.

Anja Butter, Theo Heimel, Till Martini, Sascha Peitzsch, Tilman Plehn

SciPost Phys. 15, 094 (2023) · published 14 September 2023 | Toggle abstract · pdf

The matrix element method is widely considered the ultimate LHC inference tool for small event numbers. We show how a combination of two conditional generative neural networks encodes the QCD radiation and detector effects without any simplifying assumptions, while keeping the computation of likelihoods for individual events numerically efficient. We illustrate our approach for the CP-violating phase of the top Yukawa coupling in associated Higgs and single-top production. Currently, the limiting factor for the precision of our approach is jet combinatorics.

Tanmoy Modak, Lennart Röver, Björn Malte Schäfer, Benedikt Schosser, Tilman Plehn

SciPost Phys. 15, 047 (2023) · published 3 August 2023 | Toggle abstract · pdf

Starobinsky inflation is an attractive, fundamental model to explain the Planck measurements, and its higher-order extension may allow us to probe quantum gravity effects. We show that future CMB data combined with the 21cm intensity map from SKA will meaningfully probe such an extended Starobinsky model. A combined analysis will provide a precise measurement and intriguing insight into inflationary dynamics, even accounting for correlations with astrophysical parameters.

Aishik Ghosh, Benjamin Nachman, Tilman Plehn, Lily Shire, Tim M. P. Tait, Daniel Whiteson

SciPost Phys. Core 6, 045 (2023) · published 20 June 2023 | Toggle abstract · pdf

A comprehensive uncertainty estimation is vital for the precision program of the LHC. While experimental uncertainties are often described by stochastic processes and well-defined nuisance parameters, theoretical uncertainties lack such a description. We study uncertainty estimates for cross-section predictions based on scale variations across a large set of processes. We find patterns similar to a stochastic origin, with accurate uncertainties for processes mediated by the strong force, but a systematic underestimate for electroweak processes. We propose an improved scheme, based on the scale variation of reference processes, which reduces outliers in the mapping from leading order to next-to-leading-order in perturbation theory.

Simon Badger, Anja Butter, Michel Luchmann, Sebastian Pitz, Tilman Plehn

SciPost Phys. Core 6, 034 (2023) · published 25 April 2023 | Toggle abstract · pdf

Evaluating loop amplitudes is a time-consuming part of LHC event generation. For di-photon production with jets we show that simple, Bayesian networks can learn such amplitudes and model their uncertainties reliably. A boosted training of the Bayesian network further improves the uncertainty estimate and the network precision in critical phase space regions. In general, boosted network training of Bayesian networks allows us to move between fit-like and interpolation-like regimes of network training.

Anja Butter, Tilman Plehn, Steffen Schumann, Simon Badger, Sascha Caron, Kyle Cranmer, Francesco Armando Di Bello, Etienne Dreyer, Stefano Forte, Sanmay Ganguly, Dorival Gonçalves, Eilam Gross, Theo Heimel, Gudrun Heinrich, Lukas Heinrich, Alexander Held, Stefan Höche, Jessica N. Howard, Philip Ilten, Joshua Isaacson, Timo Janßen, Stephen Jones, Marumi Kado, Michael Kagan, Gregor Kasieczka, Felix Kling, Sabine Kraml, Claudius Krause, Frank Krauss, Kevin Kröninger, Rahool Kumar Barman, Michel Luchmann, Vitaly Magerya, Daniel Maitre, Bogdan Malaescu, Fabio Maltoni, Till Martini, Olivier Mattelaer, Benjamin Nachman, Sebastian Pitz, Juan Rojo, Matthew Schwartz, David Shih, Frank Siegert, Roy Stegeman, Bob Stienen, Jesse Thaler, Rob Verheyen, Daniel Whiteson, Ramon Winterhalder, Jure Zupan

SciPost Phys. 14, 079 (2023) · published 21 April 2023 | Toggle abstract · pdf

First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.

Anja Butter, Theo Heimel, Sander Hummerich, Tobias Krebs, Tilman Plehn, Armand Rousselot, Sophia Vent

SciPost Phys. 14, 078 (2023) · published 20 April 2023 | Toggle abstract · pdf

Generative networks are opening new avenues in fast event generation for the LHC. We show how generative flow networks can reach percent-level precision for kinematic distributions, how they can be trained jointly with a discriminator, and how this discriminator improves the generation. Our joint training relies on a novel coupling of the two networks which does not require a Nash equilibrium. We then estimate the generation uncertainties through a Bayesian network setup and through conditional data augmentation, while the discriminator ensures that there are no systematic inconsistencies compared to the training data.

Anja Butter, Sascha Diefenbacher, Gregor Kasieczka, Benjamin Nachman, Tilman Plehn, David Shih, Ramon Winterhalder

SciPost Phys. 13, 087 (2022) · published 7 October 2022 | Toggle abstract · pdf

The large data rates at the LHC require an online trigger system to select relevant collisions. Rather than compressing individual events, we propose to compress an entire data set at once. We use a normalizing flow as a deep generative model to learn the probability density of the data online. The events are then represented by the generative neural network and can be inspected offline for anomalies or used for other analysis purposes. We demonstrate our new approach for a toy model and a correlation-enhanced bump hunt.

Pierre Baldi, Lukas Blecher, Anja Butter, Julian Collado, Jessica N. Howard, Fabian Keilbach, Tilman Plehn, Gregor Kasieczka, Daniel Whiteson

SciPost Phys. 13, 064 (2022) · published 23 September 2022 | Toggle abstract · pdf

QCD-jets at the LHC are described by simple physics principles. We show how super-resolution generative networks can learn the underlying structures and use them to improve the resolution of jet images. We test this approach on massless QCD-jets and on fat top-jets and find that the network reproduces their main features even without training on pure samples. In addition, we show how a slim network architecture can be constructed once we have control of the full network performance.

Marco Bellagente, Manuel Haußmann, Michel Luchmann, Tilman Plehn

SciPost Phys. 13, 003 (2022) · published 21 July 2022 | Toggle abstract · pdf

Following the growing success of generative neural networks in LHC simulations, the crucial question is how to control the networks and assign uncertainties to their event output. We show how Bayesian normalizing flow or invertible networks capture uncertainties from the training and turn them into an uncertainty on the event weight. Fundamentally, the interplay between density and uncertainty estimates indicates that these networks learn functions in analogy to parameter fits rather than binned event counts.

Tanmoy Modak, Tilman Plehn, Lennart Röver, Björn Malte Schäfer

SciPost Phys. Core 5, 037 (2022) · published 18 July 2022 | Toggle abstract · pdf

SKA will be a major step forward not only in astrophysics, but also in precision cosmology. We show how the neutral hydrogen intensity map can be combined with the Planck measurements of the CMB power spectrum, to provide a precision test of the inflaton potential. For a conservative range of redshifts we find that SKA can significantly improve current constraints on the Hubble slow-roll parameters.

Barry M. Dillon, Gregor Kasieczka, Hans Olischlager, Tilman Plehn, Peter Sorrenson, Lorenz Vogel

SciPost Phys. 12, 188 (2022) · published 9 June 2022 | Toggle abstract · pdf

Collider searches face the challenge of defining a representation of high-dimensional data such that physical symmetries are manifest, the discriminating features are retained, and the choice of representation is new-physics agnostic. We introduce JetCLR to solve the mapping from low-level data to optimized observables though self-supervised contrastive learning. As an example, we construct a data representation for top and QCD jets using a permutation-invariant transformer-encoder network and visualize its symmetry properties. We compare the JetCLR representation with alternative representations using linear classifier tests and find it to work quite well.

Ramon Winterhalder, Vitaly Magerya, Emilio Villa, Stephen P. Jones, Matthias Kerner, Anja Butter, Gudrun Heinrich, Tilman Plehn

SciPost Phys. 12, 129 (2022) · published 13 April 2022 | Toggle abstract · pdf

Numerical evaluations of Feynman integrals often proceed via a deformation of the integration contour into the complex plane. While valid contours are easy to construct, the numerical precision for a multi-loop integral can depend critically on the chosen contour. We present methods to optimize this contour using a combination of optimized, global complex shifts and a normalizing flow. They can lead to a significant gain in precision.

Ilaria Brivio, Sebastian Bruggisser, Emma Geoffray, Wolfgang Kilian, Michael Krämer, Michel Luchmann, Tilman Plehn, Benjamin Summ

SciPost Phys. 12, 036 (2022) · published 24 January 2022 | Toggle abstract · pdf

We present a global analysis of the Higgs and electroweak sector, in the SMEFT framework and matched to a UV-completion. As the UV-model we use the triplet extension of the electroweak gauge sector. The matching is performed at one loop, employing functional methods. In the SFitter analysis, we pay particular attention to theory uncertainties arising from the matching. Our results highlight the complementarity between SMEFT and model-specific analyses.

Barry M. Dillon, Tilman Plehn, Christof Sauer, Peter Sorrenson

SciPost Phys. 11, 061 (2021) · published 17 September 2021 | Toggle abstract · pdf

Autoencoders as tools behind anomaly searches at the LHC have the structural problem that they only work in one direction, extracting jets with higher complexity but not the other way around. To address this, we derive classifiers from the latent space of (variational) autoencoders, specifically in Gaussian mixture and Dirichlet latent spaces. In particular, the Dirichlet setup solves the problem and improves both the performance and the interpretability of the networks.

Wei-Shu Hou, Tanmoy Modak, Tilman Plehn

SciPost Phys. 10, 150 (2021) · published 21 June 2021 | Toggle abstract · pdf

Electroweak baryogenesis in a two-Higgs doublet model is a well-motivated and testable scenario for physics beyond the Standard Model. An attractive way of providing $CP$ violation is through flavor-changing Higgs couplings, where linking top and charm quarks is hardly constrained by flavor and $CP$-violation constraints. We show how this scenario can be conclusively tested by searching for heavy charged and neutral Higgs bosons at the LHC. While the charged Higgs signature requires a dedicated analysis, the neutral Higgs signature will be covered by a general search for same-sign top pairs.

Anja Butter, Sascha Diefenbacher, Gregor Kasieczka, Benjamin Nachman, Tilman Plehn

SciPost Phys. 10, 139 (2021) · published 10 June 2021 | Toggle abstract · pdf

A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample. We show for a simple example with increasing dimensionality how generative networks indeed amplify the training statistics. We quantify their impact through an amplification factor or equivalent numbers of sampled events.

Sebastian Bieringer, Anja Butter, Theo Heimel, Stefan Höche, Ullrich Köthe, Tilman Plehn, Stefan T. Radev

SciPost Phys. 10, 126 (2021) · published 2 June 2021 | Toggle abstract · pdf

QCD splittings are among the most fundamental theory concepts at the LHC. We show how they can be studied systematically with the help of invertible neural networks. These networks work with sub-jet information to extract fundamental parameters from jet samples. Our approach expands the LEP measurements of QCD Casimirs to a systematic test of QCD properties based on low-level jet observables. Starting with an toy example we study the effect of the full shower, hadronization, and detector effects in detail.

Mathias Backes, Anja Butter, Tilman Plehn, Ramon Winterhalder

SciPost Phys. 10, 089 (2021) · published 23 April 2021 | Toggle abstract · pdf

Event generation with neural networks has seen significant progress recently. The big open question is still how such new methods will accelerate LHC simulations to the level required by upcoming LHC runs. We target a known bottleneck of standard simulations and show how their unweighting procedure can be improved by generative networks. This can, potentially, lead to a very significant gain in simulation speed.

Ingolf Bischer, Tilman Plehn, Werner Rodejohann

SciPost Phys. 10, 039 (2021) · published 17 February 2021 | Toggle abstract · pdf

Sterile neutrinos coupling only to third-generation fermions are attractive dark matter candidates. In an EFT approach we demonstrate that they can generate the observed relic density without violating constraints from direct and indirect detection. Such an EFT represents a gauged third-generation $B-L$ model, as well as third-generation scalar or vector leptoquarks. LHC measurements naturally distinguish the different underlying models.

Martin Bauer, Patrick Foldenauer, Peter Reimitz, Tilman Plehn

SciPost Phys. 10, 030 (2021) · published 5 February 2021 | Toggle abstract · pdf

We systematically study models with light scalar and pseudoscalar dark matter candidates and their potential signals at the LHC. First, we derive cosmological bounds on models with the Standard Model Higgs mediator and with a new weak-scale mediator. Next, we study two processes inspired by the indirect and direct detection process topologies, now happening inside the LHC detectors. We find that LHC can observe very light dark matter over a huge mass range if it is produced in mediator decays and then scatters with the detector material to generate jets in the nuclear recoil.

Gregor Kasieczka, Michel Luchmann, Florian Otterpohl, Tilman Plehn

SciPost Phys. 9, 089 (2020) · published 18 December 2020 | Toggle abstract · pdf

We show how to treat systematic uncertainties using Bayesian deep networks for regression. First, we analyze how these networks separately trace statistical and systematic uncertainties on the momenta of boosted top quarks forming fat jets. Next, we propose a novel calibration procedure by training on labels and their error bars. Again, the network cleanly separates the different uncertainties. As a technical side effect, we show how Bayesian networks can be extended to describe non-Gaussian features.

Marco Bellagente, Anja Butter, Gregor Kasieczka, Tilman Plehn, Armand Rousselot, Ramon Winterhalder, Lynton Ardizzone, Ullrich Köthe

SciPost Phys. 9, 074 (2020) · published 18 November 2020 | Toggle abstract · pdf

For simulations where the forward and the inverse directions have a physics meaning, invertible neural networks are especially useful. A conditional INN can invert a detector simulation in terms of high-level observables, specifically for ZW production at the LHC. It allows for a per-event statistical interpretation. Next, we allow for a variable number of QCD jets. We unfold detector effects and QCD radiation to a pre-defined hard process, again with a per-event probabilistic interpretation over parton-level phase space.

Anja Butter, Tilman Plehn, Ramon Winterhalder

SciPost Phys. Core 3, 009 (2020) · published 5 November 2020 | Toggle abstract · pdf

Subtracting event samples is a common task in LHC simulation and analysis, and standard solutions tend to be inefficient. We employ generative adversarial networks to produce new event samples with a phase space distribution corresponding to added or subtracted input samples. We first illustrate for a toy example how such a network beats the statistical limitations of the training data. We then show how such a network can be used to subtract background events or to include non-local collinear subtraction events at the level of unweighted 4-vector events.

Tilman Plehn, Peter Reimitz, Peter Richardson

SciPost Phys. 8, 092 (2020) · published 24 June 2020 | Toggle abstract · pdf

GeV-scale dark matter is an increasingly attractive target for direct detection, indirect detection, and collider searches. Its annihilation into hadronic final states produces a challenging zoo of light hadronic resonances. We update Herwig7 to study the photon and positron spectra from annihilation through a vector mediator. It covers dark matter masses between 250 MeV and 5 GeV and includes an error estimate.

Marco Bellagente, Anja Butter, Gregor Kasieczka, Tilman Plehn, Ramon Winterhalder

SciPost Phys. 8, 070 (2020) · published 29 April 2020 | Toggle abstract · pdf

LHC analyses directly comparing data and simulated events bear the danger of using first-principle predictions only as a black-box part of event simulation. We show how simulations, for instance, of detector effects can instead be inverted using generative networks. This allows us to reconstruct parton level information from measured events. Our results illustrate how, in general, fully conditional generative networks can statistically invert Monte Carlo simulations. As a technical by-product we show how a maximum mean discrepancy loss can be staggered or cooled.

Ben G. Lillard, Tilman Plehn, Alexis Romero, Tim M. P. Tait

SciPost Phys. 8, 043 (2020) · published 17 March 2020 | Toggle abstract · pdf

Typical LHC analyses search for local features in kinematic distributions. Assumptions about anomalous patterns limit them to a relatively narrow subset of possible signals. Wavelets extract information from an entire distribution and decompose it at all scales, simultaneously searching for features over a wide range of scales. We propose a systematic wavelet analysis and show how bumps, bump-dip combinations, and oscillatory patterns are extracted. Our kinematic wavelet analysis kit KWAK provides a publicly available framework to analyze and visualize general distributions.

Sascha Diefenbacher, Hermann Frost, Gregor Kasieczka, Tilman Plehn, Jennifer M. Thompson

SciPost Phys. 8, 023 (2020) · published 7 February 2020 | Toggle abstract · pdf

Capsule networks are ideal tools to combine event-level and subjet information at the LHC. After benchmarking our capsule network against standard convolutional networks, we show how multi-class capsules extract a resonance decaying to top quarks from both, QCD di-jet and the top continuum backgrounds. We then show how its results can be easily interpreted. Finally, we use associated top-Higgs production to demonstrate that capsule networks can work on overlaying images to go beyond calorimeter information.

Sven Bollweg, Manuel Haussmann, Gregor Kasieczka, Michel Luchmann, Tilman Plehn, Jennifer Thompson

SciPost Phys. 8, 006 (2020) · published 16 January 2020 | Toggle abstract · pdf

Bayesian neural networks allow us to keep track of uncertainties, for example in top tagging, by learning a tagger output together with an error band. We illustrate the main features of Bayesian versions of established deep-learning taggers. We show how they capture statistical uncertainties from finite training samples, systematics related to the jet energy scale, and stability issues through pile-up. Altogether, Bayesian networks offer many new handles to understand and control deep learning at the LHC without introducing a visible prior effect and without compromising the network performance.

Anja Butter, Tilman Plehn, Ramon Winterhalder

SciPost Phys. 7, 075 (2019) · published 4 December 2019 | Toggle abstract · pdf

Event generation for the LHC can be supplemented by generative adversarial networks, which generate physical events and avoid highly inefficient event unweighting. For top pair production we show how such a network describes intermediate on-shell particles, phase space boundaries, and tails of distributions. In particular, we introduce the maximum mean discrepancy to resolve sharp local features. It can be extended in a straightforward manner to include for instance off-shell contributions, higher orders, or approximate detector effects.

Gregor Kasieczka, Tilman Plehn, Anja Butter, Kyle Cranmer, Dipsikha Debnath, Barry M. Dillon, Malcolm Fairbairn, Darius A. Faroughy, Wojtek Fedorko, Christophe Gay, Loukas Gouskos, Jernej F. Kamenik, Patrick T. Komiske, Simon Leiss, Alison Lister, Sebastian Macaluso, Eric M. Metodiev, Liam Moore, Ben Nachman, Karl Nordström, Jannicke Pearkes, Huilin Qu, Yannik Rath, Marcel Rieger, David Shih, Jennifer M. Thompson, Sreedevi Varma

SciPost Phys. 7, 014 (2019) · published 30 July 2019 | Toggle abstract · pdf

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established methods they rely on low-level input, for instance calorimeter output. While their network architectures are vastly different, their performance is comparatively similar. In general, we find that these new approaches are extremely powerful and great fun.

Gregor Kasieczka, Nicholas Kiefer, Tilman Plehn, Jennifer M. Thompson

SciPost Phys. 6, 069 (2019) · published 18 June 2019 | Toggle abstract · pdf

Distinguishing quarks from gluons based on low-level detector output is one of the most challenging applications of multi-variate and machine learning techniques at the LHC. We first show the performance of our 4-vector-based LoLa tagger without and after considering detector effects. We then discuss two benchmark applications, mono-jet searches with a gluon-rich signal and di-jet resonances with a quark-rich signal. In both cases an immediate benefit compared to the standard event-level analysis exists.

Anke Biekoetter, Tyler Corbett, Tilman Plehn

SciPost Phys. 6, 064 (2019) · published 5 June 2019 | Toggle abstract · pdf

We present a global analysis of the Higgs and electroweak sector based on LHC Run II and electroweak precision observables. We show which measurements provide the leading constraints on Higgs-related operators, and how the achieved LHC precision makes it necessary to combine rate measurements with electroweak precision observables. The SFitter framework allows us to include kinematic distributions beyond pre-defined ATLAS and CMS observables, independently study correlations, and avoid Gaussian assumptions for theory uncertainties. These Run II results are a step towards a precision physics program at the LHC, interpreted in terms of effective operators.

Theo Heimel, Gregor Kasieczka, Tilman Plehn, Jennifer M Thompson

SciPost Phys. 6, 030 (2019) · published 8 March 2019 | Toggle abstract · pdf

Autoencoder networks, trained only on QCD jets, can be used to search for anomalies in jet-substructure. We show how, based either on images or on 4-vectors, they identify jets from decays of arbitrary heavy resonances. To control the backgrounds and the underlying systematics we can de-correlate the jet mass using an adversarial network. Such an adversarial autoencoder allows for a general and at the same time easily controllable search for new physics. Ideally, it can be trained and applied to data in the same phase space region, allowing us to efficiently search for new physics using un-supervised learning.

Anke Biekoetter, Dorival Gonçalves, Tilman Plehn, Michihisa Takeuchi, Dirk Zerwas

SciPost Phys. 6, 024 (2019) · published 25 February 2019 | Toggle abstract · pdf

We estimate the reach of global Higgs analyses at a 27 TeV hadron collider in terms of Higgs couplings and in terms of a gauge-invariant effective Lagrangian, including invisible Higgs decays and the Higgs self-coupling. The new collider will indirectly probe new physics in the TeV range and allow for a meaningful test of the Higgs self-coupling also embedded in a global analysis.

Martin Bauer, Sascha Diefenbacher, Tilman Plehn, Michael Russell, Daniel A. Camargo

SciPost Phys. 5, 036 (2018) · published 19 October 2018 | Toggle abstract · pdf

A consistent model for vector mediators to dark matter needs to be anomaly-free and include a scalar mode from mass generation. For the leading U(1) extensions we review the structure and constraints, including kinetic mixing at loop level. The thermal relic density suggests that the vector and scalar masses are similar. For the LHC we combine a $Z'$ shape analysis with mono-jets. For the latter, we find that a shape analysis offers significant improvement over existing cut-and-count approaches. Direct detection limits strongly constrain the kinetic mixing angle and we propose a $\ell^+\ell^- E_T$ search strategy based on the scalar mediator.

Elias Bernreuther, Jan Horak, Tilman Plehn, Anja Butter

SciPost Phys. 5, 034 (2018) · published 17 October 2018 | Toggle abstract · pdf

Mono-X searches are standard dark matter search strategies at the LHC. First, we show how in the case of initial state radiation they essentially collapse to mono-jet searches. Second, we systematically study mono-X signatures from decays of heavier dark matter states. Direct detection constraints strongly limit our MSSM expectations, but largely vanish for mono-Z and mono-Higgs signals once we include light NMSSM mediators. Finally, the decay topology motivates mono-W-pair and mono-Higgs-pair searches, strengthening and complementing their mono-X counterparts.

Anja Butter, Gregor Kasieczka, Tilman Plehn, Michael Russell

SciPost Phys. 5, 028 (2018) · published 26 September 2018 | Toggle abstract · pdf

We introduce a new and highly efficient tagger for hadronically decaying top quarks, based on a deep neural network working with Lorentz vectors and the Minkowski metric. With its novel machine learning setup and architecture it allows us to identify boosted top quarks not only from calorimeter towers, but also including tracking information. We show how the performance of our tagger compares with QCD-inspired and image-recognition approaches and find that it significantly increases the performance for strongly boosted top quarks.

Anke Biekoetter, Fabian Keilbach, Rhea Moutafis, Tilman Plehn, Jennifer M. Thompson

SciPost Phys. 4, 035 (2018) · published 22 June 2018 | Toggle abstract · pdf

Searches for invisible Higgs decays in weak boson fusion are a well-known laboratory for jets and QCD studies. We present a series of results on tagging jets and central jet activity. First, precision analyses of the central jet activity require full control of single top production in some analyses. Second, the rate dependence on the size of the tagging jets is not limited to weak boson fusion. For the first time, we show how subjet information on the tagging jets and on the additional jet activity can be used to extract the Higgs signal. The additional observables relieve some of the pressure on other, critical observables. Finally, we compare the performance of weak boson fusion and associated Higgs production.