arXiv subjects

Dennis Schwarz

Publications and source records attributed to Dennis Schwarz.

14 recordsLinked to original sources

arXiv2026

The Higgs boson through the lens of electroweak precision data

The global electroweak fit tests the quantum structure of the Standard Model by confronting precision measurements with high-order theoretical predictions. This paper presents an updated Gfitter analysis using the latest experimental inputs, notably the new world average of the $W$-boson mass, and state-of-the-art theoretical calculations. The fit yields indirect determinations of precision observables, including the $W$ and Higgs-boson masses, the top-quark mass, and the effective leptonic weak mixing angle, confirming the remarkable internal consistency of the Standard Model. The analysis is further extended to the Higgs sector by combining ATLAS and CMS signal-strength measurements with electroweak precision data in the $κ$ framework. The resulting electroweak constraints on the Higgs couplings to vector bosons allow a determination of the total Higgs-boson width with a precision of about 10\% or better within a leading-logarithmic oblique interpretation, and provide bounds on invisible and undetected Higgs-boson branching fractions without using direct searches for invisible Higgs decays. The paper also presents bounds on Wilson coefficients in the Standard Model Effective Field Theory together with projections for the precision of the global electroweak fit achievable at the FCC-ee.

hep-ph↗

arXiv2025

FAIR Universe HiggsML Uncertainty Dataset and Competition

The FAIR Universe HiggsML Uncertainty Challenge focused on measuring the physical properties of elementary particles with imperfect simulators. Participants were required to compute and report confidence intervals for a parameter of interest regarding the Higgs boson while accounting for various systematic (epistemic) uncertainties. The dataset is a tabular dataset of 28 features and 280 million instances. Each instance represents a simulated proton-proton collision as observed at CERN's Large Hadron Collider in Geneva, Switzerland. The features of these simulations were chosen to capture key characteristics of different types of particles. These include primary attributes, such as the energy and three-dimensional momentum of the particles, as well as derived attributes, which are calculated from the primary ones using domain-specific knowledge. Additionally, a label feature designates each instance's type of proton-proton collision, distinguishing the Higgs boson events of interest from three background sources. As outlined in this paper, the permanent release of the dataset allows long-term benchmarking of new techniques. The leading submissions, including Contrastive Normalising Flows and Density Ratios estimation through classification, are described. Our challenge has brought together the physics and machine learning communities to advance our understanding and methodologies in handling systematic uncertainties within AI techniques.

hep-ph↗

arXiv2025

Unbinned inclusive cross-section measurements with machine-learned systematic uncertainties

We introduce a novel methodology for addressing systematic uncertainties in unbinned inclusive cross-section measurements and related collider-based inference problems. Our approach incorporates known analytic dependencies on parameters of interest, including signal strengths and nuisance parameters. When these dependencies are unknown, as is frequently the case for systematic uncertainties, dedicated neural network parametrizations provide an approximation that is trained on simulated data. The resulting machine-learned surrogate captures the complete parameter dependence of the likelihood ratio, providing a near-optimal test statistic. As a case study, we perform a first-principles inclusive cross-section measurement of $\textrm{H}\rightarrowττ$ in the single-lepton channel, utilizing simulated data from the FAIR Universe Higgs Uncertainty Challenge. Results in Asimov data, from large-scale toy studies, and using the Fisher information demonstrate significant improvements over traditional binned methods. Our computer code ``Guaranteed Optimal Log-Likelihood-based Unbinned Method'' (GOLLUM) for machine-learning and inference is publicly available.

hep-ph↗

arXiv2025

How to Unfold Top Decays

Using unfolded top-quark decay data we can measure the top quark mass, as well as search for unexpected kinematic effects. We present a new generative unfolding method for the two tasks and show how they both benefit from unbinned, high-dimensional unfolding. Unlike weight-based or iterative generative methods we include a targeted unbiasing with respect to the training data. This shows significant advantages over standard, iterative methods, in terms of applicability, flexibility and accuracy.

hep-ph↗

arXiv2025

Using the $W$ as a Standard Candle to Reach the Top: Calibrating Energy Correlator Based Top Mass Measurements

The top quark mass is a key parameter of the Standard Model, yet measuring it precisely at the Large Hadron Collider (LHC) is challenging. Inspired by the use of standard candles in cosmology, we propose a novel energy correlator-based observable, which directly accesses the dimensionless quantity $m_t$/$m_W$. We perform a Monte Carlo study to demonstrate the feasibility of the top mass extraction from Run 2, 3, and High-Luminosity LHC datasets. Our resulting $m_t$ can be defined in a well-controlled short-distance mass scheme and exhibits remarkably small uncertainties from nonperturbative effects, as well as insensitivity to parton distribution functions, outlining a roadmap for a record precision measurement at the LHC.

hep-ph↗

arXiv2024

Top Quark Mass Extractions from Energy Correlators: A Feasibility Study

In a recent article, we proposed an energy correlator-based method to achieve a precision top quark mass extraction from jet substructure, using the $W$-boson mass as a standard candle. In this paper, we perform an extensive event generator simulation study of this proposal, testing both its experimental viability, as well as its sensitivity to different subprocesses in the top quark production and decay. On the experimental side, we show that uncertainties in the jet energy scale, constituent energy scale, and tracking efficiency have a minimal effect. On the theoretical side, we find that our observable isolates the perturbative decay of the top quark, while nonperturbative physics, such as the modelling of color reconnection, and underlying event, have a negligible impact on the distribution. We conclude that our proposed measurement is resilient to the experimental and theoretical aspects of the hadron collider environment, with variations in model parameters consistently leading to $\lesssim 100~$MeV shifts in the measured top mass. Our results motivate precision theoretical calculations of the energy correlator on top decays, both analytic and using parton shower generators, and further exploration of the experimental measurement.

hep-ph↗

arXiv2024

A rotation-equivariant graph neural network for learning hadronic SMEFT effects

We introduce a graph neural network architecture designed to extract novel phenomena in the Standard Model Effective Field Theory (SMEFT) context from LHC collision data. The proposed infrared- and collinear-safe architecture is sensitive to the angular orientation of radiation patterns in jets from hadronic decays of highly energetic massive particles. Equivariance with respect to rotations around the jet axis allows for extracting the information on the angular orientation decoupled from the jet substructure. We demonstrate the robustness of the approach and its potential for future probes of the SMEFT at the LHC through toy studies and with realistic event simulations of the WZ process in the semileptonic decay channel.

hep-ph↗

arXiv2023

Precision Top Mass Measurement Using Energy Correlators

Precision top mass measurements at hadron colliders have been notoriously difficult. The fundamental challenge in the current approaches lies in achieving simultaneously high top mass sensitivity and good theoretical control. Inspired by the use of standard candles in cosmology, we overcome this problem by showing that a single energy correlator-based observable can be constructed that reflects the characteristic angular scales associated with both the $W$-boson and top quark. This gives direct access to the dimensionless quantity $m_{t}/m_{W}$, from which $m_{t}$ can be extracted in a well-defined short-distance mass scheme as a function of the well-known $m_{W}$. A Monte-Carlo-based study is performed to demonstrate the properties of our observable and the statistical feasibility of its extraction from the Run 2 and 3 and High-Luminosity LHC data sets. The resulting $m_t$ has remarkably small uncertainties from hadronization effects and is insensitive to the underlying event and parton distribution functions. Our proposed observable provides a road map for a rich program to achieve a top mass determination at the LHC with record precision.

hep-ph↗

arXiv2022

Studies of top quark properties in CMS

In this article, recent studies of top quark properties in CMS are presented. The discussed analyses are measurements of the charge asymmetry, CP violation and jet mass, all carried out in final states of $\textrm{t}\bar{\textrm{t}}$ production with one or two leptons. The data were recorded with the CMS detector in the years 2016 to 2018.

hep-ex↗

arXiv2022

Recent studies on top quark properties and mass in CMS

Studies of top quark properties using data collected by the CMS experiment are presented, including direct measurements of properties or extractions using differential cross section measurements. The latest results on top quark mass measurements using multiple kinematic distributions in a likelihood technique as well as the top quark pole mass derived from $\textrm{t}\bar{\textrm{t}}$+jet cross section measurements will be discussed.

hep-ex↗

arXiv2022

Learning the EFT likelihood with tree boosting

We develop a tree boosting algorithm for collider measurements of multiple Wilson coefficients in effective field theories describing phenomena beyond the standard model of particle physics. The design of the discriminant exploits per-event information of the simulated data sets that encodes the predictions for different values of the Wilson coefficients. This ``Boosted Information Tree'' algorithm provides nearly optimal discrimination power order-by-order in the expansion in the Wilson coefficients and approaches the optimal likelihood ratio test statistic. As a proof-of-principle, we apply the algorithm to the $\textrm{pp}\rightarrow\textrm{Zh}$ process for different types of modeling.

hep-ph↗

arXiv2022

Tree boosting for learning EFT parameters

We present a new tree boosting algorithm designed for the measurement of parameters in the context of effective field theory (EFT). To construct the algorithm, we interpret the optimized loss function of a traditional decision tree as the maximal Fisher information in Poisson counting experiments. We promote the interpretation to general EFT predictions and develop a suitable boosting method. The resulting ``Boosted Information Tree'' algorithm approximates the score, the derivative of the log-likelihood function with respect to the parameter. It thus provides a sufficient statistic in the vicinity of a reference point in parameter space where the estimator is trained. The training exploits per-event information of likelihood ratios for different theory parameter values available in the simulated EFT data sets.

hep-ph↗

arXiv2021

Testing the EFT paradigm with top quark final states in proton-proton collisions at LHC with ATLAS and CMS

With the absence of the direct detection of new physics and the large data set collected at the LHC, the search for indirect effects in precision measurements became popular. Effective field theories enable to interpret these effects that originate from physics manifested at currently unreachable energy scales in a model independent framework. In this report, an overview of new analyses performed by the ATLAS and CMS Collaborations in the field of top quark physics is given.

hep-ex↗

arXiv2020

New jet tagging techniques at CMS

The CMS experiment makes use of a large variety of algorithms to identify the origin of particle jets measured in the detector. Through the study of jet substructure properties, jets originating from quarks, gluons, W/Z/Higgs bosons, top quarks and pileup interactions are identified and categorized. We present new techniques based on machine learning approaches developed for the analysis of the data collected during the LHC Run 2 that significantly surpass the performances of classical taggers.

hep-ex↗