Manuel Szewc

University of Cincinnati

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Jure Zupan

University of Cincinnati

EÁ

Ezequiel Álvarez

National University of General San Martín

Jernej F. Kamenik

Jožef Stefan Institute

Tony Menzo

University of Cincinnati

Darius A. Faroughy

University of Cincinnati

M. Wilkinson

University of Cincinnati

Christian Bierlich

Lund University

S. Mrenna

Fermi National Accelerator Laboratory

Ahmed M. Youssef

University of Sharjah

Phil Ilten

University of Cincinnati

Cooperative Institutions

A. Alikhanyan National Laboratory

Fermi National Accelerator Laboratory

European Organization for Nuclear Research

Institute of High Energy Physics

University of Adelaide

Massachusetts Institute of Technology

University of Antwerp

Lawrence Berkeley National Laboratory

University of Cincinnati

Consejo Nacional de Investigaciones Científicas y Técnicas

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Report on scipost_202301_00014v1

Ezequiel Álvarez Manuel Szewc Alejandro Szynkman Santiago A. Tanco T. Tarutina

Recognizing hadronically decaying top-quark jets in a sample of jets, or even its total fraction in the sample, is an important step in many LHC searches for Standard Model and Beyond Standard Model physics as well.Although there exists outstanding top-tagger algorithms, their construction and their expected performance rely on Montecarlo simulations, which may induce potential biases.For these reasons we develop two simple unsupervised top-tagger algorithms based on performing Bayesian inference on a mixture model.In one of them we use as the observed variable a new geometrically-based observable Ã3 , and in the other we consider the more traditional τ 3 /τ 2 N -subjettiness ratio, which yields a better performance.As expected, we find that the unsupervised tagger performance is below existing supervised taggers, reaching expected Area Under Curve AUC ∼ 0.80 -0.81 and accuracies of about 69% -75% in a full range of sample purity.However, these performances are more robust to possible biases in the Montecarlo that their supervised counterparts.Our findings are a step towards exploring and considering simpler and unbiased taggers.

10.21468/scipost.report.6668

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Null hypothesis test for anomaly detection

Physics Letters B (2023)

Jernej F. Kamenik Manuel Szewc

We extend the use of Classification Without Labels for anomaly detection with a hypothesis test designed to exclude the background-only hypothesis. By testing for statistical independence of the two discriminating dataset regions, we are able to exclude the background-only hypothesis without relying on fixed anomaly score cuts or extrapolations of background estimates between regions. The method relies on the assumption of conditional independence of anomaly score features and dataset regions, which can be ensured using existing decorrelation techniques. As a benchmark example, we consider the LHC Olympics dataset where we show that mutual information represents a suitable test for statistical independence and our method exhibits excellent and robust performance at different signal fractions even in presence of realistic feature correlations.

Anomaly (physics)

Independence

Benchmark (surveying)

Conditional independence

Null (SQL)

Feature (linguistics)

Decorrelation

10.1016/j.physletb.2023.137836

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Report on 2209.01222v1

arXiv (Cornell University) (2022)

Darius A. Faroughy Jernej F. Kamenik Manuel Szewc Jure Zupan

We propose an extension of the existing experimental strategy for measuring branching fractions of top quark decays, targeting specifically t → jqW , where jq is a light quark jet.The improved strategy uses orthogonal b-and q-taggers, and adds a new observable, the number of light-quarktagged jets, to the already commonly used observable, the fraction of b-tagged jets in an event.Careful inclusion of the additional complementary observable significantly increases the expected statistical power of the analysis, with the possibility of excluding |V tb | = 1 at 95% C.L. at the HL-LHC, and accessing directly the standard model value of |V td | 2 + |Vts| 2 .

10.21468/scipost.report.6027

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Improvement and generalization of ABCD method with Bayesian inference

arXiv (Cornell University) (2024)

Ezequiel Álvarez Leandro Da Rold Manuel Szewc Alejandro Szynkman Santiago A. Tanco

To find New Physics or to refine our knowledge of the Standard Model at the LHC is an enterprise that involves many factors. We focus on taking advantage of available information and pour our effort in re-thinking the usual data-driven ABCD method to improve it and to generalize it using Bayesian Machine Learning tools. We propose that a dataset consisting of a signal and many backgrounds is well described through a mixture model. Signal, backgrounds and their relative fractions in the sample can be well extracted by exploiting the prior knowledge and the dependence between the different observables at the event-by-event level with Bayesian tools. We show how, in contrast to the ABCD method, one can take advantage of understanding some properties of the different backgrounds and of having more than two independent observables to measure in each event. In addition, instead of regions defined through hard cuts, the Bayesian framework uses the information of continuous distribution to obtain soft-assignments of the events which are statistically more robust. To compare both methods we use a toy problem inspired by $pp\to hh\to b\bar b b \bar b$, selecting a reduced and simplified number of processes and analysing the flavor of the four jets and the invariant mass of the jet-pairs, modeled with simplified distributions. Taking advantage of all this information, and starting from a combination of biased and agnostic priors, leads us to a very good posterior once we use the Bayesian framework to exploit the data and the mutual information of the observables at the event-by-event level. We show how, in this simplified model, the Bayesian framework outperforms the ABCD method sensitivity in obtaining the signal fraction in scenarios with $1\%$ and $0.5\%$ true signal fractions in the dataset. We also show that the method is robust against the absence of signal.

10.21468/scipostphyscore.7.3.043

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Reweighting Monte Carlo Predictions and Automated Fragmentation Variations in Pythia 8

arXiv (Cornell University) (2023)

Christian Bierlich P. Ilten Tony Menzo S. Mrenna Manuel Szewc

This work reports on a method for uncertainty estimation in simulated collider-event predictions. The method is based on a Monte Carlo-veto algorithm, and extends previous work on uncertainty estimates in parton showers by including uncertainty estimates for the Lund string-fragmentation model. This method is advantageous from the perspective of simulation costs: a single ensemble of generated events can be reinterpreted as though it was obtained using a different set of input parameters, where each event now is accompanied with a corresponding weight. This allows for a robust exploration of the uncertainties arising from the choice of input model parameters, without the need to rerun full simulation pipelines for each input parameter choice. Such explorations are important when determining the sensitivities of precision physics measurements. Accompanying code is available at https://gitlab.com/uchep/mlhad-weights-validation.

Uncertainty Quantification

Fragmentation

10.48550/arxiv.2308.13459

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Bayesian probabilistic modeling for four-top production at the LHC

Physical review. D/Physical review. D. (2022)

Ezequiel Álvarez Barry M. Dillon Darius A. Faroughy Jernej F. Kamenik Federico Lamagna

Monte Carlo (MC) generators are crucial for analyzing data in particle collider experiments. However, often even a small mismatch between the MC simulations and the measurements can undermine the interpretation of the results. This is particularly important in the context of LHC searches for rare physics processes within and beyond the standard model (SM). One of the ultimate rare processes in the SM currently being explored at the LHC, $pp\ensuremath{\rightarrow}t\overline{t}t\overline{t}$ with its large multidimensional phase-space is an ideal testing ground to explore new ways to reduce the impact of potential MC mismodeling on experimental results. We propose a novel statistical method capable of disentangling the 4-top signal from the dominant backgrounds in the same-sign dilepton channel, while simultaneously correcting for possible MC imperfections in modeling of the most relevant discriminating observables---the jet multiplicity distributions. A Bayesian mixture of multinomials is used to model the light-jet and $b$-jet multiplicities under the assumption of their conditional independence. The signal and background distributions generated from a deliberately mistuned MC simulator are used as model priors. The posterior distributions, as well as the signal and background fractions, are then learned from the data using Bayesian inference. We demonstrate that our method can mitigate the effects of large MC mismodelings in the context of a realistic $t\overline{t}t\overline{t}$ search, leading to corrected posterior distributions that better approximate the underlying truth-level spectra.

10.1103/physrevd.105.092001

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Citations (6)

Null Hypothesis Test for Anomaly Detection

arXiv (Cornell University) (2022)

Jernej F. Kamenik Manuel Szewc

Anomaly (physics)

Independence

Conditional independence

Benchmark (surveying)

Feature (linguistics)

Null (SQL)

Decorrelation

10.48550/arxiv.2210.02226

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Describing Hadronization via Histories and Observables for Monte-Carlo Event Reweighting

arXiv (Cornell University) (2024)

Christian Bierlich Phil Ilten Tony Menzo Stephen Mrenna Manuel Szewc

We introduce a novel method for extracting a fragmentation model directly from experimental data without requiring an explicit parametric form, called Histories and Observables for Monte-Carlo Event Reweighting (\homer), consisting of three steps: the training of a classifier between simulation and data, the inference of single fragmentation weights, and the calculation of the weight for the full hadronization chain. We illustrate the use of \homer on a simplified hadronization problem, a $q\bar{q}$ string fragmenting into pions, and extract a modified Lund string fragmentation function $f(z)$. We then demonstrate the use of \homer on three types of experimental data: (i) binned distributions of high level observables, (ii) unbinned event-by-event distributions of these observables, and (iii) full particle cloud information. After demonstrating that $f(z)$ can be extracted from data (the inverse of hadronization), we also show that, at least in this limited setup, the fidelity of the extracted $f(z)$ suffers only limited loss when moving from (i) to (ii) to (iii). Public code is available at https://gitlab.com/uchep/mlhad.

Hadronization

10.48550/arxiv.2410.06342

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Describing hadronization via histories and observables for Monte-Carlo event reweighting

SciPost Physics (2025)

Christian Bierlich P. Ilten Tony Menzo Stephen Mrenna Manuel Szewc

We introduce a novel method for extracting a fragmentation model directly from experimental data without requiring an explicit parametric form, called Histories and Observables for Monte-Carlo Event Reweighting (HOMER), consisting of three steps: the training of a classifier between simulation and data, the inference of single fragmentation weights, and the calculation of the weight for the full hadronization chain. We illustrate the use of HOMER on a simplified hadronization problem, a q\bar{q} qq‾ string fragmenting into pions, and extract a modified Lund string fragmentation function f(z) f(z) . We then demonstrate the use of HOMER on three types of experimental data: (i) binned distributions of high-level observables, (ii) unbinned event-by-event distributions of these observables, and (iii) full particle cloud information. After demonstrating that f(z) f(z) can be extracted from data (the inverse of hadronization), we also show that, at least in this limited setup, the fidelity of the extracted f(z) f(z) suffers only limited loss when moving from (i) to (ii) to (iii). Public code is available at https://gitlab.com/uchep/mlhad.

Hadronization

10.21468/scipostphys.18.2.054

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A Machine Learning alternative to placebo-controlled clinical trials upon new diseases: A primer.

Ezequiel Álvarez Federico Lamagna Manuel Szewc

The appearance of a new dangerous and contagious disease requires the development of a drug therapy faster than what is foreseen by usual mechanisms. Many drug therapy developments consist in investigating through different clinical trials the effects of different specific drug combinations by delivering it into a test group of ill patients, meanwhile a placebo treatment is delivered to the remaining ill patients, known as the control group. We compare the above technique to a new technique in which all patients receive a different and reasonable combination of drugs and use this outcome to feed a Neural Network. By averaging out fluctuations and recognizing different patient features, the Neural Network learns the pattern that connects the patients initial state to the outcome of the treatments and therefore can predict the best drug therapy better than the above method. In contrast to many available works, we do not study any detail of drugs composition nor interaction, but instead pose and solve the problem from a phenomenological point of view, which allows us to compare both methods. Although the conclusion is reached through mathematical modeling and is stable upon any reasonable model, this is a proof-of-concept that should be studied within other expertises before confronting a real scenario. All calculations, tools and scripts have been made open source for the community to test, modify or expand it. Finally it should be mentioned that, although the results presented here are in the context of a new disease in medical sciences, these are useful for any field that requires a experimental technique with a control group.

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