Manuel Szewc

A bstract: Measuring di-Higgs production in the four-bottom channel is challenged by overwhelming QCD backgrounds and imperfect simulations. We develop a Bayesian mixture model that simultaneously infers signal and background fractions and their individual shapes directly in the signal region. The likelihood is a nuanced combination of a one-dimensional kinematic discriminator and per-jet flavour scores; with their correlations incorporated via kinematic bins. Monte Carlo informs weak Dirichlet priors, while the posterior adjusts to the interplay of the model, priors and observed data. Using pseudo-data simulated with standard tools and with controlled mismatches, we show that the method corrects biased priors, delivers 68–95% credible intervals for the signal count, and improves dataset-level ROC/AUC relative to simple cut-and-count baselines. This study highlights how Bayesian inference can harvest information present in the signal region and self-calibrate model parameters, providing a robust route to increased sensitivity in di-Higgs searches.

We present iHOMER, an iterative version of the HOMER method to extract Lund fragmentation functions from experimental data. Through iterations, we address the information gap between latent and observable phase spaces and systematically remove bias. To quantify uncertainties on the inferred weights, we use a combination of Bayesian neural networks and uncertainty-aware regression. We find that the combination of iterations and uncertainty quantification produces well-calibrated weights that accurately reproduce the data distribution. A parametric closure test shows that the iteratively learned fragmentation function is compatible with the true fragmentation function.

Improving the understanding of signal and background distributions in signal-region is a valuable key to enhance any analysis in collider physics. This is usually a difficult task because –among others– signal and backgrounds are hard to discriminate in signal-region, simulations may reach a limit of reliability if they need to model non-perturbative QCD, and distributions are multi-dimensional and many times may be correlated within each class. Bayesian density estimation is a technique that leverages prior knowledge and data correlations to effectively extract information from data in signal-region. In this work we extend previous works on data-driven mixture models for meaningful unsupervised signal extraction in collider physics to incorporate correlations between features. Using a standard dataset of top and QCD jets, we show how simulators, despite having an expected bias, can be used to inject sufficient inductive nuance into an inference model in terms of priors to then be corrected by data and estimate the true correlated distributions between features within each class. We compare the model with and without correlations to show how the signal extraction is sensitive to their inclusion and we quantify the improvement due to the inclusion of correlations using both supervised and unsupervised metrics.

Measuring di-Higgs production in the four-bottom channel is challenged by overwhelming QCD backgrounds and imperfect simulations. We develop a Bayesian mixture model that simultaneously infers signal and background fractions and their individual shapes directly in the signal region. The likelihood is a nuanced combination of a one-dimensional kinematic discriminator and per-jet flavour scores; with their correlations incorporated via kinematic bins. Monte Carlo informs weak Dirichlet priors, while the posterior adjusts to the interplay of the model, priors and observed data. Using pseudo-data simulated with standard tools and with controlled mismatches, we show that the method corrects biased priors, delivers calibrated 68-95% credible intervals for the signal count, and improves dataset-level ROC/AUC relative to simple cut-and-count baselines. This study highlights how Bayesian inference can harvest information present in the signal region and self-calibrate model parameters, providing a robust route to increased sensitivity in di-Higgs searches.

A bstract: Crucial to many measurements at the LHC is the use of correlated multi-dimensional information to distinguish rare processes from large backgrounds, which is complicated by the poor modeling of many of the crucial backgrounds in Monte Carlo simulations. In this work, we introduce HI-SIGMA, a method to perform unbinned high-dimensional statistical inference with data-driven background distributions. In contradistinction to many applications of Simulation Based Inference in High Energy Physics, HI-SIGMA relies on generative ML models, rather than classifiers, to learn the signal and background distributions in the high-dimensional space. These ML models allow for interpretable inference while also incorporating model errors and other sources of systematic uncertainties. We showcase this methodology on a simplified version of a di-Higgs measurement in the bbγγ final state, where the di-photon resonance allows for background interpolation from sidebands into the signal region. We demonstrate that HI-SIGMA provides improved sensitivity as compared to standard classifier-based methods, and that systematic uncertainties can be straightforwardly incorporated by extending methods which have been used for histogram based analyses.

We update the HOMER method, a technique to solve a restricted version of the inverse problem of hadronization – extracting the Lund string fragmentation function f(z) f ( z ) from data using only observable information. Here, we demonstrate its utility by extracting f(z) f ( z ) from synthetic Pythia simulations using high-level observables constructed on an event-by-event basis, such as multiplicities and shape variables. Four cases of increasing complexity are considered, corresponding to e^+e^- e + e − collisions at a center-of-mass energy of 90≥v 90 ≥ v producing either a string stretched between a q q and \bar{q} q ‾ containing no gluons; the same string containing one gluon g g with fixed kinematics; the same but the gluon has varying kinematics; and the most realistic case, strings with an unrestricted number of gluons that is the end-result of a parton shower. We demonstrate the extraction of f(z) f ( z ) in each case, with the result of only a relatively modest degradation in performance of the HOMER method with the increased complexity of the string system.

We present a method for reweighting flavor selection in the Lund string fragmentation model. This is the process of calculating and applying event weights enabling fast and exact variation of hadronization parameters on pre-generated event samples. The procedure is post hoc, requiring only a small amount of additional information stored per event, and allowing for efficient estimation of hadronization uncertainties without repeated simulation. Weight expressions are derived from the hadronization algorithm itself, and validated against direct simulation for a wide range of observables and parameter shifts. The hadronization algorithm can be viewed as a hierarchical Markov process with stochastic rejections, a structure common to many complex simulations outside of high-energy physics. This perspective makes the method modular, extensible, and potentially transferable to other domains. We demonstrate the approach in Pythia, including both coverage considerations and timing benefits. For the purpose of this paper, our goal is to develop and demonstrate the the formalism, and we therefore exclude several model variations for baryon production (popcorn model, junction production) needed for proton collisions. These will be the topic of a future paper.

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} q q ‾ 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.

We project the reach of future lepton colliders for measuring CKM elements from direct observations of W decays. We focus our attention to |V _cs | and |V _cb | determinations, using FCC-ee as case study. We employ state-of-the-art jet flavor taggers to obtain the projected sensitivity, and scan over tagger performances to show their effect. We conclude that future lepton collider can sizeably improve the sensitivity on |V _cs | and |V _cb |, albeit the achievable reach will strongly depend on the level of systematic uncertainties on tagger parameters.