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This talk discusses the ARCANE reweighting technique for tackling the negative weights problem in collider event generation.
Final State Interaction (FSI) processes in $\nu$--Pb differ significantly from those in neutrino--carbon ($\nu$--C), and MINER$\nu$A is particularly sensitive to these differences, especially in pion absorption. This work develops an FSI reweighting method for $\nu$--Pb (and C) interactions in MINER$\nu$A. We compare GENIE~2, the currently used generator version, with GENIE~3 predictions of the initial hadron energy as a function of FSI fate, and apply these weights to simulated observables. We identify changes in the FSI composition, providing a framework for estimating FSI-related systematic uncertainties and interpreting their effects in the data.
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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.
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We propose and validate a new method to trace the impact of parameter variations in the simulation of multi-parton interactions and colour reconnections in the Sherpa event generator. They are reflected, at an event-by-event basis, through relative weights with respect to the central production parameters that give rise to the generated events and distributions. Our method facilitates the tuning of the Monte Carlo event generator at a dramatically reduced computational cost, alleviates parameter sensitivity studies, and enables robust quantification of parametric uncertainties on-the-fly, one of the missing ingredients for future simulations of high-energy particle collisions. The method can easily be adapted to and implemented in other event generators. To illustrate its potential, we here consider combined tunes of the multi-parton-interaction and colour-reconnection models in Sherpa using LHC proton-proton collision data at $\sqrt{s}=7\,\text{TeV}$. We furthermore calibrate the energy-scaling behaviour of dimensionful model parameters based on $\sqrt{s}=13\,\text{TeV}$ LHC data and Tevatron data taken at $\sqrt{s}=1.96\,\text{TeV}$.
Automating post-disaster damage assessment with remote sensing data is critical for faster surveys of structures impacted by natural disasters. One significant obstacle to training state-of-the-art deep neural networks to support this automation is that large quantities of labelled data are often required. However, obtaining those labels is particularly unrealistic to support post-disaster damage assessment in a timely manner. Few-shot learning methods could help to mitigate this by reducing the amount of labelled data required to successfully train a model while achieving satisfactory results. To this end, we explore a feature reweighting method to the YOLOv3 object detection architecture to achieve few-shot learning of damage assessment models on the xBD dataset. Our results show that the feature reweighting approach yield improved mAP over the baseline with significantly fewer labelled samples. In addition, we use t-SNE to analyze the class-specific reweighting vectors generated by the reweighting module in order to evaluate their inter-class and intra-class similarity. We find that the vectors form clusters based on class, and that these clusters overlap with visually similar classes. Those results show the potential to employ this few-shot learning strategy for rapid damage assessment with post-event remote sensing images.
Cross-section measurements of top-quark pair production where the hadronically decaying top quark has transverse momentum greater than 355 GeV and the other top quark decays into ℓνb are presented using 139 fb –1 of data collected by the ATLAS experiment during proton-proton collisions at the LHC. The fiducial cross-section at √s = 13 TeV is measured to be σ = 1.267 ± 0.005 ± 0.053 pb, where the uncertainties reflect the limited number of data events and the systematic uncertainties, giving a total uncertainty of 4.2%. The cross-section is measured differentially as a function of variables characterizing the tt¯ system and additional radiation in the events. The results are compared with various Monte Carlo generators, including comparisons where the generators are reweighted to match a parton-level calculation at next-to-next-to-leading order. The reweighting improves the agreement between data and theory. The measured distribution of the top-quark transverse momentum is used to search for new physics in the context of the effective field theory framework. No significant deviation from the Standard Model is observed and limits are set on the Wilson coefficients of the dimension-six operators O tG and $O$ $^{(8)}_{tq}$, where the limits on the latter are the most stringent to date.
Anomalous contributions to the electric and magnetic dipole moments of the τ lepton from new physics scenarios have brought renewed interest in the development of new charge-parity violating signatures in τ -pair production at Belle II energies, and also at higher energies of the Large Hadron Collider and the Future Circular Collider. In this paper, we discuss the effects of spin correlations, including transverse degrees of freedom, in the τ -pair production and decay. These studies include calculating analytical formulas, obtaining numerical results, and building semirealistic observables sensitive to the transverse spin correlations induced by the dipole moments of the τ lepton. The effects of such anomalous contributions to the dipole moments are introduced on top of precision simulations of e − e + → τ − τ + , q q ¯ → τ − τ + and γ γ → τ − τ + processes, involving multibody final states. The τ decays are simulated along with radiative corrections, in particular electroweak box contributions of W W and Z Z exchanges are taken into account. Respective extensions of the Standard Model amplitudes and the reweighting algorithms are implemented into the Monte Carlo, which is used to simulate τ -pair production in e − e + collisions, and the aupinner program, which is used to reweight events with τ pair produced in p p collisions. Published by the American Physical Society 2024
Neutrinos are tiny mysterious fundamental particles with small cross sections. Through neutrino physics, scientists across the world are trying to answer many intriguing questions about nature such as the dominance of matter over antimatter, CP violation in the lepton sector, number of supernovas in the early universe, etc. Detection of neutrinos requires massive particle detectors and intense neutrino beam owing to their small cross section. Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment that is planned to start taking data beginning in 2026. DUNE will consist of 4 massive detectors, the first of which will be using single-phase liquid argon time projection chamber (LArTPC) technology. The ProtoDUNE-SP experiment is a prototype of the DUNE built at the CERN neutrino platform and uses the same detector technology that will be used in DUNE first module. The ProtoDUNE-SP experiment collected months of test beam and cosmic ray data beginning in September 2018. It was built to provide a testbed for the installation of detector parts for DUNE, showing long-term stability of the detector, understanding detector response for different test beam particles (including protons, pions, electrons, kaons, muons), and measurement of hadron-argon cross sections. When a particle passes through LArTPC electron-ion pairs are produced. To reconstruct the position and energy of a particle passing through the medium knowledge of ionization electron drift velocity is essential. The electron drift velocity is distorted by an excess positive charge built up in the detector, known as space charge. This study discusses a novel technique for measuring the ionization electron drift velocity using cosmic-ray muons. The technique uses tracks that travel the entire drift distance of the TPC for drift velocity determination. Secondly, the study discusses a method for converting the charge deposited into energy. The method is carried out in two step s. In th e first step detector response for energetic cosmic ray muons crossing the entire the TPC is used to make the charge deposition uniform throughout the TPC, and in the second step stopping cosmic-ray muons are used for determining the energy scale. Finally, the study discusses a pion-argon cross section measurement based on reweighting of Monte Carlo simulations using J. Calcutt's Geant4Reweight framework. Neutrinos cannot be directly detected; they are identified based on the interaction products. Pions are a common interaction product in a neutrino interaction. For precise modeling of neutrino event generators, it is essential to understand the pion-argon interaction. Pion-argon cross section measurement serves as an important input for neutrino interaction models. The results of the pion-argon total reaction cross section using the Geant4 reweighting technique are found to be in good agreement with Geant4 predictions. The many studies carried out in the ProtoDUNE-SP experi ment wil l be useful for current and future neutrino experiments using LArTPC technology including ICARUS, MicroBooNE, DUNE
Proton conduction in acceptor-doped perovskites is fundamentally a vibronic process: mobile H + and D + do not move independently, but dynamically co-vibrate with the surrounding oxygen–metal framework. Direct experimental evidence for this behavior is presented using in situ 119 𝑆𝑛 nuclear resonance vibrational spectroscopy (NRVS) on hydrated, deuterated, and dry 𝐵𝑎𝑆𝑛 1−𝑥 𝑌 𝑥 𝑂 3−𝛿 . Hydration induces systematic redistributions in the Sn-projected phonon density of states (PDOS), including an upshift of the first spectral moment by about 0.4 meV, indicating a stiffening of the extended Sn–O network. H/D isotopic substitution leaves the Sn-projected PDOS largely unchanged, with only subtle isotope-dependent spectral reweighting, demonstrating that protonic degrees of freedom are not localized oscillators but are embedded in collective lattice modes. These results are rationalized using a classical coupled proton–phonon oscillator model that links the observed PDOS variations to changes in effective force constants and vibrational mass terms. The model captures how H + and D + participate in cooperative lattice dynamics rather than forming isolated OH/OD entities. Overall, NRVS probes proton–lattice coupling in ceramic proton conductors and quantitatively describes how protonic defects modulate host lattice dynamics to enable phonon-assisted long-range proton transport.
The likelihood ratio is a crucial quantity for statistical inference in science that enables hypothesis testing, construction of confidence intervals, reweighting of distributions, and more. Many modern scientific applications, however, make use of data- or simulation-driven models for which computing the likelihood ratio can be very difficult or even impossible. By applying the so-called “likelihood ratio trick,” approximations of the likelihood ratio may be computed using clever parametrizations of neural network-based classifiers. A number of different neural network setups can be defined to satisfy this procedure, each with varying performance in approximating the likelihood ratio when using finite training data. We present a series of empirical studies detailing the performance of several common loss functionals and parametrizations of the classifier output in approximating the likelihood ratio of two univariate and multivariate Gaussian distributions as well as simulated high-energy particle physics datasets.
Complete anomaly detection strategies that are both signal sensitive and compatible with background estimation have largely focused on resonant signals. Non-resonant new physics scenarios are relatively under-explored and may arise from off-shell effects or final states with significant missing energy. In this paper, we extend a class of weakly supervised anomaly detection strategies developed for resonant physics to the non-resonant case. Machine learning models are trained to reweight, generate, or morph the background, extrapolated from a control region. A classifier is then trained in a signal region to distinguish the estimated background from the data. The new methods are demonstrated using a semi-visible jet signature as a benchmark signal model, and are shown to automatically identify the anomalous events without specifying the signal ahead of time.
Differential and double-differential distributions of kinematic variables of leptons from decays of top-quark pairs ($t\bar{t}$) are measured using the full LHC Run 2 data sample collected with the ATLAS detector. The data were collected at a pp collision energy of $\sqrt{s}$ = 13 TeV and correspond to an integrated luminosity of 140 fb –1 . The measurements use events containing an oppositely charged e μ pair and b-tagged jets. The results are compared with predictions from several Monte Carlo generators. While no prediction is found to be consistent with all distributions, a better agreement with measurements of the lepton p T distributions is obtained by reweighting the $t\bar{t}$ sample so as to reproduce the top-quark p T distribution from an NNLO calculation. The inclusive top-quark pair production cross-section is measured as well, both in a fiducial region and in the full phase-space. The total inclusive cross-section is found to be $σ_{t\bar{t}}=$ 829 ± 1 (stat) ± 13 (syst) ± 8 (lumi) ± 2 (beam) pb, where the uncertainties are due to statistics, systematic effects, the integrated luminosity and the beam energy. This is in excellent agreement with the theoretical expectation.
A search for Higgs boson (H) production at high transverse momentum (p T ) in the WW decay channel is presented. The analysis uses proton-proton collisions at $\sqrt{s}=13$ TeV recorded by the CMS experiment in 2016–2018, corresponding to an integrated luminosity of 138 fb −1 . The visible decay products of the Higgs boson are reconstructed as a single large-radius jet with one isolated lepton or none (1ℓ and 0ℓ, respectively; ℓ = e, μ). The H-candidate jets are identified using an advanced transformer-based algorithm and are calibrated with the Lund jet plane reweighting technique. The 1ℓ channel is further split into gluon fusion, vector boson fusion, and associated production with hadronically decaying vector boson categories, while the 0ℓ channel considers all production processes inclusively. The measured cross section times the H → WW branching fraction relative to the standard model expectation is $\mu =-{0.19}_{-0.46}^{+0.48}$, indicating no evidence of a signal above the background. This measurement represents the first dedicated study of highly Lorentz-boosted H → WW decays, complementing earlier searches for high-p T Higgs boson in other decay channels.
A search for new physics in top quark production with additional final-state leptons is performed using data collected by the CMS experiment in proton-proton collisions at $ \sqrt{s} $ = 13 TeV at the LHC during 2016–2018. The data set corresponds to an integrated luminosity of 138 fb$^{−1}$. Using the framework of effective field theory (EFT), potential new physics effects are parametrized in terms of 26 dimension-six EFT operators. The impacts of EFT operators are incorporated through the event-level reweighting of Monte Carlo simulations, which allows for detector-level predictions. The events are divided into several categories based on lepton multiplicity, total lepton charge, jet multiplicity, and b-tagged jet multiplicity. Kinematic variables corresponding to the transverse momentum (p$_{T}$) of the leading pair of leptons and/or jets as well as the p$_{T}$ of on-shell Z bosons are used to extract the 95% confidence intervals of the 26 Wilson coefficients corresponding to these EFT operators. No significant deviation with respect to the standard model prediction is found.[graphic not available: see fulltext]