arXiv subjects

Mihoko M. Nojiri

Publications and source records attributed to Mihoko M. Nojiri.

At least 19 recordsLinked to original sources

arXiv2025

Reweighting and Analysing Event Generator Systematics by Neural Networks on High-Level Features

The state-of-the-art deep learning (DL) models for jet classification use jet constituent information directly, improving performance tremendously. This draws attention to interpretability, namely, the decision-making process, correlations contributing to the classification, and high-level features (HLFs) representing the difference between signal and background. We address the interpretability issue using a modular architecture called the analysis model (AM), which combines several motivated HLFs as the input. We focus on the generator systematics of the top vs. QCD classification by one of the best classifiers, Particle Transformer (ParT). Taking commonly used event generators Pythia (PY) and Herwig (HW) as examples, we demonstrate that the event weights estimated by the AM generator classifier align the HW classification score distribution to PY ones for QCD jets, with small training uncertainty. This suggests the AM is sufficient to describe simulated QCD jet features with relatively few observables, and generator systematics would also be reduced by reweighting the simulation by data. On the other hand, large event weights are required for QCD-like top jets, which leads to imperfect reweighting for both AM and ParT generator classifiers. Moreover, the AM HLFs are insufficient for describing PY and HW differences, causing lower reweighting accuracy compared with ParT. The missing features are the correlation among the collimated high-energy jet constituents, which are strongly correlated to the energy flow polynomials (EFPs) selected for top vs. QCD classification, showing the complementarity between AM HLFs and the selected EFPs.

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arXiv2025

JFlow: Model-Independent Spherical Jeans Analysis using Equivariant Continuous Normalizing Flows

The kinematics of stars in dwarf spheroidal galaxies have been studied to understand the structure of dark matter halos. However, the kinematic information of these stars is often limited to celestial positions and line-of-sight velocities, making full phase space analysis challenging. Conventional methods rely on projected analytic phase space density models with several parameters and infer dark matter halo structures by solving the spherical Jeans equation. In this paper, we introduce an unsupervised machine learning method for solving the spherical Jeans equation in a model-independent way as a first step toward model-independent analysis of dwarf spheroidal galaxies. Using equivariant continuous normalizing flows, we demonstrate that spherically symmetric stellar phase space densities and velocity dispersions can be estimated without model assumptions. As a proof of concept, we apply our method to Gaia challenge datasets for spherical models and measure dark matter mass densities for given velocity anisotropy profiles. Our method can identify halo structures accurately, even with a small number of tracer stars.

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arXiv2025

Machine Learning Electroweakino Production

The system of light electroweakinos and heavy squarks gives rise to one of the most challenging signatures to detect at the LHC. It consists of missing transverse energy recoiled against a few hadronic jets originating either from QCD radiation or squark decays. The analysis generally suffers from the large irreducible Z + jets $(Z \to ν\bar ν)$ background. In this study, we explore Machine Learning (ML) methods for efficient signal/background discrimination. Our best attempt uses both reconstructed (jets, missing transverse energy, etc.) and low-level (particle-flow) objects. We find that the discrimination performance improves as the pT threshold for soft particles is lowered from 10 GeV to 1 GeV, at the expense of larger systematic uncertainty. In many cases, the ML method provides a factor two enhancement in $S/\sqrt{(S + B)}$ from a simple kinematical selection. The sensitivity on the squark-elecroweakino mass plane is derived with this method, assuming the Run-3 and HL-LHC luminosities. Moreover, we investigate the relations between input features and the network's classification performance to reveal the physical information used in the background/signal discrimination process.

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arXiv2024

Quantum similarity learning for anomaly detection

Anomaly detection is a vital technique for exploring signatures of new physics Beyond the Standard Model (BSM) at the Large Hadron Collider (LHC). The vast number of collisions generated by the LHC demands sophisticated deep learning techniques. Similarity learning, a self-supervised machine learning, detects anomalous signals by estimating their similarity to background events. In this paper, we explore the potential of quantum computers for anomaly detection through similarity learning, leveraging the power of quantum computing to enhance the known similarity learning method. In the realm of noisy intermediate-scale quantum (NISQ) devices, we employ a hybrid classical-quantum network to search for heavy scalar resonances in the di-Higgs production channel. In the absence of quantum noise, the hybrid network demonstrates improvement over the known similarity learning method. Moreover, we employ a clustering algorithm to reduce measurement noise from limited shot counts, resulting in $9\%$ improvement in the hybrid network performance. Our analysis highlights the applicability of quantum algorithms for LHC data analysis, where improvements are anticipated with the advent of fault-tolerant quantum computers.

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arXiv2024

Jet Classification Using High-Level Features from Anatomy of Top Jets

Recent advancements in deep learning models have significantly enhanced jet classification performance by analyzing low-level features (LLFs). However, this approach often leads to less interpretable models, emphasizing the need to understand the decision-making process and to identify the high-level features (HLFs) crucial for explaining jet classification. To address this, we consider the top jet tagging problems and introduce an analysis model (AM) that analyzes selected HLFs designed to capture important features of top jets. Our AM mainly consists of the following three modules: a relation network analyzing two-point energy correlations, mathematical morphology and Minkowski functionals for generalizing jet constituent multiplicities, and a recursive neural network analyzing subjet constituent multiplicity to enhance sensitivity to subjet color charges. We demonstrate that our AM achieves performance comparable to the Particle Transformer (ParT) while requiring fewer computational resources in a comparison of top jet tagging using jets simulated at the hadronic calorimeter angular resolution scale. Furthermore, as a more constrained architecture than ParT, the AM exhibits smaller training uncertainties because of the bias-variance tradeoff. We also compare the information content of AM and ParT by decorrelating the features already learned by AM. Lastly, we briefly comment on the results of AM with finer angular resolution inputs.

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arXiv2024

Streamlined jet tagging network assisted by jet prong structure

Attention-based transformer models have become increasingly prevalent in collider analysis, offering enhanced performance for tasks such as jet tagging. However, they are computationally intensive and require substantial data for training. In this paper, we introduce a new jet classification network using an MLP mixer, where two subsequent MLP operations serve to transform particle and feature tokens over the jet constituents. The transformed particles are combined with subjet information using multi-head cross-attention so that the network is invariant under the permutation of the jet constituents. We utilize two clustering algorithms to identify subjets: the standard sequential recombination algorithms with fixed radius parameters and a new IRC-safe, density-based algorithm of dynamic radii based on HDBSCAN. The proposed network demonstrates comparable classification performance to state-of-the-art models while boosting computational efficiency drastically. Finally, we evaluate the network performance using various interpretable methods, including centred kernel alignment and attention maps, to highlight network efficacy in collider analysis tasks.

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arXiv2023

First Evaluation of Meson and $τ$ lepton Spectra and Search for Heavy Neutral Leptons at ILC Beam Dump

A beam dump experiment can be seamlessly added to the {proposed} International Linear Collider (ILC) program because the high energy electron beam should be dumped after the collision point. The ILC beam dump experiment will provide an excellent opportunity to search for new long-lived particles. Since many of them can be produced by a rare decay of standard model particles, we evaluate spectra of the mesons and $τ$ lepton at the decay based on the PHITS and PYTHIA8 simulations. As a motivated physics case, we study the projected sensitivity of heavy neutral leptons at the ILC beam dump experiment. The heavy neutral leptons can also be produced via deep inelastic scattering and $Z$ boson decay at the ILC main detector, which we include in the projection. With the multi-track signal, the reach would be greatly extended in mass and coupling, even compared with the other proposed searches.

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arXiv2022

Monojet signatures from gluino and squark decays

We study the monojet and dijet channels at the LHC as a tool for searching for squarks and gluinos. We consider two separate R-parity conserving supersymmetric scenarios. In the first scenario we postulate a large mass hierarchy between squarks ($\tilde q$) and winos ($\widetilde W$), and wino-like neutralino is assumed to be the lightest supersymmetric particle (LSP). The associated squark-wino production, $pp \to \tilde q \widetilde W$, then leads to a monojet-like signature, where the high $p_T$ jet is originated from the squark decay, $\tilde q \to q + \widetilde W$. We demonstrate that this associated production, as well as the $pp \to \widetilde W \widetilde W + {\rm jets}$ production, have a significant impact on the exclusion limit in the squark-neutralino mass plane. The second scenario postulates that the lighter of the squark and gluino is only a few GeV heavier than the LSP neutralino. The associated squark-gluino production, $pp \to \tilde q \tilde g$, then leads to a distinctive monojet signature, where the high $p_T$ jet is produced from the decay of the heavier coloured particle into the lighter one ($\tilde q \to q + \tilde g$ for $m_{\tilde q} > m_{\tilde g}$ and $\tilde g \to q + \tilde q$ for $m_{\tilde g} > m_{\tilde q}$). The lighter coloured particle is effectively regarded as an invisible particle since the decay products are soft due to the approximate mass degeneracy. We recast existing monojet and dijet analyses and find a non-trivial exclusion limit in the squark-gluino mass plane in this scenario.

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arXiv2021

Morphology for Jet Classification

We introduce a jet tagger based on a neural network analyzing the Minkowski Functionals (MFs) of pixellated jet images. The MFs are geometric measures of binary images, and they can be regarded as a generalization of the particle multiplicity, which is an important quantity in jet tagging. Their changes by dilation encode the jet constituents' geometric structures that appear at various angular scales. We explicitly show that this analysis using the MFs together with mathematical morphology can be considered a constrained convolutional neural network (CNN). Conversely, CNN could model the MFs in a certain limit, and we show their correlation in the example of tagging semi-visible jets emerging from the strong interaction of a hidden valley scenario. The MFs are independent of the IRC-safe observables commonly used in jet physics. We combine this morphological analysis with an IRC-safe relation network which models two-point energy correlations. While the resulting network uses constrained input parameters, it shows comparable dark jet and top jet tagging performances to the CNN. The architecture has significant computational advantages when the available data is limited. We show that its tagging performance is much better than that of the CNN with a small number of training samples. We also qualitatively discuss their parton-shower model dependency. The results suggest that the MFs can be an efficient parameterization of the IRC-unsafe feature space of jets.

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arXiv2020

Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions

Deep neural networks trained on jet images have been successful in classifying different kinds of jets. In this paper, we identify the crucial physics features that could reproduce the classification performance of the convolutional neural network in the top jet vs. QCD jet classification. We design a neural network that considers two types of substructural features: two-point energy correlations, and the IRC unsafe counting variables of a morphological analysis of jet images. The new set of IRC unsafe variables can be described by Minkowski functionals from integral geometry. To integrate these features into a single framework, we reintroduce two-point energy correlations in terms of a graph neural network and provide the other features to the network afterward. The network shows a comparable classification performance to the convolutional neural network. Since both networks are using IRC unsafe features at some level, the results based on simulations are often dependent on the event generator choice. We compare the classification results of Pythia 8 and Herwig 7, and a simple reweighting on the distribution of IRC unsafe features reduces the difference between the results from the two simulations.

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arXiv2020

Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra

Classification of jets with deep learning has gained significant attention in recent times. However, the performance of deep neural networks is often achieved at the cost of interpretability. Here we propose an interpretable network trained on the jet spectrum $S_{2}(R)$ which is a two-point correlation function of the jet constituents. The spectrum can be derived from a functional Taylor series of an arbitrary jet classifier function of energy flows. An interpretable network can be obtained by truncating the series. The intermediate feature of the network is an infrared and collinear safe C-correlator which allows us to estimate the importance of a $S_{2}(R)$ deposit at an angular scale R in the classification. The performance of the architecture is comparable to that of a convolutional neural network (CNN) trained on jet images, although the number of inputs and complexity of architecture is significantly simpler than the CNN classifier. We consider two examples: one is the classification of two-prong jets which differ in color charge of the mother particle, and the other is a comparison between Pythia 8 and Herwig 7 generated jets.

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arXiv2018

Spectral Analysis of Jet Substructure with Neural Networks: Boosted Higgs Case

Jets from boosted heavy particles have a typical angular scale which can be used to distinguish them from QCD jets. We introduce a machine learning strategy for jet substructure analysis using a spectral function on the angular scale. The angular spectrum allows us to scan energy deposits over the angle between a pair of particles in a highly visual way. We set up an artificial neural network (ANN) to find out characteristic shapes of the spectra of the jets from heavy particle decays. By taking the Higgs jets and QCD jets as examples, we show that the ANN of the angular spectrum input has similar performance to existing taggers. In addition, some improvement is seen when additional extra radiations occur. Notably, the new algorithm automatically combines the information of the multi-point correlations in the jet.

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arXiv2018

Flavour-violating decays of mixed top-charm squarks at the LHC

We explore signatures related to squark decays in the framework of non-minimally flavour-violating Supersymmetry. We consider a simplified model where the lightest squark consists of an admixture of charm and top flavour. By recasting the existing LHC searches for top and charm squarks, we show that the limits on squark masses from these analyses are significantly weakened when the top-charm mixing is sizeable. We propose a dedicated search for squarks based on the $tc+{E_{\mathrm{T}}^{\mathrm{miss}}}$ final state which enhances the experimental sensitivity for the case of high mixing, and we map its expected reach for the forthcoming runs of the LHC. We emphasize the role of analyses requiring a jet tagged as produced by the fragmentation of a charm quark in understanding the squark mixing pattern, thus providing a novel handle on new physics. Our results show that, in order to achieve full coverage of the parameter space of supersymmetric models, it is necessary to extend current experimental search programmes with analyses specifically targeting the cases where the lightest top-partner is a mixed state.

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arXiv2018

Monojet Signatures from Heavy Colored Particles: Future Collider Sensitivities and Theoretical Uncertainties

In models with colored particle $\mathcal{Q}$ that can decay into a dark matter candidate $X$, the relevant collider process $pp\to \mathcal{Q}\bar{\mathcal{Q}}\rightarrow X\bar{X}+$jets gives rise to events with significant transverse momentum imbalance. When the masses of $\mathcal{Q}$ and $X$ are very close, the relevant signature becomes monojet-like, and Large Hadron Collider (LHC) search limits become much less constraining. In this paper, we study the current and anticipated experimental sensitivity to such particles at the High-Luminosity LHC at $\sqrt{s}=14\,\mathrm{TeV}$ with $\mathcal{L}=3\,\mathrm{ab}^{-1}$ of data and the proposed High-Energy LHC at $\sqrt{s}=27\,\mathrm{TeV}$ with $\mathcal{L}=15\,\mathrm{ab}^{-1}$ of data. We estimate the reach for various Lorentz and QCD color representations of $\mathcal{Q}$. Identifying the nature of $\mathcal{Q}$ is very important to understanding the physics behind the monojet signature. Therefore, we also study the dependence of the observables built from the $pp\to\mathcal{Q}\bar{\mathcal{Q}} + j $ process on $\mathcal{Q}$ itself. Using the state-of-the-art Monte Carlo suites MadGraph5_aMC@NLO+Pythia8 and Sherpa, we find that when these observables are calculated at NLO in QCD with parton shower matching and multijet merging, the residual theoretical uncertainties are comparable to differences observed when varying the quantum numbers of $\mathcal{Q}$ itself. We find, however, that the precision achievable with NNLO calculations, where available, can resolve this dilemma.

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arXiv2017

False Vacuum Decay in Gauge Theory

The decay rate of a false vacuum is studied in gauge theory, paying particular attention to its gauge invariance. Although the decay rate should not depend on the gauge parameter $ξ$ according to the Nielsen identity, the gauge invariance of the result of a perturbative calculation has not been clearly shown. We give a prescription to perform a one-loop calculation of the decay rate, with which a manifestly gauge-invariant expression of the decay rate is obtained. We also discuss the renormalization necessary to make the result finite, and show that the decay rate is independent of the gauge parameter even after the renormalization.

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arXiv2017

On the Gauge Invariance of the Decay Rate of False Vacuum

We study the gauge invariance of the decay rate of the false vacuum for the model in which the scalar field responsible for the false vacuum decay has gauge quantum number. In order to calculate the decay rate, one should integrate out the field fluctuations around the classical path connecting the false and true vacua (i.e., so-called bounce). Concentrating on the case where the gauge symmetry is broken in the false vacuum, we show a systematic way to perform such an integration and present a manifestly gauge-invariant formula of the decay rate of the false vacuum.

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arXiv2016

Quark-gluon discrimination in the search for gluino pair production at the LHC

We study the impact of including quark- and gluon-initiated jet discrimination in the search for strongly interacting supersymmetric particles at the LHC. Taking the example of gluino pair production, considerable improvement is observed in the LHC search reach on including the jet substructure observables to the standard kinematic variables within a multivariate analysis. In particular, quark and gluon jet separation has higher impact in the region of intermediate mass-gap between the gluino and the lightest neutralino, as the difference between the signal and the standard model background kinematic distributions is reduced in this region. We also compare the predictions from different Monte Carlo event generators to estimate the uncertainty originating from the modelling of the parton shower and hadronization processes.

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arXiv2016

Surviving scenario of stop decays for ATLAS $\ell+jets+E^{miss}_T$ search

Recently ATLAS reported a $3.3σ$ excess in the stop search with $\ell+jets+E_T^{miss}$ channel. We try to interpret the signal by a light stop pair production in the MSSM. We find: (1) simple models where stop decays into a higgsino or a bino are not favored. (2) an extension of them can explain the data at $2σ$ level without conflicting with the other search channels. A surviving possibility includes a light stop and a light higgsino, which is expected in a natural SUSY scenario.

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