Search

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., Comment: 41 pages, 21 figures. Public code available

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., Comment: 15 figures, 3 figures

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., Comment: 24 pages, 9 figures. Matches published version

We present an strategy for measuring the off-diagonal elements of the third row of CKM matrix $|V_{tq}|$ through the branching fractions of top quark decays $t\to q W$, where $q$ is a light quark jet. This strategy is an extension of existing measurements, with the improvement rooted in the use of orthogonal $b$- and $q$-taggers that add a new observable, the number of light-quark-tagged 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 a null $|V_{td}|^2+|V_{ts}|^2$ at $95\%$ C.L. at the HL-LHC., Comment: Talk at the 16th International Workshop on Top Quark Physics (Top2023), 24-29 September 2023

We introduce a model of hadronization based on invertible neural networks that faithfully reproduces a simplified version of the Lund string model for meson hadronization. Additionally, we introduce a new training method for normalizing flows, termed MAGIC, that improves the agreement between simulated and experimental distributions of high-level (macroscopic) observables by adjusting single-emission (microscopic) dynamics. Our results constitute an important step toward realizing a machine-learning based model of hadronization that utilizes experimental data during training. Finally, we demonstrate how a Bayesian extension to this normalizing-flow architecture can be used to provide analysis of statistical and modeling uncertainties on the generated observable distributions., Comment: 26 pages, 9 figures, public code available

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., Comment: 19 pages, 7 figures, public code available

Recent advances in $b$, $c$, and $s$ quark tagging coupled with novel statistical analysis techniques will allow future high energy and high statistics electron-positron colliders, such as the FCC-ee, to place phenomenologically relevant bounds on flavor violating Higgs and $Z$ decays to quarks. We assess the FCC-ee reach for $Z/h\to bs, cu$ decays as a function of jet tagging performance. We also update the SM predictions for the corresponding branching ratios, as well as the indirect constraints on the flavor violating Higgs and $Z$ couplings to quarks. Using type III two Higgs doublet model as an example of beyond the standard model physics, we show that the searches for $h\to bs, cu$ decays at FCC-ee can probe new parameter space not excluded by indirect searches. We also reinterpret the FCC-ee reach for $Z\to bs , cu$ in terms of the constraints on models with vectorlike quarks., Comment: 22 pages, 15 figures

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 $\tilde{A}_{3}$, and in the other we consider the more traditional $\tau_{3}/\tau_{2}$ $N$-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 $\sim 0.80-0.81$ 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., Comment: 15 pages, 6 figures; Published version

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., Comment: 10 pages, 3 figures, 1 Table. Matches published version at Physics Letters B. All code is available at https://github.com/ManuelSzewc/Null_Hypothesis_Test_for_Anomaly_Detection. Comments welcome!

We propose an extension of the existing experimental strategy for measuring branching fractions of top quark decays, targeting specifically $t\to j_q W$, where $j_q$ is a light quark jet. The improved strategy uses orthogonal $b$- and $q$-taggers, and adds a new observable, the number of light-quark-tagged 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 $|V_{td}|^2+|V_{ts}|^2$., Comment: 19 pages, 7 figures

Machine-learning techniques have become fundamental in high-energy physics and, for new physics searches, it is crucial to know their performance in terms of experimental sensitivity, understood as the statistical significance of the signal-plus-background hypothesis over the background-only one. We present here a simple method that combines the power of current machine-learning techniques to face high-dimensional data with the likelihood-based inference tests used in traditional analyses, which allows us to estimate the sensitivity for both discovery and exclusion limits through a single parameter of interest, the signal strength. Based on supervised learning techniques, it can perform well also with high-dimensional data, when traditional techniques cannot. We apply the method to a toy model first, so we can explore its potential, and then to a LHC study of new physics particles in dijet final states. Considering as the optimal statistical significance the one we would obtain if the true generative functions were known, we show that our method provides a better approximation than the usual naive counting experimental results., Comment: 24 pages, 8 figures; matches version published in Eur. Phys. J. C

The classification of jets induced by quarks or gluons is important for New Physics searches at high-energy colliders. However, available taggers usually rely on modelling the data through Monte Carlo simulations, which could veil intractable theoretical and systematical uncertainties. To significantly reduce biases, we propose an unsupervised learning algorithm that, given a sample of jets, can learn the SoftDrop Poissonian rates for quark- and gluon-initiated jets and their fractions. We extract the Maximum Likelihood Estimates for the mixture parameters and the posterior probability over them. We then construct a quark-gluon tagger and estimate its accuracy in actual data to be in the $0.65-0.7$ range, below supervised algorithms but nevertheless competitive. We also show how relevant unsupervised metrics perform well, allowing for an unsupervised hyperparameter selection. Further, we find that this result is not affected by an angular smearing introduced to simulate detector effects for central jets. The presented unsupervised learning algorithm is simple; its result is interpretable and depends on very few assumptions., Comment: 18 pages including references, 9 figures. Matches version to be published at Frontiers in Artificial Intelligence, section Big Data and AI in High Energy Physics. References updated, changes in text and in figures. Comments welcome!

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, $pp\to t\bar tt \bar t$ with its large multi-dimensional phase-space is an ideal testing ground to explore new ways to reduce the impact of potential MC mismodelling 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 modelling 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 mismodellings in the context of a realistic $t\bar tt\bar t$ search, leading to corrected posterior distributions that better approximate the underlying truth-level spectra., Comment: Matches accepted version at PRD. 11 pages, 8 figures. This expanded version includes a toy model and explicit tests of the model validity for Four-Tops. Code available at https://github.com/ManuelSzewc/bayes-4tops

During the last years ATLAS and CMS have reported a number of slight to mild discrepancies in signatures of multileptons plus $b$-jets in analyses such as $t\bar t H$, $t\bar t W^\pm$, $t\bar t Z$ and $t\bar t t\bar t$. Among them, a recent ATLAS result on $t\bar t H$ production has also reported an excess in the charge asymmetry in the same-sign dilepton channel with two or more $b$-tagged jets. Motivated by these tantalizing discrepancies, we study a phenomenological New Physics model consisting of a $Z'$ boson that couples to up-type quarks via right-handed currents: $t_R\gamma^\mu \bar t_R$, $t_R\gamma^\mu \bar c_R$, and $t_R \gamma^\mu \bar u_R$. The latter vertex allows to translate the charge asymmetry at the LHC initial state protons to a final state with top quarks which, decaying to a positive lepton and a $b$-jet, provides a crucial contribution to some of the observed discrepancies. Through an analysis at a detector level, we select the region in parameter space of our model that best reproduces the data in the aforementioned $t\bar t H$ study, and in a recent ATLAS $t\bar t t \bar t$ search. We find that our model provides a better fit to the experimental data than the Standard Model for a New Physics scale of approximately $\sim$500 GeV, and with a hierarchical coupling of the $Z'$ boson that favours the top quark and the presence of FCNC currents. In order to estimate the LHC sensitivity to this signal, we design a broadband search featuring many kinematic regions with different signal-to-background ratio, and perform a global analysis. We also define signal-enhanced regions and study observables that could further distinguish signal from background. We find that the region in parameter space of our model that best fits the analysed data could be probed with a significance exceeding 3 standard deviations with just the full Run-2 dataset., Comment: 33 pages, 16 figures, appendix included. Matches JHEP version. Changes in text and in Figures. References added. Comments welcome

COVID-19 outbreaks have proven to be very difficult to isolate and extinguish before they spread out. An important reason behind this might be that epidemiological barriers consisting in stopping symptomatic people are likely to fail because of the contagion time before onset, mild cases and/or asymptomatics carriers. Motivated by these special COVID-19 features, we study a scheme for containing an outbreak in a city that consists in adding an extra firewall block between the outbreak and the rest of the city. We implement a coupled compartment model with stochastic noise to simulate a localized outbreak that is partially isolated and analyze its evolution with and without firewall for different plausible model parameters. We explore how further improvements could be achieved if the epidemic evolution would trigger policy changes for the flux and/or lock-down in the different blocks. Our results show that a substantial improvement is obtained by merely adding an extra block between the outbreak and the bulk of the city., Comment: Comments welcome. 16 pages, 4 figures

We describe a technique to learn the underlying structure of collider events directly from the data, without having a particular theoretical model in mind. It allows to infer aspects of the theoretical model that may have given rise to this structure, and can be used to cluster or classify the events for analysis purposes. The unsupervised machine-learning technique is based on the probabilistic (Bayesian) generative model of Latent Dirichlet Allocation. We pair the model with an approximate inference algorithm called Variational Inference, which we then use to extract the latent probability distributions describing the learned underlying structure of collider events. We provide a detailed systematic study of the technique using two example scenarios to learn the latent structure of di-jet event samples made up of QCD background events and either $t\bar{t}$ or hypothetical $W' \to (\phi \to WW) W$ signal events., Comment: 38 pages, 23 figures

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., Comment: Work originally aimed for COVID-19 outbreak. All scripts are open source in github.com/ManuelSzewc/ML4DT/

Current daily paper releases are becoming increasingly large and areas of research are growing in diversity. This makes it harder for scientists to keep up to date with current state of the art and identify relevant work within their lines of interest. The goal of this article is to address this problem using Machine Learning techniques. We model a scientific paper to be built as a combination of different scientific knowledge from diverse topics into a new problem. In light of this, we implement the unsupervised Machine Learning technique of Latent Dirichlet Allocation (LDA) on the corpus of papers in a given field to: i) define and extract underlying topics in the corpus; ii) get the topics weight vector for each paper in the corpus; and iii) get the topics weight vector for new papers. By registering papers preferred by a user, we build a user vector of weights using the information of the vectors of the selected papers. Hence, by performing an inner product between the user vector and each paper in the daily Arxiv release, we can sort the papers according to the user preference on the underlying topics. We have created the website IArxiv.org where users can read sorted daily Arxiv releases (and more) while the algorithm learns each users preference, yielding a more accurate sorting every day. Current IArxiv.org version runs on Arxiv categories astro-ph, gr-qc, hep-ph and hep-th and we plan to extend to others. We propose several new useful and relevant implementations to be additionally developed as well as new Machine Learning techniques beyond LDA to further improve the accuracy of this new tool., Comment: We are open to new ideas and to scientists and institutions wishing to collaborate and/or partner in further improvements for this service. With this tool the time a paper is sent is irrelevant for its order of appearance

We study the implementation of a Topic Model algorithm in four-top searches at the LHC as a test-probe of a not ideal system for applying this technique. We study this Topic Model behavior as its different hypotheses such as mutual reducibility and equal distribution in all samples shift from true. The four-top final state at the LHC is not only relevant because it does not fulfill these conditions, but also because it is a difficult and inefficient system to reconstruct and current Monte Carlo modeling of signal and backgrounds suffers from non-negligible uncertainties. We implement this Topic Model algorithm in the Same-Sign lepton channel where S/B is of order one and all backgrounds cannot have more than two b-jets at parton level. We define different mixtures according to the number of b-jets and we use the total number of jets to demix. Since only the background has an anchor bin, we find that we can reconstruct the background in the signal region independently of Monte Carlo. We propose to use this information to tune the Monte Carlo in the signal region and then compare signal prediction with data. We also explore Machine Learning techniques applied to this Topic Model algorithm and find slight improvements as well as potential roads to investigate. Although our findings indicate that still with the full LHC run 3 data the implementation would be challenging, we pursue through this work to find ways to reduce the impact of Monte Carlo simulations in four-top searches at the LHC., Comment: 26 pages, 10 figures, appendix included. Minor corrections to match JHEP version. As suggested by Referee, all the code needed to reproduce the results in the paper is uploaded to https://github.com/ManuelSzewc/Topic-Model-for-four-top-at-the-LHC/. Comments welcome

CMS has recently reported a moderate excess in the $\mu\nu jj$ final state in a second generation Leptoquark search, but they have disregarded it because the excess is not present in the $\mu\mu jj$ final state and because they do not observe the expected resonant peak in the distributions. As a proof of concept we show that a simple Leptoquark model including second and third generation couplings with non-negligible single- and non-resonant production in addition to usual pair production could explain the data: excess ($\mu\nu jj$), lack of excess ($\mu\mu jj$) and missing peak in the distributions; while being in agreement with collider constraints. We take this result and analysis as a starting point of a reconsideration of the ATLAS and CMS second generation Leptoquark searches. We also discuss which would be the consequences and modifications that should be performed in the searches to test if this deviation would correspond to a New Physics signal. We observe that low-energy flavor constraints can be avoided by adding heavier particles to the model., Comment: Matches accepted version in PRD. An appendix has been added to address low energy flavour constraints in non-resonant scenarios with large couplings of O(1). 28 pages, 11 figures

The measurements of $R_K^{(*)}$ and $R_{D}^{(*)}$ by BaBar, Belle and the LHCb collaborations could be showing a hint of lepton flavor universality violation that can be accommodated by the presence of suitable leptoquarks at the TeV scale. We consider an effective description, with leptoquarks arising as composite pseudo Nambu-Goldstone bosons, as well as anarchic partial compositeness of the SM fermions. Considering the $R_K^{(*)}$ anomaly within this framework, we study pair production of $S_3\sim(\bar 3,3)_{1/3}$ at the LHC. We focus on the component $S_3^{1/3}$ of the triplet, which decays predominantly into $t\tau$ and $b\nu$, and study the bounds from existing searches at $\sqrt{s}=13$ TeV at the LHC. We find that sbottom searches in the $b\bar{b}$+MET final state best explore the region in parameter space preferred by our model and currently exclude $S_3^{1/3}$ masses up to $\sim$1 TeV. Additional searches, considering the $t\tau$ and $t\mu$ decay modes, are required to probe the full physical parameter space. In this paper we also recast existing studies on direct leptoquark searches in the $t\tau t\tau$ channel and SM $t\bar{t}t\bar{t}$ searches, and obtain the regions in parameter space currently excluded. Practically the whole physical parameter space is currently excluded for masses up to $\sim$0.8 TeV, which could be extended up to $\sim$1 TeV with the full Run 3 dataset. We conclude that pair production searches for this leptoquark can benefit from considering the final state $t \tau b$ +MET, where the largest branching ratio is expected. We appraise that future explorations of leptoquarks explaining the B-anomalies with masses beyond the TeV should also consider single and non-resonant production in order to extend the mass reach., Comment: 20 pages, 8 figures. Matches published version in JHEP, changes in text and references added