Learning to increase matching efficiency in identifying additional b-jets in the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process

• URL: http://arxiv.org/abs/2103.09129v1
• Date: Tue, 16 Mar 2021 15:16:29 GMT
• Title: Learning to increase matching efficiency in identifying additional b-jets in the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process
• Abstract summary: The $texttbartextbbartextb$ process produces a top quark pair in association with a b quark pair. We adopt deep learning methods to learn from data to identify the additional b-jets from the $texttbartextbbartextb$ events.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The $\text{t}\bar{\text{t}}\text{H}(\text{b}\bar{\text{b}})$ process is an essential channel to reveal the Higgs properties but has an irreducible background from the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process, which produces a top quark pair in association with a b quark pair. Therefore, understanding the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process is crucial for improving the sensitivity of a search for the $\text{t}\bar{\text{t}}\text{H}(\text{b}\bar{\text{b}})$ process. To this end, when measuring the differential cross-section of the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process, we need to distinguish the b-jets originated from top quark decays, and additional b-jets originated from gluon splitting. Since there are no simple identification rules, we adopt deep learning methods to learn from data to identify the additional b-jets from the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ events. Specifically, by exploiting the special structure of the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ event data, we propose several loss functions that can be minimized to directly increase the matching efficiency, the accuracy of identifying additional b-jets. We discuss the difference between our method and another deep learning-based approach based on binary classification arXiv:1910.14535 using synthetic data. We then verify that additional b-jets can be identified more accurately by increasing matching efficiency directly rather than the binary classification accuracy, using simulated $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ event data in the lepton+jets channel from pp collision at $\sqrt{s}$ = 13 TeV.

Related papers

• Optimal Scalar Quantization for Matrix Multiplication: Closed-Form Density and Phase Transition [50.36362492608702]
  We study entrywise scalar quantization of two matrices prior to multiplication.<n>We obtain a closed-form optimal point density [ star(u) propto exp!left(-fracu26right)bigl( (1-2)+2u22bigr), qquad u=fracx_X,, and prove a correlation-driven phase transition.
  arXiv  Detail & Related papers  (2026-03-20T01:53:44Z)
• Replace, Don't Expand: Mitigating Context Dilution in Multi-Hop RAG via Fixed-Budget Evidence Assembly [1.2891210250935148]
  RAG systems often fail on multi-hop queries when the initial retrieval misses a bridge fact.<n>We propose textbfSEAL-RAG, a training-free controller that adopts a textbfreplace, don't expand'' strategy to fight context dilution.<n>We evaluate SEAL-RAG against faithful re-implementations of Basic RAG, CRAG, Self-RAG, and Adaptive-$k$ in a shared environment.
  arXiv  Detail & Related papers  (2025-12-11T16:31:29Z)
• Attention with Trained Embeddings Provably Selects Important Tokens [73.77633297039097]
  Token embeddings play a crucial role in language modeling but, despite this practical relevance, their theoretical understanding remains limited.<n>Our paper addresses the gap by characterizing the structure of embeddings obtained via gradient descent.<n>Experiments on real-world datasets (IMDB, Yelp) exhibit a phenomenology close to that unveiled by our theory.
  arXiv  Detail & Related papers  (2025-05-22T21:00:09Z)
• On the $O(\rac{\sqrt{d}}{K^{1/4}})$ Convergence Rate of AdamW Measured by $\ell_1$ Norm [54.28350823319057]
  This paper establishes the convergence rate $frac1Ksum_k=1KEleft[|nabla f(xk)|_1right]leq O(fracsqrtdCK1/4) for AdamW measured by $ell_$ norm, where $K$ represents the iteration number, $d denotes the model dimension, and $C$ matches the constant in the optimal convergence rate of SGD.
  arXiv  Detail & Related papers  (2025-05-17T05:02:52Z)
• Don't Get Lost in the Trees: Streamlining LLM Reasoning by Overcoming Tree Search Exploration Pitfalls [83.89771461061903]
  Recent advancements in tree search algorithms guided by verifiers have significantly enhanced the reasoning capabilities of large language models (LLMs) Recent advancements in tree search algorithms guided by verifiers have significantly enhanced the reasoning capabilities of large language models (LLMs) We identify two key challenges contributing to this inefficiency: $textitover-exploration$ due to redundant states with semantically equivalent content, and $textitunder-exploration$ caused by high variance in verifier scoring. We propose FETCH, a flexible, plug-and-play system compatible with various tree search algorithms.
  arXiv  Detail & Related papers  (2025-02-16T16:12:01Z)
• Model-agnostic basis functions for the 2-point correlation function of dark matter in linear theory [0.0]
  We find a basis $mathcalB$ that describes $xi_rm lin(r)$ near the baryon acoustic oscillation feature in a wide class of cosmological models. Using our basis functions in model-agnostic BAO analyses can potentially lead to significant statistical gains.
  arXiv  Detail & Related papers  (2024-10-28T18:00:01Z)
• Iterative Methods via Locally Evolving Set Process [43.405427507066065]
  Approximate Personalized PageRank (APPR) is a local variant of Gauss-Seidel. We show that APPR admits a new runtime bound $tildeO(overlineoperatornamevol(S_t)/overlinegamma_t leq 1/epsilon$. Numerical results confirm the efficiency of this novel framework and show up to a hundredfold speedup over corresponding standard solvers on real-world graphs.
  arXiv  Detail & Related papers  (2024-10-19T07:28:11Z)
• Learning Orthogonal Multi-Index Models: A Fine-Grained Information Exponent Analysis [54.57279006229212]
  Information exponent has played an important role in predicting the sample complexity of online gradient descent.<n>In this work, we show that by considering both second- and higher-order terms, we can first learn the relevant space using the second-order terms.<n>The overall sample and complexity of online SGD is $tildeO( d PL-1 )$.
  arXiv  Detail & Related papers  (2024-10-13T00:14:08Z)
• Transformer In-Context Learning for Categorical Data [51.23121284812406]
  We extend research on understanding Transformers through the lens of in-context learning with functional data by considering categorical outcomes, nonlinear underlying models, and nonlinear attention. We present what is believed to be the first real-world demonstration of this few-shot-learning methodology, using the ImageNet dataset.
  arXiv  Detail & Related papers  (2024-05-27T15:03:21Z)
• Mechanics of Next Token Prediction with Self-Attention [41.82477691012942]
  Transformer-based language models are trained on large datasets to predict the next token given an input sequence. We show that training self-attention with gradient descent learns an automaton which generates the next token in two distinct steps. We hope that these findings shed light on how self-attention processes sequential data and pave the path toward demystifying more complex architectures.
  arXiv  Detail & Related papers  (2024-03-12T21:15:38Z)
• Exact Fractional Inference via Re-Parametrization & Interpolation between Tree-Re-Weighted- and Belief Propagation- Algorithms [0.4527270266697462]
  We show how to express $Z$ as a product, $forall lambda: Z=Z(lambda)tilde Z(lambda)$, where the multiplicative correction, $tilde Z(lambda)$, is an expectation over a node-independent probability distribution. We also discuss the applicability of this approach to the problem of image de-noising.
  arXiv  Detail & Related papers  (2023-01-25T00:50:28Z)
• Enlarging the notion of additivity of resource quantifiers [62.997667081978825]
  Given a quantum state $varrho$ and a quantifier $cal E(varrho), it is a hard task to determine $cal E(varrhootimes N)$. We show that the one shot distillable entanglement of certain spherically symmetric states can be quantitatively approximated by such an augmented additivity.
  arXiv  Detail & Related papers  (2022-07-31T00:23:10Z)
• Computational Complexity of Normalizing Constants for the Product of Determinantal Point Processes [12.640283469603357]
  We study the computational complexity of computing the normalizing constant. We show that $sum_Sdet(bf A_S,S)p$ exactly for every (fixed) positive even integer $p$ is UP-hard and Mod$_3$P-hard.
  arXiv  Detail & Related papers  (2021-11-28T14:08:25Z)
• FriendlyCore: Practical Differentially Private Aggregation [67.04951703461657]
  We propose a simple and practical tool $mathsfFriendlyCore$ that takes a set of points $cal D$ from an unrestricted (pseudo) metric space as input. When $cal D$ has effective diameter $r$, $mathsfFriendlyCore$ returns a "stable" subset $cal D_Gsubseteq cal D$ that includes all points. $mathsfFriendlyCore$ can be used to preprocess the input before privately aggregating it, potentially simplifying the aggregation or boosting its accuracy
  arXiv  Detail & Related papers  (2021-10-19T17:43:50Z)
• On the Self-Penalization Phenomenon in Feature Selection [69.16452769334367]
  We describe an implicit sparsity-inducing mechanism based on over a family of kernels. As an application, we use this sparsity-inducing mechanism to build algorithms consistent for feature selection.
  arXiv  Detail & Related papers  (2021-10-12T09:36:41Z)
• On the Power of Multitask Representation Learning in Linear MDP [61.58929164172968]
  This paper presents analyses for the statistical benefit of multitask representation learning in linear Markov Decision Process (MDP) We first discover a emphLeast-Activated-Feature-Abundance (LAFA) criterion, denoted as $kappa$, with which we prove that a straightforward least-square algorithm learns a policy which is $tildeO(H2sqrtfrackappa mathcalC(Phi)2 kappa dNT+frackappa dn)
  arXiv  Detail & Related papers  (2021-06-15T11:21:06Z)
• Semi-supervised Active Regression [21.51757844385258]
  This paper studies the use of partially labelled, potentially biased data for learning tasks. The learner has access to a dataset $X in mathbbRd | X min_beta in mathbbRd | X beta - Y |2 end2 equation while making few additional label queries.
  arXiv  Detail & Related papers  (2021-06-12T03:28:43Z)
• The EM Algorithm is Adaptively-Optimal for Unbalanced Symmetric Gaussian Mixtures [36.91281862322494]
  We show that the EM algorithm adaptively achieves the minimax error rate $tildeOBig(minBigfrac1(1-2delta_*)sqrtfracdn,frac1|theta_*|sqrtfracdn,left(fracdnright)1/4BigBig)$ in no more than $OBig(frac1|theta_*|2Big
  arXiv  Detail & Related papers  (2021-03-29T14:28:17Z)
• Nearly Horizon-Free Offline Reinforcement Learning [97.36751930393245]
  We revisit offline reinforcement learning on episodic time-homogeneous Markov Decision Processes with $S$ states, $A$ actions and planning horizon $H$. We obtain the first set of nearly $H$-free sample complexity bounds for evaluation and planning using the empirical MDPs.
  arXiv  Detail & Related papers  (2021-03-25T18:52:17Z)
• An Algorithm for Learning Smaller Representations of Models With Scarce Data [0.0]
  We present a greedy algorithm for solving binary classification problems in situations where the dataset is too small or not fully representative. It relies on a trained model with loose accuracy constraints, an iterative hyperparameter pruning procedure, and a function used to generate new data.
  arXiv  Detail & Related papers  (2020-10-15T19:17:51Z)
• All you need is a second look: Towards Tighter Arbitrary shape text detection [80.85188469964346]
  Long curve text instances tend to be fragmented because of the limited receptive field size of CNN. Simple representations using rectangle or quadrangle bounding boxes fall short when dealing with more challenging arbitrary-shaped texts. textitNASK reconstructs text instances with a more tighter representation using the predicted geometrical attributes.
  arXiv  Detail & Related papers  (2020-04-26T17:03:41Z)
• Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm [100.11971836788437]
  We study the fixed-support Wasserstein barycenter problem (FS-WBP) We develop a provably fast textitdeterministic variant of the celebrated iterative Bregman projection (IBP) algorithm, named textscFastIBP.
  arXiv  Detail & Related papers  (2020-02-12T03:40:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.