Related papers: Enhancing the Sensitivity for Triple Higgs Boson S…

Downsampling is widely adopted to achieve a good trade-off between accuracy and latency for visual recognition. Unfortunately, the commonly used pooling layers are not learned, and thus cannot preserve important information. As another…

Recently, there has been a growing focus on the search for anomalous objects beyond standard model (BSM) signatures at the Large Hadron Collider (LHC). This study investigates novel signatures involving highly collimated photons, referred…

Dense prediction models are widely used for image segmentation. One important challenge is to sufficiently train these models to yield good generalizations for hard-to-learn pixels. A typical group of such hard-to-learn pixels are…

We demonstrate that in supersymmetry (SUSY) with relatively light top superpartners, h -> bb can be a very promising channel to discover the SM-like Higgs resonance at the Large Hadron Collider (LHC), although in general contexts it is…

Our proposed deeply-supervised nets (DSN) method simultaneously minimizes classification error while making the learning process of hidden layers direct and transparent. We make an attempt to boost the classification performance by studying…

This article summarises three searches for diboson resonances in the all-hadronic final state using data collected at a center-of-mass energy of $\sqrt{\rm{s}}=13$ TeV with the CMS experiment at the CERN LHC. The boson decay products are…

The search for di-Higgs production at the LHC in order to set limits on Higgs trilinear coupling and constraints on new physics is one of the main motivations for the LHC high luminosity phase. Recent experimental analyses suggest that such…

In low altitude UAV communications, accurate channel estimation remains challenging due to the dynamic nature of air to ground links, exacerbated by high node mobility and the use of large scale antenna arrays, which introduce hybrid near…

In this paper, an approach for neutral Higgs bosons search is described based on 2HDM type-I at electron-positron linear colliders operating at $ \sqrt{s}=1$ TeV. The beam is assumed to be unpolarized and fast detector simulation is…

Recent studies shows that the majority of existing deep steganalysis models have a large amount of redundancy, which leads to a huge waste of storage and computing resources. The existing model compression method cannot flexibly compress…

Recently machine learning algorithms based on deep layered artificial neural networks (DNNs) have been applied to a wide variety of high energy physics problems such as jet tagging or event classification. We explore a simple but effective…

Deep learning has become a powerful tool for medical image analysis; however, conventional Convolutional Neural Networks (CNNs) often fail to capture the fine-grained and complex features critical for accurate diagnosis. To address this…

Many domains of high energy physics analysis are starting to explore machine learning techniques. Powerful methods can be used to identify and measure rare processes from previously insurmountable backgrounds. One of the most profound…

Accurate classification of breast ultrasound images into benign, malignant, and normal categories is a critical clinical task complicated by speckle noise, acoustic shadowing, and inter-class visual ambiguity. Existing deep learning methods…

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…

The signal for a highly boosted heavy resonance competing against a background of light parton jets at the LHC can be enhanced by analyzing subjets in the "fat" jet that possibly contains the heavy resonance. Three methods for doing this…

We deploy an advanced Machine Learning (ML) environment, leveraging a multi-scale cross-attention encoder for event classification, towards the identification of the $gg\to H\to hh\to b\bar b b\bar b$ process at the High Luminosity Large…

Data recorded by the D0 experiment at the Fermilab Tevatron Collider are analyzed to search for neutral Higgs bosons produced in association with b quarks. The search is performed in the three-b-quark channel using multijet-triggered events…

A precise measurement of the Higgs boson couplings to bottom and top quarks is of paramount importance during the upcoming LHC runs. We present a comprehensive analysis for the Higgs production process in association with a…

Although the deep learning recognition model has been widely used in the condition monitoring of rotating machinery. However, it is still a challenge to understand the correspondence between the structure and function of the model and the…