Related papers: Jet Image Tagging Using Deep Learning: An Ensemble…
A deep-learning approach based on the transformer architecture is developed to distinguish between jets originating from quarks and gluons. The algorithm operates on jets with transverse momentum $p_{\text{T}} > 20$ and pseudorapidity…
Previous studies have demonstrated the utility and applicability of machine learning techniques to jet physics. In this paper, we construct new observables for the discrimination of jets from different originating particles exclusively from…
A method is introduced for distinguishing top jets (boosted, hadronically decaying top quarks) from light quark and gluon jets using jet substructure. The procedure involves parsing the jet cluster to resolve its subjets, and then imposing…
Autoencoders are widely used in machine learning applications, in particular for anomaly detection. Hence, they have been introduced in high energy physics as a promising tool for model-independent new physics searches. We scrutinize the…
A significant challenge in the tagging of boosted objects via machine-learning technology is the prohibitive computational cost associated with training sophisticated models. Nevertheless, the universality of QCD suggests that a large…
Jet measurements in heavy ion collisions at low jet momentum can provide constraints on the properties of the quark gluon plasma but are overwhelmed by a significant, fluctuating background. We build upon our previous work which…
The identification of hadronic final states plays a crucial role in the physics programme of the ATLAS Experiment at the CERN LHC. Sophisticated artificial intelligence (AI) algorithms are employed to classify jets according to their…
Jet fires are relatively small and have the least severe effects among the diverse fire accidents that can occur in industrial plants; however, they are usually involved in a process known as the domino effect, that leads to more severe…
Machine learning techniques are increasingly being applied toward data analyses at the Large Hadron Collider, especially with applications for discrimination of jets with different originating particles. Previous studies of the power of…
In recent years, deep learning has rapidly become a method of choice for the segmentation of medical images. Deep Neural Network (DNN) architectures such as UNet have achieved state-of-the-art results on many medical datasets. To further…
Jets can be used to probe the physical properties of the high energy density matter created in collisions at the Relativistic Heavy Ion Collider (RHIC). Measurements of strong suppression of inclusive hadron distributions and di-hadron…
Jet tagging is an essential categorization problem in high energy physics. In recent times, Deep Learning has not only risen to the challenge of jet tagging but also significantly improved its performance. In this article, we proposed an…
We introduce a new class of event shapes to characterize the jet-like structure of an event. Like traditional event shapes, our observables are infrared/collinear safe and involve a sum over all hadrons in an event, but like a jet…
Jets at high energy colliders are complicated objects to identify. Even if jets are widely separated, there is no reason for jets to have the same size. A single reconstruction, or interpretation, of each event can only extract a limited…
The identification of jets and their constituents is one of the key problems and challenging task in heavy ion experiments such as experiments at RHIC and LHC. The presence of huge background of soft particles pose a curse for jet finding…
This paper presents a novel method of searching for boosted hadronically decaying objects by treating them as anomalous elements of a contaminated dataset. A Variational Recurrent Neural Network (VRNN) is used to model jets as sequences of…
Deep learning-based algorithms can provide state-of-the-art accuracy for remote sensing technologies such as unmanned aerial vehicles (UAVs)/drones, potentially enhancing their remote sensing capabilities for many emergency response and…
Ensembling is a method that aims to maximize the detection performance by fusing individual detectors. While rarely mentioned in deep-learning articles applied to remote sensing, ensembling methods have been widely used to achieve high…
The suppression and modification of high-energy objects, like jets, in heavy-ion collisions provide an important window to access the degrees of freedom of the quark-gluon plasma on different length scales. Despite increasingly precise and…
Under which conditions does a jet appear as a particle--like signal from the hidden realm of quarks and gluons? Motivated by this question jet clustering conditions are formulated, in order to characterize jet clustering algorithms, which…