Phenomenology of Photon-Jets

Cited 47|Views16

Abstract

One of the challenges of collider physics is to unambiguously associate detector-based objects with the corresponding elementary physics objects. A particular example is the association of calorimeter-based objects such as "jets," identified with a standard (IR-safe) jet algorithm, with the underlying physics objects, which may be QCD-jets (arising from a scattered parton), electrons, photons or, as discussed here, photon-jets (a group of collinear photons). This separation is especially interesting in the context of Higgs search, where the signal includes events with two photons (in the Standard Model) as well as events with two photon-jets (in a variety of Beyond the Standard Model scenarios), while QCD provides ever-present background. Here we describe the implementation of techniques from the rapidly evolving area of jet substructure studies, not only to enhance the more familiar photon-QCD separation, but also to separately distinguish photon-jets, i.e., to separate usual jets into three categories: single photons, photon-jets and QCD-jets. The efficacy of these techniques for separation is illustrated through studies of simulated data. DOI: 10.1103/PhysRevD.87.014015

Translated text

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper