Quark/Gluon Discrimination and Top Tagging with Dual Attention Transformer

Jet tagging is a crucial classification task in high energy physics. Recently the performance of jet tagging has been significantly improved by the application of deep learning techniques. In this work, we propose Particle Dual Attention Transformer for jet tagging, a new transformer architecture which captures both global information and local information simultaneously. Based on the point cloud representation, we introduce the Channel Attention module to the point cloud transformer and incorporates both the pairwise particle interactions and the pairwise jet feature interactions in the attention mechanism. We demonstrate the effectiveness of the P-DAT architecture in classic top tagging and quark-gluon discrimination tasks, achieving competitive performance compared to other benchmark strategies.Comment: 15 pages, 4 figures, 3 table

Hierarchical High-Point Energy Flow Network for Jet Tagging

Jet substructure observable basis is a systematic and powerful tool for analyzing the internal energy distribution of constituent particles within a jet. In this work, we propose a novel method to insert neural networks into jet substructure basis as a simple yet efficient interpretable IRC-safe deep learning framework to discover discriminative jet observables. The Energy Flow Polynomial (EFP) could be computed with a certain summation order, resulting in a reorganized form which exhibits hierarchical IRC-safety. Thus inserting non-linear functions after the separate summation could significantly extend the scope of IRC-safe jet substructure observables, where neural networks can come into play as an important role. Based on the structure of the simplest class of EFPs which corresponds to path graphs, we propose the Hierarchical Energy Flow Networks and the Local Hierarchical Energy Flow Networks. These two architectures exhibit remarkable discrimination performance on the top tagging dataset and quark-gluon dataset compared to other benchmark algorithms even only utilizing the kinematic information of constituent particles

Heavy Bino and Slepton for Muon g-2 Anomaly

In light of very recent E989 experimental result, we investigate the possibility that heavy sparticles explain the muon g-2 anomaly. We focus on the bino-smuon loop in an effective SUSY scenario, where a light gravitino plays the role of dark matter and other sparticles are heavy. Due to the enhancement of left-right mixing of smuons by heavy higgsinos, the contribution of bino-smuon loop can sizably increase the prediction of muon g-2 to the experimental value. Under collider and vacuum stability constraints, we find that TeV scale bino and smuon can still account for the new muon g-2 anomaly. The implications for LHC phenomenology are also discussed.Comment: 10 pages,1 figure;Published in:Nucl.Phys.B 969(2021)115481,add some discussions and references, matches published versio

τ±νγγ\tau^\pm \nu \gamma\gamma and ℓ±ℓ±γγ /ETX\ell^\pm \ell^\pm \gamma \gamma {\rlap{\,/}{E}_T} X to probe the fermiophobic Higgs boson with high cutoff scales

The light fermiophobic Higgs boson $h_{\rm f}$ in the type-I two-Higgs-doublet model can evade the current search programs at the LHC since its production through the quark-antiquark annihilation and gluon fusion is not feasible. The particle can be more elusive if the model retains stability up to the Planck scale because the efficient discovery channels are missing from the existing search chart. Through the comprehensive scanning, we show that all the viable parameter points with the Planck cutoff scale require $m_{h_{\rm f}} \in[80,\, 120]{\;{\rm GeV}}$ and $M_{A/H^\pm} \in [90,\,150]{\;{\rm GeV}}$. Since $h_{\rm f}h_{\rm f}\to \gamma\gamma W^+ W^-$ and $H^\pm \to \tau^\pm \nu/h_{\rm f}W^\pm$ are dominant in this case, two final states are more efficient to probe $h_{\rm f}$ than the conventional search mode of $4\gamma+W^\pm/Z$. One is $\tau^\pm\nu \gamma\gamma$ from $pp \to H^\pm(\to\tau^\pm\nu) h_{\rm f}(\to \gamma\gamma)$ and the other is $\ell^\pm \ell^\pm \gamma\gamma {\rlap{\,/}{E}_T} X$ ($\ell^\pm=e^\pm,\mu^\pm$) from $pp \to H^\pm(\to h_{\rm f}W^\pm) h_{\rm f} \to \gamma\gamma W^+ W^-W^\pm$, $pp \to H^\pm(\to h_{\rm f} W^\pm) A(\to h_{\rm f} Z) \to \gamma\gamma W^+ W^- W^\pm Z$, and $pp \to H^+(\to h_{\rm f} W^+)H^-(\to h_{\rm f} W^-)\to \gamma\gamma W^+ W^- W^+ W^-$. The inclusive $\ell^\pm \ell^\pm \gamma\gamma {\rlap{\,/}{E}_T} X$ consists of a same-sign dilepton, two prompt photons, and missing transverse energy. We perform the signal-background analysis at the detector level. With the total integrated luminosity of $300\;{\rm fb}^{-1}$ and the 5\% background uncertainty, two proposed channels at the 14 TeV LHC yield signal significances above five in the entire viable parameter space of the fermiophobic type-I with a high cutoff scale.Comment: Final version to appear in JHE

Elevated CO2 and Warming Altered Grassland Microbial Communities in Soil Top-Layers.

As two central issues of global climate change, the continuous increase of both atmospheric CO2 concentrations and global temperature has profound effects on various terrestrial ecosystems. Microbial communities play pivotal roles in these ecosystems by responding to environmental changes through regulation of soil biogeochemical processes. However, little is known about the effect of elevated CO2 (eCO2) and global warming on soil microbial communities, especially in semiarid zones. We used a functional gene array (GeoChip 3.0) to measure the functional gene composition, structure, and metabolic potential of soil microbial communities under warming, eCO2, and eCO2 + warming conditions in a semiarid grassland. The results showed that the composition and structure of microbial communities was dramatically altered by multiple climate factors, including elevated CO2 and increased temperature. Key functional genes, those involved in carbon (C) degradation and fixation, methane metabolism, nitrogen (N) fixation, denitrification and N mineralization, were all stimulated under eCO2, while those genes involved in denitrification and ammonification were inhibited under warming alone. The interaction effects of eCO2 and warming on soil functional processes were similar to eCO2 alone, whereas some genes involved in recalcitrant C degradation showed no significant changes. In addition, canonical correspondence analysis and Mantel test results suggested that NO3-N and moisture significantly correlated with variations in microbial functional genes. Overall, this study revealed the possible feedback of soil microbial communities to multiple climate change factors by the suppression of N cycling under warming, and enhancement of C and N cycling processes under either eCO2 alone or in interaction with warming. These findings may enhance our understanding of semiarid grassland ecosystem responses to integrated factors of global climate change

Probing Light Fermiophobic Higgs Boson via diphoton jets at the HL-LHC

In this study, we explore the phenomenological signatures associated with a light fermiophobic Higgs boson, $h_{\rm f}$, within the type-I two-Higgs-doublet model at the HL-LHC. Our meticulous parameter scan illuminates an intriguing mass range for $m_{h_{\rm f}}$, spanning $[1,10]{\;{\rm GeV}}$. This mass range owes its viability to substantial parameter points, largely due to the inherent challenges of detecting the soft decay products of $h_{\rm f}$ at contemporary high-energy colliders. Given that this light $h_{\rm f}$ ensures $Br(h_{\rm f}\to\gamma\gamma)\simeq 1$, $Br(H^\pm \to h_{\rm f} W^\pm)\simeq 1$, and $M_{H^\pm}\lesssim 330{\;{\rm GeV}}$, we propose a golden discovery channel: $pp\to h_{\rm f}H^\pm\to \gamma\gamma\gamma\gamma \,l^\pm\nu$, where $l^\pm$ includes $e^\pm$ and $\mu^\pm$. However, a significant obstacle arises as the two photons from the $h_{\rm f}$ decay mostly merge into a single jet due to their proximity within $\Delta R<0.4$. This results in a final state characterized by two jets, rather than four isolated photons, thus intensifying the QCD backgrounds. To tackle this, we devise a strategy within \textsc{Delphes} to identify jets with two leading subparticles as photons, termed diphoton jets. Our thorough detector-level simulations across 18 benchmark points predominantly show signal significances exceeding the $5\sigma$ threshold at an integrated luminosity of $3{\;{\rm ab}^{-1}}$. Furthermore, our approach facilitates accurate mass reconstructions for both $m_{h_{\rm f}}$ and $M_{H^\pm}$. Notably, in the intricate scenarios with heavy charged Higgs bosons, our application of machine learning techniques provides a significant boost in significance.Comment: 36 pages with 12 figure

Content-Based Search for Deep Generative Models

The growing proliferation of customized and pretrained generative models has made it infeasible for a user to be fully cognizant of every model in existence. To address this need, we introduce the task of content-based model search: given a query and a large set of generative models, finding the models that best match the query. As each generative model produces a distribution of images, we formulate the search task as an optimization problem to select the model with the highest probability of generating similar content as the query. We introduce a formulation to approximate this probability given the query from different modalities, e.g., image, sketch, and text. Furthermore, we propose a contrastive learning framework for model retrieval, which learns to adapt features for various query modalities. We demonstrate that our method outperforms several baselines on Generative Model Zoo, a new benchmark we create for the model retrieval task.Comment: Our project page is hosted at https://generative-intelligence-lab.github.io/modelverse

Effect of “Jian-Pi-Zhi-Dong Decoction” on Gamma-Aminobutyric Acid in a Mouse Model of Tourette Syndrome

The purpose of this study was to explore the positive effects of Jian-Pi-Zhi-Dong Decoction (JPZDD) on Tourette syndrome (TS) by investigating the expression of gamma-aminobutyric acid (GABA) and its type A receptor (GABAAR) in the striatum of a TS mice model. The model was induced by 3,3′-iminodipropionitrile (IDPN) treatment; then mice were divided into 4 groups (n=22, each); control and IDPN groups were gavaged with saline and the remaining 2 groups were gavaged with tiapride and JPZDD. We recorded the stereotypic behaviors of TS mice and measured the content of GABA in striatum by HPLC and GABAAR expression by immunohistochemistry and real-time PCR. Our results showed that JPZDD inhibited the abnormal behaviors of TS model mice and decreased GABA levels and GABAAR protein and mRNA expression in the striatum of TS model mice. In brief, the mechanism by which JPZDD alleviates TS symptoms may be associated with GABAAR expression downregulation in striatum which may regulate GABA metabolism