46 research outputs found
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
and to probe the fermiophobic Higgs boson with high cutoff scales
The light fermiophobic Higgs boson 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 and .
Since and are dominant in this case, two final states are more
efficient to probe than the conventional search mode of
. One is from and the other is () from , , and . The inclusive 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
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, , within the type-I
two-Higgs-doublet model at the HL-LHC. Our meticulous parameter scan
illuminates an intriguing mass range for , spanning
. This mass range owes its viability to substantial
parameter points, largely due to the inherent challenges of detecting the soft
decay products of at contemporary high-energy colliders. Given that
this light ensures ,
, and , we propose a golden discovery channel: , where includes and
. However, a significant obstacle arises as the two photons from the
decay mostly merge into a single jet due to their proximity within
. 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 threshold at an integrated luminosity of
. Furthermore, our approach facilitates accurate mass
reconstructions for both and . 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