arXiv subjects

Tao Han

Publications and source records attributed to Tao Han.

At least 91 records · Page 5Linked to original sources

arXiv2024

Prospects for searching for sterile neutrinos with gravitational wave and $γ$-ray burst joint observations

Sterile neutrinos can influence the evolution of the universe, and thus cosmological observations can be used to detect them. Future gravitational wave (GW) observations can precisely measure absolute cosmological distances, helping to break parameter degeneracies generated by traditional cosmological observations. This advancement can lead to much tighter constraints on sterile neutrino parameters. This work provides a preliminary forecast for detecting sterile neutrinos using third-generation GW detectors in combination with future short $γ$-ray burst observations from a THESEUS-like telescope, an approach not previously explored in the literature. Both massless and massive sterile neutrinos are considered within the $Λ$CDM cosmology. We find that using GW data can greatly enhance the detection capability for massless sterile neutrinos, reaching 3$σ$ level. For massive sterile neutrinos, GW data can also greatly assist in improving the parameter constraints, but it seems that effective detection is still not feasible.

astro-ph.HE↗

arXiv2024

SignEye: Traffic Sign Interpretation from Vehicle First-Person View

Traffic signs play a key role in assisting autonomous driving systems (ADS) by enabling the assessment of vehicle behavior in compliance with traffic regulations and providing navigation instructions. However, current works are limited to basic sign understanding without considering the egocentric vehicle's spatial position, which fails to support further regulation assessment and direction navigation. Following the above issues, we introduce a new task: traffic sign interpretation from the vehicle's first-person view, referred to as TSI-FPV. Meanwhile, we develop a traffic guidance assistant (TGA) scenario application to re-explore the role of traffic signs in ADS as a complement to popular autonomous technologies (such as obstacle perception). Notably, TGA is not a replacement for electronic map navigation; rather, TGA can be an automatic tool for updating it and complementing it in situations such as offline conditions or temporary sign adjustments. Lastly, a spatial and semantic logic-aware stepwise reasoning pipeline (SignEye) is constructed to achieve the TSI-FPV and TGA, and an application-specific dataset (Traffic-CN) is built. Experiments show that TSI-FPV and TGA are achievable via our SignEye trained on Traffic-CN. The results also demonstrate that the TGA can provide complementary information to ADS beyond existing popular autonomous technologies.

cs.CV↗

arXiv2024

Electroweak Scattering at the Muon Shot

It has long been recognized that the scattering of electroweak particles at very high energies is dominated by vector boson fusion, which probes the origin of electroweak symmetry breaking and offers a unique window into the ultraviolet regime of the SM. Previous studies assume SM-like couplings and rely on the effective $W$ approximation (or electroweak parton distribution), whose validity is well-established within the SM but not yet studied in the presence of anomalous Higgs couplings. In this work, we critically examine the electroweak production of two Higgs bosons in the presence of anomalous $VVh$ and $VVhh$ couplings. We compute the corresponding helicity amplitudes and compare the cross section results in the effective $W$ approximation with the full fixed-order calculation. In particular, we identify two distinct classes of anomalous Higgs couplings, whose effects are not captured by vector boson fusion and effective $W$ approximation. Such very high energy electroweak scatterings can be probed at the Muon Shot, a multi-TeV muon collider upon which we base our study, although similar considerations apply to other high energy colliders.

hep-ph↗

arXiv2024

Top Quark Electroweak Dipole Moment at a High Energy Muon Collider

We study the sensitivity reach to probe the electroweak dipole operators associated with a top quark at a multi-TeV lepton collider. Studying the electroweak dipole operators is strongly motivated by precision physics. The operators exhibit unique chiral structure and can be enhanced with respect to others in theories beyond the Standard Model. We illustrate this point in a strongly coupled composite Higgs model. We find that a high energy $μ^+μ^-$ collider may offer unique opportunity to probe the electroweak dipole operators beyond the coverage by the LHC and future hadron colliders. The significant sensitivity is achieved by the leading channel $μ^+ μ^- \to t \bar t$ near the threshold, and substantially improved by the novel channel $μ^+ μ^- \to t \bar t h$ at high energies. We may be able to reach a new physics scale well above the collider energy for moderate couplings of the Wilson coefficients. Our main conclusions are applicable to future $e^+e^-$ colliders.

hep-ph↗

arXiv2024

Higgs-muon interactions at a multi-TeV muon collider

We establish a simple yet general parameterization of Higgs-muon interactions within the effective field theory frameworks, including both the Higgs Effective Field Theory (HEFT) and the Standard Model Effective Field Theory (SMEFT). We investigate the potential of a muon collider, operating at center-of-mass energies of 3 and 10 TeV, to probe Higgs-muon interactions. All possible processes involving the direct production of multiple electroweak bosons ($W$, $Z$, and $H$) with up to five final-state particles are considered. Our findings indicate that a muon collider can achieve greater sensitivity than the high-luminosity LHC, especially considering the independence of the Higgs decay branching fraction to muons. Notably, a 10 TeV muon collider offers exceptional sensitivity to muon-Higgs interactions, surpassing the 3 TeV option. In particular, searches based on multi-Higgs production prove highly effective for probing these couplings.

hep-ph↗

arXiv2024

WeatherFormer: Empowering Global Numerical Weather Forecasting with Space-Time Transformer

Numerical Weather Prediction (NWP) system is an infrastructure that exerts considerable impacts on modern society.Traditional NWP system, however, resolves it by solving complex partial differential equations with a huge computing cluster, resulting in tons of carbon emission. Exploring efficient and eco-friendly solutions for NWP attracts interest from Artificial Intelligence (AI) and earth science communities. To narrow the performance gap between the AI-based methods and physic predictor, this work proposes a new transformer-based NWP framework, termed as WeatherFormer, to model the complex spatio-temporal atmosphere dynamics and empowering the capability of data-driven NWP. WeatherFormer innovatively introduces the space-time factorized transformer blocks to decrease the parameters and memory consumption, in which Position-aware Adaptive Fourier Neural Operator (PAFNO) is proposed for location sensible token mixing. Besides, two data augmentation strategies are utilized to boost the performance and decrease training consumption. Extensive experiments on WeatherBench dataset show WeatherFormer achieves superior performance over existing deep learning methods and further approaches the most advanced physical model.

cs.AI↗

arXiv2024

Probing Higgs-muon interactions at a multi-TeV muon collider

We study the capabilities of a muon collider, at 3 and 10 TeV center-of-mass energy, of probing the interactions of the Higgs boson with the muon. We consider all the possible processes involving the direct production of EW bosons ($W,Z$ and $H$) with up to five particles in the final state. We study these processes both in the HEFT and SMEFT frameworks, assuming that the dominant BSM effects originate from the muon Yukawa sector. Our study shows that a Muon Collider has sensitivity beyond the LHC, as it not only relies on the Higgs-decay branching fraction to muons. A 10 TeV muon collider provides a unique sensitivity on muon and (multi-) Higgs interactions, significantly better than the 3 TeV option. We find searches based purely on multi-Higgs production to be particularly effective in probing these couplings.

hep-ph↗

arXiv2024

Standard siren cosmology in the era of the 2.5-generation ground-based gravitational wave detectors: bright and dark sirens of LIGO Voyager and NEMO

The 2.5-generation (2.5G) ground-based gravitational wave (GW) detectors LIGO Voyager and NEMO are expected to be operational in the late 2020s and early 2030s. In this work, we explore the potential of GW standard sirens observed by the 2.5G GW detectors in measuring cosmological parameters, especially for the Hubble constant. Using GWs to measure cosmological parameters is inherently challenging, especially for 2.5G detectors, given their limited capability, which results in weaker constraints on cosmological parameters from the detected standard sirens. However, the measurement of the Hubble constant using standard siren observations from Voyager and NEMO is still promising. For example, using bright sirens from Voyager and NEMO can measure the Hubble constant with a precision of about $2\%$ and $6\%$ respectively, and using the Voyager-NEMO network can improve the precision to about $1.6\%$. Moreover, bright sirens can be used to break the degeneracy of cosmological parameters generated by CMB data, and to a certain extent, 2.5G detectors can also play a role in this aspect. Observations of dark sirens by 2.5G detectors can achieve relatively good results in measuring the Hubble constant, with a precision of within $2\%$, and if combining observations of bright and dark sirens, the precision of the Hubble constant measurement can reach about $1.4\%$. Finally, we also discussed the impact of the uncertainty in the binary neutron star merger rate on the estimation of cosmological parameters. We conclude that the magnificent prospect for solving the Hubble tension is worth expecting in the era of the 2.5G ground-based GW detectors.

astro-ph.CO↗

arXiv2024

New Physics at Neutron Beam Dump

We find a new utility of neutrons, usually treated as an experimental nuisance causing unwanted background, in probing new physics signals. They can either be radiated from neutrons (neutron bremsstrahlung) or appear through secondary particles from neutron-on-target interactions, dubbed "neutron beam dump". As a concrete example, we take the FASER/FASER2 experiment as a "factory" of high-energy neutrons that interact with the iron dump. We find that neutron-initiated bremsstrahlung contributions are comparable to proton-initiated ones, in terms of the resulting flux and the range of couplings that can be probed. The neutron bremsstrahlung can be used to probe dark gauge bosons with non-zero neutron coupling. In particular, we investigate protophobic gauge bosons and find that FASER/FASER2 can probe new parameter space. We also illustrate the possibility of neutron-induced secondary particles by considering axion-like particles with electron couplings. We conclude that the physics potential of FASER/FASER2 in terms of new physics searches can be greatly extended and improved with the inclusion of neutron interactions.

hep-ph↗

arXiv2024

ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast

Data-driven weather forecast based on machine learning (ML) has experienced rapid development and demonstrated superior performance in the global medium-range forecast compared to traditional physics-based dynamical models. However, most of these ML models struggle with accurately predicting extreme weather, which is related to training loss and the uncertainty of weather systems. Through mathematical analysis, we prove that the use of symmetric losses, such as the Mean Squared Error (MSE), leads to biased predictions and underestimation of extreme values. To address this issue, we introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast. Beyond the evolution in training loss, we introduce a training-free extreme value enhancement module named ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples, thereby increasing the hit rate of low-probability extreme events. Combined with an advanced global weather forecast model, extensive experiments show that our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.

cs.LG↗

arXiv2024

LeFi: Learn to Incentivize Federated Learning in Automotive Edge Computing

Federated learning (FL) is the promising privacy-preserve approach to continually update the central machine learning (ML) model (e.g., object detectors in edge servers) by aggregating the gradients obtained from local observation data in distributed connected and automated vehicles (CAVs). The incentive mechanism is to incentivize individual selfish CAVs to participate in FL towards the improvement of overall model accuracy. It is, however, challenging to design the incentive mechanism, due to the complex correlation between the overall model accuracy and unknown incentive sensitivity of CAVs, especially under the non-independent and identically distributed (Non-IID) data of individual CAVs. In this paper, we propose a new learn-to-incentivize algorithm to adaptively allocate rewards to individual CAVs under unknown sensitivity functions. First, we gradually learn the unknown sensitivity function of individual CAVs with accumulative observations, by using compute-efficient Gaussian process regression (GPR). Second, we iteratively update the reward allocation to individual CAVs with new sampled gradients, derived from GPR. Third, we project the updated reward allocations to comply with the total budget. We evaluate the performance of extensive simulations, where the simulation parameters are obtained from realistic profiling of the CIFAR-10 dataset and NVIDIA RTX 3080 GPU. The results show that our proposed algorithm substantially outperforms existing solutions, in terms of accuracy, scalability, and adaptability.

cs.NI↗

arXiv2024

Prospects for weighing neutrinos in interacting dark energy models using joint observations of gravitational waves and $γ$-ray bursts

Cosmological observations can be used to weigh neutrinos, but this method is model-dependent, with results relying on the cosmological model considered. If we consider interactions between dark energy and dark matter, the neutrino mass constraints differ from those derived under the standard model. On the contrary, gravitational wave (GW) standard siren observations can measure absolute cosmological distances, helping to break parameter degeneracies inherent in traditional cosmological observations, thereby improving constraints on neutrino mass. This paper examines the constraints on neutrino mass within interacting dark energy (IDE) models and explores how future GW standard siren observations could enhance these results. For multi-messenger GW observations, we consider the joint observations of binary neutron star mergers by third-generation ground-based GW detectors and short $γ$-ray burst observations by missions similar to the THESEUS satellite project. Using current cosmological observations (CMB+BAO+SN), we obtain an upper limit on the neutrino mass in the IDE models of 0.15 (or 0.16) eV. With the inclusion of GW data, the upper limit on the neutrino mass improves to 0.14 eV. This indicates that in the context of IDE models, the improvement in neutrino mass constraints from GW observations is relatively limited. However, GW observations significantly enhance the constraints on other cosmological parameters, such as matter density parameter, the Hubble constant, and coupling strength between dark energy and dark matter.

astro-ph.CO↗

arXiv2024

A Comprehensive Solution to Connect Speech Encoder and Large Language Model for ASR

Recent works have shown promising results in connecting speech encoders to large language models (LLMs) for speech recognition. However, several limitations persist, including limited fine-tuning options, a lack of mechanisms to enforce speech-text alignment, and high insertion errors especially in domain mismatch conditions. This paper presents a comprehensive solution to address these issues. We begin by investigating more thoughtful fine-tuning schemes. Next, we propose a matching loss to enhance alignment between modalities. Finally, we explore training and inference methods to mitigate high insertion errors. Experimental results on the Librispeech corpus demonstrate that partially fine-tuning the encoder and LLM using parameter-efficient methods, such as LoRA, is the most cost-effective approach. Additionally, the matching loss improves modality alignment, enhancing performance. The proposed training and inference methods significantly reduce insertion errors.

cs.LG↗

arXiv2024

A comprehensive forecast for cosmological parameter estimation using joint observations of gravitational waves and short $γ$-ray bursts

In the third-generation (3G) gravitational-wave (GW) detector era, the multi-messenger GW observation for binary neutron star (BNS) merger events can exert great impacts on exploring the cosmic expansion history. In this work, we comprehensively explore the potential of 3G GW standard siren observations in cosmological parameter estimations by considering the 3G GW detectors and the future short $γ$-ray burst (GRB) detector THESEUS-like telescope joint observations. Based on the 10-year observation of different detection strategies, we predict that the numbers of detectable GW-GRB events are 334--674 with the redshifts $z<3.5$ and the inclination angles $ι<15^{\circ}$. For the cosmological analysis, we consider the $Λ$CDM, $w$CDM, $w_0w_a$CDM models, and interacting dark energy (IDE) models. We find that GW can tightly constrain the Hubble constant with precisions of $0.345\%-0.065\%$, but perform not well in constraining other cosmological parameters. Fortunately, GW could effectively break the cosmological parameter degeneracies generated by the mainstream EM observations, CMB+BAO+SN (CBS). When combining the mock GW data with the CBS data, CBS+GW can tightly constrain the equation of state of dark energy $w$ with a precision of 1.26\%, close to the standard of precision cosmology. Meanwhile, the addition of GW to CBS could improve constraints on cosmological parameters by $34.2\%-94.9\%$. In conclusion, GW standard siren observations from 3G GW detectors could play a crucial role in helping solve the Hubble tension and probe the fundamental nature of dark energy.

astro-ph.CO↗

arXiv2024

Optimizing Fictitious States for Bell Inequality Violation in Bipartite Qubit Systems

There is a significant interest in testing quantum entanglement and Bell inequality violation in high-energy experiments. Since the analyses in high-energy experiments are performed with events statistically averaged over phase space, the states used to determine observables depend on the choice of coordinates through an event-dependent basis and are thus not genuine quantum states, but rather "fictitious states." We prove that if Bell inequality violation is observed with a fictitious state, then it implies the same for a quantum sub-state. We further show analytically that the basis which diagonalizes the spin-spin correlations is optimal for constructing fictitious states, and for maximizing the violation of Bell's inequality.

hep-ph↗

arXiv2024

FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation

Data assimilation is a vital component in modern global medium-range weather forecasting systems to obtain the best estimation of the atmospheric state by combining the short-term forecast and observations. Recently, AI-based data assimilation approaches have attracted increasing attention for their significant advantages over traditional techniques in terms of computational consumption. However, existing AI-based data assimilation methods can only handle observations with a specific resolution, lacking the compatibility and generalization ability to assimilate observations with other resolutions. Considering that complex real-world observations often have different resolutions, we propose the \textit{\textbf{Fourier Neural Processes}} (FNP) for \textit{arbitrary-resolution data assimilation} in this paper. Leveraging the efficiency of the designed modules and flexible structure of neural processes, FNP achieves state-of-the-art results in assimilating observations with varying resolutions, and also exhibits increasing advantages over the counterparts as the resolution and the amount of observations increase. Moreover, our FNP trained on a fixed resolution can directly handle the assimilation of observations with out-of-distribution resolutions and the observational information reconstruction task without additional fine-tuning, demonstrating its excellent generalization ability across data resolutions as well as across tasks.

cs.LG↗

arXiv2024

FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation

Weather forecasting is a crucial yet highly challenging task. With the maturity of Artificial Intelligence (AI), the emergence of data-driven weather forecasting models has opened up a new paradigm for the development of weather forecasting systems. Despite the significant successes that have been achieved (e.g., surpassing advanced traditional physical models for global medium-range forecasting), existing data-driven weather forecasting models still rely on the analysis fields generated by the traditional assimilation and forecasting system, which hampers the significance of data-driven weather forecasting models regarding both computational cost and forecasting accuracy. In this work, we explore the possibility of coupling the data-driven weather forecasting model with data assimilation by integrating the global AI weather forecasting model, FengWu, with one of the most popular assimilation algorithms, Four-Dimensional Variational (4DVar) assimilation, and develop an AI-based cyclic weather forecasting system, FengWu-4DVar. FengWu-4DVar can incorporate observational data into the data-driven weather forecasting model and consider the temporal evolution of atmospheric dynamics to obtain accurate analysis fields for making predictions in a cycling manner without the help of physical models. Owning to the auto-differentiation ability of deep learning models, FengWu-4DVar eliminates the need of developing the cumbersome adjoint model, which is usually required in the traditional implementation of the 4DVar algorithm. Experiments on the simulated observational dataset demonstrate that FengWu-4DVar is capable of generating reasonable analysis fields for making accurate and efficient iterative predictions.

physics.ao-ph↗

arXiv2024

CRA5: Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformer

The advent of data-driven weather forecasting models, which learn from hundreds of terabytes (TB) of reanalysis data, has significantly advanced forecasting capabilities. However, the substantial costs associated with data storage and transmission present a major challenge for data providers and users, affecting resource-constrained researchers and limiting their accessibility to participate in AI-based meteorological research. To mitigate this issue, we introduce an efficient neural codec, the Variational Autoencoder Transformer (VAEformer), for extreme compression of climate data to significantly reduce data storage cost, making AI-based meteorological research portable to researchers. Our approach diverges from recent complex neural codecs by utilizing a low-complexity Auto-Encoder transformer. This encoder produces a quantized latent representation through variance inference, which reparameterizes the latent space as a Gaussian distribution. This method improves the estimation of distributions for cross-entropy coding. Extensive experiments demonstrate that our VAEformer outperforms existing state-of-the-art compression methods in the context of climate data. By applying our VAEformer, we compressed the most popular ERA5 climate dataset (226 TB) into a new dataset, CRA5 (0.7 TB). This translates to a compression ratio of over 300 while retaining the dataset's utility for accurate scientific analysis. Further, downstream experiments show that global weather forecasting models trained on the compact CRA5 dataset achieve forecasting accuracy comparable to the model trained on the original dataset. Code, the CRA5 dataset, and the pre-trained model are available at https://github.com/taohan10200/CRA5.

cs.LG↗