arXiv subjects

Renjie Wang

Publications and source records attributed to Renjie Wang.

15 recordsLinked to original sources

arXiv2026

Flavor Tomography of Long-Lived Neutrino-Mass Mediators: Probing the Neutrino Mass Ordering at the HL-LHC

The neutrino mass ordering remains unresolved because long-baseline oscillation measurements are entangled with the unknown CP phase $δ_{CP}$; a collider probe free of this degeneracy would provide a qualitatively different, complementary test. We show that when the long-lived charged particle producing a displaced-lepton signal at the High-Luminosity LHC is also the mediator responsible for Majorana neutrino masses---the \textit{shared-coupling condition}---the Casas--Ibarra parametrization determines the lepton flavor ratio $N_e/N_μ$ of time-delayed leptons in terms of Pontecorvo--Maki--Nakagawa--Sakata (PMNS) mixing parameters alone. Within the resulting \textit{shared-coupling class} of models the ratio is fixed by oscillation data, independent of all beyond-Standard-Model mass and coupling parameters up to calculable corrections bounded by charged-lepton-flavor-violation data, yielding $N_e/N_μ= 0.140$ for the Normal and $2.11$ for the Inverted Hierarchy---a separation of more than an order of magnitude between the two predictions. This prediction holds identically across the Scotogenic model, the Type-III seesaw, the inverse and linear seesaw, and lepton-portal dark matter, and is stable against oscillation-parameter uncertainties and detector-efficiency variations, which cancel in the ratio. A single efficiency-corrected decision criterion identifies the hierarchy, and explicit signal-yield estimates combined with an exact Poisson test show that $\mathcal{O}(25)$ displaced-lepton events suffice for a $3σ$ discrimination with the timing infrastructure already under construction at CMS and ATLAS---so that a positive long-lived-particle signal would simultaneously resolve the mass ordering, with no dependence on $δ_{CP}$.

hep-ph↗

arXiv2026

Probing Ultra-Compressed Scotogenic Dark Matter at the HL-LHC via 4D Spacetime Tracking

The ultra-compressed scotogenic regime, in which a 2\,GeV mass splitting between the charged inert scalar and the dark-matter fermion is motivated by and compatible with co-annihilation to the observed relic density for suitable choices of the remaining model parameters, produces decay leptons for which existing LHC displaced-lepton and disappearing-track searches retain only partial, unoptimised acceptance, leaving room for a dedicated timing-assisted strategy. We propose exploiting the 30\,ps timing resolution of the HL-LHC precision timing detectors as a fourth observable, converting the macroscopic scalar decay length of 100--1000\,mm into an arrival-time delay of 70--700\,ps---more than three orders of magnitude above the sub-picosecond delays of all prompt Standard Model backgrounds. Operating at the level of a low-$p_T$ disappearing-track stub matched to a delayed timing hit, without requiring full lepton reconstruction, our 4D timing strategy achieves signal efficiencies of 12--36\% across the probed mass range (14\% at the 200\,GeV benchmark) and zero background across $4.20\times10^6$ simulated prompt SM events, projecting 95\%\,C.L.\ exclusion up to scalar masses of $\simeq\!670$\,GeV at 3000\,fb$^{-1}$ under conservative background assumptions. The scalar lifetime is set by the Yukawa coupling $y$ through $cτ\propto y^{-2}$, while sub-eV neutrino masses constrain only the product $y^2λ_5$; the small couplings that place the charged scalar in the optimal timing window are therefore compatible with, though not uniquely fixed by, the radiative neutrino-mass mechanism, making the co-annihilation corridor---conventionally the most elusive regime---an accessible target for 4D spacetime tracking at the HL-LHC.

hep-ph↗

arXiv2026

Vector-boson-fusion timing references for four-dimensional displaced-vertex searches

Long-lived particles that decay inside the tracker are searched for via the spatial displacement of their decay vertices. However, the same decays also arrive late, providing a handle made available by a precision timing layer at the HL-LHC that displaced-vertex searches do not exploit. We study the exotic decay of a Higgs boson produced through vector-boson fusion (VBF), $pp\to hjj$ with $h\to ss$, in which the long-lived scalar decays as $s\to b\bar b$, and use the VBF tag jets to identify the hard-scatter vertex. Its prompt central tracks, timed by a CMS-MTD-like barrel layer that also times the central tracks of the displaced vertex, define a per-event start time, turning each displaced vertex into a four-dimensional object. Because a fast simulation cannot determine the absolute normalization of the heavy-flavour and instrumental displaced-vertex backgrounds, we take as the primary result a normalization-free figure of merit: the ratio of the timing-enhanced sensitivity to the spatial-only sensitivity. Adding the timing layer improves this ratio by a factor of $\simeq2.8$ at a nominal working point and by up to an order of magnitude for a tighter delayed-vertex requirement, because the heavy-flavour background that survives the spatial selection is prompt in collision time. The improvement increases with decay length, extending sensitivity into the long-lifetime regime in which purely spatial searches lose tracker acceptance. The gain is essentially mass-independent at a decay length of $\ctau\simeq100\mm$, where the trend with scalar mass reverses: lighter scalars benefit more at short lifetimes because of their larger boost, whereas heavier scalars benefit more at long lifetimes as the lighter states leak out of the tracker.

hep-ph↗

arXiv2026

CRL-VLA: Continual Vision-Language-Action Learning

Lifelong learning is critical for embodied agents in open-world environments, where reinforcement learning fine-tuning has emerged as an important paradigm to enable Vision-Language-Action (VLA) models to master dexterous manipulation through environmental interaction. Thus, Continual Reinforcement Learning (CRL) is a promising pathway for deploying VLA models in lifelong robotic scenarios, yet balancing stability (retaining old skills) and plasticity (learning new ones) remains a formidable challenge for existing methods. We introduce CRL-VLA, a framework for continual post-training of VLA models with rigorous theoretical bounds. We derive a unified performance bound linking the stability-plasticity trade-off to goal-conditioned advantage magnitude, scaled by policy divergence. CRL-VLA resolves this dilemma via asymmetric regulation: constraining advantage magnitudes on prior tasks while enabling controlled growth on new tasks. This is realized through a simple but effective dual-critic architecture with novel Goal-Conditioned Value Formulation (GCVF), where a frozen critic anchors semantic consistency and a trainable estimator drives adaptation. Experiments on the LIBERO benchmark demonstrate that CRL-VLA effectively harmonizes these conflicting objectives, outperforming baselines in both anti-forgetting and forward adaptation.

cs.AI↗

arXiv2025

Detectability of massive binary black holes with sub-mHz gravitational wave missions

Beyond LISA, proposed space-based gravitational wave (GW) missions aim to explore the sub-millihertz to microhertz frequency band, with one key objective being the detection of massive binary black hole (MBBH) mergers across cosmic distances. In this work, we investigate the detection and localization capabilities of future sub-mHz GW observatories for MBBH coalescences. Including the full galactic foreground noise, we find that signal-to-noise ratios (SNRs) can reach several thousand across a wide range of redshifts. We evaluate three representative orbital configurations--non-precessing and precessing with different inclination angles--and analyze their localization performance for various MBBH populations. In the non-precessing case, a two-hemisphere degeneracy arises when only the dominant (2,2) mode is considered, which is effectively resolved by including higher-order modes. These modes contribute to a more uniform performance across all configurations, thereby mitigating the prior advantage of precessing mission orbits. Sub-mHz missions operating in the [10 $μ$Hz, 10 mHz] band partially overlap with LISA's range but provide enhanced sensitivity to lower-frequency GWs due to their longer interferometric baselines. This results in significantly improved localization of high-mass MBBHs, enhancing the prospects for multi-messenger astronomy and precision cosmology. Moreover, the high SNRs attainable with sub-mHz detectors could enable stringent tests of general relativity and alternative theories of gravity.

gr-qc↗

arXiv2025

Learning Robotic Policy with Imagined Transition: Mitigating the Trade-off between Robustness and Optimality

Existing quadrupedal locomotion learning paradigms usually rely on extensive domain randomization to alleviate the sim2real gap and enhance robustness. It trains policies with a wide range of environment parameters and sensor noises to perform reliably under uncertainty. However, since optimal performance under ideal conditions often conflicts with the need to handle worst-case scenarios, there is a trade-off between optimality and robustness. This trade-off forces the learned policy to prioritize stability in diverse and challenging conditions over efficiency and accuracy in ideal ones, leading to overly conservative behaviors that sacrifice peak performance. In this paper, we propose a two-stage framework that mitigates this trade-off by integrating policy learning with imagined transitions. This framework enhances the conventional reinforcement learning (RL) approach by incorporating imagined transitions as demonstrative inputs. These imagined transitions are derived from an optimal policy and a dynamics model operating within an idealized setting. Our findings indicate that this approach significantly mitigates the domain randomization-induced negative impact of existing RL algorithms. It leads to accelerated training, reduced tracking errors within the distribution, and enhanced robustness outside the distribution.

cs.RO↗

arXiv2025

Dynamic Adaptive Legged Locomotion Policy via Decoupling Reaction Force Control and Gait Control

While Reinforcement Learning (RL) has achieved remarkable progress in legged locomotion control, it often suffers from performance degradation in out-of-distribution (OOD) conditions and discrepancies between the simulation and the real environments. Instead of mainly relying on domain randomization (DR) to best cover the real environments and thereby close the sim-to-real gap and enhance robustness, this work proposes an emerging decoupled framework that acquires fast online adaptation ability and mitigates the sim-to-real problems in unfamiliar environments by isolating stance-leg control and swing-leg control. Various simulation and real-world experiments demonstrate its effectiveness against horizontal force disturbances, uneven terrains, heavy and biased payloads, and sim-to-real gap.

cs.RO↗

arXiv2025

Integrating Trajectory Optimization and Reinforcement Learning for Quadrupedal Jumping with Terrain-Adaptive Landing

Jumping constitutes an essential component of quadruped robots' locomotion capabilities, which includes dynamic take-off and adaptive landing. Existing quadrupedal jumping studies mainly focused on the stance and flight phase by assuming a flat landing ground, which is impractical in many real world cases. This work proposes a safe landing framework that achieves adaptive landing on rough terrains by combining Trajectory Optimization (TO) and Reinforcement Learning (RL) together. The RL agent learns to track the reference motion generated by TO in the environments with rough terrains. To enable the learning of compliant landing skills on challenging terrains, a reward relaxation strategy is synthesized to encourage exploration during landing recovery period. Extensive experiments validate the accurate tracking and safe landing skills benefiting from our proposed method in various scenarios.

cs.RO↗

arXiv2024

Cooperative Visual-LiDAR Extrinsic Calibration Technology for Intersection Vehicle-Infrastructure: A review

In the typical urban intersection scenario, both vehicles and infrastructures are equipped with visual and LiDAR sensors. By successfully integrating the data from vehicle-side and road monitoring devices, a more comprehensive and accurate environmental perception and information acquisition can be achieved. The Calibration of sensors, as an essential component of autonomous driving technology, has consistently drawn significant attention. Particularly in scenarios involving multiple sensors collaboratively perceiving and addressing localization challenges, the requirement for inter-sensor calibration becomes crucial. Recent years have witnessed the emergence of the concept of multi-end cooperation, where infrastructure captures and transmits surrounding environment information to vehicles, bolstering their perception capabilities while mitigating costs. However, this also poses technical complexities, underscoring the pressing need for diverse end calibration. Camera and LiDAR, the bedrock sensors in autonomous driving, exhibit expansive applicability. This paper comprehensively examines and analyzes the calibration of multi-end camera-LiDAR setups from vehicle, roadside, and vehicle-road cooperation perspectives, outlining their relevant applications and profound significance. Concluding with a summary, we present our future-oriented ideas and hypotheses.

cs.CV↗

arXiv2022

USB: A Unified Semi-supervised Learning Benchmark for Classification

Semi-supervised learning (SSL) improves model generalization by leveraging massive unlabeled data to augment limited labeled samples. However, currently, popular SSL evaluation protocols are often constrained to computer vision (CV) tasks. In addition, previous work typically trains deep neural networks from scratch, which is time-consuming and environmentally unfriendly. To address the above issues, we construct a Unified SSL Benchmark (USB) for classification by selecting 15 diverse, challenging, and comprehensive tasks from CV, natural language processing (NLP), and audio processing (Audio), on which we systematically evaluate the dominant SSL methods, and also open-source a modular and extensible codebase for fair evaluation of these SSL methods. We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning. USB enables the evaluation of a single SSL algorithm on more tasks from multiple domains but with less cost. Specifically, on a single NVIDIA V100, only 39 GPU days are required to evaluate FixMatch on 15 tasks in USB while 335 GPU days (279 GPU days on 4 CV datasets except for ImageNet) are needed on 5 CV tasks with TorchSSL.

cs.LG↗

arXiv2022

LitePIG: A Lite Parameter Inference system for the Gravitational wave in the millihertz band

We present a python based parameter inference system for the gravitational wave (GW) measured in the millihertz band. This system includes the following features: the GW waveform originated from the massive black hole binaries (MBHB), the stationary instrumental gaussian noise, the higher-order harmonic modes, the full response function from the time delay interferometry (TDI) and the gaussian likelihood function with the dynamic nested parameter sampler. In particular, we highlight the role of higher-order modes. By including these modes, the luminosity distance estimation precision can be improved roughly by a factor of 50, compared with the case with only the leading order ($\ell=2,|m|=2$) mode. This is due to the response function of different harmonic modes on the inclination angle are different. Hence, it can help to break the distance-inclination degeneracy. Furthermore, we show the robustness of testing general relativity (GR) by using the higher-order harmonics. Our results show that the GW from MBHB can simultaneously constrain four of the higher harmonic amplitudes (deviation from GR) with a precision of $c_{21}=0.54^{+0.61}_{-0.82}$, $c_{32}=-0.65^{+0.22}_{-0.08}$, $c_{33}=0.56^{+0.60}_{-0.76}$ and $c_{44}=1.57^{+2.34}_{-1.90}$, respectively.

astro-ph.IM↗

arXiv2021

Hubble parameter estimation via dark sirens with the LISA-Taiji network

The Hubble parameter is one of the central parameters in modern cosmology, which describes the present expansion rate of the universe. Their values inferred from the late-time observations are systematically higher than those from the early-time measurements by about $10\%$. To come to a robust conclusion, independent probes with accuracy at percent levels are crucial. Gravitational waves from compact binary coalescence events can be formulated into the standard siren approach to provide an independent Hubble parameter measurement. The future space-borne gravitational wave observatory network, such as the LISA-Taiji network, will be able to measure the gravitational wave signals in the Millihertz bands with unprecedented accuracy. By including several statistical and instrumental noises, we show that within 5 years operation time, the LISA-Taiji network is able to constrain the Hubble parameter within $1\%$ accuracy, and possibly beats the scatters down to $0.5\%$ or even better.

astro-ph.CO↗

arXiv2019

Epitaxial Growth and Characterization of AlInN Based Core-Shell Nanowire Light Emitting Diodes Operating in the Ultraviolet Spectrum

We report on the demonstration of the first axial AlInN ultraviolet core-shell nanowire light-emitting diodes with highly stable emission in the UV wavelength range. During the epitaxial growth of AlInN layer, an AlInN shell is spontaneously formed, resulted in the reduced nonradiative recombination on nanowire surface. The AlInN nanowires exhibit high internal quantum efficiency of ~ 52% at room temperature for emission at 295nm. The peak emission wavelength can be varied from 290 nm to 355 nm by changing the growth condition. Moreover, significantly strong transverse magnetic (TM) polarized emission is recorded which is ~ 4 times stronger compared to the transverse electric (TE) polarized light at 295 nm. This study provides alternative approach for the fabrication of new type of high-performance ultraviolet light-emitters.

physics.app-ph↗

arXiv2019

The Entropic Measure Transform

We introduce the entropic measure transform (EMT) problem for a general process and prove the existence of a unique optimal measure characterizing the solution. The density process of the optimal measure is characterized using a semimartingale BSDE under general conditions. The EMT is used to reinterpret the conditional entropic risk-measure and to obtain a convenient formula for the conditional expectation of a process which admits an affine representation under a related measure. The entropic measure transform is then used provide a new characterization of defaultable bond prices, forward prices, and futures prices when the asset is driven by a jump diffusion. The characterization of these pricing problems in terms of the EMT provides economic interpretations as a maximization of returns subject to a penalty for removing financial risk as expressed through the aggregate relative entropy. The EMT is shown to extend the optimal stochastic control characterization of default-free bond prices of Gombani and Runggaldier (Math. Financ. 23(4):659-686, 2013). These methods are illustrated numerically with an example in the defaultable bond setting.

q-fin.MF↗

arXiv2016

Trading against disorderly liquidation of a large position under asymmetric information and market impact

We consider trading against a hedge fund or large trader that must liquidate a large position in a risky asset if the market price of the asset crosses a certain threshold. Liquidation occurs in a disorderly manner and negatively impacts the market price of the asset. We consider the perspective of small investors whose trades do not induce market impact and who possess different levels of information about the liquidation trigger mechanism and the market impact. We classify these market participants into three types: fully informed, partially informed and uninformed investors. We consider the portfolio optimization problems and compare the optimal trading and wealth processes for the three classes of investors theoretically and by numerical illustrations.

q-fin.TR↗