arXiv subjects

Shang Li

Publications and source records attributed to Shang Li.

At least 19 recordsLinked to original sources

arXiv2026

The large scale structure probes of dark energy

We present a brief review on the large scale structure (LSS) probes of dark energy. We cover probes that directly constrain dark energy such as baryon acoustic oscillation, redshift space distortion, weak lensing and cluster number count. We also review auxiliary probes that mitigate systematics in dark energy constraints, such as the SZ effect to constrain baryonic effect and broadband galaxy clustering to calibrate photometric redshift. We demonstrate the synergy between these probes in delivering dark energy constraint of both high precision and high accuracy.

astro-ph.CO↗

arXiv2026

Synergy between the gravitational potential decay rate and other structure growth probes in testing gravity

We test gravity by exploiting the synergy between the gravitational potential decay rate ($\mathit{DR}$) and complementary structure-growth probes: these observables respond to MG parameters with different degeneracy directions, so their combination yields stronger constraints than any single probe. We adopt the tomographic $\mathit{DR}$ measurements reported in \citep{2025ApJ...982...99D} and combine them with CMB-lensing-tomography $\Sigma_8$ measurements and $f\sigma_8$ measurements from DESI DR1 full-shape analyses and the DESI peculiar-velocity field. We apply this joint data vector to two representative frameworks: phenomenological parameterizations and the Effective Field Theory (EFT) $\alpha$-basis. For the phenomenological form $P_{\rm MG}(a)=1+P_{{\rm MG},0}\,\Omega_{\rm DE}(a)/\Omega_{\rm DE}(0)$, where $P_{\rm MG}$ denotes $\mu$, $\eta$, or $\Sigma$, we obtain $\mu_0=0.09\pm0.35$ and $\Sigma_0=0.01\pm0.06$. Compared to the measurements combination $\Sigma_8+f\sigma_8$, including $\mathit{DR}$ tightens the constraint on $\Sigma_0$ by a factor of $\sim2$. For the $(\mu_0,\eta_0)$ case we find $\mu_0=0.06^{+0.17}_{-0.23}$ and $\eta_0=-0.03^{+0.36}_{-0.46}$; relative to $\Sigma_8+f\sigma_8$, adding $\mathit{DR}$ improves the constraints on both parameters by a factor of $\sim1.5$. In the EFT $\alpha$-basis, adopting the parameterization $\alpha_i(a)=c_i\,\Omega_{\rm DE}(a)$ with $i\in\{{\rm M,B}\}$, we find $c_{\rm M}=0.64^{+0.32}_{-0.72}$ and $c{\rm B}=0.31^{+0.19}_{-0.29}$. The corresponding EFT uncertainties are about a factor of $\sim2$ smaller than those reported in \citep{2025JCAP...09..053I}, which combined DESI full-shape and BAO measurements with DES-SN5YR and CMB data. These results demonstrate the capability of $\mathit{DR}$ and the necessity of including the $\mathit{DR}$ measurements in testing gravity.

astro-ph.CO↗

arXiv2025

Relative Bruhat decomposition of wonderful compactification

In the seminal paper of Borel and Tits about reductive groups, they show some fundamental results about Bruhat cells with respect to a minimal parabolic subgroup, e.g., relative Bruhat decomposition and its geometrization, relative Bruhat order and the relation of Zariski closure and topological closure. In this paper, we show analogous results for Bruhat cells of wonderful group compactification in the sense of De Concini and Procesi. Our results can be viewed as the version at infinity of those of Borel and Tits. Our main focus is general base field. When the base field is algebraically closed, most of our results are proved by Brion and Springer.

math.AG↗

arXiv2025

Toroidal embedding of Chevalley groups over $\mathbb{Z}$

The classification of equivariant toroidal embeddings of a reductive group over an algebraically closed field is combinatorial and does not depend on the characteristic of the base field. This suggests that there should exist ``universal'' toroidal embeddings for a Chevalley group scheme over $\mathbb{Z}$ which specialize to classical toroidal embeddings via base change. In this paper, we establish the existence of ``universal'' equivariant toroidal embeddings for split reductive group schemes over $\mathbb{Z}$. We also discuss several geometric properties of these embeddings.

math.AG↗

arXiv2025

Wonderful embedding for group schemes in the Bruhat--Tits theory

For a reductive group $G$ over a discretely valued Henselian field $k$, using valuations of root datum and concave functions, the Bruhat--Tits theory defines an important class of open bounded subgroups of $G(k)$ which are essential objects in representation theory and arithmetic geometry. Moreover, these subgroups are uniquely determined by smooth affine group schemes whose generic fibers are $G$ over the ring of integers of $k$. To study these group schemes, when $G$ is adjoint and quasi-split, we systematically construct wonderful embedding for these group schemes which are uniquely determined by a big cell structure. The way that we construct our wonderful embedding is different from classical methods in the sense that we avoid embedding a group scheme into an ambient space and taking closure. We use an intrinsic and functorial method which is a variant of Artin--Weil method of birational group laws. Beyond the quasi-split case, our wonderful embedding is constructed by \'etale descent. Moreover our wonderful embedding behaves in a similar way to the classical wonderful compactification of $G$. Our results can serve as a bridge between the theory of wonderful compactifications and the Bruhat--Tits theory.

math.AG↗

arXiv2025

SNAT-YOLO: Efficient Cross-Layer Aggregation Network for Edge-Oriented Gangue Detection

To address the issues of slow detection speed,low accuracy,difficulty in deployment on industrial edge devices,and large parameter and computational requirements in deep learning-based coal gangue target detection methods,we propose a lightweight coal gangue target detection algorithm based on an improved YOLOv11.First,we use the lightweight network ShuffleNetV2 as the backbone to enhance detection speed.Second,we introduce a lightweight downsampling operation,ADown,which reduces model complexity while improving average detection accuracy.Third,we improve the C2PSA module in YOLOv11 by incorporating the Triplet Attention mechanism,resulting in the proposed C2PSA-TriAtt module,which enhances the model's ability to focus on different dimensions of images.Fourth,we propose the Inner-FocalerIoU loss function to replace the existing CIoU loss function.Experimental results show that our model achieves a detection accuracy of 99.10% in coal gangue detection tasks,reduces the model size by 38%,the number of parameters by 41%,and the computational cost by 40%,while decreasing the average detection time per image by 1 ms.The improved model demonstrates enhanced detection speed and accuracy,making it suitable for deployment on industrial edge mobile devices,thus contributing positively to coal processing and efficient utilization of coal resources.

cs.CV↗

arXiv2025

Testing General Relativity using Large Scale Structures Photometric Redshift Surveys and Cosmic Microwave Background Lensing Effect

The $E_G$ statistic provides a valuable tool for evaluating predictions of General Relativity (GR) by probing the relationship between gravitational potential and galaxy clustering on cosmological scales within the observable universe. In this study, we constrain the $E_G$ statistic using photometric redshift data from the Dark Energy Survey (DES) MagLim sample in combination with the Planck 2018 Cosmic Microwave Background (CMB) lensing map. Unlike spectroscopic redshift surveys, photometric redshift measurements are subject to significant redshift uncertainties, making it challenging to constrain the redshift distortion parameter $\beta$ with high precision. We adopt a new definition for this parameter, $\beta(z) = {f\sigma_8(z)}/{b\sigma_8(z)}$. In this formulation, we reconstruct the growth rate of structure, $f\sigma_8(z)$, using Artificial Neural Networks (ANN) method, while simultaneously utilizing model-independent constraints on the parameter $b\sigma_8(z)$, directly obtained from the DES collaboration. After obtaining the angular power spectra $C_\ell^{gg}$ (galaxy-galaxy) and $C_\ell^{g\kappa}$ (galaxy-CMB lensing) from the combination of DES photometric data and Planck lensing, we derive new measurements of the $E_G$ statistic: $E_G = 0.354 \pm 0.146$, $0.452 \pm 0.092$, $0.414 \pm 0.069$, and $0.296 \pm 0.069$ (68$\%$ C.L.) across four redshift bins: $z = 0.30, 0.47, 0.63$, and $0.80$, respectively, which are consistent with the predictions of the standard $\Lambda$CDM model. Finally, we forecast the $E_G$ statistic using future photometric redshift data from the China Space Station Telescope, combined with lensing measurements from the CMB-S4 project, indicating an achievable constraint on $E_G$ of approximately 1$\%$, improving the precision of tests for GR on cosmological scales.

astro-ph.CO↗

arXiv2024

An It\^o-type formula for some measure-valued processes and its application on controlled superprocesses

We derive an It\^o-type formula for a measure-valued process that has a decomposition analogous to a classical semimartingale. The derivation begins with a time partitioning approach similar to the classical proof of It\^o's formula. To address the new challenges arising from the measure-valued setting, we employ symmetric polynomials to approximate the second-order linear derivative of the functional on finite measures, alongside certain localization techniques. A controlled superprocess with a binary branching mechanism can be interpreted as a weak solution to a controlled stochastic partial differential equation (SPDE), which naturally leads to such a decomposition. Consequently, this It\^o-type formula makes it possible to derive the Hamilton-Jacobi-Bellman (HJB) equation and the verification theorem for controlled superprocesses with a binary branching mechanism. Additionally, we propose a heuristic definition for the viscosity solution of an equation involving derivatives on finite measures. We prove that a continuous value function is a viscosity solution in this sense and demonstrate the uniqueness of the viscosity solution when the second-order derivative term on the measure vanishes.

math.PR↗

arXiv2024

Constraints on Primordial Magnetic Fields from High Redshift Stellar Mass Density

Primordial magnetic fields (PMFs) play a pivotal role in influencing small-scale fluctuations within the primordial density field, thereby enhancing the matter power spectrum within the context of the $\Lambda$CDM model at small scales. These amplified fluctuations accelerate the early formation of galactic halos and stars, which can be observed through advanced high-redshift observational techniques. Therefore, Stellar Mass Density (SMD) observations, which provide significant opportunities for detailed studies of galaxies at small scales and high redshifts, offer a novel perspective on small-scale cosmic phenomena and constrain the characteristics of PMFs. In this study, we compile 14 SMD data points at redshifts $z > 6$ and derive stringent constraints on the parameters of PMFs, which include the amplitude of the magnetic field at a characteristic scale of $\lambda=1\,{\rm Mpc}$, denoted as $B_0$, and the spectral index of the magnetic field power spectrum, $n_{\rm B}$. At 95\% confidence level, we establish upper limits of $B_0 < 4.44$ nG and $n_{\rm B} < -2.24$, along with a star formation efficiency of approximately $f_*^0 \sim 0.1$. If we fix $n_{\rm B}$ at specific values, such as $-2.85$, $-2.9$, and $-2.95$, the 95\% upper limits for the amplitude of the magnetic field can be constrained to 1.33 nG, 2.21 nG, and 3.90 nG, respectively. Finally, we attempt to interpret recent early observations provided by James Webb Space Telescope (JWST) using the theory of PMFs, and find that by selecting appropriate PMF parameters, it is possible to explain these results without significantly increasing the star formation efficiency.

astro-ph.CO↗

arXiv2024

Restoring Real-World Degraded Events Improves Deblurring Quality

Due to its high speed and low latency, DVS is frequently employed in motion deblurring. Ideally, high-quality events would adeptly capture intricate motion information. However, real-world events are generally degraded, thereby introducing significant artifacts into the deblurred results. In response to this challenge, we model the degradation of events and propose RDNet to improve the quality of image deblurring. Specifically, we first analyze the mechanisms underlying degradation and simulate paired events based on that. These paired events are then fed into the first stage of the RDNet for training the restoration model. The events restored in this stage serve as a guide for the second-stage deblurring process. To better assess the deblurring performance of different methods on real-world degraded events, we present a new real-world dataset named DavisMCR. This dataset incorporates events with diverse degradation levels, collected by manipulating environmental brightness and target object contrast. Our experiments are conducted on synthetic datasets (GOPRO), real-world datasets (REBlur), and the proposed dataset (DavisMCR). The results demonstrate that RDNet outperforms classical event denoising methods in event restoration. Furthermore, RDNet exhibits better performance in deblurring tasks compared to state-of-the-art methods. DavisMCR are available at https://github.com/Yeeesir/DVS_RDNet.

cs.CV↗

arXiv2024

Evidence for GeV emission of the superluminous supernova SN 2017egm

Superluminous supernovae (SLSNe) are a new class of transients with luminosities $\sim10 -100$ times larger than the usual core-collapse supernovae (SNe). Their origin is still unclear and one widely discussed scenario involves a millisecond magnetar central engine. The GeV-TeV emission of SLSNe has been predicted in the literature but has not been convincingly detected yet. Here we report the results of the search for $\gamma$-ray emission in the direction of SN 2017egm, one of the closest SLSNe detected so far, using 15 years of {\it Fermi}-LAT Pass 8 data. There is a transient $\gamma$-ray source appearing about 2 months after this event and lasting a few months. Monte Carlo simulations show that the $\gamma$-ray signal has a global significance of {\it at least} 4$\sigma$. Both the peak time and the luminosity of the GeV emission are consistent with the magnetar model prediction, suggesting that such a GeV transient is the high-energy counterpart of SN 2017egm and the central engine of this SLSNe is a young magnetar.

astro-ph.HE↗

arXiv2023

End-to-end Alternating Optimization for Real-World Blind Super Resolution

Blind Super-Resolution (SR) usually involves two sub-problems: 1) estimating the degradation of the given low-resolution (LR) image; 2) super-resolving the LR image to its high-resolution (HR) counterpart. Both problems are ill-posed due to the information loss in the degrading process. Most previous methods try to solve the two problems independently, but often fall into a dilemma: a good super-resolved HR result requires an accurate degradation estimation, which however, is difficult to be obtained without the help of original HR information. To address this issue, instead of considering these two problems independently, we adopt an alternating optimization algorithm, which can estimate the degradation and restore the SR image in a single model. Specifically, we design two convolutional neural modules, namely \textit{Restorer} and \textit{Estimator}. \textit{Restorer} restores the SR image based on the estimated degradation, and \textit{Estimator} estimates the degradation with the help of the restored SR image. We alternate these two modules repeatedly and unfold this process to form an end-to-end trainable network. In this way, both \textit{Restorer} and \textit{Estimator} could get benefited from the intermediate results of each other, and make each sub-problem easier. Moreover, \textit{Restorer} and \textit{Estimator} are optimized in an end-to-end manner, thus they could get more tolerant of the estimation deviations of each other and cooperate better to achieve more robust and accurate final results. Extensive experiments on both synthetic datasets and real-world images show that the proposed method can largely outperform state-of-the-art methods and produce more visually favorable results. The codes are rleased at \url{https://github.com/greatlog/RealDAN.git}.

cs.CV↗

arXiv2023

An equivariant compactification for adjoint reductive group schemes

Wonderful compactifications of adjoint reductive groups over an algebraically closed field play an important role in algebraic geometry and representation theory. In this paper, we construct an equivariant compactification for adjoint reductive groups over arbitrary base schemes. Our compactifications parameterize classical wonderful compactifications of De Concini and Procesi as geometric fibers. Our construction is based on a variant of the Artin-Weil method of birational group laws. In particular, our construction gives a new intrinsic construction of wonderful compactifications. The Picard group scheme of our compactifications is computed. We also discuss several applications of our compactification in the study of torsors under reductive group schemes.

math.AG↗

arXiv2022

Impact of the political risk on food reserve ratio: evidence across countries

Using an unbalanced panel data covering 75 countries from 1991 to 2019, we explore how the political risk impacts on food reserve ratio. The empirical findings show that an increasing political risk negatively affect food reserve ratio, and same effects hold for both internal risk and external risk. Moreover, we find that the increasing external or internal risks both negatively affect production and exports, but external risk does not significantly impact on imports and it positively impacts on consumption, while internal risk negatively impacts on imports and consumption. The results suggest that most of governments have difficulty to raise subsequent food reserve ratio in face of an increasing political risk, no matter it is an internal risk or an external risk although the mechanisms behind the impacts are different.

econ.GN↗

arXiv2022

Using $\gamma$-ray observations of dwarf spheroidal galaxies to test the possible common origin of the W-boson mass anomaly and the GeV $\gamma$-ray/antiproton excesses

A recent result from Fermilab suggests that the measured W-boson mass deviates from the prediction of the Standard Model (SM) with a significance of $>7\sigma$, and there may exist new physics beyond the SM. It is proposed that the inert two Higgs doublet model (i2HDM) can well explain the new W-boson mass. Meanwhile, the lightest neutral scalar $S$ in the i2HDM can be stable and play the role of dark matter with a preferred dark matter mass of $\sim 54-74$ GeV. It is also found that part of the parameter space of this model can explain both the Galactic center GeV gamma-ray excess detected by $Fermi$-LAT and the GeV antiproton excess detected by AMS-02 through a $SS\rightarrow WW^*$ annihilation. In this paper, we aim to test the possible common i2HDM origin of the three anomaly/excesses using the $Fermi$-LAT observations of Milky Way dwarf spheroidal (dSph) galaxies. We perform single and stacking analyses on 19 dSphs that have J-factor measurements. We find that our upper limits are below the favored parameters and seems to be able to exclude the possibility of a common origin of the three anomaly/excesses. However, because the J-factor measurements include relatively large uncertainties, which come from the measurements of stellar kinematics, whether this model could be reliably excluded needs to be further confirmed by future observations.

astro-ph.HE↗

arXiv2022

Learning the Degradation Distribution for Blind Image Super-Resolution

Synthetic high-resolution (HR) \& low-resolution (LR) pairs are widely used in existing super-resolution (SR) methods. To avoid the domain gap between synthetic and test images, most previous methods try to adaptively learn the synthesizing (degrading) process via a deterministic model. However, some degradations in real scenarios are stochastic and cannot be determined by the content of the image. These deterministic models may fail to model the random factors and content-independent parts of degradations, which will limit the performance of the following SR models. In this paper, we propose a probabilistic degradation model (PDM), which studies the degradation $\mathbf{D}$ as a random variable, and learns its distribution by modeling the mapping from a priori random variable $\mathbf{z}$ to $\mathbf{D}$. Compared with previous deterministic degradation models, PDM could model more diverse degradations and generate HR-LR pairs that may better cover the various degradations of test images, and thus prevent the SR model from over-fitting to specific ones. Extensive experiments have demonstrated that our degradation model can help the SR model achieve better performance on different datasets. The source codes are released at \url{git@github.com:greatlog/UnpairedSR.git}.

eess.IV↗

arXiv2021

Search for gamma-ray spectral lines with the DArk Matter Particle Explorer

The DArk Matter Particle Explorer (DAMPE) is well suitable for searching for monochromatic and sharp $\gamma$-ray structures in the GeV$-$TeV range thanks to its unprecedented high energy resolution. In this work, we search for $\gamma$-ray line structures using five years of DAMPE data. To improve the sensitivity, we develop two types of dedicated data sets (including the BgoOnly data which is the first time to be used in the data analysis for the calorimeter-based gamma-ray observatories) and adopt the signal-to-noise ratio optimized regions of interest (ROIs) for different DM density profiles. No line signals or candidates are found between 10 and 300 GeV in the Galaxy. The constraints on the velocity-averaged cross section for $\chi\chi \to \gamma\gamma$ and the decay lifetime for $\chi \to \gamma\nu$, both at 95% confidence level, have been calculated and the systematic uncertainties have been taken into account. Comparing to the previous Fermi-LAT results, though DAMPE has an acceptance smaller by a factor of $\sim 10$, similar constraints on the DM parameters are achieved and below 100 GeV the lower limits on the decay lifetime are even stronger by a factor of a few. Our results demonstrate the potential of high-energy-resolution observations on dark matter detection.

astro-ph.HE↗

arXiv2021

Approaching the Limit of Image Rescaling via Flow Guidance

Image downscaling and upscaling are two basic rescaling operations. Once the image is downscaled, it is difficult to be reconstructed via upscaling due to the loss of information. To make these two processes more compatible and improve the reconstruction performance, some efforts model them as a joint encoding-decoding task, with the constraint that the downscaled (i.e. encoded) low-resolution (LR) image must preserve the original visual appearance. To implement this constraint, most methods guide the downscaling module by supervising it with the bicubically downscaled LR version of the original high-resolution (HR) image. However, this bicubic LR guidance may be suboptimal for the subsequent upscaling (i.e. decoding) and restrict the final reconstruction performance. In this paper, instead of directly applying the LR guidance, we propose an additional invertible flow guidance module (FGM), which can transform the downscaled representation to the visually plausible image during downscaling and transform it back during upscaling. Benefiting from the invertibility of FGM, the downscaled representation could get rid of the LR guidance and would not disturb the downscaling-upscaling process. It allows us to remove the restrictions on the downscaling module and optimize the downscaling and upscaling modules in an end-to-end manner. In this way, these two modules could cooperate to maximize the HR reconstruction performance. Extensive experiments demonstrate that the proposed method can achieve state-of-the-art (SotA) performance on both downscaled and reconstructed images.

eess.IV↗