arXiv subjects
Publications and source records attributed to Jian Ge.
In 1986, Gromov asked whether every complete noncompact $n$-dimensional Riemannian manifold with nonnegative Ricci curvature and scalar curvature at least one satisfies: \[ \Vol_g (B(p, R))\le C_{n}R^{n-2} \] for all $p\in M$ and $R>0$. We answer this question affirmatively using the heat-kernel Fisher metric and Nash entropy.
Answering the question "Are we alone?" requires atmospheric spectroscopy of nearby terrestrial planets. For an Earth--Sun analog, even the strongest transmission signals are expected to be of order 1 part per million (ppm). Unlike short-period planets, Earth 2.0 planets transit only about once per year, so single-transit sensitivity, rather than stacking repeated observations, is the fundamental design driver. Life 2.0 is a scalable space-mission concept linking Earth 2.0 candidates discovered by PLATO and the Earth 2.0 (ET) mission with atmospheric characterization and biosignature assessment. The baseline architecture comprises 900 one-meter space telescopes, each equipped with a high-throughput Waveguide Integrated Miniature Spectrograph and an ultra-low-read-noise CMOS detector. After independent calibration, spectra acquired simultaneously during a transit are combined, providing the photon-collecting capability of an approximately 30-m aperture at the selected spectral resolution while retaining a modular architecture. The baseline 0.2--1.05 $\mu$m range covers O$_3$, O$_2$, H$_2$O, Rayleigh scattering, and other diagnostics, with extension into the infrared as detector technologies mature. Prototype Waveguide Spectral Lens devices have demonstrated 40--66\% throughput at resolving powers from $R \sim 200$ to $R \sim 20{,}000$. Lightweight silicon-carbide mirrors and sub-electron-noise CMOS detectors support replicated production. Life 2.0 must address detector systematics, instrument stability, and stellar variability; rather than assuming these limitations disappear, it builds on calibration, detector-characterization, and data-analysis techniques advanced during the JWST era. The concept offers a scalable alternative to a monolithic 30-m-class space telescope and a staged pathway toward biosignature spectroscopy of nearby Earth-like planets.
Let $V$ be an $n$-dimensional Euclidean vector space, ,where $n\ge 4$, and $\ell = \lfloor\frac{n}{2}\rfloor$. We prove the sharp pointwise estimate \[ q_2(E) \ge -\frac{2(\ell -1)}{3\ell} \mathrm{Scal}(E) \mathrm{Id}_{\Lambda^2V^*} \] for every algebraic curvature tensor $E$ on $V$ with nonnegative sectional curvature. Applying this estimate to the decomposition $\operatorname{Rm}_{g}=K_{\min}I+E$, we obtain the vanishing of $H^2(M; \mathbb{R})$ under a dimension-dependent strict sectional-scalar curvature pinching condition. At the weak endpoint, all harmonic two-forms are parallel. Apart from the flat case, this yields $b_2(M)=0$ in odd dimensions and $b_2(M)\le 1$ in even dimensions. At even-dimensional endpoint, $b_2(M)>0$ forces $(M, g)$ to be isometric, up to scaling, to $\mathbb{CP}^{\ell}$ with its Fubini-Study metric. As a consequence every closed five-dimensional manifold satisfying the strict pinching condition implies is a rational homology sphere. An anisotropic rescaling of the same homogeneous four-frame estimate also gives the sharp pointwise sectional-scalar pinching criterion \[ K_{\min} \ge \frac{n(n-1)}{n^2-n+12}S_0 \quad \Longrightarrow \mathrm{PIC2}. \] The strict pinching places the curvature tensor in the interior of PIC2 and normalized Ricci flow brings it to a positive constant sectional curvature.
Computational efficiency is a critical requirement for transit searches in modern large-scale photometric surveys. We present Graphics Processing Units Transit Least Squares (GTLS), a GPU-accelerated implementation of the Transit Least Squares algorithm designed to reduce the computational cost of periodic transit detection while preserving TLS-like sensitivity to transit-shaped signals. GTLS parallelizes the dominant steps of the TLS search, including phase folding, transit-duration evaluation, moving-window depth estimation, and chi-squared calculation. Using Kepler-like long-cadence light curves and synthetic Kepler-like time series, we benchmark GTLS against the reference CPU implementation of TLS and the GPU-based BLS implementation in cuvarbase. On an AMD Ryzen 9 7950X CPU and an NVIDIA RTX 4090 GPU, GTLS processes a 3000-day synthetic light curve in approximately 138 seconds, compared with 3289 seconds for TLS. With two RTX 4090 GPUs, the runtime is reduced to approximately 79 seconds. In recovery tests, GTLS achieves detection performance statistically consistent with TLS, with a precision of 9.3 percent and recall of 79.4 percent, compared with 9.4 percent and 81.1 percent for TLS. These results demonstrate that GTLS enables efficient TLS-style searches for large photometric data sets from Kepler, TESS, PLATO, ET, and future missions. The source code is publicly available.
Motivated by the observational incompleteness of intermediate-to-long-period Earth-size planets, we present TransitNet, a compact attention-augmented deep-learning framework for low-SNR transit blind searches. To enable realistic method development and objective threshold calibration under blind-search conditions, we develop a unified dataset construction, benchmarking, and threshold-selection framework. On recovery benchmarks constructed from unseen Kepler targets, TransitNet attains 95.2 percent accuracy in the challenging SNR range of 6 to 8 and outperforms both TLS and BLS, achieving ROC-AUC and PR-AP values of 0.974 and 0.982, respectively. In an injected Earth-size and sub-Earth-size transit recovery experiment, TransitNet achieves a recovery rate of 93.0 percent, substantially exceeding those of TLS (63.1 percent) and BLS (60.0 percent). In addition to detection, TransitNet provides attention-based estimates of transit windows and midpoints. On an independent evaluation set, 97.4 percent of injected transits are fully covered by the estimated transit window. Applied to real Kepler observations, the model successfully recovers all 34 selected confirmed Kepler planets, with a mean absolute transit midpoint error of 1.24 hours. The model combines a compact footprint of about 1.5 MB with high inference efficiency, yielding speed-ups of about 12 to 25 times relative to CPU-TLS and about 4 to 5 times relative to CPU-BLS. These results demonstrate that TransitNet provides an accurate, scalable, and computationally efficient framework for low-SNR transit blind searches in the tested regime and motivate its extension to longer-period Earth-size planet searches.
We present DEtection in phase-folded Light curves with cOntrastive Scoring (DELOS), a deep-learning framework that uses contrastive scoring to perform blind searches for shallow transits in Kepler photometry. DELOS combines GPU-accelerated phase folding, optimized phase binning, and a custom one-dimensional convolutional encoder to assign a transit-likeness score to each folded light curve, thereby producing a score periodogram over trial periods without relying on pre-detected threshold-crossing events. Focusing on intermediate-to-long-period signals with orbital periods of 100-150 days, DELOS was trained on 20 million synthetic light curves generated with realistic transit models and Kepler-like noise properties, achieving a validation accuracy of 99.3% on the synthetic validation set. In controlled injection-recovery experiments, DELOS improves the combined precision-recall performance by 15.5% relative to Box-fitting Least Squares (BLS) and 11.25% relative to Transit Least Squares (TLS) in the low Signal-to-Noise Ratios (low-SNR) regime. It also accelerates the search by factors of approximately 3-5 and 74-80 compared with BLS and TLS, respectively. Applied to a selected Kepler validation sample, DELOS recovered all known shallow intermediate-to-long-period transit signals in the tested period range. These results demonstrate that DELOS provides an efficient and sensitive framework for low-SNR transit searches and represents a practical step toward future searches for longer-period terrestrial planets in Kepler, K2, TESS, PLATO, and Earth 2.0 data. Accordingly, this work is intended as a methodological development and validation study, with the detailed astrophysical validation of newly identified candidates deferred to future work.
In this short note, we establish an upper bound for the conjugate radius of an open $n$-dimensional Riemannian manifold under a scalar curvature lower bound and a bottom-of-spectrum upper bound. As a consequence, if $\lambda_{0}(M)=0$ and scalar curvature $\ge n(n-1)$, then the conjugate radius $\le \pi$.
In this note, we prove that the infimum of the mean curvature on any disconnected boundary component of an unbounded mean convex domain in $\mathbb{R}^n$ must be zero.
Ca II absorbers, characterized by dusty and metal-rich environments, provide unique insights into the interstellar medium of galaxies. However, their rarity and weak absorption features have hindered comprehensive studies. In this work, we present a novel dual CNN approach to detect Ca II absorption systems, analyzing over 100,000 quasar spectra from the Sloan Digital Sky Survey (SDSS) Data Release 16. Our primary CNN identifies Ca II features, while a secondary CNN cross-verifies these detections using five Fe II absorption lines. This approach yielded 1,646 Ca II absorption systems, including 525 previously known absorbers and 1,121 new discoveries, nearly tripling the size of any previously reported catalog. Among our Ca II absorbers, 95 are found to show the 2175\r{A} dust feature (2DA), corresponding to 22% of strong absorbers, 7% of weak absorbers, and $\sim$12% of the overall Ca II population at $0.8 < z_{\text{abs}} < 1.4$. Across the full redshift range of $0.36 < z_{\text{abs}} < 1.4$, $\sim$1.5% of Mg II absorbers host Ca II.
Stellar age determination for large samples of stars opens new avenues for a broad range of astronomical sciences. While precise stellar ages for evolved stars have been derived from large ground- and space-based stellar surveys, reliable age determination for cool main-sequence dwarf stars remains a challenge. In this work, we set out to estimate the age of dwarf stars from the LAMOST spectra with a data-driven approach. We build a training set by using wide binaries that the primary component has reliable isochrone age estimate thus gives the age of the secondary. This training set is further supplemented with field stars and cluster stars whose ages are known. We then train a data-driven model for inferring age from their spectra with the XGBoost algorithm. Given a spectral signal-to-noise ratio greater than 50, the age estimation precise to 10% to 25% for K-type stars, as younger stars have larger relative errors. Validations suggest that the underlying information used for our age estimation is largely attributed to the LAMOST spectral features of chemical abundances. It means our result is a manifestation of stellar chemical clock effectively acted on LAMOST spectra ($R\simeq1800$). Applying our model to the LAMOST DR10 yields a massive age catalog for $\sim4$ million dwarf stars. Statistical properties, such as the age distribution, age-abundance and age-stellar activity relations of the sample stars are discussed. The catalog is publicly accessible and can be helpful for extensive sciences from detection and characterization of Earth-like planets to Galactic archaeology.
The Weyl type $f(Q,T)$ modified gravity theory is an extension of the $f(Q)$ and $f(Q,T)$ type theories, where $T$ is the trace of the matter energy-momentum tensor, and the scalar non-metricity $Q$ is represented in its standard Weyl form, and it is fully determined by a vector field $\omega _\mu$. The theory can give a good description of the observational data, and of the evolution of the late-time Universe, including a geometric explanation of the dark energy. In this work we investigate the Big Bang Nucleosynthesis (BBN) constraints on several Weyl type $f(Q,T)$ gravity models. In particular, we consider the corrections that Weyl type $f(Q,T)$ terms induce on the freeze-out temperature $\mathcal{T}_f$, as compared to the standard $\Lambda$CDM results. We analyze in detail three distinct cosmological models, corresponding to specific choices of the functional form of $f(Q,T)$. The first model has a simple linear additive structure in $Q$ and $T$, the second model is multiplicative in $Q$ and $T$, while the third is additive in $T$ and the exponential of $Q$. For each $f(Q,T)$ we consider first the cosmological evolution in the radiation dominated era, and then we impose the observational bound on $\left|\delta \mathcal{T}_f/ \mathcal{T}_f\right|$ to obtain constraints on the model parameters from the primordial abundances of the light elements such as helium-4, deuterium and lithium-7. The abundances of helium-4 and deuterium agree with theoretical predictions, however, the lithium problem, even slightly alleviated, still persists for the considered Weyl type $f(Q,T)$ models. Generally, these models satisfy the BBN constraints, and thus they represent viable cosmologies describing the entire dynamical time scale of the evolution of the Universe.
Accurately characterizing intrinsic stellar photometric noise induced by stellar astrophysics, such as stellar activity, granulation, and oscillations, is of crucial importance for detecting transiting exoplanets. In this study, we investigate the relation between the intrinsic stellar photometric noise, as quantified by the Kepler rrmsCDPP measurement, and the level of stellar chromospheric activity, as indicated by the S-index of Ca II HK lines derived from the LAMOST spectra. Our results reveal a clear positive correlation between S-index and rrmsCDPP, and the correlation becomes more significant at higher activity levels and on longer timescales. We have therefore built an empirical relation between rrmsCDPP and S-index as well as Teff, logg, [Fe/H], and apparent magnitude with the XGBoost regression algorithm, using the LAMOST-Kepler common star sample as the training set. This method achieves a precision of ~20 ppm for inferring the intrinsic noise from the S-index and other stellar labels on a 6-hour integration duration. We have applied this empirical relation to the full LAMOST DR7 spectra database, and obtained the intrinsic noise predictions for 1,358,275 stars. The resultant catalog is publicly available and expected to be valuable for optimizing target selection for future exoplanet-hunting space missions, such as the Earth 2.0 mission.
The network flow watermarking technique associates the two communicating parties by actively modifying certain characteristics of the stream generated by the sender so that it covertly carries some special marking information. Some curious users communicating with the hidden server as a Tor client may attempt de-anonymization attacks to uncover the real identity of the hidden server by using this technique. This compromises the privacy of the anonymized communication system. Therefore, we propose a defense scheme against flow watermarking. The scheme is based on deep neural networks and utilizes generative adversarial networks to convert the original Inter-Packet Delays (IPD) into new IPDs generated by the model. We also adopt the concept of adversarial attacks to ensure that the detector will produce an incorrect classification when detecting these new IPDs. This approach ensures that these IPDs are considered "clean", effectively covering the potential watermarks. This scheme is effective against time-based flow watermarking techniques.
Fluxgate sensors are widely used in the field of low frequency and weak vector magnetic field measurement because of their good performance, such as high resolution and low power consumption. However, during the long-term continuous observation, the drift errors of the fluxgate sensor will occur due to the variable ambient temperature. This paper proposes a temperature compensation method for fluxgate sensors based on polynomial fitting. First, a physical model of the temperature & fluxgate sensor was established on the COMSOL Multiphysics simulation platform, and the influence of temperature on the measurement performance of the fluxgate sensor was analyzed. Second, according to the existing temperature-magnetic field data, a temperature compensation model of the fluxgate sensor was constructed. And compared it with other temperature compensation method, the result shows that the proposed temperature compensation method is relatively simple and can better achieve real-time compensation for sensor application scenarios. Finally, to verify the effectiveness of the proposed method, numerous laboratory experiments were implemented. The temperature drift is reduced from more than 500 nT before compensation to about 1 nT. The results show that the proposed method has a good temperature compensation effect on the data measured by the fluxgate sensor within a variable temperature background.
We present the results of time-resolved high-resolution spectroscopic observations of the very active RS Canum Venaticorum (RS CVn) star SZ Piscium (SZ Psc), obtained during two consecutive observing nights on October 24 and 25, 2011. Several optical chromospheric activity indicators are analyzed using the spectral subtraction technique, which show the remarkably different behavior between two nights. Gradually blue-shifted and strengthened excess absorption features presented in the series of the subtracted spectra (especially for the H$_{\alpha}$, He I D$_{3}$ and H$_{\beta}$ lines), as a result of active stellar prominence that is rising its height along the line of our sight, was detected in the observations on October 24. This prominence activation event was probably associated with the subsequently occurred optical flare, and part of that flare decay phase was hunted in the observations on October 25. The flare was characterized by the prominent He I D$_{3}$ line emission, as well as stronger chromospheric emission in the H$_{\alpha}$, H$_{\beta}$ and other active lines. The gradual decay of flare was accompanied by an obviously developmental absorption feature in the blue wing of the H$_{\alpha}$ and other active lines, which could be explained as cool post-flare loops which projected against the bright flare background. Therefore, a series of possibly associated magnetic activity phenomena, including flare-related prominence activation, optical flare and post-flare loops, were detected during our observations.
Of over 5,000 exoplanets identified so far, only a few hundred possess sub-Earth radii. The formation processes of these sub-Earths remain elusive, and acquiring additional samples is essential for investigating this unique population. In our study, we employ the GPFC method, a novel GPU Phase Folding algorithm combined with a Convolutional Neural Network, on Kepler photometry data. This method enhances the transit search speed significantly over the traditional Box-fitting Least Squares method, allowing a complete search of the known Kepler KOI data within days using a commercial GPU card. To date, we have identified five new ultra-short-period planets (USPs): Kepler-158d, Kepler-963c, Kepler-879c, Kepler-1489c, and KOI-4978.02. Kepler-879c with a radius of $0.4 R_\oplus$ completes its orbit around a G dwarf in 0.646716 days. Kepler-158d with a radius of $0.43 R_\oplus$ orbits a K dwarf star every 0.645088 days. Kepler-1489c with a radius of $0.51 R_\oplus$ orbits a G dwarf in 0.680741 days. Kepler-963c with a radius of $0.6 R_\oplus$ revolves around a G dwarf in 0.919783 days, and KOI-4978.02 with a radius of $0.7 R_\oplus$ circles a G dwarf in 0.941967 days. Among our findings, Kepler-879c, Kepler-158d and Kepler-963c rank as the first, the third, the fourth smallest USPs identified to date. Notably, Kepler-158d stands as the smallest USP found orbiting K dwarfs while Kepler-963c, Kepler-879c, Kepler-1489c, and KOI-4978.02 are the smallest USPs found orbiting G dwarfs. Kepler-879c, Kepler-158d, Kepler-1489c, and KOI-4978.02 are among the smallest planets that are closest to their host stars, with orbits within 5 stellar radii. In addition, these discoveries highlight GPFC's promising capability in identifying small, new transiting exoplanets within photometry data from Kepler, TESS, and upcoming space transit missions, PLATO and ET.
This paper presents GPFC, a novel Graphics Processing Unit (GPU) Phase Folding and Convolutional Neural Network (CNN) system to detect exoplanets using the transit method. We devise a fast folding algorithm parallelized on a GPU to amplify low signal-to-noise ratio transit signals, allowing a search at high precision and speed. A CNN trained on two million synthetic light curves reports a score indicating the likelihood of a planetary signal at each period. While the GPFC method has broad applicability across period ranges, this research specifically focuses on detecting ultra-short-period planets with orbital periods less than one day. GPFC improves on speed by three orders of magnitude over the predominant Box-fitting Least Squares (BLS) method. Our simulation results show GPFC achieves $97%$ training accuracy, higher true positive rate at the same false positive rate of detection, and higher precision at the same recall rate when compared to BLS. GPFC recovers $100\%$ of known ultra-short-period planets in $\textit{Kepler}$ light curves from a blind search. These results highlight the promise of GPFC as an alternative approach to the traditional BLS algorithm for finding new transiting exoplanets in data taken with $\textit{Kepler}$ and other space transit missions such as K2, TESS and future PLATO and Earth 2.0.
City-scale outdoor thermal comfort diagnostics are essential for understanding actual heat stress. However, previous research primarily focused on the street scale. Here, we present the WRF-UCM-SOLWEIG framework to achieve fine-grained thermal comfort mapping at the city scale. The background climate condition affecting thermal comfort is simulated by the Weather Research and Forecasting (WRF) model coupled with the urban canopy model (UCM) at a local-scale (500m). The most dominant factor, mean radiant temperature, is simulated using the Solar and Longwave Environmental Irradiance Geometry (SOLWEIG) model at the micro-scale (10m). The Universal Thermal Climate Index (UTCI) is calculated based on the mean radiant temperature and local climate parameters. The influence of different ground surface materials, buildings, and tree canopies is simulated in the SOLWEIG model using integrated urban morphological data. We applied this proposed framework to the city of Guangzhou, China, and investigated the intra-day variation in the impact of urban morphology during a heat wave period. Through statistical analysis, we found that the elevation in UTCI is primarily attributed to the increase in the fraction of impervious surface (ISF) during daytime, with a maximum correlation coefficient of 0.80. Tree canopy cover has a persistent cooling effect during the day. Implementing 40% of tree cover can reduce the daytime UTCI by 1.5 to 2.0 K. At nighttime, all urban features have a negligible contribution to outdoor thermal comfort. Overall, the established framework provides essential input data and references for studies and urban planners in the practice of urban (micro)climate diagnostics and planning.