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Researcher profile

Tianqi Zhao

Tianqi Zhao contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
astro-ph.HEnucl-thastro-ph.SRgr-qcSymbolic ComputationArtificial Intelligenceeess.SPMachine Learningphysics.soc-phPopulations and Evolution

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Published work

9 published item(s)

preprint2026arXiv

A New Bayesian Framework with Natural Priors to Constrain the Neutron Star Equation of State

We propose a new Bayesian framework to infer the neutron star equation of state (EOS) from mass and radius observations and neutron matter theory by defining priors that directly parameterize mass-radius space instead of pressure-energy density space. We use direct and accurate inversion approximations to map mass-radius relations to the underlying EOS. We systematically compare its EOS inferences with those inferred from traditional EOS parameterizations, taking care to quantify the systematic prior uncertainties of both. Our results show that prior uncertainties should be included in all Bayesian approaches. The more natural alternative framework provides broader coverage of the physically allowed mass-radius space, especially small radius configurations, and yields enhanced computational efficiency and substantially reduced dependence on prior choices. Our results demonstrate that direct parameterization in observed space offers a robust and efficient alternative to traditional methods.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Observable Signatures of a Quarkyonic Phase in Neutron Stars

Performing Bayesian inference on quarkyonic equation-of-state models for neutron star matter, we find they satisfy all current astrophysical observations, thus reinforcing the argument for the use of such neutron star matter equation-of-state models alongside traditional ones. To observationally differentiate between stars with and without a quarkyonic phase, we identify a novel observational signature: the slope of the mass-radius relation at some fixed mass in conjunction with the sound speed at the star's center. In this plane, we find quarkyonic stars in a region with high central sound speed and positive slope, that is distinct from purely nucleonic stars. High accuracy NS radii measurements facilitated by the next generation of detectors, coupled with ongoing studies of mapping astrophysical observables to microphysical properties like sound speed can be used for testing this signature. Our results indicate that a neutron star with these properties would be a strong evidence for existence of a quarkyonic phase or a similar crossover transition in its core.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

TFZ-Tree: An Ultra-Lightweight Waveform Classification Framework for Resource-Constrained Devices

Under the trend of multi-waveform coexistence in 6G IoT, intelligent receivers must first identify physical-layer waveform types before performing correct demodulation and resource scheduling. However, existing signal identification research largely focuses on symbol-level modulation classification. Research directly targeting physical-layer waveform types (e.g., OFDM, OTFS, LoRa) is not only extremely scarce but also heavily reliant on deep neural networks and complex time-frequency transforms, making deployment on resource-constrained terminals difficult. Symbol modulation classification methods themselves cannot circumvent the prerequisite of ``waveform identification first.'' To address this dual gap, we propose an ultra-lightweight waveform classification framework based on time-frequency multidimensional features with a cooperative Z-test tree (ZTree). The framework employs low-complexity time-domain feature extraction, and the classification backend adopts a ZTree optimized by Z-statistical testing, which uses hypothesis testing confidence to automatically control decision tree splitting and size, ensuring efficient execution on resource-limited processors. Tested on ten 6G candidate waveforms including OFDM, OTFS, DSSS, LoRa, and NB-IoT, the method achieves 99.5\% average accuracy under AWGN and 87.4\% under TDL-C multipath channels, with main confusion between OTFS and LoRa. Implemented in C on an x86 platform, single inference latency is under 4~ms. To the best of our knowledge, this is the first work achieving real-time recognition of ten IoT waveform types. Future work will target deployment acceleration on embedded MCUs. Code and dataset are open-sourced at: https://github.com/Einstein-sworder/IoT-wave.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Isolating Bounded and Unbounded Real Roots of a Mixed Trigonometric-Polynomial

Mixed trigonometric-polynomials (MTPs) are functions of the form $f(x,\sin{x}, \cos{x})$ with $f\in\mathbb{Q}[x_1,x_2,x_3]$. In this paper, an algorithm ``isolating" all the real roots of an MTP is provided and implemented. It automatically divides the real roots into two parts: one consists of finitely many ``bounded" roots in an interval $[μ_-,μ_+]$ while the other consists of probably countably many ``periodic" roots in $\mathbb{R}\backslash[μ_-,μ_+]$. For bounded roots, the algorithm returns isolating intervals and corresponding multiplicities while for periodic roots, it returns finitely many mutually disjoint small intervals $I_i\subset[-π,π]$, integers $c_i>0$ and multisets of root multiplicity $\{m_{j,i}\}_{j=1}^{c_i}$ such that any periodic root $t>μ_+$ is in the set $(\sqcup_i\cup_{k\in\mathbb{N}}(I_i+2kπ))$ and any interval $I_i+2kπ\subset(μ_+,\infty)$ contains exactly $c_i$ periodic roots with multiplicities $m_{1,i},...,m_{c_i,i}$, respectively. The effectiveness and efficiency of the algorithm are shown by experiments. %In particular, our results indicate that the ``distributions" of the roots of an MTP in the ``periods" $(-π,π]+2kπ$ sufficiently far from $0$ share a same pattern. Besides, the method used to isolate the roots in $[μ_-,μ_+]$ is applicable to any other bounded interval as well. The algorithm takes advantages of the weak Fourier sequence technique and deals with the intervals period-by-period without scaling the coordinate so to keep the length of the sequence short. The new approaches can easily be modified to decide whether there is any root, or whether there are infinitely many roots in unbounded intervals of the form $(-\infty,a)$ or $(a,\infty)$ with $a\in\mathbb{Q}$.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

ECG Heartbeat classification using deep transfer learning with Convolutional Neural Network and STFT technique

Electrocardiogram (ECG) is a simple non-invasive measure to identify heart-related issues such as irregular heartbeats known as arrhythmias. While artificial intelligence and machine learning is being utilized in a wide range of healthcare related applications and datasets, many arrhythmia classifiers using deep learning methods have been proposed in recent years. However, sizes of the available datasets from which to build and assess machine learning models is often very small and the lack of well-annotated public ECG datasets is evident. In this paper, we propose a deep transfer learning framework that is aimed to perform classification on a small size training dataset. The proposed method is to fine-tune a general-purpose image classifier ResNet-18 with MIT-BIH arrhythmia dataset in accordance with the AAMI EC57 standard. This paper further investigates many existing deep learning models that have failed to avoid data leakage against AAMI recommendations. We compare how different data split methods impact the model performance. This comparison study implies that future work in arrhythmia classification should follow the AAMI EC57 standard when using any including MIT-BIH arrhythmia dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Quasi-normal g-modes of neutron stars with quarks

Quasi-normal oscillation modes of neutron stars provide a means to probe their interior composition using gravitational wave astronomy. We compute the frequencies and damping times of composition-dependent core g-modes of neutron stars containing quark matter employing linearized perturbative equations of general relativity. We find that ignoring background metric perturbations due to the oscillating fluid, as in the Cowling approximation, underestimates the g-mode frequency by up to 10% for higher mass stars, depending on the parameters of the nuclear equation of state and how the mixed phase is constructed. The g-mode frequencies are well-described by a linear scaling with the central lepton (or combined lepton and quark) fraction for nucleonic (hybrid) stars. Our findings suggest that neutron stars with and without quarks are manifestly different with regards to their quasi-normal g-mode spectrum, and may thus be distinguished from one another in future observations of gravitational waves from merging neutron stars.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Square-free Strong Triangular Decomposition of Zero-dimensional Polynomial Systems

Triangular decomposition with different properties has been used for various types of problem solving, e.g. geometry theorem proving, real solution isolation of zero-dimensional polynomial systems, etc. In this paper, the concepts of strong chain and square-free strong triangular decomposition (SFSTD) of zero-dimensional polynomial systems are defined. Because of its good properties, SFSTD may be a key way to many problems related to zero-dimensional polynomial systems, such as real solution isolation and computing radicals of zero-dimensional ideals. Inspired by the work of Wang and of Dong and Mou, we propose an algorithm for computing SFSTD based on Gröbner bases computation. The novelty of the algorithm is that we make use of saturated ideals and separant to ensure that the zero sets of any two strong chains have no intersection and every strong chain is square-free, respectively. On one hand, we prove that the arithmetic complexity of the new algorithm can be single exponential in the square of the number of variables, which seems to be among the rare complexity analysis results for triangular-decomposition methods. On the other hand, we show experimentally that, on a large number of examples in the literature, the new algorithm is far more efficient than a popular triangular-decomposition method based on pseudo-division. Furthermore, it is also shown that, on those examples, the methods based on SFSTD for real solution isolation and for computing radicals of zero-dimensional ideals are very efficient.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Inferring incubation period distribution of COVID-19 based on SEAIR Model

To reduce the biases of traditional survey-based methods, this paper proposes an epidemic model-based approach to inference the incubation period distribution of COVID-19 utilizing the publicly reported confirmed case number. We construct an epidemic model, namely SEAIR, and take advantage of the dynamic transmission process depicted by SEAIR to estimate the onset probability in each day of exposed individuals in eight impacted countries. Based on these estimations, the general incubation probability distribution of COVID-19 has been revealed. The proposed method can avoid several biases of traditional survey-based methods. However, due to the mathematical-model-based nature of this method, the inference results are somewhat sensitive to the setting of parameters. Therefore, this method should be practiced reasonably on the basis of a certain understanding of the studied epidemic.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Quarkyonic Matter Equation of State in Beta-Equilibrium

Quark matter may appear due to a hadronic-quark transition in the core of a hybrid star. Quarkyonic matter is an approach in which both quarks and nucleons appear as quasi-particles in a crossover transition, and provides an explicit realization of early ideas concerning quark matter (e.g., the MIT bag model). This description has recently been employed by McLerran and Reddy to model chargeless (pure neutron) matter with an approach that has the virtue that the speed of sound rises quickly at a neutron-quark transition so as to satisfy observational constraints on the neutron star maximum mass ($\gtrsim2M_\odot$) and the radius of a $1.4M_\odot$ star ($R_{1.4}\lesssim 13.5$ km). Traditional models involving first-order transitions result in softer pressure-energy density relations that have difficulty satisfying these constraints except with very narrow choices of parameters. We propose a variation of quarkyonic matter involving protons and leptons whose energy can be explicitly minimized to achieve both chemical and beta equilibrium, which cannot be done in the chargeless formulation. Quarkyonic stellar models are able to satisfy observed mass and radius constraints with a wide range of model parameters, avoiding the obligatory fine-tuning of conventional hybrid star models, including requiring the transition density to be very close to the nuclear saturation density. Our formulation fits experimental and theoretical properties of the nuclear symmetry energy and pure neutron matter, and contains as few as three free parameters. This makes it an ideal tool for the study of high-density matter that is an efficient alternative to piecewise polytrope or spectral decomposition methods.

0 citations0 reviewsNo code linkedOpen access