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Researcher profile

Junjie Zhao

Junjie Zhao contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
gr-qcastro-ph.HEMachine LearningArtificial Intelligenceastro-ph.IMhep-phComputation and LanguageComputer Vision

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Published work

10 published item(s)

preprint2026arXiv

An agentic framework for gravitational-wave counterpart association in the multi-messenger era

With the detection of gravitational waves (GWs), multi-messenger astronomy has opened a new window for advancing our understanding of astrophysics, dense matter, gravitation, and cosmology. The GW sources detected to date are from mergers of compact object binaries, which possess the potential to generate detectable electromagnetic (EM) counterparts. Searching for associations between GW signals and their EM counterparts is an essential step toward enabling subsequent multi-messenger studies. In the era of next-generation GW and EM detectors, the rapid increase in the number of events brings not only unprecedented scientific opportunities, but also substantial challenges to the existing data analysis paradigm. To help address these challenges, we develop GW-Eyes, an agentic framework powered by large language models (LLMs). For the first time, GW-Eyes integrates domain-specific tools and autonomously performs counterpart association tasks between GW and candidate EM events. It supports natural language interaction to assist human experts with auxiliary tasks such as catalog management, skymap visualization, and rapid verification. Our framework leverages the complex decision-making capabilities of LLMs and their traceable reasoning processes, offering a new perspective to the multi-messenger astronomy.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

HAL: Inducing Human-likeness in LLMs with Alignment

Conversational human-likeness plays a central role in human-AI interaction, yet it has remained difficult to define, measure, and optimize. As a result, improvements in human-like behavior are largely driven by scale or broad supervised training, rather than targeted alignment. We introduce Human Aligning LLMs (HAL), a framework for aligning language models to conversational human-likeness using an interpretable, data-driven reward. HAL derives explicit conversational traits from contrastive dialogue data, combines them into a compact scalar score, and uses this score as a transparent reward signal for alignment with standard preference optimization methods. Using this approach, we align models of varying sizes without affecting their overall performance. In large-scale human evaluations, models aligned with HAL are more frequently perceived as human-like in conversation. Because HAL operates over explicit, interpretable traits, it enables inspection of alignment behavior and diagnosis of unintended effects. More broadly, HAL demonstrates how soft, qualitative properties of language--previously outside the scope for alignment--can be made measurable and aligned in an interpretable and explainable way.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Closing a spontaneous-scalarization window with binary pulsars

Benefitting from the unequaled precision of the pulsar timing technique, binary pulsars are important testbeds of gravity theories, providing some of the tightest bounds on alternative theories of gravity. One class of well-motivated alternative gravity theories, the scalar-tensor gravity, predict large deviations from general relativity for neutron stars through a nonperturbative phenomenon known as spontaneous scalarization. This effect, which cannot be tested in the Solar System, can now be tightly constrained using the latest results from the timing of a set of 7 binary pulsars (PSRs J0348+0432, J0737$-$3039A, J1012+5307, J1738+0333, J1909$-$3744, J1913+1102, and J2222$-$0137), especially with the updated parameters of PSRs J0737$-$3039A, J1913+1102, and J2222$-$0137. Using new timing results, we constrain the neutron star's effective scalar coupling, which describes how strongly neutron stars couple to the scalar field, to a level of $\left|α_{\rm A}\right| \lesssim 6 \times 10^{-3}$ in a Bayesian analysis. Our analysis is thorough, in the sense that our results apply to all neutron star masses and all reasonable equations of state of dense matters, in the full relevant parameter space. It excludes the possibility of spontaneous scalarization of neutron stars, at least within a class of scalar-tensor gravity theories.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Extending the Fisher Information Matrix in Gravitational-wave Data Analysis

The Fisher information matrix (FM) plays an important role in forecasts and inferences in many areas of physics. While giving fast parameter estimation with the Gaussian likelihood approximation in the parameter space, the FM can only give the ellipsoidal posterior contours of parameters and lose the higher-order information beyond Gaussianity. We extend the FM in gravitational-wave (GW) data analysis using the Derivative Approximation for LIkelihoods (DALI), a method to expand the likelihood while keeping it positive definite and normalizable at every order, for more accurate forecasts and inferences. When applied to the two real GW events, GW150914 and GW170817, DALI can reduce the difference between FM approximation and the real posterior by 5 times in the best case. The calculation time of DALI and FM is at the same order of magnitude, while obtaining the real full posterior will take several orders of magnitude longer. Besides more accurate approximations, higher-order correction from DALI provides a fast assessment on the FM analysis and gives suggestions for complex sampling techniques which are computationally intensive. We recommend using the DALI method as an extension to the FM method in GW data analysis to pursue better accuracy while still keeping the speed.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Simultaneous bounds on the gravitational dipole radiation and varying gravitational constant from compact binary inspirals

Compact binaries are an important class of gravitational-wave (GW) sources that can be detected by current and future GW observatories. They provide a testbed for general relativity (GR) in the highly dynamical strong-field regime. Here, we use GWs from inspiraling binary neutron stars and binary black holes to investigate dipolar gravitational radiation (DGR) and varying gravitational constant predicted by some alternative theories to GR, such as the scalar-tensor gravity. Within the parametrized post-Einsteinian framework, we introduce the parametrization of these two effects simultaneously into compact binaries&#39; inspiral waveform and perform the Fisher-information-matrix analysis to estimate their simultaneous bounds. In general, the space-based GW detectors can give a tighter limit than ground-based ones. The tightest constraints can reach $σ_B<3\times10^{-11}$ for the DGR parameter $B$ and $σ_{\dot{G}}/G < 7\times10^{-9} \, {\rm yr}^{-1} $ for the varying $G$, when the time to coalescence of the GW event is close to the lifetime of space-based detectors. In addition, we analyze the correlation between these two effects and highlight the importance of considering both effects in order to arrive at more realistic results.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Deep Image Clustering with Category-Style Representation

Deep clustering which adopts deep neural networks to obtain optimal representations for clustering has been widely studied recently. In this paper, we propose a novel deep image clustering framework to learn a category-style latent representation in which the category information is disentangled from image style and can be directly used as the cluster assignment. To achieve this goal, mutual information maximization is applied to embed relevant information in the latent representation. Moreover, augmentation-invariant loss is employed to disentangle the representation into category part and style part. Last but not least, a prior distribution is imposed on the latent representation to ensure the elements of the category vector can be used as the probabilities over clusters. Comprehensive experiments demonstrate that the proposed approach outperforms state-of-the-art methods significantly on five public datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Improved deep learning techniques in gravitational-wave data analysis

In recent years, convolutional neural network (CNN) and other deep learning models have been gradually introduced into the area of gravitational-wave (GW) data processing. Compared with the traditional matched-filtering techniques, CNN has significant advantages in efficiency in GW signal detection tasks. In addition, matched-filtering techniques are based on the template bank of the existing theoretical waveform, which makes it difficult to find GW signals beyond theoretical expectation. In this paper, based on the task of GW detection of binary black holes, we introduce the optimization techniques of deep learning, such as batch normalization and dropout, to CNN models. Detailed studies of model performance are carried out. Through this study, we recommend to use batch normalization and dropout techniques in CNN models in GW signal detection tasks. Furthermore, we investigate the generalization ability of CNN models on different parameter ranges of GW signals. We point out that CNN models are robust to the variation of the parameter range of the GW waveform. This is a major advantage of deep learning models over matched-filtering techniques.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Multiband Observation of LIGO/Virgo Binary Black Hole Mergers in the Gravitational-wave Transient Catalog GWTC-1

The Advanced LIGO and Virgo detectors opened a new era to study black holes (BHs) in our Universe. A population of stellar-mass binary BHs (BBHs) are discovered to be heavier than previously expected. These heavy BBHs provide us an opportunity to achieve multiband observation with ground-based and space-based gravitational-wave (GW) detectors. In this work, we use BBHs discovered by the LIGO/Virgo Collaboration as indubitable examples, and study in great detail the prospects for multiband observation with GW detectors in the near future. We apply the Fisher matrix to spinning, non-precessing inspiral-merger-ringdown waveforms, while taking the motion of space-based GW detectors fully into account. Our analysis shows that, detectors with decihertz sensitivity are expected to log stellar-mass BBH signals with very large signal-to-noise ratio, and provide accurate parameter estimation, including the sky location and time to coalescence. Furthermore, the combination of multiple detectors will achieve unprecedented measurement of BBH properties. As an explicit example, we present the multiband sensitivity to the generic dipole radiation for BHs, which is vastly important for the equivalence principle in the foundation of gravitation, in particular for those theories that predict curvature-induced scalarization of BHs.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Neutron Star Structure in the Minimal Gravitational Standard-Model Extension and the Implication to Continuous Gravitational Waves

Tiny violation of Lorentz invariance has been the subject of theoretic study and experimental test for a long time. We use the Standard-Model Extension (SME) framework to investigate the effect of the minimal Lorentz violation on the structure of a neutron star. A set of hydrostatic equations with modifications from Lorentz violation are derived, and then the modifications are isolated and added to the Tolman-Oppenheimer-Volkoff (TOV) equation as the leading-order Lorentz-violation corrections in relativistic systems. A perturbation solution to the leading-order modified TOV equations is found. The quadrupole moments due to the anisotropy in the structure of neutron stars are calculated and used to estimate the quadrupole radiation of a spinning neutron star with the same deformation. The calculation puts forward a new test for Lorentz invariance in the strong-field regime when continuous gravitational waves are observed in the future.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Tests of conservation laws in post-Newtonian gravity with binary pulsars

General relativity is a fully conservative theory, but there exist other possible metric theories of gravity. We consider non-conservative ones with a parameterized post-Newtonian (PPN) parameter, $ζ_2$. A non-zero $ζ_2$ induces a self-acceleration for the center of mass of an eccentric binary pulsar system, which contributes to the second time derivative of the pulsar spin frequency, $\ddotν$. In our work, using the method in Will (1992), we provide an improved analysis with four well-timed, carefully-chosen binary pulsars. In addition, we extend Will&#39;s method and derive $ζ_2$&#39;s effect on the third time derivative of the spin frequency, $\dddotν$. For PSR B1913+16, the constraint from $\dddotν$ is even tighter than that from $\ddotν$. We combine multiple pulsars with Bayesian inference, and obtain an upper limit, $\left|ζ_{2}\right|<1.3\times10^{-5}$ at 95% confidence level, assuming a flat prior in $\log_{10} \left| ζ_{2}\right|$. It improves the existing bound by a factor of three. Moreover, we propose an analytical timing formalism for $ζ_2$. Our simulated times of arrival with simplified assumptions show binary pulsars&#39; capability in limiting $ζ_{2}$, and useful clues are extracted for real data analysis in future. In particular, we discover that for PSRs B1913+16 and J0737$-$3039A, $\dddotν$ can yield more constraining limits than $\ddotν$.

0 citations0 reviewsNo code linkedOpen access