arXiv subjects

Yanyan Zheng

Publications and source records attributed to Yanyan Zheng.

8 recordsLinked to original sources

arXiv2026

Testing cosmological isotropy with gravitational waves and gamma-ray bursts

The cosmological principle asserts that the Universe is homogeneous and isotropic on large enough scales. However, alternative cosmological models can bring about anisotropies through local inhomogeneities, anisotropic evolution, or exotic physics. In addition, select studies have also hinted at mild evidence of anisotropies in SNe Ia, CMB, and GRB data, though these remain unconfirmed. In this work, we test for cosmological anisotropies using gravitational waves and gamma-ray bursts, adopting the latest O4a release from the LIGO-Virgo-KAGRA collaboration and GRBWeb (including all known GRBs since 1991). If the cosmological principle holds, the sky localisation and the characteristics of the GRBs and GWs (masses, luminosities, redshifts) should be statistically isotropic when corrected for selection biases. We employ a couple statistical methods, including angular power spectra and two-point correlation functions, and compare the results against synthetic data. The work extends previous analyses by including the most recent datasets, and the use of multiple complementary statistical tests. We find no significant evidence for anisotropy in the current GW and GRB datasets, consistent with the cosmological principle.

astro-ph.CO↗

arXiv2024

Bayesian real-time classification of multi-messenger electromagnetic and gravitational-wave observations

Because of the electromagnetic radiation produced during the merger, compact binary coalescences with neutron stars may result in multi-messenger observations. In order to follow up on the gravitational-wave signal with electromagnetic telescopes, it is critical to promptly identify the properties of these sources. This identification must rely on the properties of the progenitor source, such as the component masses and spins, as determined by low-latency detection pipelines in real time. The output of these pipelines, however, might be biased, which could decrease the accuracy of parameter recovery. Machine learning algorithms are used to correct this bias. In this work, we revisit this problem and discuss two new implementations of supervised machine learning algorithms, K-Nearest Neighbors and Random Forest, which are able to predict the presence of a neutron star and post-merger matter remnant in low-latency compact binary coalescence searches across different search pipelines and data sets. Additionally, we present a novel approach for calculating the Bayesian probabilities for these two metrics. Instead of metric scores derived from binary machine learning classifiers, our scheme is designed to provide the astronomy community well-defined probabilities. This would deliver a more direct and easily interpretable product to assist electromagnetic telescopes in deciding whether to follow up on gravitational-wave events in real time.

astro-ph.HE↗

arXiv2024

Angular power spectrum of gravitational-wave transient sources as a probe of the large-scale structure

We present a new, simulation-based inference method to compute the angular power spectrum of the distribution of foreground gravitational-wave transient events. As a first application of this method, we use the binary black hole mergers observed during the LIGO, Virgo, and KAGRA third observation run to test the spatial distribution of these sources. We find no evidence for anisotropy in their angular distribution. We discuss further applications of this method to investigate other gravitational-wave source populations and their correlations to the cosmological large-scale structure.

astro-ph.CO↗

arXiv2024

An Optically Targeted Search for Gravitational Waves emitted by Core-Collapse Supernovae during the Third Observing Run of Advanced LIGO and Advanced Virgo

We present the results from a search for gravitational-wave transients associated with core-collapse supernovae observed optically within 30 Mpc during the third observing run of Advanced LIGO and Advanced Virgo. No gravitational wave associated with a core-collapse supernova has been identified. We then report the detection efficiency for a variety of possible gravitational-wave emissions. For neutrino-driven explosions, the distance at which we reach 50% detection efficiency is up to 8.9 kpc, while more energetic magnetorotationally-driven explosions are detectable at larger distances. The distance reaches for selected models of the black hole formation, and quantum chromodynamics phase transition are also provided. We then constrain the core-collapse supernova engine across a wide frequency range from 50 Hz to 2 kHz. The upper limits on gravitational-wave energy and luminosity emission are at low frequencies down to $10^{-4}\,M_\odot c^2$ and $6 \times 10^{-4}\,M_\odot c^2$/s, respectively. The upper limits on the proto-neutron star ellipticity are down to 3 at high frequencies. Finally, by combining the results obtained with the data from the first and second observing runs of LIGO and Virgo, we improve the constraints of the parameter spaces of the extreme emission models. Specifically, the proto-neutron star ellipticities for the long-lasting bar mode model are down to 1 for long emission (1 s) at high frequency.

astro-ph.HE↗

arXiv2023

Heavy-tailed distributions of confirmed COVID-19 cases and deaths in spatiotemporal space

This paper conducts a systematic statistical analysis of the characteristics of the geographical empirical distributions for the numbers of both cumulative and daily confirmed COVID-19 cases and deaths at county, city, and state levels over a time span from January 2020 to June 2022. The mathematical heavy-tailed distributions can be used for fitting the empirical distributions observed in different temporal stages and geographical scales. The estimations of the shape parameter of the tail distributions using the Generalized Pareto Distribution also support the observations of the heavy-tailed distributions. According to the characteristics of the heavy-tailed distributions, the evolution course of the geographical empirical distributions can be divided into three distinct phases, namely the power-law phase, the lognormal phase I, and the lognormal phase II. These three phases could serve as an indicator of the severity degree of the COVID-19 pandemic within an area. The empirical results suggest important intrinsic dynamics of a human infectious virus spread in the human interconnected physical complex network. The findings extend previous empirical studies and could provide more strict constraints for current mathematical and physical modeling studies, such as the SIR model and its variants based on the theory of complex networks.

physics.soc-ph↗

arXiv2023

Precision measurement of the return distribution property of the Chinese stock market index

This paper systematically conducts an analysis of the composite index 1-min datasets over the 17-year period (2005-2021) for both the Shanghai and Shenzhen stock exchanges. To reveal the difference between the Chinese and the mature stock markets, here we precisely measure the property of return distribution of composite index over the time scale $Δt$ ranging from 1 min up to almost 4,000 min. The main findings are as follows. (1) Return distribution presents a leptokurtic, fat-tailed, and almost symmetrical shape, which is similar to that of mature markets. (2) The central part of return distribution is well described by the symmetrical Lévy $α$-stable process with a stability parameter comparable with the value of about 1.4 extracted in the U.S. stock market. (3) Return distribution can be well described by the student's t-distribution within a wider return range than the Lévy $α$-stable distribution. (4) Distinctively, the stability parameter shows a potential change when $Δt$ increases, and thus a crossover region at 15 $< Δt <$ 60 min is observed. This is different from the finding in the U.S. stock market where a single value of about 1.4 holds over 1 $\le Δt \le$ 1,000 min. (5) The tail distribution of returns at small $Δt$ decays as an asymptotic power-law with an exponent of about 3, which is a value widely existing in mature markets. However, it decays exponentially when $Δt \ge$ 240 min, which is not observed in mature markets. (6) Return distributions gradually converge to Gaussian as $Δt$ increases. This observation is different from the finding of a critical $Δt =$ 4 days in the U.S. stock market.

q-fin.ST↗

arXiv2022

Temporal and spatial evolution of the distribution related to the number of COVID-19 pandemic

This work systematically conducts a data analysis based on the numbers of both cumulative and daily confirmed COVID-19 cases and deaths in a time span through April 2020 to June 2022 for over 200 countries around the world. Such research feature aims to reveal the temporal and spatial evolution of the country-level distribution observed in COVID-19 pandemic, and obtains some interesting results as follows. (1) The distributions of the numbers for cumulative confirmed cases and deaths obey power-law in early stages of COVID-19 and stretched exponential function in subsequent course. (2) The distributions of the numbers for daily confirmed cases and deaths obey power-law in early and late stages of COVID-19 and stretched exponential function in middle stages. The crossover region between power-law and stretched exponential behaviour seems to depend on the evolution of "infection" event and "death" event. Such observation implies a kind of important symmetry related to the dynamics process of COVID-19 spreading. (3) The distributions of the normalized numbers for each metric show a temporal scaling behaviour in 2-year period, and are well described by stretched exponential function. The observation of power-law and stretched exponential behaviour in such country-level distributions suggests underlying intrinsic dynamics of a virus spreading process in human interconnected society. And thus it is important for understanding and mathematically modeling the COVID-19 pandemic.

q-bio.PE↗

arXiv2021

Detecting and reconstructing gravitational waves from the next Galactic core-collapse supernova in the Advanced Detector Era

We performed a detailed analysis of the detectability of a wide range of gravitational waves derived from core-collapse supernova simulations using gravitational-wave detector noise scaled to the sensitivity of the upcoming fourth and fifth observing runs of the Advanced LIGO, Advanced Virgo, and KAGRA. We use the coherent WaveBurst algorithm, which was used in the previous observing runs to search for gravitational waves from core-collapse supernovae. As coherent WaveBurst makes minimal assumptions on the morphology of a gravitational-wave signal, it can play an important role in the first detection of gravitational waves from an event in the Milky Way. We predict that signals from neutrino-driven explosions could be detected up to an average distance of 10 kpc, and distances of over 100 kpc can be reached for explosions of rapidly rotating progenitor stars. An estimated minimum signal-to-noise ratio of 10-25 is needed for the signals to be detected. We quantify the accuracy of the waveforms reconstructed with coherent WaveBurst and we determine that the most challenging signals to reconstruct are those produced in long-duration neutrino-driven explosions and models that form black holes a few seconds after the core bounce.

astro-ph.HE↗