arXiv subjects

Yu-Chen Wang

Publications and source records attributed to Yu-Chen Wang.

At least 19 recordsLinked to original sources

arXiv2026

No Detectable One-halo Galactic Conformity Signal with Halo-mass Estimates Consistent with Weak-lensing Constraints

One-halo galactic conformity is the tendency for satellites in halos with quenched centrals to have lower star-formation activity than those in halos with star-forming centrals at fixed halo mass. It is an important probe of the galaxy--halo connection and halo-wide quenching processes that may couple central and satellite evolution. However, its existence remains controversial, because conformity must be measured at fixed halo mass, while halo masses are difficult to estimate accurately. In this Letter, we measure one-halo conformity in SDSS using five stellar-mass-complete samples and three halo-mass estimates: an ML estimate whose star-forming and quenched stellar mass--halo mass relations (SHMRs) agree with independent weak-lensing constraints, and two conventional abundance-matching (AM) estimates. We quantify conformity as the difference in median $\log({\rm sSFR})$ between satellites of star-forming and quenched centrals, using both satellite-level and halo-level statistics. The two AM estimates produce strong positive conformity signals, consistent with previous AM-based measurements, but these signals are not reproduced with the ML halo masses. For the halo-level statistic, the representative AM-based signals are $+0.38\pm0.04$ dex and $+0.23\pm0.04$ dex for the luminosity-ranking and mass-ranking AM halo masses, detected relative to no conformity at about $10\sigma$ and $6\sigma$, respectively. In contrast, the ML result is consistent with no conformity, $+0.00\pm0.03$ dex; the satellite-level statistic gives a similar result. Thus, with halo-mass estimates consistent with weak-lensing constraints, we find no detectable one-halo conformity signal in the present SDSS sample, suggesting that the strong AM-based signal is largely driven by halo-mass estimation biases.

astro-ph.GA↗

arXiv2026

Inelastic Dark Matter and High-Energy Recoil Signatures in LZ

We study the inelastic dark matter (iDM) that consists of two-component dark matter particles $(\chi_1,\chi_2)$ and serves as a minimal extension beyond the commonly used one-component DM models. A distinctive feature of such iDM scattering with the target nuclei is to favorably produce signals at high nuclear recoil (NR) energy region in both exothermic and endothermic processes. In particular, for the exothermic dark matter scenario, the signal does not rely on the tail of the Boltzmann velocity distribution. As a result, the required DM-nucleus scattering cross section is significantly reduced, which in turn helps to substantially relax the stringent constraints imposed by IceCube neutrino searches. Using the inelastic DM-nucleus scattering, we naturally explain the newly reported event excess at high recoil energy with the LUX-ZEPLIN (LZ) experiment.

hep-ph↗

arXiv2026

Exact tunneling splittings of rotationally excited states from symmetrized path-integral molecular dynamics

We extend our previous symmetrized path-integral molecular dynamics approach to calculate tunneling splittings of molecules in rotationally excited states. In this new formalism, the system is rigorously projected onto selected rotational manifolds and states of a chosen symmetry through an Eckart spring, which connects the two end beads of the ring polymer via a permutation--inversion--rotation operation. This method is numerically exact within statistical uncertainty once convergence with respect to all simulation parameters has been achieved. Importantly, it enables the simultaneous extraction of tunneling splittings for multiple total angular-momentum quantum numbers $J$ from a single set of simulations, without additional computational cost relative to the original approach. After validating the formalism by computing the rotational levels of water (beyond the rigid-rotor approximation), we apply it to ammonia and obtain rotationally resolved tunneling splittings in excellent agreement with exact variational benchmarks. Except for small errors due to the underlying potential energy surface, the results capture the experimentally observed trend that the tunneling splitting decreases with $J$.

physics.chem-ph↗

arXiv2026

Exact tunneling splittings from path-integral hybrid Monte Carlo with enveloping bridging potentials

A path-integral hybrid Monte Carlo approach with enveloping bridging potentials (PIHMC-EBP) is proposed for calculating numerically exact tunneling splittings in molecular systems. The central idea is to construct an approximately barrierless bridging potential that smoothly connects symmetry-related regions of ring-polymer phase space, enabling direct sampling of the free-energy profile from which the relevant splittings are obtained. Two tailored nonlocal updates are designed to enhance the sampling of slow collective motions. Compared with path-integral molecular dynamics using thermodynamic integration, PIHMC-EBP requires neither quadrature nor time-step convergence checks, thereby substantially reducing the manual effort required to analyze the results. Applications to malonaldehyde (and its deuterated isotopologue) and the HCl dimer using state-of-the-art potential energy surfaces provide the most precise tunneling splittings reported to date for both systems, while simultaneously reducing the overall computational cost by several times and three orders of magnitude, respectively. Finally, application to the water dimer yields the first numerically exact path-integral calculations of the ground-state tunneling splittings on three different potential energy surfaces, all obtained simultaneously by reweighting a single set of trajectories.

physics.chem-ph↗

arXiv2025

An Improved Machine Learning Approach for Radio Frequency Interference Mitigation in FAST-SETI Survey Archival Data

The search for extraterrestrial intelligence (SETI) commensal surveys aim to scan the sky to detect technosignatures from extraterrestrial life. A major challenge in SETI is the effective mitigation of radio frequency interference (RFI), a critical step that is particularly vital for the highly sensitive Five-hundred-meter Aperture Spherical radio Telescope (FAST). While initial RFI mitigation (e.g., removal of persistent and drifting narrowband RFI) are essential, residual RFI often persists, posing significant challenges due to its complex and various nature. In this paper, we propose and apply an improved machine learning approach, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, to identify and mitigate residual RFI in FAST-SETI commensal survey archival data from July 2019. After initial RFI mitigation, we successfully identify and remove 36977 residual RFIs (accounting for $\sim$ 77.87\%) within approximately 1.678 seconds using the DBSCAN algorithm. This result shows that we have achieved a 7.44\% higher removal rate than previous machine learning methods, along with a 24.85\% reduction in execution time. We finally find interesting candidate signals consistent with previous studies, and retain one candidate signal following further analysis. Therefore, DBSCAN algorithm can mitigate more residual RFI with higher computational efficiency while preserving the candidate signals that we are interested in.

astro-ph.IM↗

arXiv2025

Search for Light Inelastic Dark Matter with Low-Energy Ionization Signatures in PandaX-4T

Direct detection of light dark matter (DM) is generally difficult due to its small recoil energy. The inelastic scattering of DM can produce unique signatures in the DM direct detection experiments. Using the low-energy unpaired ionization data from PandaX-4T, we newly analyze the probe of the exothermic inelastic dark matter (ineDM). We demonstrate that PandaX-4T can probe the ineDM mass-splitting down to 0.05 keV and probe the ineDM mass to the sub-GeV range. For the ineDM with a dark photon mediator, we use the PandaX-4T data to impose stringent bounds on the mixing parameter between the dark photon and photon.

hep-ph↗

arXiv2025

From Halos to Galaxies. X: Decoding Galaxy SEDs with Physical Priors and Accurate Star Formation History Reconstruction

The spectral energy distribution (SED) of galaxies is essential for deriving fundamental properties like stellar mass and star formation history (SFH). However, conventional methods, including both parametric and non-parametric approaches, often fail to accurately recover the observed cosmic star formation rate (SFR) density due to oversimplified or unrealistic assumptions about SFH and their inability to account for the complex SFH variations across different galaxy populations. To address this issue, we introduce a novel approach that improves galaxy broadband SED analysis by incorporating physical priors derived from hydrodynamical simulations. Tests using IllustrisTNG simulations demonstrate that our method can reliably determine galaxy physical properties from broadband photometry, including stellar mass within 0.05 dex, current SFR within 0.3 dex, and fractional stellar formation time within 0.2 dex, with a negligible fraction of catastrophic failures. When applied to the Sloan Digital Sky Survey (SDSS) main photometric galaxy sample with spectroscopic redshift, our estimates of stellar mass and SFR are consistent with the widely used MPA-JHU and GSWLC catalogs. Notably, using the derived SFHs of individual SDSS galaxies, we estimate the cosmic SFR density and stellar mass density with remarkable consistency to direct observations up to $z \sim 6$. This demonstrates a significant advancement in deriving SFHs from SEDs that closely align with observational data. Consequently, our method can reliably recover observed spectral indices such as $\rm D_{\rm n}(4000)$ and $\rm Hδ_{\rm A}$ by synthesizing the full spectra of galaxies using the estimated SFHs and metal enrichment histories, relying solely on broadband photometry as input. Furthermore, this method is extremely computationally efficient compared to conventional approaches.

astro-ph.GA↗

arXiv2025

From Halos to Galaxies. VI. Improved halo mass estimation for SDSS groups and measurement of the halo mass function

In $Λ$CDM cosmology, galaxies form and evolve in their host dark matter (DM) halos. Halo mass is crucial for understanding the halo-galaxy connection. The abundance matching (AM) technique has been widely used to derive the halo masses of galaxy groups. However, quenching of the central galaxy can decouple the coevolution of its stellar mass and DM halo mass. Different halo assembly histories can also result in significantly different final stellar mass of the central galaxies. These processes can introduce substantial uncertainties in the halo masses derived from the AM method, particularly leading to a systematic bias between groups with star-forming centrals (blue groups) and passive centrals (red groups). To improve, we developed a new machine learning (ML) algorithm that accounts for these effects and is trained on simulations. Our results show that the ML method eliminates the systematic bias in the derived halo masses for blue and red groups and is, on average, $\sim1/3$ more accurate than the AM method. With careful calibration of observable quantities from simulations and observations from SDSS, we apply our ML model to the SDSS Yang et al. groups to derive their halo masses down to $10^{11.5}\mathrm{M_\odot}$ or even lower. The derived SDSS group halo mass function agrees well with the theoretical predictions, and the derived stellar-to-halo mass relations for both red and blue groups matches well with those obtained from direct weak lensing measurements. These new halo mass estimates enable more accurate investigation of the galaxy-halo connection and the role of the halos in galaxy evolution.

astro-ph.GA↗

arXiv2024

Probing Light Inelastic Dark Matter from Direct Detection

For dark matter (DM) direct detections, the kinematic effects such as those of the inelastic scattering can play important role in light DM searches. The light DM detection is generally difficult because of its small recoil energy. But the recoil energy of the exothermic inelastic DM scattering could exceed the detection threshold due to the contribution from the DM mass-splitting, making the direct detection of sub-GeV DM feasible. In this work, we systematically study signatures of the light exothermic inelastic DM from the recoil spectra including both the DM-electron scattering and Migdal effect. Such inelastic DM has mass around (sub-)GeV scale with DM mass-splitting of $O(1-10^2)$keV. We analyze the direct detection sensitivities to such light inelastic DM. For different inelastic DM masses and mass-splittings, we find that the DM-electron recoil and Migdal effect can contribute significantly and differently to the direct detection signatures. The DM-lepton and/or DM-quark interactions may vary for different DM models, and their interplay leads to a diversity in the recoil spectra. Hence, it is important to perform a combined analysis to include both the DM-electron recoil and Migdal effect. We further demonstrate that this analysis has strong impacts on the cosmological and laboratory bounds for the inelastic DM.

hep-ph↗

arXiv2024

A Stochastic Approach to Reconstructing the Speed of Light in Cosmology

The Varying Speed of Light (VSL) model describes how the speed of light in a vacuum changes with cosmological redshift. Despite numerous models, there is little observational evidence for this variation. While the speed of light can be accurately measured by physical means, cosmological methods are rarely used. Previous studies quantified the speed of light at specific redshifts using Gaussian processes and reconstructed the redshift-dependent function $c(z)$. It is crucial to quantify the speed of light across varying redshifts. We use the latest data on angular diameter distances $D_A(z)$ and Hubble parameters $H(z)$ from baryon acoustic oscillation (BAO) and cosmic chronometer measurements in the redshift interval $z\in[0.07,1.965]$. The speed of light $c(z)$ is determined using Gaussian and deep Gaussian processes to reconstruct $H(z)$, $D_A(z)$, and $D^{\prime}_A(z)$. Furthermore, we conduct comparisons across three distinct models, encompassing two renowned VSL models. We get the result of the parameters constraints in the models (1) for the ``$c$-c" model, $c_0=29492.6 \pm^{6.2}_{5.3} \mathrm{~km} \mathrm{~s}^{-1}$. (2) For the ``$c$-cl" model, $c_0=29665.5 \pm^{11.2}_{11.4}\mathrm{~km} \mathrm{~s}^{-1}$ and $n=0.05535 \pm^{0.00008}_{0.00007}$. (3) For the ``$c$-CPL" model, $c_0=29555.7 \pm^{13.3}_{13.2} \mathrm{~km} \mathrm{~s}^{-1}$ and $n=-0.0607 \pm 0.0001$. Based on our findings, it may be inferred that Barrow's classical VSL model is not a suitable fit for our data. In contrast, the widely recognized Chevallier-Polarski-Linder (CPL) VSL model, under some circumstances, as well as the universal ``c is constant" model, demonstrate a satisfactory ability to account for our findings.

astro-ph.CO↗

arXiv2023

SETI at FAST in China

Since the commencement of the first SETI observation in 2019, China's Search for Extraterrestrial Intelligence program has garnered momentum through domestic support and international collaborations. Several observations targeting exoplanets and nearby stars have been conducted with the FAST. In 2023, the introduction of the Far Neighbour Project(FNP) marks a substantial leap forward, driven by the remarkable sensitivity of the FAST telescope and some of the novel observational techniques. The FNP seeks to methodically detect technosignatures from celestial bodies, including nearby stars, exoplanetary systems, Milky Way globular clusters, and more. This paper provides an overview of the progress achieved by SETI in China and offers insights into the distinct phases comprising the FNP. Additionally, it underscores the significance of this project's advancement and its potential contributions to the field.

astro-ph.EP↗

arXiv2023

Novel Cosmological Joint Constraints in Multidimensional Observables Space with Redshift-free Inferences

Cosmology constraints serve as a crucial criterion in discriminating cosmological models. The traditional combined method to constrain the cosmological parameters designates the corresponding theoretical value and observational data as functions of redshift, however, sometimes the redshift cannot be measured directly, or the measurement error is large, or the definition of redshift is controversial. In this paper, we propose a novel joint method to constrain parameters that eliminates the redshift $z$ and makes full use of the multiple observables $\left\lbrace \mathcal{F}_{1,\mathrm{obs}},\mathcal{F}_{2,\mathrm{obs}},\cdots,\mathcal{F}_{M,\mathrm{obs}}\right\rbrace$ spanning in $M$-dimensional joint observables space. Considering the generality of the mathematical form of the cosmological models and the guidance from low to high dimensions, we firstly validate our method in a three-dimensional joint observables space spanned by $H(z)$, $fσ_{8}(z)$ and $D_{A}(z)$, where the three coordinates can be considered redshift-free measurements of the same celestial body (or shared-redshift data reconstructed model independently). Our results are consistent with the traditional combined method but with lower errors, yielding $H_0=68.7\pm0.1\mathrm{~km} \mathrm{~s}^{-1}\mathrm{~Mpc}^{-1}$, $Ω_{m0}=0.289\pm0.003$, $σ_{8}=0.82\pm0.01$ and showing alleviated parametric degeneracies to some extent. In principle, our joint constraint method allows an extended form keeping the redshift information as an independent coordinate and can also be readily degraded to the form of a traditional combined method to constrain parameters.

astro-ph.CO↗

arXiv2023

A Search for Extraterrestrial Technosignatures in Archival FAST Survey Data Using a New Procedure

The "search for extraterrestrial intelligence" (SETI) commensal surveys aim to scan the sky to find possible technosignatures from the extraterrestrial intelligence (ETI). The mitigation of radio frequency interference (RFI) is an important step, especially for the most sensitive Five-hundred-meter Aperture Spherical radio Telescope (FAST), which can detect more weak RFI. In this paper, we propose several new techniques for RFI mitigation, and use our procedure to search for ETI signals from the archival data of FAST's first SETI commensal survey. We detect the persistent narrowband RFI by setting a threshold of the signals' sky separation, and detect the drifting RFI (and potentially other types of RFI) using the Hough transform. We also use the clustering algorithms to remove more RFI and select candidates. The results of our procedure are compared to the earlier work on the same FAST data. We find that our methods, though relatively simpler in computation, remove more RFI (99.9912% compared to 99.9063% in the earlier work), but preserve the simulated ETI signals except those (5.1%) severely affected by the RFI. We also report more interesting candidate signals, about a dozen of which are new candidates that are not previously reported. In addition, we find that the proposed Hough transform method, with suitable parameters, also has the potential to remove the broadband RFI. We conclude that our methods can effectively remove the vast majority of the RFI while preserving and finding the candidate signals that we are interested in.

astro-ph.IM↗

arXiv2023

Kernel Selection for Gaussian Process in Cosmology: with Approximate Bayesian Computation Rejection and Nested Sampling

Gaussian Process (GP) has gained much attention in cosmology due to its ability to reconstruct cosmological data in a model-independent manner. In this study, we compare two methods for GP kernel selection: Approximate Bayesian Computation (ABC) Rejection and nested sampling. We analyze three types of data: cosmic Chronometer data (CC), Type Ia Supernovae (SNIa), and Gamma Ray Burst (GRB), using five kernel functions. To evaluate the differences between kernel functions, we assess the strength of evidence using Bayes factors. Our results show that, for ABC Rejection, the Matérn kernel with $ν$=5/2 (M52 kernel) outperformes the commonly used Radial Basis Function (RBF) kernel in approximating all three datasets. Bayes factors indicate that the M52 kernel typically supports the observed data better than the RBF kernel, but with no clear advantage over other alternatives. However, nested sampling gives different results, with the M52 kernel losing its advantage. Nevertheless, Bayes factors indicate no significant dependence of the data on each kernel.

astro-ph.CO↗

arXiv2023

Test of Artificial Neural Networks in Likelihood-free Cosmological Constraints: A Comparison of IMNN and DAE

In the procedure of constraining the cosmological parameters with the observational Hubble data and the type Ia supernova data, the combination of Masked Autoregressive Flow and Denoising Autoencoder can perform a good result. The above combination extracts the features from OHD with DAE, and estimates the posterior distribution of cosmological parameters with MAF. We ask whether we can find a better tool to compress large data in order to gain better results while constraining the cosmological parameters. Information maximising neural networks, a kind of simulation-based machine learning technique, was proposed at an earlier time. In a series of numerical examples, the results show that IMNN can find optimal, non-linear summaries robustly. In this work, we mainly compare the dimensionality reduction capabilities of IMNN and DAE. We use IMNN and DAE to compress the data into different dimensions and set different learning rates for MAF to calculate the posterior. Meanwhile, the training data and mock OHD are generated with a simple Gaussian likelihood, the spatially flat ΛCDM model and the real OHD data. To avoid the complex calculation in comparing the posterior directly, we set different criteria to compare IMNN and DAE.

astro-ph.CO↗

arXiv2022

Diagrammatic quantum Monte Carlo toward the calculation of transport properties in disordered semiconductors

A new diagrammatic quantum Monte Carlo approach is proposed to deal with the imaginary time propagator involving both dynamic disorder (i.e., electron-phonon interactions) and static disorder of local or nonlocal nature in a unified and numerically exact way. The establishment of the whole framework relies on a general reciprocal-space expression and a generalized Wick's theorem for the static disorder. Since the numerical cost is independent of the system size, various physical quantities such as the thermally averaged coherence, Matsubara one-particle Green's function and current autocorrelation function can be efficiently evaluated in the thermodynamic limit (infinite in the system size). The validity and performance of the proposed approach are systematically examined in a broad parameter regimes. This approach, combined with proper numerical analytic continuation methods and first-principles calculations, is expected to be a versatile tool toward the calculation of various transport properties like mobilities in realistic semiconductors involving multiple electronic energy bands, high-frequency optical and low-frequency acoustic phonons, different forms of dynamic and static disorders, anisotropy, etc.

cond-mat.mtrl-sci↗

arXiv2022

GeV Scale Inelastic Dark Matter with Dark Photon Mediator via Direct Detection and Cosmological/Laboratory Constraints

We propose a new candidate of GeV scale inelastic dark matter (DM). Our construction has an anomaly-free $U(1)_X^{}$ gauge group with dark photon mediator, and can realize either scalar or fermionic inelastic DM. It is highly predictive and testable. We study the scattering rate of light inelastic DM with electrons in the XENON1T experiment and with nuclei in the XENON1T, CRESST-III, CDEX-1B and DarkSide-50 experiments. We resolve the recent XENON1T anomaly via electron recoil detection. Combining the XENON1T constraints from both electron recoils and nuclear recoils (including Migdal effect), we predict the inelastic DM mass $\lesssim 1.5$GeV. We further analyze the bounds by the DM relic abundance, the lifetime of the heavier DM component, and laboratory constraints, from which we identify the viable parameter space for the future probe. This provides an important benchmark for the theories and experimental tests of GeV scale inelastic DM.

hep-ph↗

arXiv2021

Likelihood-free Cosmological Constraints with Artificial Neural Networks: An Application on Hubble Parameters and SNe Ia

The errors of cosmological data generated from complex processes, such as the observational Hubble parameter data (OHD) and the Type Ia supernova (SN Ia) data, cannot be accurately modeled by simple analytical probability distributions, e.g. Gaussian distribution. To constrain cosmological parameters from these data, likelihood-free inference is usually used to bypass the direct calculation of the likelihood. In this paper, we propose a new procedure to perform likelihood-free cosmological inference using two artificial neural networks (ANN), the Masked Autoregressive Flow (MAF) and the denoising autoencoder (DAE). Our procedure is the first to use DAE to extract features from data, in order to simplify the structure of MAF needed to estimate the posterior. Tested on simulated Hubble parameter data with a simple Gaussian likelihood, the procedure shows the capability of extracting features from data and estimating posterior distributions without the need of tractable likelihood. We demonstrate that it can accurately approximate the real posterior, achieve performance comparable to the traditional MCMC method, and the MAF gets better training results for small number of simulation when the DAE is added. We also discuss the application of the proposed procedure to OHD and Pantheon SN Ia data, and use them to constrain cosmological parameters from the non-flat $Λ$CDM model. For SNe Ia, we use fitted light curve parameters to find constraints on $H_0,Ω_m,Ω_Λ$ similar to relevant work, using less empirical distributions. In addition, this work is also the first to use Gaussian process in the procedure of OHD simulation.

astro-ph.CO↗