arXiv subjects

Fei Qin

Publications and source records attributed to Fei Qin.

At least 37 records · Page 2Linked to original sources

arXiv2024

The MOST Hosts Survey: spectroscopic observation of the host galaxies of ~40,000 transients using DESI

We present the MOST Hosts survey (Multi-Object Spectroscopy of Transient Hosts). The survey is planned to run throughout the five years of operation of the Dark Energy Spectroscopic Instrument (DESI) and will generate a spectroscopic catalog of the hosts of most transients observed to date, in particular all the supernovae observed by most public, untargeted, wide-field, optical surveys (PTF/iPTF, SDSS II, ZTF, DECAT, DESIRT). Scientific questions for which the MOST Hosts survey will be useful include Type Ia supernova cosmology, fundamental plane and peculiar velocity measurements, and the understanding of the correlations between transients and their host galaxy properties. Here, we present the first release of the MOST Hosts survey: 21,931 hosts of 20,235 transients. These numbers represent 36% of the final MOST Hosts sample, consisting of 60,212 potential host galaxies of 38,603 transients (a transient can be assigned multiple potential hosts). Of these galaxies, 40% do not appear in the DESI primary target list and therefore require a specific program like MOST Hosts. Of all the transients in the MOST Hosts list, only 26.7% have existing classifications, and so the survey will provide redshifts (and luminosities) for nearly 30,000 transients. A preliminary Hubble diagram and a transient luminosity-duration diagram are shown as examples of future potential uses of the MOST Hosts survey. The survey will also provide a training sample of spectroscopically observed transients for photometry-only classifiers, as we enter an era when most newly observed transients will lack spectroscopic classification. The MOST Hosts DESI survey data will be released through the Wiserep platform on a rolling cadence and updated to match the DESI releases. Dates of future releases and updates are available through the https://mosthosts.desi.lbl.gov website.

astro-ph.HE↗

arXiv2024

Constructing the three-dimensional extinction density maps using V-net

One of the major challenges we face is how to quickly and accurately create the three-dimensional (3D) density distributions of interstellar dust in the Milky Way using extinction and distance measurements of large samples of stars. In this study, we introduce a novel machine-learning approach that utilizes a convolution neural network, specifically a V-net, to infer the 3D distribution of dust density. Experiments are performed within two regions located towards the Galactic anti-center. The neural network is trained and tested using 10,000 simulations of dust density and line-of-sight extinction maps. Evaluation of the test sample confirms the successful generation of dust density maps from extinction maps by our model. Additionally, the performance of the trained network is evaluated using data from the literature. Our results demonstrate that our model is capable of capturing detailed dust density variations and can recover dust density maps while reducing the ``fingers of god" effect. Moving forward, we plan to apply this model to real observational data to obtain the fine distribution of dust at large and small scales in the Milky Way.

astro-ph.GA↗

arXiv2024

The DESI One-Percent survey: exploring the Halo Occupation Distribution of Emission Line Galaxies with AbacusSummit simulations

The One-Percent survey of the Dark Energy Spectroscopic Instrument collected ~ 270k emission line galaxies (ELGs) at 0.8 < z < 1.6. The high completeness of the sample allowed the clustering to be measured down to scales never probed before, 0.04 Mpc/h in rp for the projected 2-point correlation function (2PCF) and 0.17 Mpc/h in galaxy pair separation s for the 2PCF monopole and quadrupole. The most striking feature of the measurements is a strong signal at the smallest scales, below 0.2 Mpc/h in rp and 1 Mpc/h in s. We analyze these data in the halo occupation distribution framework. We consider different distributions for central galaxies, a standard power law for satellites with no condition on the presence of a central galaxy and explore several extensions of these models. For all considered models, the mean halo mass of the sample is found to be log10 ~ 11.9. We obtain a satellite mean occupation function which agrees with physically motivated ELG models only if we introduce central-satellite conformity, meaning that the satellite occupation is conditioned by the presence of central galaxies of the same type. To achieve in addition a good modeling of the clustering between 0.1 and 1 Mpc/h in rp, we allow for ELG positioning outside of the halo virial radius and find 0.5% of ELGs residing in the outskirts of halos. Furthermore, the satellite velocity dispersion inside halos is found to be ~ 30% larger than that of the halo dark matter particles. These are the main findings of our work. We investigate assembly bias as a function of halo concentration, local density or local density anisotropies and observe no significant change in our results. We split the data sample in two redshift bins and report no significant evolution with redshift. Lastly, changing the cosmology in the modeling impacts only slightly our results.

astro-ph.CO↗

arXiv2023

A Hybrid Frame Structure Design of OTFS for Multi-tasks Communications

Orthogonal time frequency space (OTFS) is a promising waveform in high mobility scenarios for it fully exploits the time-frequency diversity using a discrete Fourier transform (DFT) based two dimensional spreading. However, it trades off the processing latency for performance and may not fulfill the stringent latency requirements in some services. This fact motivates us to design a hybrid frame structure where the OTFS and Orthogonal Frequency Division Multiplexing (OFDM) are orthogonally multiplexed in the time domain, which can adapt to both diversity-preferred and latency-preferred tasks. As we identify that this orthogonality is disrupted after channel coupling, we provide practical algorithms to mitigate the inter symbol interference between (ISI) the OTFS and OFDM, and the numerical results ensure the effectiveness of the hybrid frame structure.

eess.SP↗

arXiv2023

The Galaxy Number Density Profile of Haloes

More precise measurements of galaxy clustering will be provided by the next generation of galaxy surveys such as DESI, WALLABY and SKA. To utilize this information to improve our understanding of the Universe, we need to accurately model the distribution of galaxies in their host dark matter halos. In this work we present a new galaxy number density profile of haloes, which makes predictions for the positions of galaxies in the host halo, different to the widely adopted Navarro-Frenk-White (NFW) profile, since galaxies tend to be found more in the outskirts of halos (nearer the virial radius) than an NFW profile. The parameterised galaxy number density profile model of haloes is fit and tested using the DARKSAGE semi-analytic model of galaxy formation. We find that our galaxy number density profile model of haloes can accurately reproduce the halo occupation distribution and galaxy two-point correlation function of the DARKSAGE simulation. We also derive the analytic expressions for the circular velocity and gravitational potential energy for this profile model. We use the SDSS DR10 galaxy group catalogue to validate this galaxy number density profile model of haloes. Compared to the NFW profile, we find that our model more accurately predicts the positions of galaxies in their host halo and the galaxy two-point correlation function.

astro-ph.CO↗

arXiv2023

Target Selection for the DESI Peculiar Velocity Survey

We describe the target selection and characteristics of the DESI Peculiar Velocity Survey, the largest survey of peculiar velocities (PVs) using both the fundamental plane (FP) and the Tully-Fisher (TF) relationship planned to date. We detail how we identify suitable early-type galaxies (ETGs) for the FP and suitable late-type galaxies (LTGs) for the TF relation using the photometric data provided by the DESI Legacy Imaging Survey DR9. Subsequently, we provide targets for 373 533 ETGs and 118 637 LTGs within the DESI 5-year footprint. We validate these photometric selections using existing morphological classifications. Furthermore, we demonstrate using survey validation data that DESI is able to measure the spectroscopic properties to sufficient precision to obtain PVs for our targets. Based on realistic DESI fiber assignment simulations and spectroscopic success rates, we predict the final DESI PV Survey will obtain $\sim$133 000 FP-based and $\sim$53 000 TF-based PV measurements over an area of 14 000 $\mathrm{deg^{2}}$. We forecast the ability of using these data to measure the clustering of galaxy positions and PVs from the combined DESI PV and Bright Galaxy Surveys (BGS), which allows for cancellation of cosmic variance at low redshifts. With these forecasts, we anticipate a $4\%$ statistical measurement on the growth rate of structure at $z<0.15$. This is over two times better than achievable with redshifts from the BGS alone. The combined DESI PV and BGS will enable the most precise tests to date of the time and scale dependence of large-scale structure growth at $z<0.15$.

astro-ph.CO↗

arXiv2023

Reconstructing the cosmological density and velocity fields from redshifted galaxy distributions using V-net

The distribution of matter that is measured through galaxy redshift and peculiar velocity surveys can be harnessed to learn about the physics of dark matter, dark energy, and the nature of gravity. To improve our understanding of the matter of the Universe, we can reconstruct the full density and velocity fields from the galaxies that act as tracer particles. In this paper, we use the simulated halos as proxies for the galaxies. We use a convolutional neural network, a V-net, trained on numerical simulations of structure formation to reconstruct the density and velocity fields. We find that, with detailed tuning of the loss function, the V-net could produce better fits to the density field in the high-density and low-density regions, and improved predictions for the probability distribution of the amplitudes of the velocities. However, the weights will reduce the precision of the estimated $β$ parameter. We also find that the redshift-space distortions of the halo catalogue do not significantly contaminate the reconstructed real-space density and velocity field. We estimate the velocity field $β$ parameter by comparing the peculiar velocities of halo catalogues to the reconstructed velocity fields, and find the estimated $β$ values agree with the fiducial value at the 68\% confidence level.

astro-ph.CO↗

arXiv2023

Topological structures of energy flow: Poynting vector skyrmions

Topological properties of energy flow of light are fundamentally interesting and have rich practical applications in optical manipulations. Here, skyrmion-like structures formed by Poynting vectors are unveiled in the focal region of a pair of counter-propagating cylindrical vector vortex beams in free space. A Néel-Bloch-Néel skyrmion type transformation of Poynting vectors is observed along the light propagating direction within a volume with subwavelength feature sizes. The corresponding skyrmion type can be determined by the phase singularities of the individual components of the coherently superposed electromagnetic field in the focal region. This work reveals a new family member of optical skyrmions and may introduce novel physical phenomena associated with light scattering and optical force.

physics.optics↗

arXiv2022

HI HOD -- I. The Halo Occupation Distribution of HI Galaxies

The next generation of galaxy surveys will provide more precise measurements of galaxy clustering than have previously been possible. The 21-cm radio signals that are emitted from neutral atomic hydrogen (HI) gas will be detected by large-area radio surveys such as WALLABY and the SKA, and deliver galaxy positions and velocities that can be used to measure galaxy clustering statistics. But, to harness this information to improve our cosmological understanding, and learn about the physics of dark matter and dark energy, we need to accurately model the manner in which galaxies detected in HI trace the underlying matter distribution of the Universe. For this purpose, we develop a new HI-based Halo Occupation Distribution (HOD) model, which makes predictions for the number of galaxies present in dark matter halos conditional on their HI mass. The parameterised HOD model is fit and validated using the Dark Sage semi-analytic model, where we show that the HOD parameters can be modelled by simple linear and quadratic functions of HI mass. However, we also find that the clustering predicted by the HOD depends sensitively on the radial distributions of the HI galaxies within their host dark matter halos, which does not follow the NFW profile in the Dark Sage simulation. As such, this work enables -- for the first time -- a simple prescription for placing galaxies of different HI mass within dark matter halos in a way that is able to reproduce the HI mass-dependent galaxy clustering and HI mass function simultaneously and without requiring knowledge of the optical properties of the galaxies. Further efforts are required to demonstrate that this model can be used to produce large ensembles of mock galaxy catalogues for upcoming surveys.

astro-ph.CO↗

arXiv2022

A measurement of the Integrated Sachs-Wolfe Effect with the Rapid ASKAP Continuum Survey

The evolution of the gravitational potentials on large scales due to the accelerated expansion of the Universe is an important and independent probe of dark energy, known as the integrated Sachs-Wolfe (ISW) effect. We measure this ISW effect through cross-correlating the cosmic microwave background maps from the Planck satellite with a radio continuum galaxy distribution map from the recent Rapid ASKAP Continuum Survey (RACS). We detect a positive cross-correlation at $\sim 2.8\,σ$ relative to the null hypothesis of no correlation. We parameterise the strength of the ISW effect through an amplitude parameter and find the constraints to be $A_{\mathrm{ISW}} = 0.94^{+0.42}_{-0.41}$, which is consistent with the prediction of an accelerating universe within the current concordance cosmological model, $Λ$CDM. The credible interval on this parameter is independent of the different bias models and redshift distributions that were considered when marginalising over the nuisance parameters. We also detect a power excess in the galaxy auto-correlation angular power spectrum on large scales ($\ell \leq 40$), and investigate possible systematic causes.

astro-ph.CO↗

arXiv2022

The Sloan Digital Sky Survey Peculiar Velocity Catalogue

We present a new catalogue of distances and peculiar velocities (PVs) of $34,059$ early-type galaxies derived from Fundamental Plane (FP) measurements using data from the Sloan Digital Sky Survey (SDSS). This $7016\,\mathrm{deg}^{2}$ homogeneous sample comprises the largest set of peculiar velocities produced to date and extends the reach of PV surveys up to a redshift limit of $z=0.1$. Our SDSS-based FP distance measurements have a mean uncertainty of 23%. Alongside the data, we produce an ensemble of 2,048 mock galaxy catalogues that reproduce the data selection function, and are used to validate our fitting pipelines and check for systematic errors. We uncover a significant trend between group richness and mean surface brightness within the sample, which may hint at an environmental dependence within the FP or the presence of unresolved systematics, and can result in biased peculiar velocities. This is removed using multiple FP fits as function of group richness, a procedure made tractable through a new analytic derivation for the integral of a 3D Gaussian over non-trivial limits. Our catalogue is calibrated to the zero-point of the CosmicFlows-III sample with an uncertainty of $0.004$ dex (not including cosmic variance or the error within CosmicFlows-III itself), which is validated using independent cross-checks with the predicted zero-point from the 2M++ reconstruction of our local velocity field. Finally, as an example of what is possible with our new catalogue, we obtain preliminary bulk flow measurements up to a depth of $135\,h^{-1}\mathrm{Mpc}$. We find a slightly larger-than-expected bulk flow at high redshift, although this could be caused by the presence of the Shapley supercluster which lies outside the SDSS PV footprint.

astro-ph.CO↗

arXiv2022

Practical Issues and Challenges in CSI-based Integrated Sensing and Communication

Next-generation mobile communication network (i.e., 6G) has been envisioned to go beyond classical communication functionality and provide integrated sensing and communication (ISAC) capability to enable more emerging applications, such as smart cities, connected vehicles, AIoT and health care/elder care. Among all the ISAC proposals, the most practical and promising approach is to empower existing wireless network (e.g., WiFi, 4G/5G) with the augmented ability to sense the surrounding human and environment, and evolve wireless communication networks into intelligent communication and sensing network (e.g., 6G). In this paper, based on our experience on CSI-based wireless sensing with WiFi/4G/5G signals, we intend to identify ten major practical and theoretical problems that hinder real deployment of ISAC applications, and provide possible solutions to those critical challenges. Hopefully, this work will inspire further research to evolve existing WiFi/4G/5G networks into next-generation intelligent wireless network (i.e., 6G).

cs.NI↗

arXiv2021

Deep Learning Based OFDM Channel Estimation Using Frequency-Time Division and Attention Mechanism

In this paper, we propose a frequency-time division network (FreqTimeNet) to improve the performance of deep learning (DL) based OFDM channel estimation. This FreqTimeNet is designed based on the orthogonality between the frequency domain and the time domain. In FreqTimeNet, the input is processed by parallel frequency blocks and parallel time blocks sequentially. By introducing the attention mechanism using the SNR information, an attention based FreqTimeNet (AttenFreqTimeNet) is proposed. Using 3rd Generation Partnership Project (3GPP) channel models, the mean square error (MSE) performance of FreqTimeNet and AttenFreqTimeNet under different scenarios is evaluated. A method for constructing mixed training data is proposed, which could address the generalization problem in DL. It is observed that AttenFreqTimeNet outperforms FreqTimeNet, and FreqTimeNet outperforms other DL networks with reasonable complexity.

cs.IT↗

arXiv2021

Cosmic Flow Measurement and Mock Sampling Algorithm of Cosmicflows-4 Tully-Fisher Catalogue

Measurements of cosmic flows enable us to test whether cosmological models can accurately describe the evolution of the density field in the nearby Universe. In this paper, we measure the low-order kinematic moments of the cosmic flow field, namely bulk flow and shear moments, using the Cosmicflows-4 Tully-Fisher catalogue (CF4TF). To make accurate cosmological inferences with the CF4TF sample, it is important to make realistic mock catalogues. We present the mock sampling algorithm of CF4TF. These mock can accurately realize the survey geometry and luminosity selection function, enabling researchers to explore how these systematics affect the measurements. These mocks can also be further used to estimate the covariance matrix and errors of power spectrum and two-point correlation function in future work. In this paper, we use the mocks to test the cosmic flow estimator and find that the measurements are unbiased. The measured bulk flow in the local Universe is 376 $\pm$ 23 (error) $\pm$ 183 (cosmic variance) km s$^{-1}$ at depth $d_{\text{MLE}}=35$ Mpc $h^{-1}$, to the Galactic direction of $(l,b)=(298\pm 3^{\circ}, -6\pm 3^{\circ})$. Both the measured bulk and shear moments are consistent with the concordance $Λ$ Cold Dark Matter cosmological model predictions.

astro-ph.CO↗

arXiv2021

Gaussianization of peculiar velocities and bulk flow measurement

The line-of-sight peculiar velocities are good indicators of the gravitational fluctuation of the density field. Techniques have been developed to extract cosmological information from the peculiar velocities in order to test the cosmological models. These techniques include measuring cosmic flow, measuring two-point correlation and power spectrum of the peculiar velocity fields, reconstructing the density field using peculiar velocities. However, some measurements from these techniques are biased due to the non-Gaussianity of the estimated peculiar velocities. Therefore, we use the 2MTF survey to explore a power transform that can Gaussianize the estimated peculiar velocities. We find a tight linear relation between the transformation parameters and the measurement errors of log-distance ratio. To show an example for the implement of the Gaussianized peculiar velocities in cosmology, we develop a bulk flow estimator and estimate bulk flow from the Gaussianized peculiar velocities. We use 2MTF mocks to test the algorithm, we find the algorithm yields unbiased measurements. We also find this technique gives smaller measurement errors compared to other techniques. Under the Galactic coordinates, at the depth of $30$ $h^{-1}$ Mpc, we measure a bulk flow of $332\pm27$ km s$^{-1}$ in the direction $(l,b)=(293\pm 5^{\circ}, 13\pm 4^{\circ})$. The measurement is consistent with the $Λ$CDM prediction.

astro-ph.CO↗

arXiv2020

Topological-darkness-assisted phase regulation for atomically thin meta-optics

Two-dimensional (2D) noble-metal dichalcogenides have emerged as a new platform for the realization of versatile flat optics with a considerable degree of miniaturization. However, light field manipulation at the atomic scale is widely considered unattainable since the vanishing thickness and intrinsic losses of 2D materials completely suppress both resonances and phase accumulation effects. Empowered by conventionally perceived adverse effects of intrinsic losses, we show that the structured PtSe2 films integrated with a uniform substrate can regulate nontrivial singular phase and realize atomic-thick meta-optics in the presence of topological darkness. We experimentally demonstrate a series of atomic-thick binary meta-optics that allows angle-robust and high unit-thickness diffraction efficiency of 0.96%/nm in visible frequencies, given its thickness of merely 4.3 nm. Our results unlock the potential of a new class of 2D flat optics for light field manipulation at an atomic thickness.

physics.optics↗

arXiv2020

Artificial neural network based chemical mechanisms for computationally efficient modeling of kerosene combustion

To effectively simulate the combustion of hydrocarbon-fueled supersonic engines, such as rocket-based combined cycle (RBCC) engines, a detailed mechanism for chemistry is usually required but computationally prohibitive. In order to accelerate chemistry calculation, an artificial neural network (ANN) based methodology was introduced in this study. This methodology consists of two different layers: self-organizing map (SOM) and back-propagation neural network (BPNN). The SOM is for clustering the dataset into subsets to reduce the nonlinearity, while the BPNN is for regression for each subset. The entire methodology was subsequently employed to establish a skeleton mechanism of kerosene combustion with 41 species. The training data was generated by RANS simulations of the RBCC combustion chamber, and then fed into the SOM-BPNN with six different topologies (three different SOM topologies and two different BPNN topologies). By comparing the predicted results of six cases with those of the conventional ODE solver, it is found that if the topology is properly designed, high-precision results in terms of ignition, quenching and mass fraction prediction can be achieved. As for efficiency, 8~ 20 times speedup of the chemical system integration was achieved, indicating that it has great potential for application in complex chemical mechanisms for a variety of fuels.

physics.flu-dyn↗

arXiv2020

A Deep Learning Framework for Hydrogen-fueled Turbulent Combustion Simulation

The high cost of high-resolution computational fluid/flame dynamics (CFD) has hindered its application in combustion related design, research and optimization. In this study, we propose a new framework for turbulent combustion simulation based on the deep learning approach. An optimized deep convolutional neural network (CNN) inspired from a U-Net architecture and inception module is designed for constructing the framework of the deep learning solver, named CFDNN. CFDNN is then trained on the simulation results of hydrogen combustion in a cavity with different inlet velocities. After training, CFDNN can not only accurately predict the flow and combustion fields within the range of the training set, but also shows an extrapolation ability for prediction outside the training set. The results from CFDNN solver show excellent consistency with the conventional CFD results in terms of both predicted spatial distributions and temporal dynamics. Meanwhile, two orders of magnitude of acceleration is achieved by using CFDNN solver compared to the conventional CFD solver. The successful development of such a deep learning-based solver opens up new possibilities of low-cost, high-accuracy simulations, fast prototyping, design optimization and real-time control of combustion systems such as gas turbines and scramjets.

physics.flu-dyn↗