arXiv subjects

Jiali Wang

Publications and source records attributed to Jiali Wang.

At least 37 records · Page 2Linked to original sources

arXiv2022

Searching Extra-tidal Features around the Globular Cluster Whiting 1

Whiting 1 is a faint and young globular cluster in the halo of the Milky Way, and was suggested to have originated in the Sagittarius spherical dwarf galaxy (Sgr dSph). In this paper, we use the deep DESI Legacy Imaging Surveys to explore tentative spatial connection between Whiting 1 and the Sgr dSph. We redetermine the fundamental parameters of Whiting 1 and use the best-fitting isochrone (age $τ$=6.5 Gyr, metalicity Z=0.005 and $\rm d_{\odot}$=26.9 kpc) to construct a theoretical matched filter for the extra-tidal features searching. Without any smooth technique to the matched filter density map, we detect a round-shape feature with possible leading and trailing tails on either side of the cluster. This raw image is not totally new compared to old discoveries, but confirms that no more large-scale features can be detected under a depth of r<=22.5 mag. In our results, the whole feature stretches 0.1-0.2 degree along the orbit of Whiting 1, which gives a much larger area than the cluster core. The tails on both sides of the cluster align along the orbital direction of the Sgr dSph as well as the cluster itself, which implies that these debris are probably stripped remnants of Whiting 1 by the Milky Way.

astro-ph.GA↗

arXiv2022

Photometric redshifts and Galaxy Clusters for DES DR2, DESI DR9, and HSC-SSP PDR3 Data

Photometric redshift (photo-z) is a fundamental parameter for multi-wavelength photometric surveys, while galaxy clusters are important cosmological probers and ideal objects for exploring the dense environmental impact on galaxy evolution. We extend our previous work on estimating photo-z and detecting galaxy clusters to the latest data releases of the Dark Energy Spectroscopic Instrument (DESI) imaging surveys, Dark Energy Survey (DES), and Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) imaging surveys and make corresponding catalogs publicly available for more extensive scientific applications. The photo-z catalogs include accurate measurements of photo-z and stellar mass for about 320, 293, and 134 million galaxies with $r<23$, $i<24$, and $i<25$ in DESI DR9, DES DR2, and HSC-SSP PDR3 data, respectively. The photo-z accuracy is about 0.017, 0.024, and 0.029 and the general redshift coverage is $z<1$, $z<1.2$, and $z<1.6$, respectively for those three surveys. The uncertainties of the logarithmic stellar mass that is inferred from stellar population synthesis fitting is about 0.2 dex. With the above photo-z catalogs, galaxy clusters are detected using a fast cluster-finding algorithm. A total of 532,810, 86,963, and 36,566 galaxy clusters with the number of members larger than 10 are discovered for DESI, DES, and HSC-SSP, respectively. Their photo-z accuracy is at the level of 0.01. The total mass of our clusters are also estimated by using the calibration relations between the optical richness and the mass measurement from X-ray and radio observations. The photo-z and cluster catalogs are available at ScienceDB (https://www.doi.org/10.11922/sciencedb.o00069.00003) and PaperData Repository (https://doi.org/10.12149/101089).

astro-ph.GA↗

arXiv2022

A Deep Learning Approach to Probabilistic Forecasting of Weather

We discuss an approach to probabilistic forecasting based on two chained machine-learning steps: a dimensional reduction step that learns a reduction map of predictor information to a low-dimensional space in a manner designed to preserve information about forecast quantities; and a density estimation step that uses the probabilistic machine learning technique of normalizing flows to compute the joint probability density of reduced predictors and forecast quantities. This joint density is then renormalized to produce the conditional forecast distribution. In this method, probabilistic calibration testing plays the role of a regularization procedure, preventing overfitting in the second step, while effective dimensional reduction from the first step is the source of forecast sharpness. We verify the method using a 22-year 1-hour cadence time series of Weather Research and Forecasting (WRF) simulation data of surface wind on a grid.

stat.ML↗

arXiv2022

SN 2012ij: A low-luminosity type Ia supernova and evidence for continuous distribution from 91bg-like explosion to normal ones

In this paper, we present photometric and spectroscopic observations of a subluminous type Ia supernova (SN Ia) 2012ij, which has an absolute $B$-band peak magnitude $M_{B,\rm{max}}$ = $-$17.95 $\pm$ 0.15 mag. The $B$-band light curve exhibits a fast post-peak decline with $Δm_{15}(B)$ = 1.86 $\pm$ 0.05 mag. All the $R$ and $I$/$i$-band light curves show a weak secondary peak/shoulder feature at about 3 weeks after the peak, like some transitional subclass of SNe Ia, which could result from an incomplete merger of near-infrared (NIR) double peaks. The spectra are characterized by Ti~{\sc ii} and strong Si~{\sc ii} $λ$5972 absorption features that are usually seen in low-luminosity objects like SN 1999by. The NIR spectrum before maximum light reveals weak carbon absorption features, implying the existence of unburned materials. We compare the observed properties of SN 2012ij with those predicted by the sub-Chandrasekhar-mass and the Chandrasekhar-mass delayed-detonation models, and find that both optical and NIR spectral properties can be explained to some extent by these two models. By comparing the secondary maximum features in $I$ and $i$ bands, we suggest that SN 2012ij is a transitional object linking normal SNe Ia to typical 91bg-like ones. From the published sample of SNe Ia from the $Carnegie~Supernova~Project~II$ (CSP-II), we estimate that the fraction of SN 2012ij-like SNe Ia is not lower than $\sim$ 2%.

astro-ph.HE↗

arXiv2021

Efficient high-dimensional variational data assimilation with machine-learned reduced-order models

Data assimilation (DA) in the geophysical sciences remains the cornerstone of robust forecasts from numerical models. Indeed, DA plays a crucial role in the quality of numerical weather prediction, and is a crucial building block that has allowed dramatic improvements in weather forecasting over the past few decades. DA is commonly framed in a variational setting, where one solves an optimization problem within a Bayesian formulation using raw model forecasts as a prior, and observations as likelihood. This leads to a DA objective function that needs to be minimized, where the decision variables are the initial conditions specified to the model. In traditional DA, the forward model is numerically and computationally expensive. Here we replace the forward model with a low-dimensional, data-driven, and differentiable emulator. Consequently, gradients of our DA objective function with respect to the decision variables are obtained rapidly via automatic differentiation. We demonstrate our approach by performing an emulator-assisted DA forecast of geopotential height. Our results indicate that emulator-assisted DA is faster than traditional equation-based DA forecasts by four orders of magnitude, allowing computations to be performed on a workstation rather than a dedicated high-performance computer. In addition, we describe accuracy benefits of emulator-assisted DA when compared to simply using the emulator for forecasting (i.e., without DA).

math.DS↗

arXiv2021

A Bayesian-network-based cybersecurity adversarial risk analysis framework with numerical examples

Cybersecurity risk analysis plays an essential role in supporting organizations make effective decision about how to manage and control cybersecurity risk. Cybersecurity risk is a function of the interplay between the defender, i.e., the organisation, and the attacker: decisions and actions made by the defender second guess the decisions and actions taken by the attacker and vice versa. Insight into this game between these two agents provides a means for the defender to identify and make optimal decisions. To date, the adversarial risk analysis framework has provided a decision-analytical approach to solve such game problems in the presence of uncertainty and uses Monte Carlo simulation to calculate and identify optimal decisions. We propose an alternative framework to construct and solve a serial of sequential Defend-Attack models, that incorporates the adversarial risk analysis approach, but uses a new class of influence diagrams algorithm, called hybrid Bayesian network inference, to identify optimal decision strategies. Compared to Monte Carlo simulation the proposed hybrid Bayesian network inference is more versatile because it provides an automated way to compute hybrid Defend-Attack models and extends their use to involve mixtures of continuous and discrete variables, of any kind. More importantly, the hybrid Bayesian network approach is novel in that it supports dynamic decision making whereby new real-time observations can update the Defend-Attack model in practice. We also extend the Defend-Attack model to support cases involving extra variables and longer decision sequence. Examples are presented, illustrating how the proposed framework can be adjusted for more complicated scenarios, including dynamic decision making.

cs.GT↗

arXiv2021

Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss

The Stackelberg prediction game (SPG) has been extensively used to model the interactions between the learner and data provider in the training process of various machine learning algorithms. Particularly, SPGs played prominent roles in cybersecurity applications, such as intrusion detection, banking fraud detection, spam filtering, and malware detection. Often formulated as NP-hard bi-level optimization problems, it is generally computationally intractable to find global solutions to SPGs. As an interesting progress in this area, a special class of SPGs with the least squares loss (SPG-LS) have recently been shown polynomially solvable by a bisection method. However, in each iteration of this method, a semidefinite program (SDP) needs to be solved. The resulted high computational costs prevent its applications for large-scale problems. In contrast, we propose a novel approach that reformulates a SPG-LS as a single SDP of a similar form and the same dimension as those solved in the bisection method. Our SDP reformulation is, evidenced by our numerical experiments, orders of magnitude faster than the existing bisection method. We further show that the obtained SDP can be reduced to a second order cone program (SOCP). This allows us to provide real-time response to large-scale SPG-LS problems. Numerical results on both synthetic and real world datasets indicate that the proposed SOCP method is up to 20,000+ times faster than the state of the art.

math.OC↗

arXiv2021

Galaxy Clusters from the DESI Legacy Imaging Surveys. I. Cluster Detection

Based on the photometric redshift catalog of Zou H. et al. (2019), we apply a fast clustering algorithm to identify 540,432 galaxy clusters at $z\lesssim1$ in the DESI legacy imaging surveys, which cover a sky area of about 20,000 deg$^2$. Monte-Carlo simulations indicate that the false detection rate of our detecting method is about 3.1\%. The total masses of galaxy clusters are derived using a calibrated richness--mass relation that are based on the observations of X-ray emission and Sunyaev \& Zel'dovich effect. The median redshift and mass of our detected clusters are about 0.53 and $1.23\times10^{14} M_\odot$, respectively. Comparing with previous clusters identified using the data of the Sloan Digital Sky Survey (SDSS), we can recognize most of them, especially those with high richness. Our catalog will be used for further statistical studies on galaxy clusters and environmental effects on the galaxy evolution, etc.

astro-ph.GA↗

arXiv2021

Fast and accurate learned multiresolution dynamical downscaling for precipitation

This study develops a neural network-based approach for emulating high-resolution modeled precipitation data with comparable statistical properties but at greatly reduced computational cost. The key idea is to use combination of low- and high- resolution simulations to train a neural network to map from the former to the latter. Specifically, we define two types of CNNs, one that stacks variables directly and one that encodes each variable before stacking, and we train each CNN type both with a conventional loss function, such as mean square error (MSE), and with a conditional generative adversarial network (CGAN), for a total of four CNN variants. We compare the four new CNN-derived high-resolution precipitation results with precipitation generated from original high resolution simulations, a bilinear interpolater and the state-of-the-art CNN-based super-resolution (SR) technique. Results show that the SR technique produces results similar to those of the bilinear interpolator with smoother spatial and temporal distributions and smaller data variabilities and extremes than the original high resolution simulations. While the new CNNs trained by MSE generate better results over some regions than the interpolator and SR technique do, their predictions are still not as close as the original high resolution simulations. The CNNs trained by CGAN generate more realistic and physically reasonable results, better capturing not only data variability in time and space but also extremes such as intense and long-lasting storms. The new proposed CNN-based downscaling approach can downscale precipitation from 50~km to 12~km in 14~min for 30~years once the network is trained (training takes 4~hours using 1~GPU), while the conventional dynamical downscaling would take 1~month using 600 CPU cores to generate simulations at the resolution of 12~km over contiguous United States.

cs.LG↗

arXiv2020

New Determination of Fundamental Properties of Palomar 5 Using Deep DESI Imaging Data

The legacy imaging surveys for the Dark Energy Spectroscopic Instrument project provides multiplecolor photometric data, which are about 2 mag deeper than the SDSS. In this study, we redetermine the fundamental properties for an old halo globular cluster of Palomar 5 based on these new imaging data, including structure parameters, stellar population parameters, and luminosity and mass functions. These characteristics, together with its tidal tails, are key for dynamical studies of the cluster and constraining the mass model of the Milky Way. By fitting the King model to the radial surface density profile of Palomar 5, we derive the core radius of $r_c$ = 2.96' $\pm$ 0.11', tidal radius of $r_t$ = 17.99' $\pm$ 1.49', and concentration parameter of $c$ = 0.78 $\pm$ 0.04. We apply a Bayesian analysis method to derive the stellar population properties and get an age of 11.508 $\pm$ 0.027 Gyr, metallicity of [Fe/H] = -1.798 $\pm$ 0.014, reddening of $E(B-V)$ = 0.0552 $\pm$ 0.0005, and distance modulus of $(m-M)_0$ = 16.835 $\pm$ 0.006. The main-sequence luminosity and mass functions for both the cluster center, and tidal tails are investigated. The luminosity and mass functions at different distances from the cluster center suggest that there is obvious spatial mass segregation. Many faint low-mass stars have been evaporated at the cluster center and the tidal tails are enhanced by low-mass stars. Both the concentration and relaxation times suggest that Palomar 5 is a totally relaxed system.

astro-ph.GA↗

arXiv2020

A Mysterious Ring in Dark Space?

We report the discovery of a low-surface-brightness (27.42 mag arcsec^(-2) in g band) nebula, which has a ring-like shape in the Beijing-Arizona Sky Survey (BASS). Positive detections have been found in multiband data from far ultraviolet to far infrared, except the z band from BASS and W1, W2 from the Wide-field Infrared Survey Explorer. The reddening of the nebula E(B - V) ~ 0.02 mag is estimated from Infrared Astronomical Satellite (IRAS) 100 micron intensity and HI column density. With the help of the 3D reddening map from Pan-STARRS 1, the Two Micron All Sky Survey, and Gaia, the distance to the nebula of about 500 pc from Earth is derived. Such a low-surface-brightness nebula whose energy can be interpreted by the diffuse Galactic light could account for the optical counterpart of the infrared cirrus, which was detected by IRAS more than 30 yr ago. The ring-like structure might be the ultimate phase of an evolved planetary nebula, while the central white dwarf star has been ejected from the nebula for an unclear reason. On the other hand, the ring structure being a superposition of two close filaments might be another reasonable explanation. Considering the lack of spectroscopic data and uncertainty in the distance measurement, these interpretations need to be checked by future observations.

astro-ph.GA↗

arXiv2020

The Third Data Release of the Beijing-Arizona Sky Survey

The Beijing-Arizona Sky Survey (BASS) is a wide and deep imaging survey to cover a 5400 deg$^2$ area in the Northern Galactic Cap with the 2.3m Bok telescope using two filters ($g$ and $r$ bands). The Mosaic $z$-band Legacy Survey (MzLS) covers the same area in $z$ band with the 4m Mayall telescope. These two surveys will be used for spectroscopic targeting of the Dark Energy Spectroscopic Instrument (DESI). The BASS survey observations were completed in 2019 March. This paper describes the third data release (DR3) of BASS, which contains the photometric data from all BASS and MzLS observations between 2015 January and 2019 March. The median astrometric precision relative to {\it Gaia} positions is about 17 mas and the median photometric offset relative to the PanSTARRS1 photometry is within 5 mmag. The median $5σ$ AB magnitude depths for point sources are 24.2, 23.6, and 23.0 mag for $g$, $r$, and $z$ bands, respectively. The photometric depth within the survey area is highly homogeneous, with the difference between the 20\% and 80\% depth less than 0.3 mag. The DR3 data, including raw data, calibrated single-epoch images, single-epoch photometric catalogs, stacked images, and co-added photometric catalogs, are publicly accessible at \url{http://batc.bao.ac.cn/BASS/doku.php?id=datarelease:home}.

astro-ph.GA↗

arXiv2019

An Imputation model by Dirichlet Process Mixture of Elliptical Copulas for Data of Mixed Type

Copula-based methods provide a flexible approach to build missing data imputation models of multivariate data of mixed types. However, the choice of copula function is an open question. We consider a Bayesian nonparametric approach by using an infinite mixture of elliptical copulas induced by a Dirichlet process mixture to build a flexible copula function. A slice sampling algorithm is used to sample from the infinite dimensional parameter space. We extend the work on prior parallel tempering used in finite mixture models to the Dirichlet process mixture model to overcome the mixing issue in multimodal distributions. Using simulations, we demonstrate that the infinite mixture copula model provides a better overall fit compared to their single component counterparts, and performs better at capturing tail dependence features of the data. Simulations further show that our proposed model achieves more accurate imputation especially for continuous variables and better inferential results in some analytic models. The proposed model is applied to a medical data set of acute stroke patients in Australia.

stat.ME↗

arXiv2018

The First Data Release of the Beijing-Arizona Sky Survey

The Beijing-Arizona Sky Survey (BASS) is a new wide-field legacy imaging survey in the northern Galactic cap using the 2.3m Bok telescope. The survey will cover about 5400 deg$^2$ in the $g$ and $r$ bands, and the expected 5$σ$ depths (corrected for the Galactic extinction) in the two bands are 24.0 and 23.4 mag, respectively. BASS started observations in January 2015, and has completed about 41% of the whole area as of July 2016. The first data release contains both calibrated images and photometric catalogs obtained in 2015 and 2016. The depths of single-epoch images in the two bands are 23.4 and 22.9 mag, and the full depths of three epochs are about 24.1 and 23.5 mag, respectively.

astro-ph.GA↗

arXiv2018

The Second Data Release of the Beijing-Arizona Sky Survey

This paper presents the second data release (DR2) of the Beijing-Arizona Sky Survey (BASS). BASS is an imaging survey of about 5400 deg$^2$ in $g$ and $r$ bands using the 2.3 m Bok telescope. DR2 includes the observations as of July 2017 obtained by BASS and Mayall $z$-band Legacy Survey (MzLS). This is our first time to include the MzLS data covering the same area as BASS. BASS and MzLS have respectively completed about 72% and 76% of their observations. The two surveys will be served for the spectroscopic targeting of the upcoming Dark Energy Spectroscopic Instrument. Both BASS and MzLS data are reduced by the same pipeline. We have updated the basic data reduction and photometric methods in DR2. In particular, source detections are performed on stacked images, and photometric measurements are co-added from single-epoch images based on these sources. The median 5$σ$ depths with corrections of the Galactic extinction are 24.05, 23.61, and 23.10 mag for $g$, $r$, and $z$ bands, respectively. The DR2 data products include stacked images, co-added catalogs, and single-epoch images and catalogs. The BASS website (http://batc.bao.ac.cn/BASS/) provides detailed information and links to download the data.

astro-ph.IM↗

arXiv2018

Photometric calibration for the Beijing-Arizona Sky Survey and Mayall z-band Legacy Survey

We present the photometric calibration of the Beijing-Arizona Sky Survey (BASS) and Mayall z-band Legacy Survey (MzLS), which are two of the three wide-field optical legacy imaging surveys to provide the baseline targeting data for the Dark Energy Spectroscopic Instrument (DESI) project. The method of our photometric calibration is subdivided into the external and internal processes. The former utilizes the point-source objects of Pan-STARRS1 survey (PS1) as the reference standards to achieve the zero points of the absolute flux for individual exposures. And then the latter revise the zero points to make them consistent across the survey based on multiple tilings and large offset overlaps. Our process achieves a homogeneous photometric calibration over most of the sky with precision better than 10 mmag for g and r bands, 15 mmag for z band. The accuracy of the calibration is better than 1% at the bright end (16-18 mag) over most of the survey area.

astro-ph.IM↗

arXiv2017

Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking

Dynamical downscaling with high-resolution regional climate models may offer the possibility of realistically reproducing precipitation and weather events in climate simulations. As resolutions fall to order kilometers, the use of explicit rather than parametrized convection may offer even greater fidelity. However, these increased model resolutions both allow and require increasingly complex diagnostics for evaluating model fidelity. In this study we use a suite of dynamically downscaled simulations of the summertime U.S. (WRF driven by NCEP) with systematic variations in parameters and treatment of convection as a test case for evaluation of model precipitation. In particular, we use a novel rainstorm identification and tracking algorithm that allocates essentially all rainfall to individual precipitation events (Chang et al. 2016). This approach allows multiple insights, including that, at least in these runs, model wet bias is driven by excessive areal extent of precipitating events. Biases are time-dependent, producing excessive diurnal cycle amplitude. We show that this effect is produced not by new production of events but by excessive enlargement of long-lived precipitation events during daytime, and that in the domain average, precipitation biases appear best represented as additive offsets. Of all model configurations evaluated, convection-permitting simulations most consistently reduced biases in precipitation event characteristics.

stat.AP↗

arXiv2017

Ages and structural and dynamical parameters of two globular clusters in the M81 group

GC-1 and GC-2 are two globular clusters (GCs) in the remote halo of M81 and M82 in the M81 group discovered by Jang et al. using the {\it Hubble Space Telescope} ({\it HST}) images. These two GCs were observed as part of the Beijing--Arizona--Taiwan--Connecticut (BATC) Multicolor Sky Survey, using 14 intermediate-band filters covering a wavelength range of 4000--10000 Å. We accurately determine these two clusters' ages and masses by comparing their spectral energy distributions (from 2267 to 20000~Å, comprising photometric data in the near-ultraviolet of the {\it Galaxy Evolution Explorer}, 14 BATC intermediate-band, and Two Micron All Sky Survey near-infrared $JHK_{\rm s}$ filters) with theoretical stellar population-synthesis models, resulting in ages of $15.50\pm3.20$ for GC-1 and $15.10\pm2.70$ Gyr for GC-2. The masses of GC-1 and GC-2 obtained here are $1.77-2.04\times 10^6$ and $5.20-7.11\times 10^6 \rm~M_\odot$, respectively. In addition, the deep observations with the Advanced Camera for Surveys and Wide Field Camera 3 on the {\it HST} are used to provide the surface brightness profiles of GC-1 and GC-2. The structural and dynamical parameters are derived from fitting the profiles to three different models; in particular, the internal velocity dispersions of GC-1 and GC-2 are derived, which can be compared with ones obtained based on spectral observations in the future. For the first time, in this paper, the $r_h$ versus $M_V$ diagram shows that GC-2 is an ultra-compact dwarf in the M81 group.

astro-ph.GA↗