arXiv subjects

Yu Bai

Publications and source records attributed to Yu Bai.

At least 145 records · Page 8Linked to original sources

arXiv2019

Precision Higgs Physics at CEPC

The discovery of the Higgs boson with its mass around 125 GeV by the ATLAS and CMS Collaborations marked the beginning of a new era in high energy physics. The Higgs boson will be the subject of extensive studies of the ongoing LHC program. At the same time, lepton collider based Higgs factories have been proposed as a possible next step beyond the LHC, with its main goal to precisely measure the properties of the Higgs boson and probe potential new physics associated with the Higgs boson. The Circular Electron Positron Collider~(CEPC) is one of such proposed Higgs factories. The CEPC is an $e^+e^-$ circular collider proposed by and to be hosted in China. Located in a tunnel of approximately 100~km in circumference, it will operate at a center-of-mass energy of 240~GeV as the Higgs factory. In this paper, we present the first estimates on the precision of the Higgs boson property measurements achievable at the CEPC and discuss implications of these measurements.

hep-ex↗

arXiv2019

Proximal algorithms for constrained composite optimization, with applications to solving low-rank SDPs

We study a family of (potentially non-convex) constrained optimization problems with convex composite structure. Through a novel analysis of non-smooth geometry, we show that proximal-type algorithms applied to exact penalty formulations of such problems exhibit local linear convergence under a quadratic growth condition, which the compositional structure we consider ensures. The main application of our results is to low-rank semidefinite optimization with Burer-Monteiro factorizations. We precisely identify the conditions for quadratic growth in the factorized problem via structures in the semidefinite problem, which could be of independent interest for understanding matrix factorization.

math.OC↗

arXiv2019

A Method to Search for Black Hole Candidates with Giant Companions by LAMOST

We propose a method to search for stellar-mass black hole (BH) candidates with giant companions from spectroscopic observations. Based on the stellar spectra of LAMOST Data Release 6, we obtain a sample of seven giants in binaries with large radial velocity variation $ΔV_R > 80~{\rm km~s^{-1}}$. With the effective temperature, surface gravity, and metallicity provided by LAMOST, and the parallax given by {\it Gaia}, we can estimate the mass and radius of the giant, and therefore evaluate the possible mass of the optically invisible star in the binary. We show that the sources in our sample are potential BH candidates, and are worthy of dynamical measurement by further spectroscopic observations. Our method may be particularly valid for the selection of BH candidates in binaries with unknown orbital periods.

astro-ph.SR↗

arXiv2019

X-ray Activity from Different Types of Stars

X-ray emission is an important indicator of stellar activity. In this paper, we study stellar X-ray activity using the XMM-Newton and LAMOST data for different types of stars. We provide a sample including 1259 X-ray emitting stars, of which 1090 have accurate stellar parameter estimations. Our sample size is much larger than those in previous works. We find a bimodal distribution of X-ray to optical flux ratio (log(fX/fV)) for G and K stars. We interpret that this bimodality is due to two subpopulations with different coronal heating rates. Furthermore, using the full widths at half maxima calculated from Hα and H\b{eta} lines, we show that these stars in the inactive peaks have smaller rotational velocities. This is consistent with the magnetic dynamo theory that stars with low rotational velocities have low levels of stellar activity. We also examine the correlation between log(fX/fV) and luminosity of the excess emission in the Hα line, and find a tight relation between the coronal and chromospheric activity indicators.

astro-ph.SR↗

arXiv2019

Analysis of Sequential Quadratic Programming through the Lens of Riemannian Optimization

We prove that a "first-order" Sequential Quadratic Programming (SQP) algorithm for equality constrained optimization has local linear convergence with rate $(1-1/κ_R)^k$, where $κ_R$ is the condition number of the Riemannian Hessian, and global convergence with rate $k^{-1/4}$. Our analysis builds on insights from Riemannian optimization -- we show that the SQP and Riemannian gradient methods have nearly identical behavior near the constraint manifold, which could be of broader interest for understanding constrained optimization.

math.OC↗

arXiv2018

Alternative Interpretation of Lens Aberration on OTF

This paper demonstrates a method of interpreting the mechanism of aberration of optical systems based on non-Fourier transform optical transfer function (OTF). According to the parameters of object plane cosine fringe, we obtain the position on the pupil plane of the two beams of the interference pair emitted by the cosine fringes. By combining the aberrations of the lens, the phase of the cosine fringe of each interference pair in the image plane can be obtained. Then the intensity of the cosine fringe formed by all the interference pattern is superimposed to obtain the brightness distribution of the cosine fringe on the image plane. After that, the OTF of the aberration system can be obtained on the basis of the cosine fringe modulation on the image plane and the phase shift. Finally, taking the non-Fourier transform OTF theory with wave analyze the complex optical system, providing help for the optical system design and imaging quality evaluation.

physics.optics↗

arXiv2018

A Combined Chandra and LAMOST Study of Stellar Activity

We probed stellar X-ray activity over a wide range of stellar parameters, using {\it Chandra} and LAMOST data. We measured the X-ray-to-bolometric luminosity ratio ($R_X = L_X/L_{\rm bol}$) for 484 main-sequence stars, and found a bimodal distribution for G and K types. We interpret this bimodality as evidence of two sub-populations with different coronal temperatures, which are caused by different coronal heating rates. Using the metallicity and velocity information, we find both of the two sub-populations are mostly located in the thin disk. We find no trend of $R_X$ with stellar age for stars older than $\sim$ 4 Gyr; there is a trough in the $R_X$ vs age distribution, with the lowest range of $R_X$ appearing at ages around 2 Gyr. We then examined the correlation between $R_X$ and $R_{\rm Hα}$ (proxy of chromospheric activity): we find that the two quantities are well correlated, as found in many earlier studies. Finally, we selected a sample of twelve stars with X-ray flares, and studied the light-curve morphology of the flares. The variety of flare profiles and timescales observed in our sample suggests the contribution of different processes of energy release.

astro-ph.SR↗

arXiv2018

Machine learning Applied to Star-Galaxy-QSO Classification and Stellar Effective Temperature Regression

In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised machine-learning algorithm, random forests (RF), to the star/galaxy/QSO classification and the stellar effective temperature regression based on the combination of LAMOST and SDSS spectroscopic data. This combination enable us to obtain reliable predictions with one of the largest training sample ever used. The training samples are built with nine-color data set of about three million objects for the classification and seven-color data set of over one million stars for the regression. The performance of the classification and regression is examined with the validation and the blind tests on the objects in the RAVE, 6dFGS, UVQS and APOGEE surveys. We demonstrate that the RF is an effective algorithm with the classification accuracies higher than 99\% for the stars and the galaxies, and higher than 94\% for the QSOs. These accuracies are higher than the machine-learning results in the former studies. The total standard deviations of the regression are smaller than 200 K that is similar to those of some spectrum-based methods. The machine-learning algorithm with the broad-band photometry provides us a more efficient approach to deal with massive amounts of astrophysical data than traditional color-cuts and SED fit.

astro-ph.GA↗

arXiv2018

Liquid Epitaxial Growth of Two-dimensional Non-layer structured hybrid Perovskite

The hybrid perovskite CH3NH3PbX3 (X= Cl, Br, I) is a promising material for developing novel optoelectronic devices. Owing to the intrinsic non-layer structure, it remains challenging to synthesize molecularly thin CH3NH3PbX3 with large size. Here, we report a low-cost and highly efficient fabrication route to obtain large-scale single-crystalline 2D CH3NH3PbX3 perovskites on a mica substrate via liquid epitaxy. The 2D perovskite is characterized as 8 nm in thickness and hundreds of micrometers in lateral size. First-principles calculations suggest the strong potassium-halogen interactions at the perovskite/mica interface lower the interface energy of perovskites, driving their fast in-plane growth. Spectroscopic investigations reveal 2D CH3NH3PbBr3 possess small exciton binding energy of 30 meV, allowing a superior visible-light photodetector with a photoresponsivity of 126 A/W and a bandwidth exceeded 80 kHz. These features demonstrate that liquid epitaxy is a bottom-up approach to fabricate the non-layer structured 2D perovskites, which offer a new material platform for the device applications and fundamental investigations.

cond-mat.mtrl-sci↗

arXiv2018

Machine Learning Classification of Gaia Data Release 2

Machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We apply the machine learning classification to 85,613,922 objects in the $Gaia$ data release 2, based on the combination of the Pan-STARRS 1 and AllWISE data. The classification results are cross-matched with Simbad database, and the total accuracy is 91.9%. Our sample is dominated by stars, $\sim$ 98%, and galaxies makes up 2%. For the objects with negative parallaxes, about 2.5\% are galaxies and QSOs, while about 99.9% are stars if the relative parallax uncertainties are smaller than 0.2. Our result implies that using the threshold of 0 $< σ_π/π<$ 0.2 could yield a very clean stellar sample.

astro-ph.SR↗

arXiv2018

Effects of Threshold Energy on Reconstructions of Properties of Low-Mass WIMPs in Direct Dark Matter Detection Experiments

In this paper, we revisit our model-independent methods developed for reconstructing properties of Weakly Interacting Massive Particles (WIMPs) by using measured recoil energies from direct Dark Matter detection experiments directly and take into account more realistically non-negligible threshold energy. All expressions for reconstructing the mass and the (ratios between the) spin-independent and the spin-dependent WIMP-nucleon couplings have been modified. We focus on low-mass (m_chi <~ 15 GeV) WIMPs and present the numerical results obtained by Monte Carlo simulations. Constraints caused by non-negligible threshold energy and technical treatments for improving reconstruction results will also be discussed.

hep-ph↗

arXiv2018

Gaia Calibrated UV Luminous Stars in LAMOST

We take advantage of the Gaia data release 2 to present 275 and 1,774 ultraviolet luminous stars in the FUV and the NUV. These stars are 5$σ$ exceeding the centers of the reference frame that is built with over one million UV stars in the log $g$ vs $T_\textrm{eff}$ diagram. The Galactic extinction is corrected with the 3D dusty map. In order to limit the Lutz-Kelker effect to an insignificant level, we select the stars with the relative uncertainties of the luminosity less than 40% and the trigonometric parallaxes less than 20%. We cross-identified our sample with the catalogs of RR Lyr stars and possible white dwarf main-sequence binaries, and find they compose $\sim$ 62% and $\sim$ 16% of our sample in the FUV and NUV, respectively. This catalog provides a unique sample to study stellar activity, spectrally unresolved compact main-sequence binaries and variable stars.

astro-ph.SR↗

arXiv2018

The UV Emission of Stars in LAMOST Survey I. Catalogs

We present the ultraviolet magnitudes for over three million stars in the LAMOST survey, in which 2,202,116 stars are detected by $GALEX$. For 889,235 undetected stars, we develop a method to estimate their upper limit magnitudes. The distribution of (FUV $-$ NUV) shows that the color declines with increasing effective temperature for stars hotter than 7000 K in our sample, while the trend disappears for the cooler stars due to upper atmosphere emission from the regions higher than their photospheres. For stars with valid stellar parameters, we calculate the UV excesses with synthetic model spectra, and find that the (FUV $-$ NUV) vs. $R'_{\mathrm{FUV}}$ can be fitted with a linear relation and late-type dwarfs tend to have high UV excesses. There are 87,178 and 1,498,103 stars detected more than once in the visit exposures of $GALEX$ in the FUV and NUV, respectively. We make use of the quantified photometric errors to determine statistical properties of the UV variation, including intrinsic variability and the structure function on the timescale of days. The overall occurrence of possible false positives is below 1.3\% in our sample. UV absolute magnitudes are calculated for stars with valid parallaxes, which could serve as a possible reference frame in the NUV. We conclude that the colors related to UV provide good criteria to distinguish between M giants and M dwarfs, and the variability of RR Lyrae stars in our sample is stronger than that of other A and F stars.

astro-ph.SR↗

arXiv2017

CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices

Large-scale deep neural networks (DNNs) are both compute and memory intensive. As the size of DNNs continues to grow, it is critical to improve the energy efficiency and performance while maintaining accuracy. For DNNs, the model size is an important factor affecting performance, scalability and energy efficiency. Weight pruning achieves good compression ratios but suffers from three drawbacks: 1) the irregular network structure after pruning; 2) the increased training complexity; and 3) the lack of rigorous guarantee of compression ratio and inference accuracy. To overcome these limitations, this paper proposes CirCNN, a principled approach to represent weights and process neural networks using block-circulant matrices. CirCNN utilizes the Fast Fourier Transform (FFT)-based fast multiplication, simultaneously reducing the computational complexity (both in inference and training) from O(n2) to O(nlogn) and the storage complexity from O(n2) to O(n), with negligible accuracy loss. Compared to other approaches, CirCNN is distinct due to its mathematical rigor: it can converge to the same effectiveness as DNNs without compression. The CirCNN architecture, a universal DNN inference engine that can be implemented on various hardware/software platforms with configurable network architecture. To demonstrate the performance and energy efficiency, we test CirCNN in FPGA, ASIC and embedded processors. Our results show that CirCNN architecture achieves very high energy efficiency and performance with a small hardware footprint. Based on the FPGA implementation and ASIC synthesis results, CirCNN achieves 6-102X energy efficiency improvements compared with the best state-of-the-art results.

cs.CV↗

arXiv2017

TAPAS: Two-pass Approximate Adaptive Sampling for Softmax

TAPAS is a novel adaptive sampling method for the softmax model. It uses a two pass sampling strategy where the examples used to approximate the gradient of the partition function are first sampled according to a squashed population distribution and then resampled adaptively using the context and current model. We describe an efficient distributed implementation of TAPAS. We show, on both synthetic data and a large real dataset, that TAPAS has low computational overhead and works well for minimizing the rank loss for multi-class classification problems with a very large label space.

cs.LG↗

arXiv2017

The Landscape of Empirical Risk for Non-convex Losses

Most high-dimensional estimation and prediction methods propose to minimize a cost function (empirical risk) that is written as a sum of losses associated to each data point. In this paper we focus on the case of non-convex losses, which is practically important but still poorly understood. Classical empirical process theory implies uniform convergence of the empirical risk to the population risk. While uniform convergence implies consistency of the resulting M-estimator, it does not ensure that the latter can be computed efficiently. In order to capture the complexity of computing M-estimators, we propose to study the landscape of the empirical risk, namely its stationary points and their properties. We establish uniform convergence of the gradient and Hessian of the empirical risk to their population counterparts, as soon as the number of samples becomes larger than the number of unknown parameters (modulo logarithmic factors). Consequently, good properties of the population risk can be carried to the empirical risk, and we can establish one-to-one correspondence of their stationary points. We demonstrate that in several problems such as non-convex binary classification, robust regression, and Gaussian mixture model, this result implies a complete characterization of the landscape of the empirical risk, and of the convergence properties of descent algorithms. We extend our analysis to the very high-dimensional setting in which the number of parameters exceeds the number of samples, and provide a characterization of the empirical risk landscape under a nearly information-theoretically minimal condition. Namely, if the number of samples exceeds the sparsity of the unknown parameters vector (modulo logarithmic factors), then a suitable uniform convergence result takes place. We apply this result to non-convex binary classification and robust regression in very high-dimension.

stat.ML↗

arXiv2016

An investigation of a magnetic cataclysmic variable with a period of 14.1 ks

Magnetic cataclysmic variables (CVs) contain a white dwarf with magnetic field strong enough to control the accretion flow from a late type secondary. In this paper, we discover a magnetic CV (CXOGSG J215544.4+380116) from the $Chandra$ archive data. The X-ray light curves show a significant period of 14.1 ks, and the X-ray spectra can be described by a multi-temperature hot thermal plasma, suggesting the source as a magnetic CV. The broad dip in the X-ray light curve is due to the eclipse of the primary magnetic pole, and the additional dip in the bright phase of the soft and medium bands may be caused by the accretion stream crossing our line of sight to the primary pole. Follow-up optical spectra show features of an M2--M4 dwarf dominating the red band and a WD which is responsible for the weak upturn in the blue band. The mass ($\sim$ 0.4 $M_{\odot}$) and radius ($\sim$ 0.4 $R_{\odot}$) for the M dwarf are obtained using CV evolution models and empirical relations between the orbital period and the mass/radius. The estimated low X-ray luminosity and accretion rate may suggest the source as a low-accretion-rate polar. In addition, Very Large Array observations reveal a possible radio counterpart to the X-ray source, but with a low significance. Further radio observations with high quality are needed to confirm the radio counterpart and explore the properties of this binary system.

astro-ph.SR↗

arXiv2016

Time-Variable Aluminum Absorption in the Polar AR Ursae Majoris, and an Updated Estimate for the Mass of the White Dwarf

We present spectra of the extreme polar AR Ursae Majoris (AR UMa) which display a clear Al I absorption doublet, alongside spectra taken less than a year earlier in which that feature is not present. Re-examination of earlier SDSS spectra indicates that the Al I absorption doublet was also present $\approx$8 years before our first non-detection. We conclude that this absorbing material is unlikely to be on the surface of either the white dwarf (WD) or the donor star. We suggest that this Al I absorption feature arises in circumstellar material, perhaps produced by the evaporation of asteroids as they approach the hot WD. The presence of any remaining reservoir of rocky material in AR UMa might help to constrain the prior evolution of this unusual binary system. We also apply spectral decomposition to find the stellar parameters of the M dwarf companion, and attempt to dynamically measure the mass of the WD in AR UMa by considering both the radial velocity curves of the H$_β$ emission line and the Na I absorption line. Thereby we infer a mass range for the WD in AR UMa of 0.91 $M_{\odot}$ $<$ $M_{\mathrm{WD}}$ $<$ 1.24 $M_{\odot}$.

astro-ph.HE↗