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Yun Wang

Publications and source records attributed to Yun Wang.

At least 271 records · Page 15Linked to original sources

Effects of NII and H$α$ Line Blending on the WFIRST Galaxy Redshift Survey

The Wide Field Infrared Survey Telescope (WFIRST) will conduct a galaxy redshift survey using the H$α$ emission line primarily for spectroscopic redshift determination. Due to the modest spectroscopic resolution of the grism, the H$α$ and the neighboring [NII] lines are blended, leading to a redshift bias that depends on the [NII]/H$α$ ratio, which is correlated with a galaxy's metallicity, hence mass and ultimately environment. We investigate how this bias propagates into the galaxy clustering and cosmological parameters obtained from the WFIRST. Using simulation, we explore the effect of line blending on redshift-space distortion and baryon acoustic oscillation (BAO) measurements. We measure the BAO parameters $α_{\parallel}$, $α_{\perp}$, the logarithmic growth factor $f_{v}$, and calculate their errors based on the correlations between the line ratio and large-scale structure. We find $Δα_{\parallel} = 0.31 \pm 0.23 \%$ ($0.26\pm0.17\%$), $Δα_{\perp} = -0.10\pm0.10\%$ ($-0.12 \pm 0.11 \%$), and $Δf_{v} = 0.17\pm0.33\%$ ($-0.20 \pm 0.30\%$) for redshift 1.355--1.994 (0.700--1.345), which use approximately 18$\%$, 9$\%$, and 7$\%$ of the systematic error budget in a root-sum-square sense. These errors may already be tolerable but further mitigations are discussed. Biases due to the environment-independent redshift error can be mitigated by measuring the redshift error probability distribution function. High-spectral-resolution re-observation of a few thousand galaxies would be required (if by direct approach) to reduce them to below 25$\%$ of the error budget. Finally, we outline the next steps to improve the modeling of [NII]-induced blending biases and their interaction with other redshift error sources.

astro-ph.CO↗

ATLAS Probe: Breakthrough Science of Galaxy Evolution, Cosmology, Milky Way, and the Solar System

ATLAS (Astrophysics Telescope for Large Area Spectroscopy) Probe is a concept for a NASA probe-class space mission. It is the follow-up space mission to WFIRST, boosting its scientific return by obtaining deep IR slit spectroscopy for 70% of all galaxies imaged by a 2000 sq deg WFIRST High Latitude Survey at z>0.5. ATLAS will measure accurate and precise redshifts for 200M galaxies out to z < 7, and deliver spectra that enable a wide range of diagnostic studies of the physical properties of galaxies over most of cosmic history. ATLAS Probe science spans four broad categories: (1) Revolutionizing galaxy evolution studies by tracing the relation between galaxies and dark matter from galaxy groups to cosmic voids and filaments, from the epoch of reionization through the peak era of galaxy assembly; (2) Opening a new window into the dark Universe by weighing the dark matter filaments using 3D weak lensing with spectroscopic redshifts, and obtaining definitive measurements of dark energy and modification of General Relativity using galaxy clustering; (3) Probing the Milky Way's dust-enshrouded regions, reaching the far side of our Galaxy; and (4) Exploring the formation history of the outer Solar System by characterizing Kuiper Belt Objects. ATLAS Probe is a 1.5m telescope with a field of view of 0.4 sq deg, and uses Digital Micro-mirror Devices (DMDs) as slit selectors. It has a spectroscopic resolution of R = 1000 over 1-4 microns, and a spectroscopic multiplex factor >5,000. ATLAS is designed to fit within the NASA probe-class space mission cost envelope; it has a single instrument, a telescope aperture that allows for a lighter launch vehicle, and mature technology. ATLAS Probe will lead to transformative science over the entire range of astrophysics: from galaxy evolution to the dark Universe, from Solar System objects to the dusty regions of the Milky Way.

astro-ph.IM↗

Reconstructing the weak lensing magnification distribution of Type Ia supernovae

Weak lensing of Type Ia supernovae (SNe Ia) is a systematic uncertainty in the use of SNe Ia as standard candles, as well as an independent cosmological probe, if the corresponding magnification distribution can be extracted from data. We study the peak brightness distribution of SNe Ia in the Pantheon sample, and find that the high $z$ sub-sample shows distinct weak lensing signatures compared to the low $z$ subsample: a long tail at the bright end due to high magnifications and a shift of the peak brightness toward the faint end, consistent with findings from earlier work. We have developed a technique to reconstruct the weak lensing magnification distribution of SNe Ia, $p(μ)$, from the measured SN Ia flux distribution, and applied it to the Pantheon sample. We find that $p(μ)$ can be reconstructed at a significance better than 2$σ$ for the subsample of SNe Ia at $z>0.7$ (124 SNe Ia), and at a lower significance for the SNe Ia at $z>0.9$ (49 SNe Ia), due to the small number of SNe Ia at high redshifts. The large number of $z>1$ SNe Ia from future surveys will enable the use of $p(μ)$ reconstructed from SNe Ia as an independent cosmological probe.

astro-ph.CO↗

Robust and model-independent cosmological constraints from distance measurements

We present a systematic analysis of the cosmological constraints from the "Pantheon Sample" of 1048 Type Ia Supernovae (SNe Ia) in the redshift range $0.01<z<2.3$ compiled by Scolnic et al. (2018). Applying the flux-averaging method for detecting unknown systematic effects, we find that the "Pantheon" sample has been well calibrated and the bias caused by unknown systematic errors has been minimized. We present the estimate of distances measured from SNe Ia and reconstruct the expansion history of the Universe. The results are in agreement with a simple cosmological constant model and reveals the possible improvements that future SN Ia observations from WFIRST and LSST can target. We have derived distance priors using the CMB data from the Planck 2018 final data release, and combine them with SNe Ia and BAO data, to explore the impact from the systematic errors of SNe Ia on the combined cosmological parameter constraints. Using the combined data set of SNe Ia, BAO, and CMB distance priors, we measure the dark energy density function $X(z)=ρ_X(z)/ρ_X(0)$ as a free function (defined as a cubic spline of its values at $z=0.33, 0.67, 1.0$), along with the cosmological parameters ($Ω_k$, $Ω_m$, $Ω_b$, $H_0$). We find no deviation from a flat Universe dominated by a cosmological constant ($X(z)=1$), and $H_0=68.4\pm 0.9\text{km s}^{-1}\text{Mpc}^{-1}$, straddling the Planck team's measurement of $H_0=67.4\pm 0.5\text{km s}^{-1}\text{Mpc}^{-1}$, and Riess et al. (2018) measurement of $H_0=73.52\pm 1.62\text{km s}^{-1}\text{Mpc}^{-1}$. Adding $H_0=73.52\pm 1.62\text{km s}^{-1}\text{Mpc}^{-1}$ as a prior to the combined data set leads to the time dependence of the dark energy density at $z\sim 0.33$ at 68\% confidence level. Not including the systematic errors on SNe Ia has a similar but larger effect on the dark energy density measurement.

astro-ph.CO↗

Stability of periodic peakons for the Novikov equation

The Novikov equation is an integrable Camassa-Holm type equation with cubic nonlinearity and admits the periodic peakons. In this paper, it is shown that the periodic peakons are the global periodic weak solutions to the Novikov equation and we also prove the orbital stability of the periodic peakons for Novikov equation. By using the invariants of the equation and controlling the extrema of the solution, it is demonstrated that the shapes of these periodic peakons are stable under small perturbations in the energy space.

math.AP↗

Existence of peakons for a cubic generalization of the Camassa-Holm equation

In this paper, we study the following generalized Camassa-Holm equation with both cubic and quadratic nonlinearities: $$ m_{t}+k_{1}(3uu_{x}m+u^2m_{x})+k_{2}(2mu_{x}+m_{x}u)=0, \quad m=u-u_{xx}, $$ which is presented as a linear combination of the Novikov equation and the Camassa-Holm equation with constants $k_{1}$ and $k_{2}$. The model is a cubic generalization of the Camassa-Holm equation. It is shown that the equation admits single-peaked soliton and periodic peakons.

math.AP↗

Regularity results for the time-harmonic Maxwell equations with impedance boundary condition

This paper considers the time-harmonic Maxwell equations with impedance boundary condition.We present $H^2$-norm bound and other high-order norm bounds for strong solutions. The $H^2$-estimate have been derived in [M. Dauge, M. Costabel and S. Nicaise, Tech. Rep. 10-09, IRMAR (2010)] for the case with homogeneous boundary condition. Unfortunately, their method can not be applied to the inhomogeneous case. The main novelty of this paper is that we follow the spirit of the $H^1$-estimate in [R. Hiptmair, A. Moiola and I. Perugia, Math. Models Methods Appl. Sci., 21(2011), pp. 2263-2287] and modify the proof by applying two inequalities of Friedrichs' type to make the $H^1$-estimate move into $H^2$-estimate and $W^{m, p}$-estimate.Finally, the dependence of the regularity estimates on the wave number is obtained, which will play an important role in the convergence analysis of the numerical solutions for the time-harmonic Maxwell equations.

math.AP↗

Solvability of generalized div-curl system and Friedrichs inequalities

In this article, we consider the solvability of two generalized div-curl systems. They are referred to as the equations of magnetostatics and electro- , resp.. Necessary compatibility conditions on the data for the existence of solutions are fully described, and existence of $W^{m, p}$-solutions is proved. Moreover, we give some description for the null spaces. As a corollary, we give the estimates of gradient via generalized ${\rm div}$ and ${\rm curl}$ in $L^p$-framework, which can be considered as Friedrichs inequalities. Furthermore, two decompositions of Sobolev spaces are given.

math.AP↗

Photometric classification and redshift estimation of LSST Supernovae

Supernova (SN) classification and redshift estimation using photometric data only have become very important for the Large Synoptic Survey Telescope (LSST), given the large number of SNe that LSST will observe and the impossibility of spectroscopically following up all the SNe. We investigate the performance of a SN classifier that uses SN colors to classify LSST SNe with the Random Forest classification algorithm. Our classifier results in an AUC of 0.98 which represents excellent classification. We are able to obtain a photometric SN sample containing 99$\%$ SNe Ia by choosing a probability threshold. We estimate the photometric redshifts (photo-z) of SNe in our sample by fitting the SN light curves using the SALT2 model with nested sampling. We obtain a mean bias ($\left $) of 0.012 with $σ\left( \frac{z_\mathrm{phot}-z_\mathrm{spec}}{1+z_\mathrm{spec}}\right) = 0.0294$ without using a host-galaxy photo-z prior, and a mean bias ($\left $) of 0.0017 with $σ\left( \frac{z_\mathrm{phot}-z_\mathrm{spec}}{1+z_\mathrm{spec}}\right) = 0.0116$ using a host-galaxy photo-z prior. Assuming a flat $ΛCDM$ model with $Ω_m=0.3$, we obtain $Ω_m$ of $0.305\pm0.008$ (statistical errors only), using the simulated LSST sample of photometric SNe Ia (with intrinsic scatter $σ_\mathrm{int}=0.11$) derived using our methodology without using host-galaxy photo-z prior. Our method will help boost the power of SNe from the LSST as cosmological probes.

astro-ph.CO↗

WFIRST Science Investigation Team "Cosmology with the High Latitude Survey" Annual Report 2017

Cosmic acceleration is the most surprising cosmological discovery in many decades. Testing and distinguishing among possible explanations requires cosmological measurements of extremely high precision probing the full history of cosmic expansion and structure growth and, ideally, compare and contrast matter and relativistic tracers of the gravity potential. This program is one of the defining objectives of the Wide-Field Infrared Survey Telescope (WFIRST), as set forth in the New Worlds, New Horizons report (NWNH) in 2010. The WFIRST mission has the ability to improve these measurements by 1-2 orders of magnitude compared to the current state of the art, while simultaneously extending their redshift grasp, greatly improving control of systematic effects, and taking a unified approach to multiple probes that provide complementary physical information and cross-checks of cosmological results. We describe in this annual report the activities of the Science Investigation Team (SIT) "Cosmology with the High Latitude Survey (HLS)" during the year 2017. This team was selected by NASA in December 2015 in order to address the stringent challenges of the WFIRST dark energy (DE) program through the Project's formulation phase. This SIT has elected to jointly address Galaxy Redshift Survey, Weak Lensing and Cluster Growth and thus fully embrace the fact that the imaging and spectroscopic elements of the HLS will be realized as an integrated observing program, and they jointly impose requirements on performance and operations. WFIRST is designed to be able to deliver a definitive result on the origin of cosmic acceleration. It is not optimized for Figure of Merit sensitivity but for control of systematic uncertainties and for having multiple techniques each with multiple cross-checks. Our SIT work focuses on understanding the potential systematics in the WFIRST DE measurements.

astro-ph.CO↗

Comparing the Max and Noisy-Or Pooling Functions in Multiple Instance Learning for Weakly Supervised Sequence Learning Tasks

Many sequence learning tasks require the localization of certain events in sequences. Because it can be expensive to obtain strong labeling that specifies the starting and ending times of the events, modern systems are often trained with weak labeling without explicit timing information. Multiple instance learning (MIL) is a popular framework for learning from weak labeling. In a common scenario of MIL, it is necessary to choose a pooling function to aggregate the predictions for the individual steps of the sequences. In this paper, we compare the "max" and "noisy-or" pooling functions on a speech recognition task and a sound event detection task. We find that max pooling is able to localize phonemes and sound events, while noisy-or pooling fails. We provide a theoretical explanation of the different behavior of the two pooling functions on sequence learning tasks.

cs.SD↗

Multiple Instance Deep Learning for Weakly Supervised Small-Footprint Audio Event Detection

State-of-the-art audio event detection (AED) systems rely on supervised learning using strongly labeled data. However, this dependence severely limits scalability to large-scale datasets where fine resolution annotations are too expensive to obtain. In this paper, we propose a small-footprint multiple instance learning (MIL) framework for multi-class AED using weakly annotated labels. The proposed MIL framework uses audio embeddings extracted from a pre-trained convolutional neural network as input features. We show that by using audio embeddings the MIL framework can be implemented using a simple DNN with performance comparable to recurrent neural networks. We evaluate our approach by training an audio tagging system using a subset of AudioSet, which is a large collection of weakly labeled YouTube video excerpts. Combined with a late-fusion approach, we improve the F1 score of a baseline audio tagging system by 17%. We show that audio embeddings extracted by the convolutional neural networks significantly boost the performance of all MIL models. This framework reduces the model complexity of the AED system and is suitable for applications where computational resources are limited.

cs.SD↗

Empirical Modeling of the Redshift Evolution of the [NII]/H$α$-ratio for Galaxy Redshift Surveys

We present an empirical parameterization of the [NII]/H$α$ flux ratio as a function of stellar mass and redshift valid at 0 < z < 2.7 and 8.5 < log(M) < 11.0. This description can easily be applied to (i) simulations for modeling [NII]$\lambda6584$ line emission, (ii) deblend [NII] and H$α$ in current low-resolution grism and narrow-band observations to derive intrinsic H$α$ fluxes, and (iii) to reliably forecast the number counts of H$α$ emission-line galaxies for future surveys, such as those planned for Euclid and the Wide Field Infrared Survey Telescope (WFIRST). Our model combines the evolution of the locus on the Baldwin, Phillips & Terlevich (BPT) diagram measured in spectroscopic data out to z = 2.5 with the strong dependence of [NII]/H$α$ on stellar mass and [OIII]/H$β$ observed in local galaxy samples. We find large variations in the [NII]/H$α$ flux ratio at a fixed redshift due to its dependency on stellar mass; hence, the assumption of a constant [NII] flux contamination fraction can lead to a significant under- or overestimate of H$α$ luminosities. Specifically, measurements of the intrinsic H$α$ luminosity function derived from current low-resolution grism spectroscopy assuming a constant 29% contamination of [NII] can be overestimated by factors of ~8 at log(L) > 43.0 for galaxies at redshifts z = 1.5. This has implications for the prediction of H$α$ emitters for Euclid and WFIRST. We also study the impact of blended H$α$ and [NII] on the accuracy of measured spectroscopic redshifts.

astro-ph.GA↗

A Light-Weight Multimodal Framework for Improved Environmental Audio Tagging

The lack of strong labels has severely limited the state-of-the-art fully supervised audio tagging systems to be scaled to larger dataset. Meanwhile, audio-visual learning models based on unlabeled videos have been successfully applied to audio tagging, but they are inevitably resource hungry and require a long time to train. In this work, we propose a light-weight, multimodal framework for environmental audio tagging. The audio branch of the framework is a convolutional and recurrent neural network (CRNN) based on multiple instance learning (MIL). It is trained with the audio tracks of a large collection of weakly labeled YouTube video excerpts; the video branch uses pretrained state-of-the-art image recognition networks and word embeddings to extract information from the video track and to map visual objects to sound events. Experiments on the audio tagging task of the DCASE 2017 challenge show that the incorporation of video information improves a strong baseline audio tagging system by 5.3\% absolute in terms of $F_1$ score. The entire system can be trained within 6~hours on a single GPU, and can be easily carried over to other audio tasks such as speech sentimental analysis.

cs.SD↗

Predicting Hα emission line galaxy counts for future galaxy redshift surveys

Knowledge of the number density of H$α$ emitting galaxies is vital for assessing the scientific impact of the Euclid and WFIRST missions. In this work we present predictions from a galaxy formation model, Galacticus, for the cumulative number counts of H$α$-emitting galaxies. We couple Galacticus to three different dust attenuation methods and examine the counts using each method. A $χ^2$ minimisation approach is used to compare the model predictions to observed galaxy counts and calibrate the dust parameters. We find that weak dust attenuation is required for the Galacticus counts to be broadly consistent with the observations, though the optimum dust parameters return large values for $χ^2$, suggesting that further calibration of Galacticus is necessary. The model predictions are also consistent with observed estimates for the optical depth and the H$α$ luminosity function. Finally we present forecasts for the redshift distributions and number counts for two Euclid-like and one WFIRST-like survey. For a Euclid-like survey with redshift range $0.9\leqslant z\leqslant 1.8$ and H$α+{\rm [NII]}$ blended flux limit of $2\times 10^{-16}{\rm erg}\,{\rm s}^{-1}\,{\rm cm}^{-2}$ we predict a number density between 3900--4800 galaxies per square degree. For a WFIRST-like survey with redshift range $1\leqslant z\leqslant 2$ and blended flux limit of $1\times 10^{-16}{\rm erg}\,{\rm s}^{-1}\,{\rm cm}^{-2}$ we predict a number density between 10400--15200 galaxies per square degree.

astro-ph.GA↗

Impact of Junction Depth and Abruptness on the Activation and the Leakage Current in Germanium n$^{+}$/p Junctions

The phosphorous activation in Ge n$^{+}$/p junctions is compared in terms of junction depth, by using laser spike annealing at 860°C for 400$μ$s. The reverse junction leakage is found to strongly depend on the abruptness of dopant profiles. A shallow and abrupt junction is shown to have lower phosphorous activation level, due to surface dose loss, and higher band-to-band tunneling (BTBT) leakage, when compared to the deep junction. Simulations were carried out to evaluate the lowest achievable OFF-state currents (I$_{OFF}$) for Ge double-gate FETs when using such an abrupt junction. Our results indicate that a Ge body thickness smaller than 5 nm is required to suppress the BTBT leakage and meet the requirement for the high performance devices defined by the International Technology Roadmap for Semiconductors (I$_{OFF}$ = 10$^{-7}$ A/$μ$m).

physics.app-ph↗

Modeling galaxy clustering on small scales to tighten constraints on dark energy and modified gravity

We present a new approach to measuring cosmic expansion history and growth rate of large scale structure using the anisotropic two dimensional galaxy correlation function (2DCF) measured from data; it makes use of the empirical modeling of small-scale galaxy clustering derived from numerical simulations by Zheng et al. (2013). We validate this method using mock catalogues, before applying it to the analysis of the CMASS sample from the Sloan Digital Sky Survey Data Release 10 (DR10) of the Baryon Oscillation Spectroscopic Survey (BOSS). We find that this method enables accurate and precise measurements of cosmic expansion history and growth rate of large scale structure. Modeling the 2DCF fully including nonlinear effects and redshift space distortions (RSD) in the scale range of 16 to 144 Mpc/h, we find H(0.57)r_s(z_d)/c=0.0459 +/- 0.0006, D_A(0.57)/r_s(z_d)=9.011 +/- 0.073, and f_g(0.57)σ_8(0.57)=0.476 +/- 0.050, which correspond to precisions of 1.3%, 0.8%, and 10.5% respectively. We have defined r_s(z_d) to be the sound horizon at the drag epoch computed using a simple integral, f_g(z) as the growth rate at redshift z, and σ_8(z) as the matter power spectrum normalization on 8Mpc/h scale at z. We find that neglecting the small-scale information significantly weakens the constraints on H(z) and D_A(z), and leads to a biased estimate of f_g(z). Our results indicate that we can significantly tighten constraints on dark energy and modified gravity by reliably modeling small-scale galaxy clustering.

astro-ph.CO↗

Exploring uncertainties in dark energy constraints using current observational data with Planck 2015 distance priors

We present the distance priors that we have derived from the 2015 Planck data, and use these in combination with the latest observational data from Type Ia Supernovae (SNe Ia) and galaxy clustering, to explore the systematic uncertainties in dark energy constraints. We use the Joint Lightcurve Analysis (JLA) set of 740 SNe Ia, galaxy clustering measurements of H(z)s and D_A(z)/s (where s is the sound horizon at the drag epoch) from the Sloan Digital Sky Survey (SDSS) at z=0.35 and z=0.57 (BOSS DR12). We find that the combined dark energy constraints are insensitive to the assumptions made in the galaxy clustering measurements (whether they are for BAO only or marginalized over RSD), which indicates that as the analysis of galaxy clustering data becomes more accurate and robust, the systematic uncertainties are reduced. On the other hand, we find that flux-averaging SNe Ia at z>= 0.5 significantly tightens the dark energy constraints, and excludes a flat universe with a cosmological constant at 68% confidence level, assuming a dark energy equation of state linear in the cosmic scale factor. Flux-averaging has the most significant effect when we allow dark energy density function X(z) to be a free functions given by the cubic spline of its value at z=0, 1/3, 2/3, 1; the measured X(z) deviates from a cosmological constant at more than 95% confidence level for 0.4 < z < 0.7. Since flux-averaging reduces the bias in the SN distance measurements, this may be an indication that we have arrived in the era when the SN distance measurements are limited by systematic uncertainties.

astro-ph.CO↗