arXiv subjects

Shirley Ho

Publications and source records attributed to Shirley Ho.

At least 109 records · Page 6Linked to original sources

arXiv2020

Report from the Tri-Agency Cosmological Simulation Task Force

The Tri-Agency Cosmological Simulations (TACS) Task Force was formed when Program Managers from the Department of Energy (DOE), the National Aeronautics and Space Administration (NASA), and the National Science Foundation (NSF) expressed an interest in receiving input into the cosmological simulations landscape related to the upcoming DOE/NSF Vera Rubin Observatory (Rubin), NASA/ESA's Euclid, and NASA's Wide Field Infrared Survey Telescope (WFIRST). The Co-Chairs of TACS, Katrin Heitmann and Alina Kiessling, invited community scientists from the USA and Europe who are each subject matter experts and are also members of one or more of the surveys to contribute. The following report represents the input from TACS that was delivered to the Agencies in December 2018.

astro-ph.CO↗

arXiv2020

Using the Marked Power Spectrum to Detect the Signature of Neutrinos in Large-Scale Structure

Cosmological neutrinos have their greatest influence in voids: these are the regions with the highest neutrino to dark matter density ratios. The marked power spectrum can be used to emphasize low density regions over high density regions, and therefore is potentially much more sensitive than the power spectrum to the effects of neutrino masses. Using 22,000 N-body simulations from the Quijote suite, we quantify the information content in the marked power spectrum of the matter field, and show that it outperforms the standard power spectrum by setting constraints improved by a factor larger than 2 on all cosmological parameters. The combination of marked and standard power spectrum allows to place a 4.3σ constraint on the minimum sum of the neutrino masses with a volume equal to 1 (Gpc/h)^3 and without CMB priors. Combinations of different marked power spectra yield a 6σ constraint within the same conditions.

astro-ph.CO↗

arXiv2019

Astrometry with the Wide-Field InfraRed Space Telescope

The Wide-Field InfraRed Space Telescope (WFIRST) will be capable of delivering precise astrometry for faint sources over the enormous field of view of its main camera, the Wide-Field Imager (WFI). This unprecedented combination will be transformative for the many scientific questions that require precise positions, distances, and velocities of stars. We describe the expectations for the astrometric precision of the WFIRST WFI in different scenarios, illustrate how a broad range of science cases will see significant advances with such data, and identify aspects of WFIRST's design where small adjustments could greatly improve its power as an astrometric instrument.

astro-ph.IM↗

arXiv2019

Learning Symbolic Physics with Graph Networks

We introduce an approach for imposing physically motivated inductive biases on graph networks to learn interpretable representations and improved zero-shot generalization. Our experiments show that our graph network models, which implement this inductive bias, can learn message representations equivalent to the true force vector when trained on n-body gravitational and spring-like simulations. We use symbolic regression to fit explicit algebraic equations to our trained model's message function and recover the symbolic form of Newton's law of gravitation without prior knowledge. We also show that our model generalizes better at inference time to systems with more bodies than had been experienced during training. Our approach is extensible, in principle, to any unknown interaction law learned by a graph network, and offers a valuable technique for interpreting and inferring explicit causal theories about the world from implicit knowledge captured by deep learning.

cs.LG↗

arXiv2019

From Dark Matter to Galaxies with Convolutional Neural Networks

Cosmological simulations play an important role in the interpretation of astronomical data, in particular in comparing observed data to our theoretical expectations. However, to compare data with these simulations, the simulations in principle need to include gravity, magneto-hydrodyanmics, radiative transfer, etc. These ideal large-volume simulations (gravo-magneto-hydrodynamical) are incredibly computationally expensive which can cost tens of millions of CPU hours to run. In this paper, we propose a deep learning approach to map from the dark-matter-only simulation (computationally cheaper) to the galaxy distribution (from the much costlier cosmological simulation). The main challenge of this task is the high sparsity in the target galaxy distribution: space is mainly empty. We propose a cascade architecture composed of a classification filter followed by a regression procedure. We show that our result outperforms a state-of-the-art model used in the astronomical community, and provides a good trade-off between computational cost and prediction accuracy.

astro-ph.CO↗

arXiv2019

Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates

We demonstrate an algorithm for learning a flexible color-magnitude diagram from noisy parallax and photometry measurements using a normalizing flow, a deep neural network capable of learning an arbitrary multi-dimensional probability distribution. We present a catalog of 640M photometric distance posteriors to nearby stars derived from this data-driven model using Gaia DR2 photometry and parallaxes. Dust estimation and dereddening is done iteratively inside the model and without prior distance information, using the Bayestar map. The signal-to-noise (precision) of distance measurements improves on average by more than 48% over the raw Gaia data, and we also demonstrate how the accuracy of distances have improved over other models, especially in the noisy-parallax regime. Applications are discussed, including significantly improved Milky Way disk separation and substructure detection. We conclude with a discussion of future work, which exploits the normalizing flow architecture to allow us to exactly marginalize over missing photometry, enabling the inclusion of many surveys without losing coverage.

astro-ph.IM↗

arXiv2019

Learning to Predict the Cosmological Structure Formation

Matter evolved under influence of gravity from minuscule density fluctuations. Non-perturbative structure formed hierarchically over all scales, and developed non-Gaussian features in the Universe, known as the Cosmic Web. To fully understand the structure formation of the Universe is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the Universe and employ a large ensemble of computer simulations to compare with the observed data in order to extract the full information of our own Universe. However, to evolve trillions of galaxies over billions of years even with the simplest physics is a daunting task. We build a deep neural network, the Deep Density Displacement Model (hereafter D$^3$M), to predict the non-linear structure formation of the Universe from simple linear perturbation theory. Our extensive analysis, demonstrates that D$^3$M outperforms the second order perturbation theory (hereafter 2LPT), the commonly used fast approximate simulation method, in point-wise comparison, 2-point correlation, and 3-point correlation. We also show that D$^3$M is able to accurately extrapolate far beyond its training data, and predict structure formation for significantly different cosmological parameters. Our study proves, for the first time, that deep learning is a practical and accurate alternative to approximate simulations of the gravitational structure formation of the Universe.

astro-ph.CO↗

arXiv2019

Alignment between filaments and galaxy spins from the MaNGA integral-field survey

Halos and galaxies acquire their angular momentum during the collapse of surrounding large-scale structure. This process imprints alignments between galaxy spins and nearby filaments and sheets. Low mass halos grow by accretion onto filaments, aligning their spins with the filaments, whereas high mass halos grow by mergers along filaments, generating spins perpendicular to the filament. We search for this alignment signal using filaments identified with the "Cosmic Web Reconstruction" algorithm applied to the Sloan Digital Sky Survey Main Galaxy Sample and galaxy spins from the MaNGA integral-field unit survey. MaNGA produces a map of the galaxy's rotational velocity, allowing direct measurement of the galaxy's spin direction, or unit angular momentum vector projected onto the sky. We find no evidence for alignment between galaxy spins and filament directions. We do find hints of a mass-dependent alignment signal, which is in 2-3$σ$ tension with the mass-dependent alignment signal in the MassiveBlack-II and Illustris hydrodynamical simulations. However, the tension vanishes when galaxy spin is measured using the H$α$ emission line velocity rather than stellar velocity. Finally, in simulations we find that the mass-dependent transition from aligned to anti-aligned dark matter halo spins is not necessarily present in stellar spins: we find a stellar spin transition in Illustris but not in MassiveBlack-II, highlighting the sensitivity of spin-filament alignments to feedback prescriptions and subgrid physics.

astro-ph.GA↗

arXiv2019

HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks

One of the most promising ways to observe the Universe is by detecting the 21cm emission from cosmic neutral hydrogen (HI) through radio-telescopes. Those observations can shed light on fundamental astrophysical questions only if accurate theoretical predictions are available. In order to maximize the scientific return of these surveys, those predictions need to include different observables and be precise on non-linear scales. Currently, one of the best ways to achieve this is via cosmological hydrodynamic simulations; however, the computational cost of these simulations is high -- tens of millions of CPU hours. In this work, we use Wasserstein Generative Adversarial Networks (WGANs) to generate new high-resolution ($35~h^{-1}{\rm kpc}$) 3D realizations of cosmic HI at $z=5$. We do so by sampling from a 100-dimension manifold, learned by the generator, that characterizes the fully non-linear abundance and clustering of cosmic HI from the state-of-the-art simulation IllustrisTNG. We show that different statistical properties of the produced samples -- 1D PDF, power spectrum, bispectrum, and void size function -- match very well those of IllustrisTNG, and outperform state-of-the-art models such as Halo Occupation Distributions (HODs). Our WGAN samples reproduce the abundance of HI across 9 orders of magnitude, from the Ly$α$ forest to Damped Lyman Absorbers. WGAN can produce new samples orders of magnitude faster than hydrodynamic simulations.

astro-ph.CO↗

arXiv2019

From Dark Matter to Galaxies with Convolutional Networks

Cosmological surveys aim at answering fundamental questions about our Universe, including the nature of dark matter or the reason of unexpected accelerated expansion of the Universe. In order to answer these questions, two important ingredients are needed: 1) data from observations and 2) a theoretical model that allows fast comparison between observation and theory. Most of the cosmological surveys observe galaxies, which are very difficult to model theoretically due to the complicated physics involved in their formation and evolution; modeling realistic galaxies over cosmological volumes requires running computationally expensive hydrodynamic simulations that can cost millions of CPU hours. In this paper, we propose to use deep learning to establish a mapping between the 3D galaxy distribution in hydrodynamic simulations and its underlying dark matter distribution. One of the major challenges in this pursuit is the very high sparsity in the predicted galaxy distribution. To this end, we develop a two-phase convolutional neural network architecture to generate fast galaxy catalogues, and compare our results against a standard cosmological technique. We find that our proposed approach either outperforms or is competitive with traditional cosmological techniques. Compared to the common methods used in cosmology, our approach also provides a nice trade-off between time-consumption (comparable to fastest benchmark in the literature) and the quality and accuracy of the predicted simulation. In combination with current and upcoming data from cosmological observations, our method has the potential to answer fundamental questions about our Universe with the highest accuracy.

astro-ph.CO↗

arXiv2019

The detection of the imprint of filaments on cosmic microwave background lensing

Galaxy redshift surveys, such as 2dF, SDSS, 6df, GAMA and VIPERS, have shown that the spatial distribution of matter forms a rich web, known as the cosmic web. The majority of galaxy survey analyses measure the amplitude of galaxy clustering as a function of scale, ignoring information beyond a small number of summary statistics. Since the matter density field becomes highly non-Gaussian as structure evolves under gravity, we expect other statistical descriptions of the field to provide us with additional information. One way to study the non-Gaussianity is to study filaments, which evolve non-linearly from the initial density fluctuations produced in the primordial Universe. In our study, we report the first detection of CMB (Cosmic Microwave Background) lensing by filaments and we apply a null test to confirm our detection. Furthermore, we propose a phenomenological model to interpret the detected signal and we measure how filaments trace the matter distribution on large scales through filament bias, which we measure to be around 1.5. Our study provides a new scope to understand the environmental dependence of galaxy formation. In the future, the joint analysis of lensing and Sunyaev-Zel'dovich observations might reveal the properties of `missing baryons', the vast majority of the gas which resides in the intergalactic medium and has so far evaded most observations.

astro-ph.CO↗

arXiv2019

Illuminating the dark universe with a very high density galaxy redshift survey over a wide area

The nature of dark energy remains a profound mystery 20 years after the discovery of cosmic acceleration. A very high number density galaxy redshift survey over a wide area (HD GRS Wide) spanning the redshift range of 0.5<z<4 using the same tracer, carried out using massively parallel wide field multi-object slit spectroscopy from space, will provide definitive dark energy measurements with minimal observational systematics by design. The HD GRS Wide will illuminate the nature of dark energy, and lead to revolutionary advances in particle physics and cosmology. It will also trace the cosmic web of dark matter and provide key insight into large-scale structure in the Universe. The required observational facility can be established as part of the probe portfolio by NASA within the next decade.

astro-ph.CO↗

arXiv2019

Cosmic voids: a novel probe to shed light on our Universe

Cosmic voids, the less dense patches of the Universe, are promising laboratories to extract cosmological information. Thanks to their unique low density character, voids are extremely sensitive to diffuse components such as neutrinos and dark energy, and represent ideal environments to study modifications of gravity, where the effects of such modifications are expected to be more prominent. Robust void-related observables, including for example redshift-space distortions (RSD) and weak lensing around voids, are a promising way to chase and test new physics. Cosmological analysis of the large-scale structure of the Universe predominantly relies on the high density regions. Current and upcoming surveys are designed to optimize the extraction of cosmological information from these zones, but leave voids under-exploited. A dense, large area spectroscopic survey with imaging capabilities is ideal to exploit the power of voids fully. Besides helping illuminate the nature of dark energy, modified gravity, and neutrinos, this survey will give access to a detailed map of under-dense regions, providing an unprecedented opportunity to observe and study a so far under-explored galaxy population.

astro-ph.CO↗

arXiv2019

Probing Gravitational Lensing of the CMB with SDSS-IV Quasars

We study the cross-correlation between the Planck CMB lensing convergence map and the eBOSS quasar overdensity obtained from the Sloan Digital Sky Survey (SDSS) IV, in the redshift range $0.9 < z < 2.2$. We detect the CMB lensing convergence-quasar cross power spectrum at $5.4 σ$ significance. The cross power spectrum provides a quasar clustering bias measurement that is expected to be particularly robust against systematic effects. The redshift distribution of the quasar sample has a median redshift $z \approx 1.55$, and an effective redshift about $1.51$. The best fit bias of the quasar sample is $b_q = 2.43 \pm 0.45$, corresponding to a host halo mass of $\log_{10}\left( \frac{M}{h^{-1} M_\odot} \right) = 12.54^{+0.25}_{-0.36}$. This is broadly consistent with the previous literature on quasars with a similar redshift range and selection. Since our constraint on the bias comes from the cross-correlation between quasars and CMB lensing, we expect it to be robust to a wide range of possible systematic effects that may contaminate the auto correlation of quasars. We checked for a number of systematic effects from both CMB lensing and quasar overdensity, and found that all systematics are consistent with null within $2 σ$. The data is not sensitive to a possible scale dependence of the bias at present, but we expect that as the number of quasars increases (in future surveys such as DESI), it is likely that strong constraints on the scale dependence of the bias can be obtained.

astro-ph.CO↗

arXiv2019

Detecting Galaxy-Filament Alignments in the Sloan Digital Sky Survey III

Previous studies have shown the filamentary structures in the cosmic web influence the alignments of nearby galaxies. We study this effect in the LOWZ sample of the Sloan Digital Sky Survey using the "Cosmic Web Reconstruction" filament catalogue. We find that LOWZ galaxies exhibit a small but statistically significant alignment in the direction parallel to the orientation of nearby filaments. This effect is detectable even in the absence of nearby galaxy clusters, which suggests it is an effect from the matter distribution in the filament. A nonparametric regression model suggests that the alignment effect with filaments extends over separations of 30-40 Mpc. We find that galaxies that are bright and early-forming align more strongly with the directions of nearby filaments than those that are faint and late-forming; however, trends with stellar mass are less statistically significant, within the narrow range of stellar mass of this sample.

astro-ph.CO↗

arXiv2019

The Wide Field Infrared Survey Telescope: 100 Hubbles for the 2020s

The Wide Field Infrared Survey Telescope (WFIRST) is a 2.4m space telescope with a 0.281 deg^2 field of view for near-IR imaging and slitless spectroscopy and a coronagraph designed for > 10^8 starlight suppresion. As background information for Astro2020 white papers, this article summarizes the current design and anticipated performance of WFIRST. While WFIRST does not have the UV imaging/spectroscopic capabilities of the Hubble Space Telescope, for wide field near-IR surveys WFIRST is hundreds of times more efficient. Some of the most ambitious multi-cycle HST Treasury programs could be executed as routine General Observer (GO) programs on WFIRST. The large area and time-domain surveys planned for the cosmology and exoplanet microlensing programs will produce extraordinarily rich data sets that enable an enormous range of Archival Research (AR) investigations. Requirements for the coronagraph are defined based on its status as a technology demonstration, but its expected performance will enable unprecedented observations of nearby giant exoplanets and circumstellar disks. WFIRST is currently in the Preliminary Design and Technology Completion phase (Phase B), on schedule for launch in 2025, with several of its critical components already in production.

astro-ph.IM↗

arXiv2019

Gravitational redshift profiles of MaNGA BCGs

The gravitational potential well of an $M>10^{13}$ $M_\odot$ galaxy will lead to a gravitational redshift differential of order 1 km/s between the galaxy core and its outskirts. Current surveys of massive galaxies with spatially resolved spectroscopy have reached a size which makes feasible attempts to detect gravitational redshifts within galaxies. We use spectra from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) experiment to attempt a measurement of the averaged stellar redshift profile of large elliptical galaxies. We find that systematic effects (possibly related to charge transfer or wavelength calibration accuracy) make the standard MaNGA data pipeline unsuitable for measuring the relevant sub km/s wavelength shifts. We therefore develop a cross-correlation technique to mitigate these effects, but caution that we are working beyond the design accuracy of the MaNGA experiment. With a sample of $272$ galaxies in halos with $\log (M/M_\odot)>13$, we attempt a measurement of the gravitational redshift profile, achieving $1 σ$ errors of size $\sim 0.5$ km/s, but are unable to make a significant detection of the signal. Even without a detection, our measurement can be used to limit the dark matter mass within the half light radius of elliptical galaxies to $1.2$ times the stellar mass, at the 68% confidence level. We also perform weighting scheme tests and split sample tests, and address target selection issues and other relativistic effects, including the transverse Doppler effect and relativistic beaming of stars. Future detections could lead to new constraints on the galaxy mass distribution that are different from kinematic and lensing determinations and open a window on galaxy properties and tests of gravity.

astro-ph.GA↗

arXiv2019

Effects of baryons on the gravitational redshift profile of ΛCDM halos

Gravitational redshifts and other relativistic effects are beginning to be studied in the context of galaxy clustering. Distortions consistent with those expected in General Relativity have been measured in galaxy cluster redshift profiles by Wojtak et al. and others and in the the cross-correlation function of galaxy populations by Alam et al. On scales below ~20 Mpc/$h$ simulations have shown that gravitational redshift dominates over other effects. However, this signal is related to the shape and depth of gravitational potentials, and therefore the matter density in galaxies and galaxy clusters that is responsible for them. We investigate the effects of baryonic physics on the gravitational redshift profiles of massive (group and cluster-sized) halos. We compare the profiles of different components in halos taken from the MassiveBlack-II cosmological hydrodynamic simulation and a dark matter-only version of the same simulation. We find that inclusion of baryons, cooling, star formation and feedback significantly alters the relevant inner density profiles. These baryonic effects lead to overall increases in both gravitational redshifts and the transverse relativistic Doppler effects by up to ~50% for group size halos. We show how modified Navarro Frenk White halo profiles can be used to parametrize these differences, and provide relevant halo profile fits.

astro-ph.CO↗