scholarly journals Strong Lensing Mass Reconstruction: from Frontier Fields to the Typical Lensing Clusters of Future Surveys

2015 ◽  
Vol 11 (A29B) ◽  
pp. 793-794
Author(s):  
Keren Sharon ◽  
Michael D. Gladders ◽  
Jane R. Rigby ◽  
Matthew B. Bayliss ◽  
Eva Wuyts ◽  
...  
Keyword(s):  
Mass Density ◽  
High Redshift ◽  
High Mass ◽  
Quality Data ◽  
High Quality Data ◽  
Strong Lensing ◽  
Modeling Techniques ◽  
Lens Modeling ◽  
High Redshift Universe ◽  
Mass Reconstruction

AbstractDriven by the unprecedented wealth of high quality data that is accumulating for the Frontier Fields, they are becoming some of the best-studied strong lensing clusters to date, and probably the next few years. As will be discussed intensively in this focus meeting, the FF prove transformative for many fields: from studies of the high redshift Universe, to the assembly and structure of the clusters themselves. The FF data and the extensive collaborative effort around this program will also allow us to examine and improve upon current lens modeling techniques. Strong lensing is a powerful tool for mass reconstruction of the cores of galaxy clusters of all scales, providing an estimate of the total (dark and seen) projected mass density distribution out to 0.5 Mpc. Though SL mass may be biased by contribution from structures along the line of sight, its strength is that it is relatively insensitive to assumptions on cluster baryon astrophysics and dynamical state. Like the Frontier Fields clusters, the most “famous” strong lensing clusters are at the high mass end; they lens dozens of background sources into multiple images, providing ample lensing constraints. In this talk, I will focus on how we can leverage what we learn from modeling the FF clusters in strong lensing studies of the hundreds of clusters that will be discovered in upcoming surveys. In typical clusters, unlike the Frontier Fields, the Bullet Cluster and A1689, we observe only one to a handful of background sources, and have limited lensing constraints. I will describe the limitations that such a configuration imposes on strong lens modeling, highlight measurements that are robust to the richness of lensing evidence, and address the sources of uncertainty and what sort of information can help reduce those uncertainties. This category of lensing clusters is most relevant to the wide cluster surveys of the future.

scholarly journals The effect of dust bias on the census of neutral gas and metals in the high-redshift Universe due to SDSS-II quasar colour selection

2019 ◽  
Vol 486 (3) ◽  
pp. 4377-4397 ◽  
Author(s):  
Jens-Kristian Krogager ◽  
Johan P U Fynbo ◽  
Palle Møller ◽  
Pasquier Noterdaeme ◽  
Kasper E Heintz ◽  
...  
Keyword(s):  
Mass Density ◽  
High Redshift ◽  
Target Selection ◽  
Neutral Hydrogen ◽  
Sloan Digital Sky Survey ◽  
Selection Probability ◽  
Optical Extinction ◽  
Neutral Gas ◽  
High Redshift Universe ◽  
The Impact

ABSTRACT We present a systematic study of the impact of a dust bias on samples of damped Ly α absorbers (DLAs). This bias arises as an effect of the magnitude and colour criteria utilized in the Sloan Digital Sky Survey (SDSS) quasar target selection up until data release 7 (DR7). The bias has previously been quantified assuming only a contribution from the dust obscuration. In this work, we apply the full set of magnitude and colour criteria used up until SDSS-DR7 in order to quantify the full impact of dust biasing against dusty and metal-rich DLAs. We apply the quasar target selection algorithm on a modelled population of intrinsic colours, and by exploring the parameter space consisting of redshift, ($z_{\rm{\small QSO}}$and zabs), optical extinction, and H i column density, we demonstrate how the selection probability depends on these variables. We quantify the dust bias on the following properties derived for DLAs at z ≈ 3: the incidence rate, the mass density of neutral hydrogen and metals, and the average metallicity. We find that all quantities are significantly affected. When considering all uncertainties, the mass density of neutral hydrogen is underestimated by 10–50 per cent, and the mass density in metals is underestimated by 30–200 per cent. Lastly, we find that the bias depends on redshift. At redshift z = 2.2, the mass density of neutral hydrogen and metals might be underestimated by up to a factor of 2 and 5, respectively. Characterizing such a bias is crucial in order to accurately interpret and model the properties and metallicity evolution of absorption-selected galaxies.

scholarly journals Theoretical considerations for star formation at low and high redshift

2015 ◽  
Vol 11 (S315) ◽  
pp. 247-253
Author(s):  
Bruce G. Elmegreen
Keyword(s):  
Star Formation ◽  
Mass Density ◽  
High Redshift ◽  
Distribution Functions ◽  
High Mass ◽  
Formation Processes ◽  
Accretion Rates ◽  
Major Merger ◽  
Cloud Cores ◽  
Self Gravity

AbstractStar formation processes in strongly self-gravitating cloud cores should be similar at all redshifts, forming single or multiple stars with a range of masses determined by local magneto-hydrodynamics and gravity. The formation processes for these cores, however, as well as their structures, temperatures, Mach numbers, etc., and the boundedness and mass distribution functions of the resulting stars, should depend on environment, as should the characteristic mass, density, and column density at which cloud self-gravity dominates other forces. Because the environments for high and low redshift star formation differ significantly, we expect the resulting gas to stellar conversion details to differ also. At high redshift, the universe is denser and more gas-rich, so the active parts of galaxies are denser and more gas rich too, leading to slightly shorter gas consumption timescales, higher cloud pressures, and denser, more massive, bound stellar clusters at the high mass end. With shorter consumption times corresponding to higher relative cosmic accretion rates, and with the resulting higher star formation rates and their higher feedback powers, the ISM has greater turbulent speeds relative to the rotation speeds, thicker gas disks, and larger cloud and star complex sizes at the characteristic Jeans length. The result is a more chaotic appearance at high redshift, bridging the morphology gap between today's quiescent spirals and today's major-mergers, with neither spiral nor major-merger processes actually in play at that time. The result is also a thick disk at early times, and after in-plane accretion from relatively large clump torques, a classical bulge. Today's disks are thinner, and torque-driven accretion is slower outside of inner barred regions. This paper reviews the basic processes involved with star formation in order to illustrate its evolution over time and environment.

scholarly journals RELICS: Strong Lensing Analysis of MACS J0417.5–1154 and Predictions for Observing the Magnified High-redshift Universe with JWST

2019 ◽  
Vol 873 (1) ◽  
pp. 96 ◽  
Author(s):  
Guillaume Mahler ◽  
Keren Sharon ◽  
Carter Fox ◽  
Dan Coe ◽  
Mathilde Jauzac ◽  
...  
Keyword(s):  
High Redshift ◽  
Strong Lensing ◽  
High Redshift Universe

scholarly journals Search for Lensing Signatures in the Gravitational-Wave Observations from the First Half of LIGO–Virgo’s Third Observing Run

2021 ◽  
Vol 923 (1) ◽  
pp. 14
Author(s):  
R. Abbott ◽  
T. D. Abbott ◽  
S. Abraham ◽  
F. Acernese ◽  
K. Ackley ◽  
...  
Keyword(s):  
Gravitational Wave ◽  
Gravitational Lensing ◽  
High Redshift ◽  
High Mass ◽  
Sufficient Evidence ◽  
Multiple Image ◽  
Compact Binary ◽  
Strong Lensing ◽  
Merger Rate ◽  
Gravitational Wave Background

Abstract We search for signatures of gravitational lensing in the gravitational-wave signals from compact binary coalescences detected by Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) and Advanced Virgo during O3a, the first half of their third observing run. We study: (1) the expected rate of lensing at current detector sensitivity and the implications of a non-observation of strong lensing or a stochastic gravitational-wave background on the merger-rate density at high redshift; (2) how the interpretation of individual high-mass events would change if they were found to be lensed; (3) the possibility of multiple images due to strong lensing by galaxies or galaxy clusters; and (4) possible wave-optics effects due to point-mass microlenses. Several pairs of signals in the multiple-image analysis show similar parameters and, in this sense, are nominally consistent with the strong lensing hypothesis. However, taking into account population priors, selection effects, and the prior odds against lensing, these events do not provide sufficient evidence for lensing. Overall, we find no compelling evidence for lensing in the observed gravitational-wave signals from any of these analyses.

scholarly journals From starburst to quiescence: post-starburst galaxies and their large-scale clustering over cosmic time

2021 ◽  
Author(s):  
Aaron Wilkinson ◽  
Omar Almaini ◽  
Vivienne Wild ◽  
David Maltby ◽  
William G Hartley ◽  
...  
Keyword(s):  
Large Scale ◽  
Near Infrared ◽  
High Redshift ◽  
Cosmic Time ◽  
Rapid Quenching ◽  
High Mass ◽  
Evolutionary Pathway ◽  
Star Forming ◽  
Halo Masses ◽  
High Redshift Universe

Abstract We present the first study of the large-scale clustering of post-starburst (PSB) galaxies in the high redshift Universe (0.5 < z < 3.0). We select ∼4000 PSB galaxies photometrically, the largest high-redshift sample of this kind, from two deep large-scale near-infrared surveys: the UKIDSS Ultra Deep Survey (UDS) DR11 and the Cosmic Evolution Survey (COSMOS). Using angular cross-correlation techniques, we estimate the halo masses for this large sample of PSB galaxies and compare them with quiescent and star-forming galaxies selected in the same fields. We find that low-mass, low-redshift (0.5 < z < 1.0) PSB galaxies preferentially reside in very high-mass dark matter haloes (Mhalo > 1014 M⊙), suggesting they are likely to be infalling satellite galaxies in cluster-like environments. High-mass PSB galaxies are more weakly clustered at low redshifts, but they reside in higher mass haloes with increasing look-back time, suggesting strong redshift-dependent halo downsizing. These key results are consistent with previous results suggesting that two main channels are responsible for the rapid quenching of galaxies. While high-redshift (z > 1) galaxies appear to be quenched by secular feedback mechanisms, processes associated with dense environments are likely to be the key driver of rapid quenching in the low-redshift Universe (z < 1). Finally, we show that the clustering of photometrically selected PSBs are consistent with them being direct descendants of highly dust-enshrouded sub-millimetre galaxies (SMGs), providing tantalising evidence for the oft-speculated evolutionary pathway from starburst to quiescence.

What Can Digitization Do For Formulated Product Innovation and Development

2020 ◽  
Author(s):  
James McDonagh ◽  
William Swope ◽  
Richard L. Anderson ◽  
Michael Johnston ◽  
David J. Bray
Keyword(s):  
High Performance ◽  
Quality Data ◽  
High Quality Data ◽  
Base Level ◽  
Hybrid Approaches ◽  
Digital Ecosystem ◽  
Recent Developments ◽  
Chemical Simulation ◽  
High Level ◽  
Physical And Chemical

Digitization offers significant opportunities for the formulated product industry to transform the way it works and develop new methods of business. R&D is one area of operation that is challenging to take advantage of these technologies due to its high level of domain specialisation and creativity but the benefits could be significant. Recent developments of base level technologies such as artificial intelligence (AI)/machine learning (ML), robotics and high performance computing (HPC), to name a few, present disruptive and transformative technologies which could offer new insights, discovery methods and enhanced chemical control when combined in a digital ecosystem of connectivity, distributive services and decentralisation. At the fundamental level, research in these technologies has shown that new physical and chemical insights can be gained, which in turn can augment experimental R&D approaches through physics-based chemical simulation, data driven models and hybrid approaches. In all of these cases, high quality data is required to build and validate models in addition to the skills and expertise to exploit such methods. In this article we give an overview of some of the digital technology demonstrators we have developed for formulated product R&D. We discuss the challenges in building and deploying these demonstrators.<br>

Collecting High-Quality Data

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Social Desirability ◽  
Measurement Errors ◽  
Monitoring And Evaluation ◽  
Quality Data ◽  
Social Desirability Bias ◽  
High Quality ◽  
High Quality Data ◽  
Evaluation Systems ◽  
Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

scholarly journals The nature of giant clumps in high-z discs: a deep-learning comparison of simulations and observations

2020 ◽  
Vol 501 (1) ◽  
pp. 730-746
Author(s):  
Omri Ginzburg ◽  
Marc Huertas-Company ◽  
Avishai Dekel ◽  
Nir Mandelker ◽  
Gregory Snyder ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Main Sequence ◽  
High Redshift ◽  
High Mass ◽  
Galactic Centre ◽  
Preferential Formation ◽  
Maximum Resolution ◽  
Supernova Feedback ◽  
Disc Galaxies

ABSTRACT We use deep learning to explore the nature of observed giant clumps in high-redshift disc galaxies, based on their identification and classification in cosmological simulations. Simulated clumps are detected using the 3D gas and stellar densities in the VELA zoom-in cosmological simulation suite, with ${\sim}25\ \rm {pc}$ maximum resolution, targeting main-sequence galaxies at 1 &lt; z &lt; 3. The clumps are classified as long-lived clumps (LLCs) or short-lived clumps (SLCs) based on their longevity in the simulations. We then train neural networks to detect and classify the simulated clumps in mock, multicolour, dusty, and noisy HST-like images. The clumps are detected using an encoder–decoder convolutional neural network (CNN), and are classified according to their longevity using a vanilla CNN. Tests using the simulations show our detector and classifier to be ${\sim}80{{\ \rm per\ cent}}$ complete and ${\sim}80{{\ \rm per\ cent}}$ pure for clumps more massive than ∼107.5 M⊙. When applied to observed galaxies in the CANDELS/GOODS S+N fields, we find both types of clumps to appear in similar abundances in the simulations and the observations. LLCs are, on average, more massive than SLCs by ∼0.5 dex, and they dominate the clump population above Mc ≳ 107.6 M⊙. LLCs tend to be found closer to the galactic centre, indicating clump migration to the centre or preferential formation at smaller radii. The LLCs are found to reside in high-mass galaxies, indicating better clump survivability under supernova feedback there, due to clumps being more massive in these galaxies. We find the clump masses and radial positions in the simulations and the observations to agree within a factor of 2.

scholarly journals Velocity Analysis Using Separated Diffractions for Lunar Penetrating Radar Obtained by Yutu-2 Rover

2021 ◽  
Vol 13 (7) ◽  
pp. 1387
Author(s):  
Chao Li ◽  
Jinhai Zhang
Keyword(s):  
Dielectric Permittivity ◽  
Lunar Regolith ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Quality Data ◽  
High Quality Data ◽  
Shallow Subsurface ◽  
Subsurface Structures ◽  
Ground Penetrating ◽  
Permittivity Model

The high-frequency channel of lunar penetrating radar (LPR) onboard Yutu-2 rover successfully collected high quality data on the far side of the Moon, which provide a chance for us to detect the shallow subsurface structures and thickness of lunar regolith. However, traditional methods cannot obtain reliable dielectric permittivity model, especially in the presence of high mix between diffractions and reflections, which is essential for understanding and interpreting the composition of lunar subsurface materials. In this paper, we introduce an effective method to construct a reliable velocity model by separating diffractions from reflections and perform focusing analysis using separated diffractions. We first used the plane-wave destruction method to extract weak-energy diffractions interfered by strong reflections, and the LPR data are separated into two parts: diffractions and reflections. Then, we construct a macro-velocity model of lunar subsurface by focusing analysis on separated diffractions. Both the synthetic ground penetrating radar (GPR) and LPR data shows that the migration results of separated reflections have much clearer subsurface structures, compared with the migration results of un-separated data. Our results produce accurate velocity estimation, which is vital for high-precision migration; additionally, the accurate velocity estimation directly provides solid constraints on the dielectric permittivity at different depth.

scholarly journals Redistribution Preferences, Inequality Information, and Partisan Motivated Reasoning in the United States

Societies ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 65
Author(s):  
Clem Brooks ◽  
Elijah Harter
Keyword(s):  
Party Identification ◽  
Motivated Reasoning ◽  
The United States ◽  
Quality Data ◽  
Historical Time ◽  
High Quality Data ◽  
Redistributive Policies ◽  
Election Studies ◽  
New Evidence ◽  
The U.S

In an era of rising inequality, the U.S. public’s relatively modest support for redistributive policies has been a puzzle for scholars. Deepening the paradox is recent evidence that presenting information about inequality increases subjects’ support for redistributive policies by only a small amount. What explains inequality information’s limited effects? We extend partisan motivated reasoning scholarship to investigate whether political party identification confounds individuals’ processing of inequality information. Our study considers a much larger number of redistribution preference measures (12) than past scholarship. We offer a second novelty by bringing the dimension of historical time into hypothesis testing. Analyzing high-quality data from four American National Election Studies surveys, we find new evidence that partisanship confounds the interrelationship of inequality information and redistribution preferences. Further, our analyses find the effects of partisanship on redistribution preferences grew in magnitude from 2004 through 2016. We discuss implications for scholarship on information, motivated reasoning, and attitudes towards redistribution.

Sign in / Sign up

Export Citation Format

Share Document