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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

COSMIC SHEAR RESULTS FROM THE DEEP LENS SURVEY. I. JOINT CONSTRAINTS ON {Omega} {sub M} AND {sigma}{sub 8} WITH A TWO-DIMENSIONAL ANALYSIS

We present a cosmic shear study from the Deep Lens Survey (DLS), a deep BVRz multi-band imaging survey of five 4 deg{sup 2} fields with two National Optical Astronomy Observatory (NOAO) 4 m telescopes at Kitt Peak and Cerro Tololo. For both telescopes, the change of the point-spread-function (PSF) shape across the focal plane is complicated, and the exposure-to-exposure variation of this position-dependent PSF change is significant. We overcome this challenge by modeling the PSF separately for individual exposures and CCDs with principal component analysis (PCA). We find that stacking these PSFs reproduces the final PSF pattern on the mosaic image with high fidelity, and the method successfully separates PSF-induced systematics from gravitational lensing effects. We calibrate our shears and estimate the errors, utilizing an image simulator, which generates sheared ground-based galaxy images from deep Hubble Space Telescope archival data with a realistic atmospheric turbulence model. For cosmological parameter constraints, we marginalize over shear calibration error, photometric redshift uncertainty, and the Hubble constant. We use cosmology-dependent covariances for the Markov Chain Monte Carlo analysis and find that the role of this varying covariance is critical in our parameter estimation. Our current non-tomographic analysis alone constrains the {Omega} {sub M}-{sigma}{sub 8} likelihood contour tightly, providing a joint constraint of {Omega} {sub M} = 0.262 {+-} 0.051 and {sigma}{sub 8} = 0.868 {+-} 0.071. We expect that a future DLS weak-lensing tomographic study will further tighten these constraints because explicit treatment of the redshift dependence of cosmic shear more efficiently breaks the {Omega} {sub M}-{sigma}{sub 8} degeneracy. Combining the current results with the Wilkinson Microwave Anisotropy Probe 7 year (WMAP7) likelihood data, we obtain {Omega} {sub M} = 0.278 {+-} 0.018 and {sigma}{sub 8} = 0.815 {+-} 0.020.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

A REST-FRAME OPTICAL VIEW ON z {approx} 4 GALAXIES. I. COLOR AND AGE DISTRIBUTIONS FROM DEEP IRAC PHOTOMETRY OF THE IUDF10 AND GOODS SURVEYS

We present a study of rest-frame UV-to-optical color distributions for z {approx} 4 galaxies based on the combination of deep HST/ACS+WFC3/IR data with Spitzer/IRAC imaging. In particular, we use new, ultra-deep data from the IRAC Ultradeep Field program (IUDF10), together with previous, public IRAC data over the GOODS fields. Our sample contains a total of {approx}2600 galaxies selected as B-dropout Lyman-break Galaxies in the HUDF and its deep parallel field HUDF09-2, as well as GOODS-North/South. This sample is used to investigate the UV continuum slopes {beta} and Balmer break colors (J{sub 125} - [4.5]) as a function of rest-frame optical luminosity (using [4.5] to avoid optical emission lines). We find that galaxies at M{sub z} < -21.5 (roughly corresponding to L{sup *}{sub z{approx}4}) are significantly redder than their lower luminosity counterparts. The UV continuum slopes and the J{sub 125} - [4.5] colors are well correlated, indicating that the dust reddening at these redshifts is better described by an SMC-like extinction curve, rather than the typically assumed Calzetti reddening. After dust correction, we find that the galaxy population shows mean stellar population ages in the range 10{sup 8.5} to 10{sup 9} yr, with a dispersion of {approx}0.5 dex, and only weak trends as a function of luminosity. Only a small fraction of galaxies shows Balmer break colors consistent with extremely young ages, younger than 100 Myr. Under the assumption of smooth star-formation histories, this fraction is 12%-19% for galaxies at M{sub z} < -19.75. Our results are consistent with a gradual build-up of stars and dust in galaxies at z > 4 with only a small fraction of stars being formed in short, intense bursts of star-formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

The GISMO two-millimeter deep field in GOODS-N

We present deep continuum observations using the GISMO camera at a wavelength of 2 mm centered on the Hubble Deep Field in the GOODS-N field. These are the first deep field observations ever obtained at this wavelength. The 1σ sensitivity in the innermost ∼4' of the 7' diameter map is ∼135 μJy beam{sup –1}, a factor of three higher in flux/beam sensitivity than the deepest available SCUBA 850 μm observations, and almost a factor of four higher in flux/beam sensitivity than the combined MAMBO/AzTEC 1.2 mm observations of this region. Our source extraction algorithm identifies 12 sources directly, and another 3 through correlation with known sources at 1.2 mm and 850 μm. Five of the directly detected GISMO sources have counterparts in the MAMBO/AzTEC catalog, and four of those also have SCUBA counterparts. HDF850.1, one of the first blank-field detected submillimeter galaxies, is now detected at 2 mm. The median redshift of all sources with counterparts of known redshifts is z-tilde =2.91±0.94. Statistically, the detections are most likely real for five of the seven 2 mm sources without shorter wavelength counterparts, while the probability for none of them being real is negligible.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Faint submillimeter galaxies revealed by multifield deep ALMA observations: number counts, spatial clustering, and a dark submillimeter line emitter

We present the statistics of faint submillimeter/millimeter galaxies (SMGs) and serendipitous detections of a submillimeter/millimeter line emitter (SLE) with no multi-wavelength continuum counterpart revealed by the deep ALMA observations. We identify faint SMGs with flux densities of 0.1-1.0 mJy in the deep Band-6 and Band-7 maps of 10 independent fields that reduce cosmic variance effects. The differential number counts at 1.2 mm are found to increase with decreasing flux density down to 0.1 mJy. Our number counts indicate that the faint (0.1-1.0 mJy, or SFR{sub IR} ∼ 30-300 M {sub ☉} yr{sup –1}) SMGs contribute nearly a half of the extragalactic background light (EBL), while the remaining half of the EBL is mostly contributed by very faint sources with flux densities of <0.1 mJy (SFR{sub IR} ≲ 30 M {sub ☉} yr{sup –1}). We conduct counts-in-cells analysis with multifield ALMA data for the faint SMGs, and obtain a coarse estimate of galaxy bias, b {sub g} < 4. The galaxy bias suggests that the dark halo masses of the faint SMGs are ≲ 7 × 10{sup 12} M {sub ☉}, which is smaller than those of bright (>1 mJy) SMGs, but consistent with abundant high-z star-forming populations, such as sBzKs, LBGs, and LAEs. Finally, we report the serendipitous detection of SLE-1, which has no continuum counterparts in our 1.2 mm-band or multi-wavelength images, including ultra deep HST/WFC3 and Spitzer data. The SLE has a significant line at 249.9 GHz with a signal-to-noise ratio of 7.1. If the SLE is not a spurious source made by the unknown systematic noise of ALMA, the strong upper limits of our multi-wavelength data suggest that the SLE would be a faint galaxy at z ≳ 6.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

The infrared medium-deep survey. II. How to trigger radio AGNs? Hints from their environments

Activity at the centers of galaxies, during which the central supermassive black hole is accreting material, is nowadays accepted to be rather ubiquitous and most probably a phase of every galaxy's evolution. It has been suggested that galactic mergers and interactions may be the culprits behind the triggering of nuclear activity. We use near-infrared data from the new Infrared Medium-Deep Survey and the Deep eXtragalactic Survey of the VIMOS-SA22 field and radio data at 1.4 GHz from the FIRST survey and a deep Very Large Array survey to study the environments of radio active galactic nuclei (AGNs) over an area of ∼25 deg{sup 2} and down to a radio flux limit of 0.1 mJy and a J-band magnitude of 23 mag AB. Radio AGNs are predominantly found in environments similar to those of control galaxies at similar redshift, J-band magnitude, and (M{sub u} – M{sub r} ) rest-frame color. However, a subpopulation of radio AGNs is found in environments up to 100 times denser than their control sources. We thus preclude merging as the dominant triggering mechanism of radio AGNs. By fitting the broadband spectral energy distribution of radio AGNs in the least and most dense environments, we find that those in the least dense environments show higher radio-loudness, higher star formation efficiencies, and higher accretion rates, typical of the so-called high-excitation radio AGNs. These differences tend to disappear at z > 1. We interpret our results in terms of a different triggering mechanism for these sources that is driven by mass loss through winds of young stars created during the observed ongoing star formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

CANDIDATE CLUSTERS OF GALAXIES AT z > 1.3 IDENTIFIED IN THE SPITZER SOUTH POLE TELESCOPE DEEP FIELD SURVEY

We present 279 galaxy cluster candidates at z > 1.3 selected from the 94 deg{sup 2} Spitzer South Pole Telescope Deep Field (SSDF) survey. We use a simple algorithm to select candidate high-redshift clusters of galaxies based on Spitzer/IRAC mid-infrared data combined with shallow all-sky optical data. We identify distant cluster candidates adopting an overdensity threshold that results in a high purity (80%) cluster sample based on tests in the Spitzer Deep, Wide-Field Survey of the Boötes field. Our simple algorithm detects all three 1.4 < z ≤ 1.75 X-ray detected clusters in the Boötes field. The uniqueness of the SSDF survey resides not just in its area, one of the largest contiguous extragalactic fields observed with Spitzer, but also in its deep, multi-wavelength coverage by the South Pole Telescope (SPT), Herschel/SPIRE, and XMM-Newton. This rich data set will allow direct or stacked measurements of Sunyaev-Zel'dovich effect decrements or X-ray masses for many of the SSDF clusters presented here, and enable a systematic study of the most distant clusters on an unprecedented scale. We measure the angular correlation function of our sample and find that these candidates show strong clustering. Employing the COSMOS/UltraVista photometric catalog in order to infer the redshift distribution of our cluster selection, we find that these clusters have a comoving number density n{sub c}=(0.7{sub −0.6}{sup +6.3})×10{sup −7} h{sup 3} Mpc{sup −3} and a spatial clustering correlation scale length r {sub 0} = (32 ± 7) h {sup –1} Mpc. Assuming our sample is comprised of dark matter halos above a characteristic minimum mass, M {sub min}, we derive that at z = 1.5 these clusters reside in halos larger than M{sub min}=1.5{sub −0.7}{sup +0.9}×10{sup 14} h{sup −1} M{sub ⊙}. We find that the mean mass of our cluster sample is equal to M{sub mean}=1.9{sub −0.8}{sup +1.0}×10{sup 14} h{sup −1} M{sub ⊙}; thus, our sample contains the progenitors of present-day massive galaxy clusters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

A MULTIWAVELENGTH STUDY OF TADPOLE GALAXIES IN THE HUBBLE ULTRA DEEP FIELD

Multiwavelength data are essential in order to provide a complete picture of galaxy evolution and to inform studies of galaxies’ morphological properties across cosmic time. Here we present the results of a multiwavelength investigation of the morphologies of “tadpole” galaxies at intermediate redshift (0.314 < z < 3.175) in the Hubble Ultra Deep Field. These galaxies were previously selected from deep Hubble Space Telescope (HST) F775W data based on their distinct asymmetric knot-plus-tail morphologies. Here we use deep Wide Field Camera 3 near-infrared imaging in addition to the HST optical data in order to study the rest-frame UV/optical morphologies of these galaxies across the redshift range 0.3 < z < 3.2. This study reveals that the majority of these galaxies do retain their general asymmetric morphology in the rest-frame optical over this redshift range, if not the distinct “tadpole” shape. The average stellar mass of tadpole galaxies is lower than that of field galaxies, with the effect being slightly greater at higher redshift within the errors. Estimated from spectral energy distribution fits, the average age of tadpole galaxies is younger than that of field galaxies in the lower-redshift bin, and the average metallicity is lower (whereas the specific star formation rate for tadpoles is roughly the same as field galaxies across the redshift range probed here). These average effects combined support the conclusion that this subset of galaxies is in an active phase of assembly, either late-stage merging or cold gas accretion causing localized clumpy star formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

CANDELS MULTIWAVELENGTH CATALOGS: SOURCE IDENTIFICATION AND PHOTOMETRY IN THE CANDELS UKIDSS ULTRA-DEEP SURVEY FIELD

We present the multiwavelength-ultraviolet to mid-infrared-catalog of the UKIRT Infrared Deep Sky Survey (UKIDSS) Ultra-Deep Survey field observed as part of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS). Based on publicly available data, the catalog includes the CANDELS data from the Hubble Space Telescope (near-infrared WFC3 F125W and F160W data and visible ACS F606W and F814W data); u-band data from CFHT/Megacam; B, V, R{sub c} , i', and z' band data from Subaru/Suprime-Cam; Y and K{sub s} band data from VLT/HAWK-I; J, H, and K band data from UKIDSS (Data Release 8); and Spitzer/IRAC data (3.6, 4.5 {mu}m from SEDS; 5.8 and 8.0 {mu}m from SpUDS). The present catalog is F160W-selected and contains 35, 932 sources over an area of 201.7 arcmin{sup 2} and includes radio- and X-ray-detected sources and spectroscopic redshifts available for 210 sources.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

THE HST EXTREME DEEP FIELD (XDF): COMBINING ALL ACS AND WFC3/IR DATA ON THE HUDF REGION INTO THE DEEPEST FIELD EVER

The eXtreme Deep Field (XDF) combines data from 10 years of observations with the Hubble Space Telescope Advanced Camera for Surveys (ACS) and the Wide-Field Camera 3 Infra-Red (WFC3/IR) into the deepest image of the sky ever in the optical/near-IR. Since the initial observations of the Hubble Ultra-Deep Field (HUDF) in 2003, numerous surveys and programs, including supernovae follow-up, HUDF09, CANDELS, and HUDF12, have contributed additional imaging data across this region. However, these images have never been combined and made available as one complete ultra-deep image dataset. We combine them now with the XDF program. Our new and improved processing techniques provide higher quality reductions of the total dataset. All WFC3/IR and optical ACS data sets have been fully combined and accurately matched, resulting in the deepest imaging ever taken at these wavelengths, ranging from 29.1 to 30.3 AB mag (5σ in a 0.''35 diameter aperture) in 9 filters. The combined image therefore reaches to 31.2 AB mag 5σ (32.9 at 1σ) for a flat f {sub ν} source. The gains in the optical for the four filters done in the original ACS HUDF correspond to a typical improvement of 0.15 mag, with gains of 0.25 mag in the deepest areas. Such gains are equivalent to adding ∼130 to ∼240 orbits of ACS data to the HUDF. Improved processing alone results in a typical gain of ∼0.1 mag. Our 5σ (optical+near-IR) SExtractor catalogs reveal about 14,140 sources in the full field and about 7121 galaxies in the deepest part of the XDF.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Deep chalcogen donors and electron localization in aluminum nitride

Abstract We show with hybrid density functional theory calculations that chalcogen donors other than oxygen (i.e. S N , Se N , and Te N ) give rise to deep donor states in aluminum nitride. These donors trap a localized electron in their neutral charge state, leading to deep ( + / 0 ) donor levels that are 0.45 eV or more from the conduction-band edge. As such, this behavior is distinct from the DX behavior leads to deep (+/−) levels which affects other donors such as O N and Si Al . We highlight how these results hint at the formation of small electron polarons in AlN, which are found to be unstable in the bulk, but metastable when bound to donor dopants like Si Al and the chalocogens, with activation energies on the order of 0.2–0.3 eV. These results indicate that S, Se, and Te are not shallow donor dopants in aluminum nitride and identify origins of the experimentally observed ∼200–300 meV activation energies for dopant activation in donor-doped samples.

aluminum nitride↗
DOE OSTI2023

Slicing with deep learning models at ProtoDUNE-SP

DUNE is a cutting-edge experiment aiming to study neutrinos in detail, with a special focus on the flavor oscillation mechanism. The prototype of the DUNE Far Detector Single Phase TPC (ProtoDUNE-SP) was built and operated at CERN with a full set of reconstruction tools. To implement these reconstruction tools, Pandora, a multi-algorithm framework, has been developed. A large number of these algorithms, some of them being exploiting traditional clustering, detector physics and deep learning approaches, have been applied to images to gradually build up a picture out of singular events. One of such algorithms is the Pandora slicing algorithm which aims to partition the detector hits of an event in sets called slices. Each slice represents a single interaction in the detector and should identify all the hits related to the interacting particle and its subsequent decay products. We expect the order of tens of slices per event in ProtoDUNE-SP. In this paper we present a deep learning approach to the problem, designing a model able to outperform the state-of-the-art slicing algorithm which is currently implemented within Pandora. We assess the performance of our tool in terms of efficiency and accuracy, while exploiting hardware accelerating setups. The ultimate goal is to incorporate this deep learning approach in the Pandora reconstruction tool.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate the application of domain adversarial neural networks (DANNs) in this context. DANNs are designed to be trained in one domain (simulated data) but tested in a second domain (physics data) and utilize unlabeled data from the second domain so that during training only features which are unable to discriminate between the domains are promoted. MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at Fermilab. A-dependent cross sections are an important part of the physics program, and these measurements require vertex finding in complicated events. To illustrate the impact of the DANN we used a modified set of simulation in place of physics data during the training of the DANN and then used the label of the modified simulation during the evaluation of the DANN. In conclusion, we find that deep learning based methods offer significant advantages over our prior track-based reconstruction for the task of vertex finding, and that DANNs are able to improve the performance of deep networks by leveraging available unlabeled data and by mitigating network performance degradation rooted in biases in the physics models used for training.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Deep decarbonization and U.S. biofuels production: a coordinated analysis with a detailed structural model and an integrated multisectoral model

Scenarios for deep decarbonization involve biomass for biofuels, biopower, and bioproducts, and they often include negative emissions via carbon capture and storage or utilization. However, critical questions remain about the feasibility of rapid growth to high levels of biomass utilization, given biomass and land availability as well as historical growth rates of the biofuel industry. We address these questions through a unique coordinated analysis and comparison of carbon pricing effects on biomass utilization growth in the United States using a multisectoral integrated assessment model, the Global Change Analysis Model (GCAM), and a biomass-to-biofuels system dynamics model, the Bioenergy Scenario Model (BSM). We harmonized and varied key factors—such as carbon prices, vehicle electrification, and arable land availability—in the two models. We varied the rate of biorefinery construction, the fungibility of feedstock types across conversion processes, and policy incentives in BSM. The rate of growth in biomass deployment under a carbon price in both models is within the range of current literature. However, the reallocation of land to biomass feedstocks would need to overcome bottlenecks to achieve growth consistent with deep decarbonization scenarios. Investments as a result of near-term policy incentives can develop technology and expand capacity—reducing costs, enabling flexibility in feedstock use, and improving stability—but if biomass demand is high, these investments might not overcome land reallocation bottlenecks. Biomass utilization for deep decarbonization relies on extraordinary growth in biomass availability and industrial capacity. In this paper, we quantify and describe the potential challenges of this rapid change.

09 BIOMASS FUELS↗
DOE OSTI2025

Feature learning and generalization in deep networks with orthogonal weights

Fully-connected deep neural networks with weights initialized from independent Gaussian distributions can be tuned to criticality, which prevents the exponential growth or decay of signals propagating through the network. However, such networks still exhibit fluctuations that grow linearly with the depth of the network, which may impair the training of networks with width comparable to depth. We show analytically that rectangular networks with tanh activations and weights initialized from the ensemble of orthogonal matrices have corresponding preactivation fluctuations which are independent of depth, to leading order in inverse width. Moreover, we demonstrate numerically that, at initialization, all correlators involving the neural tangent kernel (NTK) and its descendants at leading order in inverse width—which govern the evolution of observables during training—saturate at a depth of ~20, rather than growing without bound as in the case of Gaussian initializations. We speculate that this structure preserves finite-width feature learning while reducing overall noise, thus improving both generalization and training speed in deep networks with depth comparable to width. We provide some experimental justification by relating empirical measurements of the NTK to the superior performance of deep non-linear orthogonal networks trained under full-batch gradient descent on the MNIST and CIFAR-10 classification tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network

Abstract Single-cell RNA sequencing (scRNA-seq) permits researchers to study the complex mechanisms of cell heterogeneity and diversity. Unsupervised clustering is of central importance for the analysis of the scRNA-seq data, as it can be used to identify putative cell types. However, due to noise impacts, high dimensionality and pervasive dropout events, clustering analysis of scRNA-seq data remains a computational challenge. Here, we propose a new deep structural clustering method for scRNA-seq data, named scDSC, which integrate the structural information into deep clustering of single cells. The proposed scDSC consists of a Zero-Inflated Negative Binomial (ZINB) model-based autoencoder, a graph neural network (GNN) module and a mutual-supervised module. To learn the data representation from the sparse and zero-inflated scRNA-seq data, we add a ZINB model to the basic autoencoder. The GNN module is introduced to capture the structural information among cells. By joining the ZINB-based autoencoder with the GNN module, the model transfers the data representation learned by autoencoder to the corresponding GNN layer. Furthermore, we adopt a mutual supervised strategy to unify these two different deep neural architectures and to guide the clustering task. Extensive experimental results on six real scRNA-seq datasets demonstrate that scDSC outperforms state-of-the-art methods in terms of clustering accuracy and scalability. Our method scDSC is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DHUDBlab/scDSC.

Gan, Yanglan↗
DOE OSTI2021

Deep graph representations embed network information for robust disease marker identification

We report that the accurate disease diagnosis and prognosis based on omics data rely on the effective identification of robust prognostic and diagnostic markers that reflect the states of the biological processes underlying the disease pathogenesis and progression. In this article, we present GCNCC, a Graph Convolutional Network-based approach for Clustering and Classification, that can identify highly effective and robust network-based disease markers. Based on a geometric deep learning framework, GCNCC learns deep network representations by integrating gene expression data with protein interaction data to identify highly reproducible markers with consistently accurate prediction performance across independent datasets possibly from different platforms. GCNCC identifies these markers by clustering the nodes in the protein interaction network based on latent similarity measures learned by the deep architecture of a graph convolutional network, followed by a supervised feature selection procedure that extracts clusters that are highly predictive of the disease state. By benchmarking GCNCC based on independent datasets from different diseases (psychiatric disorder and cancer) and different platforms (microarray and RNA-seq), we show that GCNCC outperforms other state-of-the-art methods in terms of accuracy and reproducibility.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Deep reinforcement learning of cell movement in the early stage of C.elegans embryogenesis

Motivation: Cell movement in the early phase of Caenorhabditis elegans development is regulated by a highly complex process in which a set of rules and connections are formulated at distinct scales. Previous efforts have demonstrated that agent-based, multi-scale modeling systems can integrate physical and biological rules and provide new avenues to study developmental systems. However, the application of these systems to model cell movement is still challenging and requires a comprehensive understanding of regulatory networks at the right scales. Recent developments in deep learning and reinforcement learning provide an unprecedented opportunity to explore cell movement using 3D time-lapse microscopy images. Results: We present a deep reinforcement learning approach within an agent-based modeling system to characterize cell movement in the embryonic development of C.elegans. Our modeling system captures the complexity of cell movement patterns in the embryo and overcomes the local optimization problem encountered by traditional rule-based, agent-based modeling that uses greedy algorithms. Here, we tested our model with two real developmental processes: the anterior movement of the Cpaaa cell via intercalation and the rearrangement of the superficial left-right asymmetry. In the first case, the model results suggested that Cpaaa’s intercalation is an active directional cell movement caused by the continuous effects from a longer distance (farther than the length of two adjacent cells), as opposed to a passive movement caused by neighbor cell movements. In the second case, a leader-follower mechanism well explained the collective cell movement pattern in the asymmetry rearrangement. These results showed that our approach to introduce deep reinforcement learning into agent-based modeling can test regulatory mechanisms by exploring cell migration paths in a reverse engineering perspective. Lastly, this model opens new doors to explore the large datasets generated by live imaging. Availability and implementation: Source code is available at https://github.com/zwang84/drl4cellmovement.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Dark Energy Survey Year 3 Results: Deep Field optical + near-infrared images and catalogue

ABSTRACT We describe the Dark Energy Survey (DES) Deep Fields, a set of images and associated multiwavelength catalogue (ugrizJHKs) built from Dark Energy Camera (DECam) and Visible and Infrared Survey Telescope for Astronomy (VISTA) data. The DES Deep Fields comprise 11 fields (10 DES supernova fields plus COSMOS), with a total area of ∼30 sq. deg. in ugriz bands and reaching a maximum i-band depth of 26.75 (AB, 10σ, 2 arcsec). We present a catalogue for the DES 3-yr cosmology analysis of those four fields with full 8-band coverage, totalling 5.88 sq. deg. after masking. Numbering 2.8 million objects (1.6 million post-masking), our catalogue is drawn from images coadded to consistent depths of r = 25.7, i = 25, and z = 24.3 mag. We use a new model-fitting code, built upon established methods, to deblend sources and ensure consistent colours across the u-band to Ks-band wavelength range. We further detail the tight control we maintain over the point-spread function modelling required for the model fitting, astrometry and consistency of photometry between the four fields. The catalogue allows us to perform a careful star–galaxy separation and produces excellent photometric redshift performance (NMAD = 0.023 at i < 23). The Deep-Fields catalogue will be made available as part of the cosmology data products release, following the completion of the DES 3-yr weak lensing and galaxy clustering cosmology work.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗