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At least 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary

Deep learning (DL) is a subdomain of artificial intelligence algorithms capable of automatically evaluating subtle graphical features to make highly accurate predictions, which was recently popularized in multiple imaging-related tasks. Because of its capabilities to analyze medical imaging such as radiology scans and digitized pathology specimens, DL has significant clinical potential as a diagnostic or prognostic tool. Coupled with rapidly increasing quantities of digital medical data, numerous novel research questions and clinical applications of DL within medicine have already been explored. Similarly, DL research and applications within hematology are rapidly emerging, although these are still largely in their infancy. Given the exponential rise of DL research for hematologic conditions, it is essential for the practising hematologist to be familiar with the broad concepts and pitfalls related to these new computational techniques. This narrative review provides a visual glossary for key deep learning principles, as well as a systematic review of published investigations within malignant and non-malignant hematologic conditions, organized by the different phases of clinical care. In order to assist the unfamiliar reader, this review highlights key portions of current literature and summarizes important considerations for the critical understanding of deep learning development and implementations in clinical practice.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SPRUCE Deep Peat Heating (DPH) Peat Water Content and Temperature for Experimental Plot Cores, June 2014 through June 2015

This data set provides the peat water content and peat temperature at time of sampling for peat cores collected before and during the SPRUCE Deep Peat Heating (DPH) study. Cores were collected during three sampling events: 03 June 2014, 09 September 2014, and 16 June 2015. Two cores were extracted from hollow locations in each of the 10 experimental plots (4, 6, 8, 10, 11, 13, 16, 17, 19, and 20). The five ambient plots (5, 7, 14, and 21) were not included in the DPH sampling efforts. See Table 1. Cores were partitioned into samples at 11 depth increments: 0-10, 10-20, 20-30, 30-40, 40-50, 50-75, 75-100, 100-125, 125-150, 150-175, and 175-200 cm below surface of the hollow. The DPH study was undertaken to evaluate possible rapid responses of deep peat carbon (C) stocks, microbial communities, and biogeochemical cycling processes to unprecedented temperatures. In June 2014, deep soil heating was initiated at 5 warming levels (+0, +2.25, +4.5, +6.75 and +9 °C) with 2 replicate plots per level. Warming levels at depth were achieved over a 25 (+ 2.25 °C) to 60-day (+9 °C) period depending on the target treatment temperatures. Data file – There is one comma-separated (*.csv) file with peat core water content and temperature at time of sampling, plus the amount of dried archived material retained after analyses. User Note: In this third release, four variables related to extrapolations of peat core temperature were removed. See the Data Set History table for additional details. 25-150, 150-175, and 175-200 cm below surface of the hollow.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Estimating Watershed Subsurface Permeability From Stream Discharge Data Using Deep Neural Networks

Subsurface permeability is a key parameter in watershed models that controls the contribution from the subsurface flow to stream flows. Since the permeability is difficult and expensive to measure directly at the spatial extent and resolution required by fully distributed watershed models, estimation through inverse modeling has had a long history in subsurface hydrology. The wide availability of stream surface flow data, compared to groundwater monitoring data, provides a new data source to infer soil and geologic properties using integrated surface and subsurface hydrologic models. As most of the existing methods have shown difficulty in dealing with highly nonlinear inverse problems, we explore the use of deep neural networks for inversion owing to their successes in mapping complex, highly nonlinear relationships. We train various deep neural network (DNN) models with different architectures to predict subsurface permeability from stream discharge hydrograph at the watershed outlet. The training data are obtained from ensemble simulations of hydrographs corresponding to an permeability ensemble using a fully-distributed, integrated surface-subsurface hydrologic model. The trained model is then applied to estimate the permeability of the real watershed using its observed hydrograph at the outlet. Our study demonstrates that the permeabilities of the soil and geologic facies that make significant contributions to the outlet discharge can be more accurately estimated from the discharge data. Their estimations are also more robust with observation errors. Compared to the traditional ensemble smoother method, DNNs show stronger performance in capturing the nonlinear relationship between permeability and stream hydrograph to accurately estimate permeability. Our study sheds new light on the value of the emerging deep learning methods in assisting integrated watershed modeling by improving parameter estimation, which will eventually reduce the uncertainty in predictive watershed models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Editorial: Deep-sea observation equipment and exploration technology

The increasing interest in deep-sea exploration is a crucial catalyst for driving advancements in deep-sea observation technology research. Numerous sophisticated sensors have been developed to facilitate comprehensive studies in deep-sea physics, chemistry, and geology. For example, cutting-edge small and compact seabed samplers are now employed on various platforms, such as Autonomous Underwater Vehicles (AUVs), Remotely Operated Vehicles (ROVs), and Human-Operated Vehicles (HOVs), enabling observation of the hadal zone. Additionally, Micro-electromechanical systems (MEMS) sensors are routinely utilized for ocean wave observation. This Research Topic aims to foster new insights into ocean exploration and observation technology, including ocean sensor technology, ocean observation platforms, and ocean observation reliability technology.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Effects of Slope Aspect and Rainfall on Belowground Deep Fine Root Traits and Aboveground Tree Height

The vertical root distribution and rooting depth are the main belowground plant functional traits used to indicate drought resistance in arid and semiarid regions. The effects of the slope aspect on the aboveground traits are visible but not the belowground deep root traits. We aimed to investigate the fine root traits of the locust tree ( Robinia pseudoacacia L.) planted on southerly and northerly aspects, and the variations in the rooting depth in regions with different rainfall, as well as assessing how deep rooting, might affect the response to drought in a loess region. We selected three study sites with different rainfall amounts, with six sampling plots at each site (three each with southerly and northerly aspects). Soil core samples were collected down to the depth where no roots were present. The locust trees tended to develop deeper fine roots rather than greater heights. The tree height and diameter were greater for locust trees on northerly aspects, whereas trees on southerly aspects had significantly deeper rooting depths. Fine root traits (root length, root area, and root dry weight density) were higher in the southerly aspect for both Changwu and Ansai, but lower in Suide. The ratio of the root front depth tree height ranged from 1.04 to 3.17, which was higher on southerly than northerly aspects, and it increased as the rainfall decreased. Locust tree growth traits (belowground fine root and aboveground tree height) were positively correlated with the mean annual rainfall. The soil moisture content of the topsoil decreased as the rainfall decreased, but the pattern varied in the deep layer. Our results suggest that the variations in the belowground rooting depth under different slope aspects may be related to plant survival strategies. The vertical extension of the rooting depth and tree height may be key functional traits that determine plant growth in drought-prone regions.

Lihui, Ma↗
DOE OSTI2025

Applying Deep Learning for Wildfire Identification: Economical and Accessible Solutions Leveraging Small Datasets

Wildfires significantly impact human health, air quality, visibility, weather, and climate change and cause substantial economic losses. While state and county-operated air quality monitors provide critical insights during wildfires, they are not available in all regions. This highlights the need for affordable, accessible tools that allow the general public to assess air quality impacts. In this study, we apply machine learning with deep neural networks to diagnose air quality rapidly from sky images taken at the Pacific Northwest National Laboratory in Richland, WA, USA. Using a convolutional neural network (CNN) framework, we trained a deep learning model to classify air quality indices based on sky images. By leveraging transfer learning, our approach fine-tunes a pre-trained model on a small dataset of sky images, significantly reducing training time while maintaining high accuracy. Our results demonstrate the potential of deep learning to provide rapid air quality diagnostics during wildfire episodes, offering early warnings to the public and enabling timely mitigation strategies, particularly for vulnerable populations. Additionally, we show that lower respiratory infections pose the highest health risk during acute smoke exposures. Reactive oxygen species (ROS) from wildfire particles further exacerbate health risks by triggering inflammation and other adverse effects.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Disposal of High-Level Nuclear Waste in Deep Horizontal Drillholes

Spent nuclear fuel and high-level radioactive waste can be disposed in deep horizontal drillholes in sedimentary, metamorphic or igneous rocks. Horizontal drillhole disposal has safety, operational and economic benefits: the repository is deep in the brine-saturated zone far below aquifers in a reducing environment of formations that can be shown to have been isolated from the surface for exceedingly long times; its depth provides safety against inadvertent intrusion, earthquakes and near-surface perturbations; it can be placed close to the reactors and interim storage facilities, minimizing transportation; disposal costs per ton of waste can be kept substantially lower than for mined repositories by its smaller size, reduced infrastructure needs and staged implementation; and, if desired, the waste could be retrieved using “fishing” technology. In the proposed disposal concept, corrosion-resistant canisters containing unmodified fuel assemblies from commercial reactors would be placed end-to-end in up to 50 cm diameter horizontal drillholes, a configuration that reduces mechanical stresses and keeps the temperatures below the boiling point of the brine. Other high-level wastes, such as capsules containing 137 Cs and 90 Sr, can be disposed in small-diameter horizontal drillholes. We provide an overview of this novel disposal concept and its technology, discuss some of its safety aspects and compare it to mined repositories and the deep vertical borehole disposal concept.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Deep Borehole Disposal Safety Case

The safety case for deep borehole disposal of nuclear wastes contains a safety strategy, an assessment basis, and a safety assessment. The safety strategy includes strategies for management, siting and design, and assessment. The assessment basis considers site selection, pre-closure, and post-closure, which includes waste and engineered barriers, the geosphere/natural barriers, and the biosphere and surface environment. The safety assessment entails a pre-closure safety analysis, a post-closure performance assessment, and confidence enhancement analyses. This work outlines the assessment basis and safety assessment aspects of a deep borehole disposal safety case. The safety case presented here is specific to deep borehole disposal of Cs and Sr capsules, but is generally applicable to other waste forms, such as spent nuclear fuel. The safety assessments for pre-closure and post-closure are briefly summarized from other sources; key issues for confidence enhancement are described in greater detail. These confidence enhancement analyses require building the technical basis for geologically old, reducing, highly saline brines at the depth of waste emplacement, and using reactive-transport codes to predict their movement in post-closure. The development and emplacement of borehole seals above the waste emplacement zone is also important to confidence enhancement.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Deep Learning for Land Cover Classification Using Only a Few Bands

There is an emerging interest in using hyperspectral data for land cover classification. The motivation behind using hyperspectral data is the notion that increasing the number of narrowband spectral channels would provide richer spectral information and thus help improve the land cover classification performance. Although hyperspectral data with hundreds of channels provide detailed spectral signatures, the curse of dimensionality might lead to degradation in the land cover classification performance. Moreover, in some practical applications, hyperspectral data may not be available due to cost, data storage, or bandwidth issues, and RGB and near infrared (NIR) could be the only image bands available for land cover classification. Light detection and ranging (LiDAR) data is another type of data to assist land cover classification especially if the land covers of interest have different heights. In this paper, we examined the performance of two Convolutional Neural Network (CNN)-based deep learning algorithms for land cover classification using only four bands (RGB+NIR) and five bands (RGB+NIR+LiDAR), where these limited number of image bands were augmented using Extended Multi-attribute Profiles (EMAP). The deep learning algorithms were applied to a well-known dataset used in the 2013 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest. With EMAP augmentation, the two deep learning algorithms were observed to achieve better land cover classification performance using only four bands as compared to that using all 144 hyperspectral bands.

58 GEOSCIENCES↗
DOE OSTI2016

Deep mixing in stellar variability: improved method, statistics, and applications

The preliminary results on deep-mixing manifestations in stellar variability are tested using our improved method and extended data set. We measure the timescales τ {sub m} of the stochastic change in the spectral power of rotational harmonics with numbers m ≤ 3 in the light curves of 1361 main-sequence stars from the Kepler mission archive. We find that the gradient [log(τ{sub 2})−log(τ{sub 1})]/[log(2)−log(1)] has a histogram maximum at −2/3, demonstrating agreement with Kolmogorov’s theory of turbulence and therefore confirming the manifestation of deep mixing. The squared amplitudes of the first and second rotational harmonics, corrected for integral photometry distortion, also show a quasi-Kolmogorov character with spectral index ≈−5/3. Moreover, the reduction of τ {sub 1} and τ {sub 2} to the timescales τ {sub lam1} and τ {sub lam2} of laminar convection in the deep stellar layers reveals the proximity of both τ {sub lam1} and τ {sub lam2} to the turnover time τ {sub MLT} of standard mixing length theory. Considering this result, we use the obtained stellar variability timescales instead of τ {sub MLT} in our analysis of the relation between stellar activity and the Rossby number P/τ {sub MLT}. Comparison of our diagrams with previous results and theoretical expectations shows that best-fit correspondence is achieved for τ {sub lam1}, which can therefore be used as an analog of τ {sub MLT}. This means that the laminar component (giant cells) of stellar turbulent convection indeed plays an important role in the physics of stars. Additionally, we estimate the diffusivity of magnetic elements in stellar photospheres.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Deep Spectroscopy with DESI for Photometric Redshift Training and Calibration

Deep spectroscopic samples can improve photometric redshift (photo-z) estimates and reduce uncertainties on redshift distributions. Such improvements can increase the cosmological constraining power of large imaging-based experiments such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) and mitigate what may be a limiting systematic effect. We present results from the “DESI-Deep pilot” program, which was designed to assess the capability of the Dark Energy Spectroscopic Instrument (DESI) on the 4m Mayall telescope to measure redshifts of galaxies as faint as expected lensing samples for early LSST data (m i ≤ 24.5). We find that DESI is remarkably efficient at this task, with redshift success rates comparable to the results of observations from 10 m class telescopes with only ∼2 × longer integration time (rather than ∼8 × longer as would be expected from aperture-area scaling), while simultaneously achieving ∼30 times larger multiplexing. We also find that the signal-to-noise ratio of the spectra scales as expected for background-limited observations even for the longest exposure times (∼7 hr) and faintest targets in the program. These results demonstrate that DESI could provide the definitive redshift sample for the early years of LSST with a modest investment of observing time. Based upon the results of this program, we provide updated predictions for the time required to collect benchmark samples for photo-z training and calibration using a variety of spectroscopic facilities. Finally, we describe a potential “DESI-Deep” survey designed to train and calibrate photo-z’s for imaging experiments, and provide forecasts of its impact on cosmological inference.

Dey, Biprateep [University of Toronto; University ↗
DOE OSTI2016

The SXDF-ALMA 2 arcmin{sup 2} deep survey: stacking rest-frame near-infrared selected objects

We present stacking analyses on our ALMA deep 1.1 mm imaging in the Subaru/XMM-Newton Deep Survey Field using 1.6 and 3.6 μm selected galaxies in the CANDELS WFC3 catalog. We detect a stacked flux of ∼0.03–0.05 mJy, corresponding to L{sub IR}<10{sup 11} L{sub ⊙} and a star formation rate (SFR) of ∼15 M{sub ⊙} yr{sup −1} at z = 2. We find that galaxies that are brighter in the rest-frame near-infrared tend to also be brighter at 1.1 mm, and galaxies fainter than m{sub 3.6μm}=23 do not produce detectable 1.1 mm emission. This suggests a correlation between stellar mass and SFR, but outliers to this correlation are also observed, suggesting strongly boosted star formation or extremely large extinction. We also find tendencies that redder galaxies and galaxies at higher redshifts are brighter at 1.1 mm. Our field contains z∼2.5 Hα emitters and a bright single-dish source. However, we do not find evidence of bias in our results caused by the bright source. By combining the fluxes of sources detected by ALMA and fluxes of faint sources detected with stacking, we recover a 1.1 mm surface brightness of up to 20.3 ± 1.2 Jy deg{sup −2}, comparable to the extragalactic background light measured by COBE. Based on the fractions of optically faint sources in our and previous ALMA studies and the COBE measurements, we find that approximately half of the cosmic star formation may be obscured by dust and missed by deep optical surveys. Much deeper and wider ALMA imaging is therefore needed to better constrain the obscured cosmic star formation history.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Deep Imaging of the HCG 95 Field. I. Ultra-diffuse Galaxies

We present a detection of 89 candidates of ultra-diffuse galaxies (UDGs) in a 4.9 degree{sup 2} field centered on the Hickson Compact Group 95 (HCG 95) using deep g- and r-band images taken with the Chinese Near Object Survey Telescope. This field contains one rich galaxy cluster (Abell 2588 at z = 0.199) and two poor clusters (Pegasus I at z = 0.013 and Pegasus II at z = 0.040). The 89 candidates are likely associated with the two poor clusters, giving about 50–60 true UDGs with a half-light radius r{sub e}>1.5 kpc and a central surface brightness μ(g,0)>24.0 mag arcsec{sup −2}. Deep z{sup ′}-band images are available for 84 of the 89 galaxies from the Dark Energy Camera Legacy Survey (DECaLS), confirming that these galaxies have an extremely low central surface brightness. Moreover, our UDG candidates are spread over a wide range in g − r color, and ∼26% are as blue as normal star-forming galaxies, which is suggestive of young UDGs that are still in formation. Interestingly, we find that one UDG linked with HCG 95 is a gas-rich galaxy with H i mass 1.1×10{sup 9} M {sub ⊙} detected by the Very Large Array, and has a stellar mass of M{sub ⋆}∼1.8×10{sup 8} M {sub ⊙}. This indicates that UDGs at least partially overlap with the population of nearly dark galaxies found in deep H i surveys. Our results show that the high abundance of blue UDGs in the HCG 95 field is favored by the environment of poor galaxy clusters residing in H i-rich large-scale structures.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Stripped Elliptical Galaxies as Probes of ICM Physics. III. Deep Chandra Observations of NGC 4552: Measuring the Viscosity of the Intracluster Medium

We present results from a deep (200 ks) Chandra observation of the early-type galaxy NGC 4552 (M89), which is falling into the Virgo cluster. Previous shallower X-ray observations of this galaxy showed a remnant gas core, a tail to the South of the galaxy, and twin “horns” attached to the northern edge of the gas core. In our deeper data, we detect a diffuse, low surface brightness extension to the previously known tail, and measure the temperature structure within the tail. We combine the deep Chandra data with archival XMM-Newton observations to put a strong upper limit on the diffuse emission of the tail out to a large distance (10× the radius of the remnant core) from the galaxy center. In our two previous papers, we presented the results of hydrodynamical simulations of ram pressure stripping specifically for M89 falling into the Virgo cluster and investigated the effect of intracluster medium (ICM) viscosity. In this paper, we compare our deep data with our specifically tailored simulations and conclude that the observed morphology of the stripped tail in NGC 4552 is most similar to the inviscid models. We conclude that, to the extent the transport processes can be simply modeled as a hydrodynamic viscosity, the ICM viscosity is negligible. More generally, any micro-scale description of the transport processes in the high- β plasma of the cluster ICM must be consistent with the efficient mixing observed in the stripped tail on macroscopic scales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Low-energy Electron-track Imaging for a Liquid Argon Time-projection-chamber Telescope Concept Using Probabilistic Deep Learning

The GammaTPC is an MeV-scale single-phase liquid argon time-projection-chamber gamma-ray telescope concept with a novel dual-scale pixel-based charge-readout system. It promises to enable a significant improvement in sensitivity to MeV-scale gamma rays over previous telescopes. The novel pixel-based charge readout allows for imaging of the tracks of electrons scattered by Compton interactions of incident gamma rays. The two primary contributors to the accuracy of a Compton telescope in reconstructing an incident gamma-ray’s original direction are its energy and position resolution. In this work, we focus on using deep learning to optimize the reconstruction of the initial position and direction of electrons scattered in Compton interactions, including using probabilistic models to estimate predictive uncertainty. We show that the deep-learning models are able to predict locations of Compton scatters of MeV-scale gamma rays from simulated 500 μm pixel-based data to better than 1 mm rms error and are sensitive to the initial direction of the scattered electron. We compare and contrast different deep-learning uncertainty estimation algorithms for reconstruction applications. Additionally, we show that event-by-event estimates of the uncertainty of the locations of the Compton scatters can be used to select those events that were reconstructed most accurately, leading to improvement in locating the origin of gamma-ray sources on the sky.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

DELVE-DEEP Survey: The Faint Satellite System of NGC 55

We report the first comprehensive census of the satellite dwarf galaxies around NGC 55 (2.1 Mpc) as a part of the DECam Local Volume Exploration DEEP (DELVE-DEEP) survey. NGC 55 is one of four isolated, Magellanic analogs in the Local Volume around which DELVE-DEEP aims to identify faint dwarfs and other substructures. We employ two complementary detection methods: one targets fully resolved dwarf galaxies by identifying them as stellar overdensities, while the other focuses on semiresolved dwarf galaxies, detecting them through shredded unresolved light components. As shown through extensive tests with injected galaxies, our search is sensitive to candidates down to M V ≲ −6.6 and surface brightness μ ≲ 28.5 mag arcsec 2 , and ∼80% complete down to M V ≲ −7.8. We do not report any new confirmed satellites beyond two previously known systems, ESO 294–010 and NGC 55-dw1. We construct the satellite luminosity function of NGC 55 and find it to be consistent with the predictions from cosmological simulations. As one of the first complete luminosity functions for a Magellanic analog, our results provide a glimpse of the constraints on low-mass-host satellite populations that will be further explored by upcoming surveys, such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time.

Medoff, Jonah [Univ. of Chicago, IL (United States↗
DOE OSTI2026

The SPT-deep Cluster Catalog: Sunyaev–Zel’dovich Selected Clusters from Combined SPT-3G and SPTpol Measurements over 100 Square Degrees

We present a catalog of 500 galaxy cluster candidates in the SPT-Deep field: a 100 deg$^{2}$ field that combines data from the SPT-3G and SPTpol surveys to reach noise levels of 3.0, 2.2, and 9.0 μK-arcmin at 95, 150, and 220 GHz, respectively. Candidates are selected via the thermal Sunyaev–Zel’dovich (SZ) effect with a minimum significance of ξ = 4.0, resulting in a catalog of purity ∼89%. Optical data from the Dark Energy Survey and infrared data from the Spitzer Space Telescope are used to confirm 442 cluster candidates. The clusters span 0.12 < z ≲ 1.8 and 1.0 × 10$^{14}$M$_{⊙}$/h$_{70}$ < M$_{500c}$ < 8.7 × 10$^{14}$M$_{⊙}$/h$_{70}$. The sample’s median redshift is 0.74, and the median mass is 1.7 × 10$^{14}$M$_{⊙}$/h$_{70}$; these are the lowest median mass and highest median redshift of any SZ-selected sample to date. We assess the effect of infrared emission from cluster member galaxies on cluster selection by performing a joint fit to the infrared dust and tSZ signals by combining measurements from SPT and overlapping submillimeter data from Herschel/SPIRE. We find that at high redshift (z > 1), the tSZ signal is reduced by $17.9_{−3.2}^{+3.8}$%$(3.8_{−0.7}^{+0.9}$%$)$ at 150 GHz (95 GHz) due to dust contamination. We repeat our cluster finding method on dust-nulled SPT maps and find the resulting catalog is consistent with the nominal SPT-Deep catalog, suggesting dust contamination does not significantly impact the SPT-Deep selection function; we attribute this lack of bias to the inclusion of the SPT 220 GHz band.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) is forecast to collect a large sample of Type Ia supernovae (SNe Ia) expected to be instrumental in unveiling the nature of dark energy. The feat, however, requires accurately measuring the two components of the Hubble diagram, distance modulus and redshift. Distance is estimated from SN Ia parameters extracted from light-curve fits, where the average quality of light curves is primarily driven by survey parameters. An optimal observing strategy is thus critical for measuring cosmological parameters with high accuracy. We present in this paper a three-stage analysis to assess the impact of the deep drilling (DD) strategy parameters on three critical aspects of the survey: redshift completeness, the number of well-measured SNe Ia, and cosmological measurements. We demonstrate that the current DD survey plans (internal LSST simulations) are characterized by a low completeness (z ~ 0.55–0.65), and irregular and low cadences (several days), which dramatically decrease the size of the well-measured SN Ia sample. We propose a method providing the number of visits required to reach higher redshifts. We use the results to design a set of optimized DD surveys for SN Ia cosmology taking full advantage of spectroscopic resources for host galaxy redshift measurements. The most accurate cosmological measurements are achieved with deep rolling surveys characterized by a high cadence (1 day), a rolling strategy (at least two seasons of observation per field), and ultradeep (z ≳ 0.8) and deep (z ≳ 0.6) fields. A deterministic scheduler including a gap recovery mechanism is critical to achieving a high-quality DD survey.

79 ASTRONOMY AND ASTROPHYSICS↗