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DOE OSTI2022

Constraining the baryonic feedback with cosmic shear using the DES Year-3 small-scale measurements

ABSTRACT We use the small scales of the Dark Energy Survey (DES) Year-3 cosmic shear measurements, which are excluded from the DES Year-3 cosmological analysis, to constrain the baryonic feedback. To model the baryonic feedback, we adopt a baryonic correction model and use the numerical package baccoemu to accelerate the evaluation of the baryonic non-linear matter power spectrum. We design our analysis pipeline to focus on the constraints of the baryonic suppression effects, utilizing the implication given by a principal component analysis on the Fisher forecasts. Our constraint on the baryonic effects can then be used to better model and ameliorate the effects of baryons in producing cosmological constraints from the next-generation large-scale structure surveys. We detect the baryonic suppression on the cosmic shear measurements with a ∼2σ significance. The characteristic halo mass for which half of the gas is ejected by baryonic feedback is constrained to be $M_c \gt 10^{13.2} \, h^{-1} \, \mathrm{M}_{\odot }$ (95 per cent C.L.). The best-fitting baryonic suppression is $\sim 5{{\ \rm per\ cent}}$ at $k=1.0 \, {\rm Mpc}\ h^{-1}$ and $\sim 15{{\ \rm per\ cent}}$ at $k=5.0 \, {\rm Mpc} \ h^{-1}$. Our findings are robust with respect to the assumptions about the cosmological parameters, specifics of the baryonic model, and intrinsic alignments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Galaxy cluster matter profiles - I. Self-similarity, mass calibration, and observable-mass relation validation employing cluster mass posteriors

We present a study of the weak lensing inferred matter profiles ΔΣ(R) of 698 South Pole Telescope (SPT) thermal Sunyaev-Zel’dovich effect (tSZE) selected and MCMF optically confirmed galaxy clusters in the redshift range 0.25 < z < 0.94 that have associated weak gravitational lensing shear profiles from the Dark Energy Survey (DES). Rescaling these profiles to account for the mass dependent size and the redshift dependent density produces average rescaled matter profiles ΔΣ(R/R200c)/(ρcritR200c) with a lower dispersion than the unscaled ΔΣ(R) versions, indicating a significant degree of self-similarity. Galaxy clusters from hydrodynamical simulations also exhibit matter profiles that suggest a high degree of self-similarity, with RMS variation among the average rescaled matter profiles with redshift and mass falling by a factor of approximately six and 23, respectively, compared to the unscaled average matter profiles. We employed this regularity in a new Bayesian method for weak lensing mass calibration that employs the so-called cluster mass posterior P(M200|ζ̂, λ̂, z), which describes the individual cluster masses given their tSZE (ζ̂) and optical (λ̂, z) observables. This method enables simultaneous constraints on richness λ-mass and tSZE detection significance ζ-mass relations using average rescaled cluster matter profiles. We validated the method using realistic mock datasets and present observable-mass relation constraints for the SPT×DES sample, where we constrained the amplitude, mass trend, redshift trend, and intrinsic scatter. Our observable-mass relation results are in agreement with the mass calibration derived from the recent cosmological analysis of the SPT×DES data based on a cluster-by-cluster lensing calibration. Our new mass calibration technique offers a higher efficiency when compared to the single cluster calibration technique. We present new validation tests of the observable-mass relation that indicate the underlying power-law form and scatter are adequate to describe the real cluster sample but that also suggest a redshift variation in the intrinsic scatter of the λ-mass relation may offer a better description. In addition, the average rescaled matter profiles offer high signal-to-noise ratio (S/N) constraints on the shape of real cluster matter profiles, which are in good agreement with available hydrodynamical ΛCDM simulations. This high S/N profile contains information about baryon feedback, the collisional nature of dark matter, and potential deviations from general relativity.Key words: gravitational lensing: weak / galaxies: clusters: general / large-scale structure of Universe

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Monte Carlo control loops for cosmic shear cosmology with DES Year 1 data

Weak lensing by large-scale structure is a powerful probe of cosmology and of the dark universe. This cosmic shear technique relies on the accurate measurement of the shapes and redshifts of background galaxies and requires precise control of systematic errors. Monte Carlo control loops (MCCL) is a forward modeling method designed to tackle this problem. It relies on the ultra fast image generator (UFig) to produce simulated images tuned to match the target data statistically, followed by calibrations and tolerance loops. Here, we present the first end-to-end application of this method, on the Dark Energy Survey (DES) Year 1 wide field imaging data. We simultaneously measure the shear power spectrum $C_ℓ$ and the redshift distribution $n(z)$ of the background galaxy sample. The method includes maps of the systematic sources, point spread function (PSF), an approximate Bayesian computation (ABC) inference of the simulation model parameters, a shear calibration scheme, and a fast method to estimate the covariance matrix. We find a close statistical agreement between the simulations and the DES Y1 data using an array of diagnostics. In a nontomographic setting, we derive a set of $C_ℓ$ and $n(z)$ curves that encode the cosmic shear measurement, as well as the systematic uncertainty. Following a blinding scheme, we measure the combination of $Ω_m$, $σ_8$, and intrinsic alignment amplitude $A_{IA}$, defined as $S_8D_{IA}=σ_8(Ω_m/0.3)^{0.5}D_{IA}$, where $D_{IA}=1-0.11(A_{IA}-1)$. We find $S_8D_{IA}=0.8954_{-0.039}^{+0.054}$, where systematics are at the level of roughly 60% of the statistical errors. We discuss these results in the context of earlier cosmic shear analyses of the DES Y1 data. Our findings indicate that this method and its fast runtime offer good prospects for cosmic shear measurements with future wide-field surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Large-Scale Classification of Urban Structural Units From Remote Sensing Imagery

Remote sensing in combination with deep learning has become instrumental for efficiently and accurately classifying land-use and land-cover across large geographic areas. These technologies have also been successful in characterizing urban environments in terms of their structural units, structure types, or morphological regions. In these approaches, an urban area is partitioned into regions that exhibit homogeneous physical characteristics. However, existing approaches are typically limited to a single city, use inconsistent typologies, and lack scalability and generalization capacity. In this article, we propose an urban structural units categorization scheme and demonstrate its utility by applying it to 13 cities. Inspired by the lack of scalability and generalization capacity in urban structural units mapping, we extend the reach of deep learning and conduct a set of classification experiments in all 13 cities. These experiments offer insights into the strengths and limitations of deep neural networks for classifying urban structural units over diverse geographic regions and on heterogeneous collections of satellite imagery. The efficacy of the proposed deep learning approach is compared to a baseline method of multiscale image features and support vector machines. Our validation on five cities shows that better performance is achieved with deep neural networks. Additionally, we evaluate the impact of input size, model depth, and spatial pyramid pooling to assess the generalization capacity of deep neural networks.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Dark energy survey year 1 results: Constraining baryonic physics in the Universe

ABSTRACT Measurements of large-scale structure are interpreted using theoretical predictions for the matter distribution, including potential impacts of baryonic physics. We constrain the feedback strength of baryons jointly with cosmology using weak lensing and galaxy clustering observables (3 × 2pt) of Dark Energy Survey (DES) Year 1 data in combination with external information from baryon acoustic oscillations (BAO) and Planck cosmic microwave background polarization. Our baryon modelling is informed by a set of hydrodynamical simulations that span a variety of baryon scenarios; we span this space via a Principal Component (PC) analysis of the summary statistics extracted from these simulations. We show that at the level of DES Y1 constraining power, one PC is sufficient to describe the variation of baryonic effects in the observables, and the first PC amplitude (Q1) generally reflects the strength of baryon feedback. With the upper limit of Q1 prior being bound by the Illustris feedback scenarios, we reach $\sim 20{{\ \rm per\ cent}}$ improvement in the constraint of $S_8=\sigma _8(\Omega _{\rm m}/0.3)^{0.5}=0.788^{+0.018}_{-0.021}$ compared to the original DES 3 × 2pt analysis. This gain is driven by the inclusion of small-scale cosmic shear information down to 2.5 arcmin, which was excluded in previous DES analyses that did not model baryonic physics. We obtain $S_8=0.781^{+0.014}_{-0.015}$ for the combined DES Y1+Planck EE+BAO analysis with a non-informative Q1 prior. In terms of the baryon constraints, we measure $Q_1=1.14^{+2.20}_{-2.80}$ for DES Y1 only and $Q_1=1.42^{+1.63}_{-1.48}$ for DESY1+Planck EE+BAO, allowing us to exclude one of the most extreme AGN feedback hydrodynamical scenario at more than 2σ.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Detection of Cosmological 21 cm Emission with the Canadian Hydrogen Intensity Mapping Experiment

We present a detection of 21 cm emission from large-scale structure (LSS) between redshift 0.78 and 1.43 made with the Canadian Hydrogen Intensity Mapping Experiment. Radio observations acquired over 102 nights are used to construct maps that are foreground filtered and stacked on the angular and spectral locations of luminous red galaxies (LRGs), emission-line galaxies (ELGs), and quasars (QSOs) from the eBOSS clustering catalogs. We find decisive evidence for a detection when stacking on all three tracers of LSS, with the logarithm of the Bayes factor equal to 18.9 (LRG), 10.8 (ELG), and 56.3 (QSO). An alternative frequentist interpretation, based on the likelihood ratio test, yields a detection significance of 7.1σ (LRG), 5.7σ (ELG), and 11.1σ (QSO). These are the first 21 cm intensity mapping measurements made with an interferometer. We constrain the effective clustering amplitude of neutral hydrogen (H I ), defined as ${{ \mathcal A }}_{{\rm{H}}\,{\rm\small{I}}}\equiv {10}^{3}\,{{\rm{\Omega }}}_{{\rm{H}}\,{\rm\small{I}}}\left({b}_{{\rm{H}}\,{\rm\small{I}}}+\langle \,f{\mu }^{2}\rangle \right)$, where Ω H I is the cosmic abundance of H I , b H I is the linear bias of H I , and $\langle$fμ 2 $\rangle$ = 0.552 encodes the effect of redshift-space distortions at linear order. We find ${{ \mathcal A }}_{{\rm{H}}\,{\rm\small{I}}}={1.51}_{-0.97}^{+3.60}$ for LRGs (z = 0.84), ${{ \mathcal A }}_{{\rm{H}}\,{\rm\small{I}}}={6.76}_{-3.79}^{+9.04}$ for ELGs (z = 0.96), and ${{ \mathcal A }}_{{\rm{H}}\,{\rm\small{I}}}={1.68}_{-0.67}^{+1.10}$ for QSOs (z = 1.20), with constraints limited by modeling uncertainties at nonlinear scales. We are also sensitive to bias in the spectroscopic redshifts of each tracer, and we find a nonzero bias Δ v = - 66 ± 20 km s -1 for the QSOs. We split the QSO catalog into three redshift bins and have a decisive detection in each, with the upper bin at z = 1.30 producing the highest-redshift 21 cm intensity mapping measurement thus far.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Non-local contribution from small scales in galaxy–galaxy lensing: comparison of mitigation schemes

ABSTRACT Recent cosmological analyses with large-scale structure and weak lensing measurements, usually referred to as 3 × 2pt, had to discard a lot of signal to noise from small scales due to our inability to accurately model non-linearities and baryonic effects. Galaxy–galaxy lensing, or the position–shear correlation between lens and source galaxies, is one of the three two-point correlation functions that are included in such analyses, usually estimated with the mean tangential shear. However, tangential shear measurements at a given angular scale θ or physical scale R carry information from all scales below that, forcing the scale cuts applied in real data to be significantly larger than the scale at which theoretical uncertainties become problematic. Recently, there have been a few independent efforts that aim to mitigate the non-locality of the galaxy–galaxy lensing signal. Here, we perform a comparison of the different methods, including the Y-transformation, the point-mass marginalization methodology, and the annular differential surface density statistic. We do the comparison at the cosmological constraints level in a combined galaxy clustering and galaxy–galaxy lensing analysis. We find that all the estimators yield equivalent cosmological results assuming a simulated Rubin Observatory Legacy Survey of Space and Time (LSST) Year 1 like set-up and also when applied to DES Y3 data. With the LSST Y1 set-up, we find that the mitigation schemes yield ∼1.3 times more constraining S8 results than applying larger scale cuts without using any mitigation scheme.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Challenges of standard halo models in constraining galaxy properties from cosmic infrared background anisotropies

The halo model, combined with halo occupation distribution (HOD) prescriptions, is widely used to interpret cosmic infrared background (CIB) anisotropies and extract physical information about star-forming galaxies and their connection to large-scale structures. Recent CIB-specific implementations of the halo model have adopted more physical parameterizations. However, the extent to which these models can reliably recover meaningful physical parameters remains uncertain. We assessed whether the current parameterization of CIB halo models is sufficient to recover astrophysical quantities, such as star formation efficiency, η(M h , z), and halo mass at which the peak of star formation efficiency occurs, M max , when fit to mock data. We also assessed whether discrepancies arise from assumptions about galaxy emission (the HOD ingredients) or from more fundamental components in the halo model, such as bias and matter clustering. We fit the M21 CIB HOD model, implemented within the halo model framework, to mock CIB power spectra and star formation rate density (SFRD) data generated from the SIDES-Uchuu simulation, and compared the best-fit parameters to the known simulation inputs. We then repeated the analysis using a simplified version of the simulation (SSU), explicitly designed to match the HOD assumptions. A detailed comparison of model and simulation outputs was carried out to trace the origin of observed discrepancies. While the M21 HOD model provides a good fit to the mock data, it failed to recover the intrinsic parameters accurately, particularly the halo mass at which star formation efficiency peaks. This mismatch persists even when fitting data generated with the same model assumptions. We find strong agreement (within 5%) in the emission-related components (SFRD, emissivity), but observe a scale- and redshift-dependent offset exceeding 20% in the two-halo term of the CIB power spectrum. This likely arises from limitations in the treatment of halo bias and matter clustering within the linear approximation. Additionally, incorporating scatter in the SFR–halo mass relation and the spectral energy distribution (SED) templates significantly affects the shot noise (∼50%), but has only a modest impact (less than 10%) on the clustered component. These results suggest that recovering physical parameters from CIB clustering requires improvements to the cosmological ingredients of the halo model framework, such as adopting scale-dependent halo bias and nonlinear matter power spectra in addition to careful modeling of emission physics.

cosmic background radiation↗
DOE OSTI2022

Dark Energy Survey Year 3 results: Measurement of the baryon acoustic oscillations with three-dimensional clustering

The three-dimensional correlation function offers an effective way to summarize the correlation of the large-scale structure even for imaging galaxy surveys. We have applied the projected three-dimensional correlation function, ξ p to measure the baryonic acoustic oscillations (BAO) scale on the first-three years Dark Energy Survey data. The sample consists of about 7 million galaxies in the redshift range 0.6< z p < 1.1 over a footprint of 4108 deg 2 . Our theory modeling includes the impact of realistic true redshift distributions beyond Gaussian photo-z approximation. ξ p is obtained by projecting the three-dimensional correlation to the transverse direction. To increase the signal-to-noise of the measurements, we have considered a Gaussian stacking window function in place of the commonly used top-hat. ξ p is sensitive to D M (z eff )/r s , the ratio between the comoving angular diameter distance and the sound horizon. Using the full sample, D M (z eff )/r s is constrained to be19.00 ± 0.67 (top-hat) and 19.15 ± 0.58 (Gaussian) at z eff = 0.835. The constraint is weaker than the angular correlation w constraint 18.84 ± 0.50), and we trace this to the fact that the BAO signals are heterogeneous across redshift. While ξ p responds to the heterogeneous signals by enlarging the error bar, w can still give a tight bound on D M s in this case. When a homogeneous BAO-signal subsample in the range 0.7 < z p <1.0 (z eff = 0.845) is considered, ξp yields 19.80 ± 0.67 (top-hat) and 19.84 ± 0.53 (Gaussian). The latter is mildly stronger than the w constraint (19.86 ± 0.55). We find that the ξ p results are more sensitive to photo-z because ξ p keeps the three-dimensional clustering information causing it to be more prone to photo-z noise. The Gaussian window gives more robust results than the top-hat as the former is designed to suppress the low signal modes. ξ p and the angular statistics such as w have their own pros and cons, and they serve an important crosscheck with each other.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Cosmological implications from two decades of spectroscopic surveys at the Apache Point Observatory

We present the cosmological implications from final measurements of clustering using galaxies, quasars, and Lyα forests from the completed Sloan Digital Sky Survey (SDSS) lineage of experiments in large-scale structure. These experiments, composed of data from SDSS, SDSS-II, BOSS, and eBOSS, offer independent measurements of baryon acoustic oscillation (BAO) measurements of angular-diameter distances and Hubble distances relative to the sound horizon, r d , from eight different samples and six measurements of the growth rate parameter, fσ 8 , from redshift-space distortions (RSD). This composite sample is the most constraining of its kind and allows us to perform a comprehensive assessment of the cosmological model after two decades of dedicated spectroscopic observation. We show that the BAO data alone are able to rule out dark-energy-free models at more than eight standard deviations in an extension to the flat, ΛCDM model that allows for curvature. When combined with Planck Cosmic Microwave Background (CMB) measurements of temperature and polarization, under the same model, the BAO data provide nearly an order of magnitude improvement on curvature constraints relative to primary CMB constraints alone. Independent of distance measurements, the SDSS RSD data complement weak lensing measurements from the Dark Energy Survey (DES) in demonstrating a preference for a flat ΛCDM cosmological model when combined with Planck measurements. The combined BAO and RSD measurements indicate σ 8 =0.85±0.03, implying a growth rate that is consistent with predictions from Planck temperature and polarization data and with General Relativity. When combining the results of SDSS BAO and RSD, Planck, Pantheon Type Ia supernovae (SNe Ia), and DES weak lensing and clustering measurements, all multiple-parameter extensions remain consistent with a ΛCDM model. Regardless of cosmological model, the precision on each of the three parameters, Ω Λ , H 0 , and σ 8 , remains at roughly 1%, showing changes of less than 0.6% in the central values between models. In a model that allows for free curvature and a time-evolving equation of state for dark energy, the combined samples produce a constraint Ω K =-0.0022±0.0022. The dark energy constraints lead to w 0 =-0.909±0.081 and w a =$-0.49_{-0.30}^{+0.35}$, corresponding to an equation of state of w p =-1.018±0.032 at a pivot redshift z p =0.29 and a Dark Energy Task Force Figure of Merit of 94. The inverse distance ladder measurement under this model yields H 0 =68.18±0.79 km s -1 Mpc -1 , remaining in tension with several direct determination methods; the BAO data allow Hubble constant estimates that are robust against the assumption of the cosmological model. In addition, the BAO data allow estimates of H 0 that are independent of the CMB data, with similar central values and precision under a ΛCDM model. Our most constraining combination of data gives the upper limit on the sum of neutrino masses at Σm ν <0.115 eV (95% confidence). Finally, we consider the improvements in cosmology constraints over the last decade by comparing our results to a sample representative of the period 2000-2010. We compute the relative gain across the five dimensions spanned by w, Ω k , Σm ν , H 0 , and σ 8 and find that the SDSS BAO and RSD data reduce the total posterior volume by a factor of 40 relative to the previous generation. Finally, adding again the Planck, DES, and Pantheon SN Ia samples leads to an overall contraction in the five-dimensional posterior volume of 3 orders of magnitude.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Dark Energy Survey year 6 results: Magnification modeling and its impact on galaxy clustering and galaxy-galaxy lensing cosmology

Gravitational lensing magnification alters the observed spatial distribution of galaxies and must be accounted for to prevent biases in cosmological probes of the large-scale structure. We investigate its effects on the Dark Energy Survey Year 6 galaxy clustering and galaxy-galaxy lensing analyses using the fiducial lens (position tracer) sample M ag L im++. Magnification bias is parameterized by a coefficient that describes the response of the number of selected objects per unlensed area element to a change in the lensing convergence. We quantify this coefficient using the BALROG synthetic source injection catalog to account for the complexity of the selection function, and compare these results with simplified estimates. The resulting values of the magnification coefficients for each redshift bin are [3.16 ± 0.08, 2.76 ± 0.21, 4.09 ± 0.15, 4.42 ± 0.16, 4.90 ± 0.29, 4.83 ± 0.25]. Relative to Year 3, this analysis provides more precise and accurate magnification bias estimates through a larger BALROG area and reweighting to better match the data properties. Here, the cosmological results are robust when tested against various magnification parameter prior choices and also when adding cross-clustering between lens redshift bins. Neglecting magnification, however, introduces significant systematic shifts: relative to the fiducial analysis with Gaussian priors centered on the BALROG -derived estimates, we observe shifts of 1.37σ in S 8 and -0.84σ in Ω m (with cosmic shear included: -0.61σ in S 8 and -0.71σ in Ω m ), in agreement with findings from simulated data, demonstrating that magnification must be modeled to avoid biases. Freeing the magnification bias in lens bin 2 leads to unphysical negative values, further justifying its exclusion from the fiducial Year 6 analysis.

Cosmological parameters↗
DOE OSTI2026

Selectivity mechanisms of ion intercalation in Prussian blue analogs

Prussian blue analogs (PBAs) are a family of materials with facile, reversible, and selective ion transport capability for various ions via electrochemical intercalation, owing to their vacancy structure. The large tunable compositional space of PBAs allows for manipulation of intercalation behavior and selectivity by controlling structural vacancy level through choice of transition metal centers and modifications to the synthesis process. However, a lack of understanding of the mechanisms of ion selectivity hinders the material’s design process. Here, for this work, we investigated the origins of ion selectivity using a model PBA, copper hexacyanoferrate, and focused on eight technologically and biologically prominent ions, for which we determined a sequence of selectivity: Rb + > K + > Na + > Ba 2+ > Sr 2+ ≈ Ca 2+ > Mg 2+ > Li + . We provide electrochemical, structural, and redox evidence of strong correlation between the ion identity, the dominant charge-compensating redox, and preferred occupancy site. Specifically, using synchrotron anomalous X-ray diffraction (AXRD), we reveal that monovalent ions exhibit significant association with the corner sites of the unit cell and iron redox, whereas divalent ions display affinity toward the center site with higher ratios of copper redox. Informed by selectivity results, we applied CuHCFe to Li purification and achieved 99.9% purity. Our findings demonstrate an approach to elucidating ion intercalation behavior in order to distinguish and manipulate material properties to optimize separation performance.

Prussian blue analog↗
DOE OSTI2020

Mutational signatures in tumours induced by high and low energy radiation in Trp53 deficient mice

Lonising radiation (IR) is a recognised carcinogen responsible for cancer development in patients previously treated using radiotherapy, and in individuals exposed as a result of accidents at nuclear energy plants. However, the mutational signatures induced by distinct types and doses of radiation are unknown. Here, we analyse the genetic architecture of mammary tumours, lymphomas and sarcomas induced by high ( 56 Fe-ions) or low (gamma) energy radiation in mice carrying Trp53 loss of function alleles. In mammary tumours, high-energy radiation is associated with induction of focal structural variants, leading to genomic instability and Met amplification. Gamma-radiation is linked to large-scale structural variants and a point mutation signature associated with oxidative stress. The genomic architecture of carcinomas, sarcomas and lymphomas arising in the same animals are significantly different. Our study illustrates the complex interactions between radiation quality, germline Trp53 deficiency and tissue/cell of origin in shaping the genomic landscape of IR-induced tumours.

Science & Technology - Other Topics↗
DOE OSTI2025

ODIN: Probing the LAE Ly α Luminosity Function across Cosmic Time and Different Environments

The ubiquity and relative ease of discovery make 2 ≲ z ≲ 5 Lyα emitting galaxies (LAEs) ideal tracers for large-scale structure of the distant Universe. In addition, because Lyα is a resonance line, but frequently observed at large equivalent width, it is potentially a probe of galaxy evolution. The LAE Lyα luminosity function (LF) is an essential measurement for making progress on both of these topics. Although several studies have computed the LAE LF, very few have delved into how the function varies with environment. The large area and depth of the One-hundred-deg 2 DECam Imaging in Narrowbands (ODIN) survey makes such measurements possible at the cosmic noon redshifts of z ∼ 2.4, 3.1, and 4.5. In this initial work, we present algorithms needed to rigorously compute the LAE LF, and test them on the ∼16,000 ODIN LAEs found in the extended COSMOS field. Using these limited samples, we find weak evidence that protocluster environments suppress the numbers of faint LAEs compared to the field. We also find that the LF decreases in number density and evolves towards a steeper faint-end slope over cosmic time from z ∼ 4.5 to z ∼ 2.4.

Lyman-alpha galaxies↗
DOE OSTI2024

Spatially Local Surrogate Modeling of Subgrid-Scale Effects in Idealized Atmospheric Flows: A Deep Learned Approach Using High-Resolution Simulation Data

Abstract We introduce a machine learned surrogate model from high-resolution simulation data to capture the subgrid-scale effects in dry, stratified atmospheric flows. We use deep neural networks (NNs) to model the spatially local state differences between a coarse-resolution simulation and a high-resolution simulation. The setup enables the capture of both dissipative and antidissipative effects in the state differences. The NN model is able to accurately capture the state differences in offline tests outside the training regime. In online tests intended for production use, the NN-coupled coarse simulation has higher accuracy over a significant period of time compared to the coarse-resolution simulation without any correction. We provide evidence of the capability of the NN model to accurately capture high-gradient regions in the flow field. With the accumulation of the errors, the NN-coupled simulation becomes computationally unstable after approximately 90 coarse simulation time steps. Insights gained from these surrogate models further pave the way for formulating stable, complex, physics-based spatially local NN models which are driven by traditional subgrid-scale turbulence closure models. Significance Statement Flows in the atmosphere are highly chaotic and turbulent, comprising flow structures of broad scales. For effective computational modeling of atmospheric flows, the effects of the small- and large-scale structures need to be captured by the simulations. Capturing the small-scale structures requires fine-resolution simulations. Even with the current state-of-the-art supercomputers, it can be prohibitively expensive to simulate these flows when computed for the entire earth over climate time scales. Thus, it is necessary to focus on the larger-scale structures using a coarse-resolution simulation while capturing the effects of the smaller-scale structures using some parameterization (approximation) scheme and incorporating it into the coarse-resolution simulation. We use machine learning to model the effects of the small-scale structures (subgrid-scale effects) in atmospheric flows. Data from a fine-resolution simulation is used to compute the missing subgrid-scale effects in coarse-resolution simulations. We then use machine learning models to approximate these differences between the coarse- and fine-resolution simulations. We see improved accuracy for the coarse-resolution simulations when corrected using these machine learned models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Crystal structure of [Th 3 (Cp*) 3 (O)(OH) 3 ] 2 Cl 2 (N 3 ) 6 : a discrete molecular capsule built from multinuclear organothorium cluster cations

An unusually large and structurally complex charge-neutral polynuclear cluster, hexa-μ 2 -azido-di-μ 3 -chlorido-hexa-μ 2 -hydroxido-di-μ 3 -oxido-hexakis(pentamethylcyclopentadienyl)hexathorium–diethyl ether–tetrahydrofuran (1/0.56/1.44), [Th 3 (C 10 H 15 ) 6 Cl 3 (N 3 ) 6 (OH) 6 O 2 ]·0.56C 4 H 10 O·1.44C 4 H 8 O or [Th 3 (Cp*) 3 (O)(OH) 3 ] 2 Cl 2 (N 3 ) 6 ·0.56C 4 H 10 O·1.44C 4 H 8 O (Cp* = [pentamethylcyclopentadienyl]) − , has been crystallized as a mixed tetrahydrofuran/diethyl ether solvate and structurally characterized. The molecule contains a number of unusual features, the most notable being a finite yet exceptionally long cyclic metal-azido chain. These rare features are the consequence of both sterically protecting Cp* ligands and highly bridging oxide and hydroxide ligands in the same system and illustrate the interesting new possibilities that can arise from combining organometallic and solvothermal f -block element chemistry.

Kelley, Steven P.↗
DOE OSTI2024

CryoTransformer: a transformer model for picking protein particles from Cryo-EM micrographs

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them. However, the widely used template-based particle picking process requires some manual particle picking and is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) can potentially automate particle picking, the current AI methods pick particles with low precision or low recall. The erroneously picked particles can severely reduce the quality of reconstructed protein structures, especially for the micrographs with low signal-to-noise-ratio (SNR).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Collaborative Research: Large Scale Intensity Mapping of Neutral Hydrogen with the Tianlai Pathfinders

This program developed new analysis methods for current and upcoming cosmological hydrogen 21cm intensity mapping data from the Tianlai experiment, located in a radio quiet area of Western China. Such mapping provides important clues to the evolution of gas and galaxies in the early universe. The experiment measured the three-dimensional large-scale structure of the universe via hydrogen emission from distant galaxies. The primary goal was to develop ways to handle the large foreground signal, which has been previously quantified in a pathfinder 21cm program conducted on NSF's Green Bank Telescope.

79 ASTRONOMY AND ASTROPHYSICS↗