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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Establishing resolution-improved NMR spectroscopy in high magnetic fields with unknown spatiotemporal variations

A half-century quest for higher magnetic fields has been an integral part of the progress undergone in the Nuclear Magnetic Resonance (NMR) study of materials’ structure and dynamics. Because 2D NMR relies on systematic changes in coherences’ phases as a function of an encoding time varied over a series of independent experiments, it generally cannot be applied in temporally unstable fields. This precludes most NMR methods from being used to characterize samples situated in hybrid or resistive magnets that are capable of achieving extremely high magnetic field strength. Recently, “ultrafast” NMR has been developed into an effective and widely applicable methodology enabling the acquisition of a multidimensional NMR spectrum in a single scan; it can therefore be used to partially mitigate the effects of temporally varying magnetic fields. Nevertheless, the strong interference of fluctuating fields with the spatial encoding of ultrafast NMR still severely restricts measurement sensitivity and resolution. Here, we introduce a strategy for obtaining high resolution NMR spectra that exploits the immunity of intermolecular zero-quantum coherences (iZQCs) to field instabilities and inhomogeneities. The spatial encoding of iZQCs is combined with a J-modulated detection scheme that removes the influence of arbitrary field inhomogeneities during acquisition. This new method can acquire high-resolution one-dimensional NMR spectra in large inhomogeneous and fluctuating fields, and it is tested with fields experimentally modeled to mimic those of resistive and resistive-superconducting hybrid magnets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Demonstration of a quantum key distribution network in urban fibre-optic communication lines

We report the results of the implementation of a quantum key distribution (QKD) network using standard fibre communication lines in Moscow. The developed QKD network is based on the paradigm of trusted repeaters and allows a common secret key to be generated between users via an intermediate trusted node. The main feature of the network is the integration of the setups using two types of encoding, i.e. polarisation encoding and phase encoding. One of the possible applications of the developed QKD network is the continuous key renewal in existing symmetric encryption devices with a key refresh time of up to 14 s. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Global biogeography of microbial nitrogen-cycling traits in soil

Microorganisms drive much of the Earth’s nitrogen (N) cycle, but we still lack a global overview of the abundance and composition of the microorganisms carrying out soil N processes. To address this gap, we characterized the biogeography of microbial N traits, defined as eight N-cycling pathways, using publically available soil metagenomes. The relative frequency of N pathways varied consistently across soils, such that the frequencies of the individual N pathways were positively correlated across the soil samples. Habitat type, soil carbon, and soil N largely explained the total N pathway frequency in a sample. In contrast, we could not identify major drivers of the taxonomic composition of the N functional groups. Further, the dominant genera encoding a pathway were generally similar among habitat types. The soil samples also revealed an unexpectedly high frequency of bacteria carrying the pathways required for dissimilatory nitrate reduction to ammonium, a little-studied N process in soil. Finally, phylogenetic analysis showed that some microbial groups seem to be N-cycling specialists or generalists. For instance, taxa within the Deltaproteobacteria encoded all eight N pathways, whereas those within the Cyanobacteria primarily encoded three pathways. Overall, this trait-based approach provides a baseline for investigating the relationship between microbial diversity and N cycling across global soils.

Nelson, Michaeline B.↗
DOE OSTI2018

Structural and functional insights into the unique CBS–CP12 fusion protein family in cyanobacteria

Cyanobacteria are important photosynthetic organisms inhabiting a range of dynamic environments. This phylum is distinctive among photosynthetic organisms in containing genes encoding uncharacterized cystathionine β-synthase (CBS)–chloroplast protein (CP12) fusion proteins. These consist of two domains, each recognized as stand-alone photosynthetic regulators with different functions described in cyanobacteria (CP12) and plants (CP12 and CBSX). Here we show that CBS–CP12 fusion proteins are encoded in distinct gene neighborhoods, several unrelated to photosynthesis. Most frequently, CBS–CP12 genes are in a gene cluster with thioredoxin A (TrxA), which is prevalent in bloom-forming, marine symbiotic, and benthic mat cyanobacteria. Focusing on a CBS–CP12 from Microcystis aeruginosa PCC 7806 encoded in a gene cluster with TrxA, we reveal that the domain fusion led to the formation of a hexameric protein. We show that the CP12 domain is essential for hexamerization and contains an ordered, previously structurally uncharacterized N-terminal region. We provide evidence that CBS–CP12, while combining properties of both regulatory domains, behaves different from CP12 and plant CBSX. It does not form a ternary complex with phosphoribulokinase (PRK) and glyceraldehyde-3-phosphate dehydrogenase. Instead, CBS–CP12 decreases the activity of PRK in an AMP-dependent manner. We propose that the novel domain architecture and oligomeric state of CBS–CP12 expand its regulatory function beyond those of CP12 in cyanobacteria.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

The regulatory and transcriptional landscape associated with carbon utilization in a filamentous fungus

Filamentous fungi, such as Neurospora crassa , are very efficient in deconstructing plant biomass by the secretion of an arsenal of plant cell wall-degrading enzymes, by remodeling metabolism to accommodate production of secreted enzymes, and by enabling transport and intracellular utilization of plant biomass components. Although a number of enzymes and transcriptional regulators involved in plant biomass utilization have been identified, how filamentous fungi sense and integrate nutritional information encoded in the plant cell wall into a regulatory hierarchy for optimal utilization of complex carbon sources is not understood. Here, we performed transcriptional profiling of N. crassa on 40 different carbon sources, including plant biomass, to provide data on how fungi sense simple to complex carbohydrates. From these data, we identified regulatory factors in N. crassa and characterized one (PDR-2) associated with pectin utilization and one with pectin/hemicellulose utilization (ARA-1). Using in vitro DNA affinity purification sequencing (DAP-seq), we identified direct targets of transcription factors involved in regulating genes encoding plant cell wall-degrading enzymes. In particular, our data clarified the role of the transcription factor VIB-1 in the regulation of genes encoding plant cell wall-degrading enzymes and nutrient scavenging and revealed a major role of the carbon catabolite repressor CRE-1 in regulating the expression of major facilitator transporter genes. These data contribute to a more complete understanding of cross talk between transcription factors and their target genes, which are involved in regulating nutrient sensing and plant biomass utilization on a global level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Photo-printing of faceted DNA patchy particles

Patchy particles with shape complementarity can serve as building blocks for assembling colloidal superstructures. Alternatively, encoding information on patches using DNA can direct assembly into a variety of crystalline or other preprogrammed structures. Here, we present a tool where DNA is used both to engineer shape and to encode information on colloidal particles. Two reactive oil emulsions with different but complementary DNA (cDNA) brushes are assembled into CsCl-like crystalline lattices. The DNA brushes are recruited to and ultimately localized at the junctions between neighboring droplets, which gives rise to DNA-encoded faceted patches. The emulsions are then solidified by ultraviolet (UV) polymerization, producing faceted patchy particles. The facet size and DNA distribution are determined by the balance between the DNA binding energy and the elastic deformation energy of droplets. This method leads to a variety of new patchy particles with directional interactions in scalable quantities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Adaptive nonequilibrium design of actin-based metamaterials: Fundamental and practical limits of control

The adaptive and surprising emergent properties of biological materials self-assembled in far-from-equilibrium environments serve as an inspiration for efforts to design nanomaterials. In particular, controlling the conditions of self-assembly can modulate material properties, but there is no systematic understanding of either how to parameterize external control or how controllable a given material can be. Here, we demonstrate that branched actin networks can be encoded with metamaterial properties by dynamically controlling the applied force under which they grow and that the protocols can be selected using multi-task reinforcement learning. These actin networks have tunable responses over a large dynamic range depending on the chosen external protocol, providing a pathway to encoding “memory” within these structures. Interestingly, we obtain a bound that relates the dissipation rate and the rate of “encoding” that gives insight into the constraints on control—both physical and information theoretical. Taken together, these results emphasize the utility and necessity of nonequilibrium control for designing self-assembled nanostructures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Structural insights into VRC01-class bnAb precursors with diverse light chains elicited in the IAVI G001 human vaccine trial

The development of germline-targeting vaccines represents a potentially transformative strategy to elicit broadly neutralizing antibodies (bnAbs) against HIV and other antigenically diverse pathogens. Here, we report on structural characterization of vaccine-elicited VRC01-class bnAb precursors in the IAVI G001 Phase 1 clinical trial with the eOD-GT8 60mer nanoparticle as immunogen. High-resolution X-ray structures of eOD-GT8 monomer complexed with Fabs of five VRC01-class bnAb precursors with >90% germline identity revealed a conserved mode of binding to the HIV CD4-binding site via IGHV1-2-encoded heavy chains, mirroring mature bnAb interactions. The light-chain V-gene diversity emulated VRC01 bnAbs and stabilized antigen engagement, while their conserved five-residue LCDR3 motifs prevented steric clashes. Notably, the VRC01-class bnAb precursors accommodated the N276 glycan, a key barrier in HIV Env recognition, through structural rearrangements in HCDR3 or LCDR1, despite its absence in the immunogen. Surface plasmon resonance analysis showed that 87% of elicited antibodies retained glycan binding capacity, albeit with reduced affinity. These findings validate the ability of eOD-GT8 60mer nanoparticles to prime VRC01-class bnAb precursors with native-like paratopes but with intrinsic glycan adaptability. Structural mimicry of mature bnAbs was observed even with limited somatic hypermutation, indicating that critical features are encoded in the germline repertoire. The structures highlight how germline-encoded features drive bnAb-like recognition at early stages. This work provides molecular evidence supporting germline targeting in humans and provides guidance for designing booster immunogens to shepherd affinity maturation toward broad neutralization.

Science & Technology - Other Topics↗
DOE OSTI2018

Iodate Reduction by Shewanella oneidensis Does Not Involve Nitrate Reductase

Microbial iodate (IO 3 - ) reduction is a major component of the iodine biogeochemical reaction network and is the basis of alternative strategies for remediation of iodine-contaminated environments. The molecular mechanism of microbial IO 3 - reduction, however, is not well understood. In microorganisms displaying IO 3 - and nitrate (NO 3 - ) reduction activities, NO 3 - reductase is postulated to reduce IO 3 - as alternate electron acceptor. In the present study, whole genome analyses of 25 NO 3 - -reducing Shewanella strains identified various combinations of genes encoding one assimilatory (cytoplasmic Nas) and three dissimilatory (membrane-associated Nar and periplasmic Napα and Napβ) NO 3 - reductases. S. oneidensis was the only Shewanella strain whose genome encoded a single NO 3 - reductase (Napβ). Terminal electron acceptor competition experiments in S. oneidensis batch cultures amended with both NO 3 - and IO 3 - demonstrated that neither NO 3 - nor IO 3 - reduction activities were competitively inhibited by the presence of the competing electron acceptor. The lack of involvement of S. oneidensis Napβ in IO 3 - reduction was confirmed via phenotypic analysis of an in-frame gene deletion mutant lacking napβΑ (encoding the NO 3 - -reducing NapβA catalytic subunit). S. oneidensis ΔnapβA was unable to reduce NO 3 - , yet reduced IO 3 - at rates higher than the wild-type strain. Thus, NapβA is required for dissimilatory NO 3 - reduction by S. oneidensis , while neither the assimilatory (Nas) nor dissimilatory (Napα, Napβ, and Nar) NO 3 - reductases are required for IO 3 - reduction. These findings oppose the traditional view that NO 3 - reductase reduces IO 3 - as alternate electron acceptor and indicate that S. oneidensis reduces IO 3 - via an as yet undiscovered enzymatic mechanism.

attenuation↗
DOE OSTI2017

Simultaneous 3D MR elastography of the in vivo mouse brain

The feasibility of sample interval modulation (SLIM) magnetic resonance elastography (MRE) for the in vivo mouse brain is assessed, and an alternative SLIM-MRE encoding method is introduced. In SLIMMRE, the phase accumulation for each motion direction is encoded simultaneously by varying either the start time of the motion encoding gradient (MEG), SLIM-phase constant (SLIM-PC), or the initial phase of the MEG, SLIM-phase varying (SLIM-PV). SLIM-PC provides gradient moment nulling, but the mutual gradient shift necessitates increased echo time (TE). SLIM-PV requires no increased TE, but exhibits nonuniform flow compensation. Comparison was to conventional MRE using six C57BL/6 mice. For SLIMPC, the Spearman’s rank correlation to conventional MRE for the shear storage and loss modulus images were 80% and 76%, respectively, and likewise for SLIM-PV, 73% and 69%, respectively. The results of the Wilcoxon rank sum test showed that there were no statistically significant differences between the spatially averaged shear moduli derived from conventional-MRE, SLIM-PC, and SLIM-PV acquisitions. Both SLIM approaches were comparable to conventional MRE scans with Spearman’s rank correlation of 69%-80% and with 3 times reduction in scan time. As a result, the SLIM-PC method had the best correlation, and SLIM-PV may be a useful tool in experimental conditions, where both measurement time and T2 relaxation is critical.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Dynamically generated decoherence-free subspaces and subsystems on superconducting qubits

Abstract Decoherence-free subspaces and subsystems (DFS) preserve quantum information by encoding it into symmetry-protected states unaffected by decoherence. An inherent DFS of a given experimental system may not exist; however, through the use of dynamical decoupling (DD), one can induce symmetries that support DFSs. Here, we provide the first experimental demonstration of DD-generated decoherence-free subsystem logical qubits. Utilizing IBM Quantum superconducting processors, we investigate two and three-qubit DFS codes comprising up to six and seven noninteracting logical qubits, respectively. Through a combination of DD and error detection, we show that DFS logical qubits can achieve up to a 23% improvement in state preservation fidelity over physical qubits subject to DD alone. This constitutes a beyond-breakeven fidelity improvement for DFS-encoded qubits. Our results showcase the potential utility of DFS codes as a pathway toward enhanced computational accuracy via logical encoding on quantum processors.

Physics↗
DOE OSTI2011

Galaxy bias and non-linear structure formation in general relativity

Length scales probed by the large scale structure surveys are becoming closer and closer to the horizon scale. Further, it has been recently understood that non-Gaussianity in the initial conditions could show up in a scale dependence of the bias of galaxies at the largest possible distances. It is therefore important to take General Relativistic effects into account. Here we provide a General Relativistic generalization of the bias that is valid both for Gaussian and for non-Gaussian initial conditions. The collapse of objects happens on very small scales, while long-wavelength modes are always in the quasi linear regime. Around every small collapsing region, it is therefore possible to find a reference frame that is valid for arbitrary times and where the space time is almost flat: the Fermi frame. Here the Newtonian approximation is applicable and the equations of motion are the ones of the standard N-body codes. The effects of long-wavelength modes are encoded in the mapping from the cosmological frame to the local Fermi frame. At the level of the linear bias, the effect of the long-wavelength modes on the dynamics of the short scales is all encoded in the local curvature of the Universe, which allows us to define a General Relativistic generalization of the bias in the standard Newtonian setting. We show that the bias due to this effect goes to zero as the square of the ratio between the physical wavenumber and the Hubble scale for modes longer than the horizon, confirming the intuitive picture that modes longer than the horizon do not have any dynamical effect. On the other hand, the bias due to non-Gaussianities does not need to vanish for modes longer than the Hubble scale, and for non-Gaussianities of the local kind it goes to a constant. As a further application of our setup, we show that it is not necessary to perform large N-body simulations to extract information about long-wavelength modes: N-body simulations can be done on small scales and long-wavelength modes are encoded simply by adding curvature to the simulation, as well as rescaling the time and the scale.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Adaptive machine learning for time-varying systems: low dimensional latent space tuning

Machine learning (ML) tools such as encoder-decoder convolutional neural networks (CNN) can represent incredibly complex nonlinear functions which map between combinations of images and scalars. For example, CNNs can be used to map combinations of accelerator parameters and images which are 2D projections of the 6D phase space distributions of charged particle beams as they are transported between various particle accelerator locations. Despite their strengths, applying ML to time-varying systems, or systems with shifting distributions, is an open problem, especially for large systems for which collecting new data for re-training is impractical or interrupts operations. Particle accelerators are one example of large time-varying systems for which collecting detailed training data requires lengthy dedicated beam measurements which may no longer be available during regular operations. We present a novel method of adaptive ML for time-varying systems. Our approach is to map very high (N ≈ 100k) dimensional inputs (a combination of scalar parameters and images) into the low dimensional (N ≈ 2) latent space at the output of the encoder section of an encoder-decoder CNN. We then actively tune the low dimensional latent space-based representation of complex system dynamics by the addition of an adaptively tuned feedback vector directly before the decoder sections builds back up to our image-based high-dimensional phase space density representations. This method allows us to learn correlations within and to quickly tune the characteristics of incredibly large parameter space systems and to track their evolution in real time based on feedback without massive new data sets for re-training. We demonstrate that our method can accurately predict and track the phase space of charged particle beams at various locations in a particle accelerator by adaptively adjusting in real-time while the unknown input beam distribution of the accelerator is changing in shape, charge, and offset and while the RF system of the accelerator itself is also changing in an unpredictable way. For FACET-II we demonstrate that such an approach has the potential to use transverse deflecting cavity and energy spread spectrum beam measurements to accurately predict 2D projections of the 6D phase space of the electron beam at the plasma wakefield acceleration interaction point where such diagnostics are unavailable.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Two-dimensional coherent spectrum of high-spin models via a quantum computing approach

Here in this work we present and benchmark a quantum computing approach to calculate the two-dimensional coherent spectrum (2DCS) of high-spin models. Our approach is based on simulating their real-time dynamics in the presence of several magnetic field pulses, which are spaced in time. We utilize the adaptive variational quantum dynamics simulation algorithm for the study due to its compact circuits, which enables simulations over sufficiently long times to achieve the required resolution in frequency space. Specifically, we consider an antiferromagnetic quantum spin model that incorporates Dzyaloshinskii-Moriya interactions and single-ion anisotropy. The obtained 2DCS spectra exhibit distinct peaks at multiples of the magnon frequency, arising from transitions between different eigenstates of the unperturbed Hamiltonian. By comparing the one-dimensional coherent spectrum with 2DCS, we demonstrate that 2DCS provides a higher resolution of the energy spectrum. We further investigate how the quantum resources scale with the magnitude of the spin using two different binary encodings of the high-spin operators: the standard binary encoding and the Gray code. At low magnetic fields both encodings require comparable quantum resources, but at larger field strengths the Gray code is advantageous. Numerical simulations for spin models with increasing number of sites indicate a polynomial system-size scaling for quantum resources. Lastly, we compare the numerical 2DCS with experimental results on a rare-earth orthoferrite system. The observed strength of the magnonic high-harmonic generation signals in the 2DCS of the quantum high-spin model aligns well with the experimental data, showing significant improvement over the corresponding mean-field results.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Differentiable Earth mover’s distance for data compression at the high-luminosity LHC

Abstract The Earth mover’s distance (EMD) is a useful metric for image recognition and classification, but its usual implementations are not differentiable or too slow to be used as a loss function for training other algorithms via gradient descent. In this paper, we train a convolutional neural network (CNN) to learn a differentiable, fast approximation of the EMD and demonstrate that it can be used as a substitute for computing-intensive EMD implementations. We apply this differentiable approximation in the training of an autoencoder-inspired neural network (encoder NN) for data compression at the high-luminosity LHC at CERN The goal of this encoder NN is to compress the data while preserving the information related to the distribution of energy deposits in particle detectors. We demonstrate that the performance of our encoder NN trained using the differentiable EMD CNN surpasses that of training with loss functions based on mean squared error.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Comparative genomics of the genus Desulfitobacterium

The Desulfitobacterium genus comprises anaerobic Gram-positive bacteria, of which the majority are facultative organohalide respirers. We here present the genomes of eight strains of Desulfitobacterium spp., including five strains of Desulfitobacterium hafniense, one strain each from D. dichloroeliminans and D. metallireducens, and one strain that had not been assigned to any species prior to this study. The newly sequenced genomes were compared with four previously published desulfitobacterial genomes. The average genome sizes are 5.5, 4.3 and 3.4 Mbp for D. hafniense, D. dehalogenans and D. dichloroeliminans/metallireducens, respectively. The genomes encode up to seven reductive dehalogenases, the genomes of both D. hafniense DP7 and D. metallireducens 853–15A T did not encode any reductive dehalogenase. The latter result was a surprise as D. metallireducens 853–15A T has been reported to carry out organohalide respiration. Unlike reported for the pce ABCT gene cluster, the other reductive dehalogenase gene clusters do not show any signs of being genetically mobile. All analyzed desulfitobacterial genomes encode a complete cobalamin synthesis pathway. A menaquinone synthesis pathway was found in all strains except D. dichloroeliminans DCA1T. The detailed analysis of the genome sequence of 12 desulfitobacteria from four different species confirmed that this genus has an extremely large metabolic repertoire.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Occurrence of NDM-1-producing Morganella morganii and Proteus mirabilis in a single patient in Portugal: probable in vivo transfer by conjugation

Abstract Objectives To decipher the genetics of acquisition of carbapenemase-encoding genes identified in two carbapenem-resistant Enterobacteriaceae recovered from a single patient in Portugal. Methods Carbapenemase genes were searched by PCR assays and mating-out assays were performed to further characterize the plasmid support of the carbapenemase genes. Genetic characterization of the plasmid supports was performed by whole-plasmid sequencing using the Illumina technology. Results We identified here two NDM-1-producing isolates, namely a Morganella morganii and a Proteus mirabilis, sharing the same blaNDM-1-positive plasmid. This 154 kb plasmid belonged to the IncA/C2 type, recently renamed IncC, and co-harboured two AmpC β-lactamase genes, namely blaCMY-4 and blaDHA-1, in addition to the 16S rRNA methylase gene armA encoding high-level resistance to aminoglycosides. In addition, the M. morganii isolate produced the CTX-M-33 extended-spectrum β-lactamase possessing weak carbapenemase activity, encoded by another plasmid. Conclusions We showed here that, in addition to KPC-type and OXA-181 carbapenemases, which have been identified as widespread in this country, another concern is the emergence of NDM-1-producing enterobacterial isolates in Portugal. We demonstrated here the in vivo plasmid transfer of a blaNDM-1-positive plasmid leading to dissemination of this carbapenemase gene within different enterobacterial species in a single patient.

Aires-de-Sousa, Marta↗
DOE OSTI2022

Photometric redshifts from SDSS images with an interpretable deep capsule network

ABSTRACT Studies of cosmology, galaxy evolution, and astronomical transients with current and next-generation wide-field imaging surveys like the Rubin Observatory Legacy Survey of Space and Time are all critically dependent on estimates of photometric redshifts. Capsule networks are a new type of neural network architecture that is better suited for identifying morphological features of the input images than traditional convolutional neural networks. We use a deep capsule network trained on ugriz images, spectroscopic redshifts, and Galaxy Zoo spiral/elliptical classifications of ∼400 000 Sloan Digital Sky Survey galaxies to do photometric redshift estimation. We achieve a photometric redshift prediction accuracy and a fraction of catastrophic outliers that are comparable to or better than current methods for SDSS main galaxy sample-like data sets (r ≤ 17.8 and zspec ≤ 0.4) while requiring less data and fewer trainable parameters. Furthermore, the decision-making of our capsule network is much more easily interpretable as capsules act as a low-dimensional encoding of the image. When the capsules are projected on a two-dimensional manifold, they form a single redshift sequence with the fraction of spirals in a region exhibiting a gradient roughly perpendicular to the redshift sequence. We perturb encodings of real galaxy images in this low-dimensional space to create synthetic galaxy images that demonstrate the image properties (e.g. size, orientation, and surface brightness) encoded by each dimension. We also measure correlations between galaxy properties (e.g. magnitudes, colours, and stellar mass) and each capsule dimension. We publicly release our code, estimated redshifts, and additional catalogues at https://biprateep.github.io/encapZulate-1.

79 ASTRONOMY AND ASTROPHYSICS↗