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Introducing the Texas Euclid Survey for Lyα (TESLA) Survey: Initial Study Correlating Galaxy Properties to Lyα Emission
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Gamma Neutron Correlated Noise of the EUCLID Critical Experiment
In this work, we present results from gamma noise measurements of a fast plutonium critical system for the first time. These results are compared to the more traditional method of neutron noise. Additionally, we investigate cross correlations of neutron and gamma signals and discuss the advantages of doing so.
Photometric Redshifts with the LSST. II. The Impact of Near-infrared and Near-ultraviolet Photometry
Accurate photometric redshift (photo-z) estimates are essential to the cosmological science goals of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). In this work, we use simulated photometry for mock galaxy catalogs to explore how LSST photo-z estimates can be improved by the addition of near-infrared (NIR) and/or ultraviolet (UV) photometry from the Euclid, Wide-Field InfrarRed Survey Telescope (WFIRST), and/or Cosmological Advanced Survey Telescope for Optical and ultraviolet Research (CASTOR) space telescopes. Generally, we find that deeper optical photometry can reduce the standard deviation of the photo-z estimates more than adding NIR or UV filters but that additional filters are the only way to significantly lower the fraction of galaxies with catastrophically under- or overestimated photo-z. For Euclid, we find that the addition of JH 5σ photometric detections can reduce the standard deviation for galaxies with z > 1 (z > 0.3) by ∼20% (∼10%), and the fraction of outliers by ∼40% (∼25%). For WFIRST, we show how the addition of deep YJHK photometry could reduce the standard deviation by ≳50% at z > 1.5 and drastically reduce the fraction of outliers to just ∼2% overall. For CASTOR, we find that the addition of its UV- and u-band photometry could reduce the standard deviation by ∼30% and the fraction of outliers by ∼50% for galaxies with z < 0.5. We also evaluate the photo-z results within sky areas that overlap with both the NIR and UV surveys and when spectroscopic training sets built from the surveys’ small-area deep fields are used.
Uncovering Where Compensating Errors Could Hide in ENDF/B-VIII.0
Unconstrained physics spaces between two or more nuclear data observables in a library occur when their values can be simultaneously adjusted without violating the uncertainties in either differential information or simulations of relevant integral experiments. Differential data are often too imprecise to fully bound all nuclear data observables of interest for application simulations. Integral data are simulated with combinations of nuclear data so that an error in one observable may be hidden by a counterbalancing error in another. In this manner compensating errors may lurk within nuclear data libraries and these errors have the potential to undermine the predictive power of neutron transport simulations, particularly in situations where there is no conclusive validation experiment that resembles the application of interest. The EUCLID project (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) developed a preliminary workflow to identify these unconstrained physics spaces by bringing together results from a large collection of integral experiments with their simulated counter-parts as well as differential information that have a one-to-one correspondence to nuclear data. This wealth of information is processed by machine learning tools for subsequent refinement by human experts. Here, we show how the EUCLID work-flow is executed by applying it first to 239 Pu and then to 9 Be nuclear data in ENDF/B-VIII.0.
How to Safely Build 100-plus Kilograms of Weapons-Grade Plutonium
The goal of the EUCLID (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) project was to reduce compensating errors by utilizing machine learning to both help determine which reactions contain compensating errors as well as optimizing an experiment which can be used to maximally reduce these errors. Compensating errors can adversely impact the predictive power of application simulations, and therefore it’s useful to further constrain nuclear data and reduce these errors. The EUCLID project included building two configurations at the National Criticality Experiments Research Center (NCERC). These two configurations had very different geometries (one was cube-like and one was slab-like). Previous works focus on selection of the target experiment(s), radiation transport capabilities developed in the project, the experiment optimization, and the performance of the experiments. This work will focus only on the safety aspects of performing this experiment, which utilized over 100 kg of weapons-grade plutonium.
Uncovering Where Compensating Errors Could Hide in ENDF/B-VIII.0
Unconstrained physics spaces between two or more nuclear data observables in a library occur when their values can be simultaneously adjusted without violating the uncertainties in either differential information or simulations of relevant integral experiments. Differential data are often too imprecise to fully bound all nuclear data observables of interest for application simulations. Integral data are simulated with combinations of nuclear data so that an error in one observable may be hidden by a counterbalancing error in another. In this manner compensating errors may lurk within nuclear data libraries and these errors have the potential to undermine the predictive power of neutron transport simulations, particularly in situations where there is no conclusive validation experiment that resembles the application of interest. The EUCLID project (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) developed a preliminary workflow to identify these unconstrained physics spaces by bringing together results from a large collection of integral experiments with their simulated counter-parts as well as differential information that have a one-to-one correspondence to nuclear data. This wealth of information is processed by machine learning tools for subsequent refinement by human experts. Here, we show how the EUCLID work-flow is executed by applying it first to 239 Pu and then to 9 Be nuclear data in ENDF/B-VIII.0.
Mitigating the effects of undersampling in weak lensing shear estimation with metacalibration
ABSTRACT metacalibration is a state-of-the-art technique for measuring weak gravitational lensing shear from well-sampled galaxy images. We investigate the accuracy of shear measured with metacalibration from fitting elliptical Gaussians to undersampled galaxy images. In this case, metacalibration introduces aliasing effects leading to an ensemble multiplicative shear bias about 0.01 for Euclid and even larger for the Roman Space Telescope, well exceeding the missions’ requirements. We find that this aliasing bias can be mitigated by computing shapes from weighted moments with wider Gaussians as weight functions, thereby trading bias for a slight increase in variance of the measurements. We show that this approach is robust to the point-spread function in consideration and meets the stringent requirements of Euclid for galaxies with moderate to high signal-to-noise ratios. We therefore advocate metacalibration as a viable shear measurement option for weak lensing from upcoming space missions.
On the detectability of strong lensing in near-infrared surveys
We present new lensing frequency estimates for existing and forthcoming deep near-infrared surveys, including those from JWST and VISTA. The estimates are based on the JAdes extragalactic Ultradeep Artificial Realisations (JAGUAR) galaxy catalogue accounting for the full photometry and morphologies for each galaxy. Due to the limited area of the JAGUAR simulations, they are less suited to wide-area surveys; however, we also present extrapolations to the surveys carried out by Euclid and the Nancy Grace Roman Space Telescope. The methodology does not make assumptions about the nature of the lens itself and probes a wide range of lens masses. The lenses and sources are selected from the same catalogue and extend the analysis from the visible bands into the near-infrared. After generating realistic simulated lensed sources and selecting those that are detectable with SNR > 20, we verify the lensing frequency expectations against published lens samples selected in the visible, finding them to be broadly consistent. We find that JWST could yield ~65 lensed systems in COSMOS-Web, of which ~25 per cent have source redshifts >4. Deeper, narrower programs (e.g. JADES-Medium) will probe more typical source galaxies (in flux and mass) but will find fewer systems (~25). Of the surveys we investigate, we find 55–80 per cent have detectable multiple imaging. Forthcoming NIR surveys will likely reveal new and diverse strong lens systems including lensed sources that are at higher redshift (JWST) and dustier, more massive and older (Euclid NISP) than those typically detected in the corresponding visible surveys.
Dark Energy Survey Year 3 results: Simulation-based cosmological inference with wavelet harmonics, scattering transforms, and moments of weak lensing mass maps. Validation on simulations
Beyond-two-point statistics contain additional information on cosmological as well as astrophysical and observational (systematics) parameters. In this methodology paper we provide an end-to-end simulationbased analysis of a set of Gaussian and non-Gaussian weak lensing statistics using detailed mock catalogs of the Dark Energy Survey (DES). Here, we implement: 1) second and third moments; 2) wavelet phase harmonics (WPH); 3) the scattering transform (ST). Our analysis is fully based on simulations, it spans a space of seven $νw$CDM cosmological parameters, and it forward models the most relevant sources of systematics of the data (masks, noise variations, clustering of the sources, intrinsic alignments, and shear and redshift calibration). We implement a neural network compression of the summary statistics, and we estimate the parameter posteriors using a likelihood-free-inference approach. We validate the pipeline extensively, and we find that WPH exhibits the strongest performance when combined with second moments, followed by ST, and then by third moments. The combination of all the different statistics further enhances constraints with respect to second moments, up to 25 percent, 15 percent, and 90 percent for S 8 , Ω m , and the figure-of-merit FoM S8;Ωm , respectively. We further find that non-Gaussian statistics improve constraints on w and on the amplitude of intrinsic alignment with respect to second moments constraints. The methodological advances presented here are suitable for application to Stage IV surveys from Euclid, Rubin-LSST, and Roman with additional validation on mock catalogs for each survey. In a companion paper we present an application to DES Year 3 data.
Interpenetrating Plasma Simulations
This project investigated multifluid models for simulating interpenetrating plasmas. These models solve the Euler equations for each plasma stream along with an appropriate model for electron dynamics. The project developed a novel code, Euclid, that simulates these models using high-order conservative spatial discretization methods and explicit and semi-implicit time integration methods. Euclid was used to solve several multifluid test cases representative of plasma dynamics in high-energy density physics experiments. Kinetic simulations of interpenetrating flows using the LOKI code revealed novel physical effects of the ion-ion streaming instability. A new multi-flow collision operator was developed for the LOKI code. A simple model of the ion-ion streaming instability was also developed. Several presentations were given on the project results; three journal articles have been published, one has been submitted, and two are planned.
Investigating Fission Reaction Rate Ratio Sensitivities [Slides]
One EUCLID project goal is to create a NEW capability in the MCNP6 code. This is being tested on reaction rate sensitivities and compared with SENSMG results. Reaction rate ratio sensitivities were added to EUCLID sensitivity library and investigated to determine value toward designing optimized experiments aimed at reducing compensating errors in nuclear data. The use of adjustment using multiple responses (and underlying/required capabilities) may benefit many applications. These include a tool for adjustment using multiple integral responses (ND adjustment and validation community); sensitivities of additional responses (ND adjustment and validation community); sensitivity capabilities (users in many application areas including safeguards/nonproliferation, criticality safety, etc.); and experiment optimization capability (applications which can benefit from integral experiments). Team is working to build and test future tools and to understand compensating errors
Formatting and V&V of Consistent 238,240−24 2Pu $\overline{v}_p$ Evaluated Mean Values and Covariances
This report is in answer to the Nuclear Criticality Safety Program FY24 quarter 4 milestone that requires: “Format and V&V nu-bar means and covariances” for 238,240-242 Pu average prompt fission neutron multiplicities, $\overline{ν}$ p , that were obtained by a consistent evaluation leveraging the fission-event generator CGMF and a detailed uncertainty quantification of experimental data. It is described how nuclear data mean values and covariances were formatted using ENDFtk. Implementing the new 238,240-242 Pu $\overline{ν}$ p into the ENDF/B-VIII.1β 4 library leads to only small overall changes in criticality values of the Jezebel, Dirty Jezebel, Jupiter-001, Jupiter-002, EUCLID 3x2 and EUCLID 8x1 critical assemblies. Simulated k eff uncertainties due to $\overline{ν}$ p covariances change only little if cross-isotope covariances are considered or not for those assemblies with low percentage content of minor Pu isotopes. However, for the Dirty Jezebel critical assembly, that has a sizeable 240 Pu and non-negligible 241 Pu content, the simulated k eff uncertainties due to considering or neglecting cross-isotope $\overline{ν}$ p covariances is 443 versus 374 pcm.