Results for “Statistical error” — Page 55

DOE OSTI2026

Surrogate model evaluation and building energy benchmarking for commercial buildings

Building energy consumption benchmarking involves challenges associated with various energy patterns for different building types; heating, ventilating, and air-conditioning (HVAC) system types; and climates. Given significant variation in energy use patterns, accurate prediction of long-term energy use using surrogate models remains challenging. Multiple linear regression (MLR) is commonly used for building energy benchmarking because of its simple structure; however, it lacks accuracy compared to other black-box models. Although many studies have compared surrogate models and offer guidance on model selection based on metrics, they do not provide detailed analysis on improving the surrogate model accuracy. In this paper, we implement a surrogate model using polynomial ridge regression (i.e., MLR with interaction terms combined with ridge regularization) for small office and retail strip mall buildings across six HVAC system types and all climate zones, for electricity and natural gas in baseline and proposed scenarios. A simulation workflow is developed using OpenStudio TM /EnergyPlus TM to generate simulation data using measures over a wide range of efficiency inputs. Enhancements based on statistical insights are used for improving the model accuracy using filters, input transformations, and change points. Surrogate models achieved average coefficient of variation of the root mean squared error (CVRMSE) values of 2.17, 1.06, 2.05, and 3.26 for proposed electricity, proposed natural gas, baseline electricity, and baseline natural gas, respectively, with enhancements reducing CVRMSE by an average of 14.9% across all combinations. We provide model interpretation via Shapley additive explanations to determine which input variables most influence energy consumption and provide supportive arguments for enhancements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Methane flux from transplanted soil monoliths depends on moisture, but not origin

Soils both produce and consume methane (CH 4 ), a potent greenhouse gas that contributes to climate change. In coastal forests, upland soils are shifting from being CH 4 sinks to sources as sea levels rise, increasingly flooding soils with little prior inundation history. Ecosystem CH 4 budgets are highly uncertain due in part to the difficulty in separating fluxes measured at the soil surface into individual production and consumption processes which are likely to have different responses to future environmental conditions. Here, we measured growing season CH 4 fluxes from soil monoliths transplanted four years prior along an inundation and salinity gradient to determine how changes in abiotic conditions control CH 4 flux rates. To parse net fluxes measured at the soil surface into their component gross rates, we paired field measurements with a stable isotope pool dilution incubation of surface soils. Throughout the growing season, net soil surface CH 4 flux was positively correlated with soil moisture (p < 0.01), with lowland-located soils tending towards CH 4 sources (mean 0.349 ± 1.11 mg CH 4 -C m -2 hr -1 , error is standard deviation) and upland-located soils tending towards CH 4 sinks (-0.003 ± 0.003 mg CH 4 -C m -2 hr -1 ). Transplanted soils’ fluxes were statistically identical to their native neighbors once microtopography-driven differences in soil moisture were controlled for. The pool dilution experiment revealed that production and consumption rates were similar in upland and lowland surface soils (2.82 ± 3.29 µmol CH 4 g -1 dry soil d -1 production, 3.47 ± 2.03 µmol CH 4 g -1 dry soil d -1 consumption), indicating the majority of production likely occurs at depth in lowland soils. Both gross and net fluxes from transplanted soils showed no effect of soil origin after four years, suggesting low resistance of CH 4 cycling to global change drivers. Our results indicate the strength of the coastal forest CH 4 sink is likely to decrease in proportion to sea-level rise.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

BLIND EXTRACTION OF AN EXOPLANETARY SPECTRUM THROUGH INDEPENDENT COMPONENT ANALYSIS

Blind-source separation techniques are used to extract the transmission spectrum of the hot-Jupiter HD189733b recorded by the Hubble/NICMOS instrument. Such a 'blind' analysis of the data is based on the concept of independent component analysis. The detrending of Hubble/NICMOS data using the sole assumption that nongaussian systematic noise is statistically independent from the desired light-curve signals is presented. By not assuming any prior or auxiliary information but the data themselves, it is shown that spectroscopic errors only about 10%-30% larger than parametric methods can be obtained for 11 spectral bins with bin sizes of {approx}0.09 {mu}m. This represents a reasonable trade-off between a higher degree of objectivity for the non-parametric methods and smaller standard errors for the parametric de-trending. Results are discussed in light of previous analyses published in the literature. The fact that three very different analysis techniques yield comparable spectra is a strong indication of the stability of these results.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

The luminosity function at z ∼ 8 from 97 Y-band dropouts: Inferences about reionization

We present the largest search to date for Y-band dropout galaxies (z ∼ 8 Lyman break galaxies, LBGs) based on 350 arcmin{sup 2} of Hubble Space Telescope observations in the V, Y, J, and H bands from the Brightest of Reionizing Galaxies (BoRG) survey. In addition to previously published data, the BoRG13 data set presented here includes approximately 50 arcmin{sup 2} of new data and deeper observations of two previous BoRG pointings, from which we present 9 new z ∼ 8 LBG candidates, bringing the total number of BoRG Y-band dropouts to 38 with 25.5 ≤ m{sub J} ≤ 27.6 (AB system). We introduce a new Bayesian formalism for estimating the galaxy luminosity function, which does not require binning (and thus smearing) of the data and includes a likelihood based on the formally correct binomial distribution as opposed to the often-used approximate Poisson distribution. We demonstrate the utility of the new method on a sample of 97 Y-band dropouts that combines the bright BoRG galaxies with the fainter sources published in Bouwens et al. from the Hubble Ultra Deep Field and Early Release Science programs. We show that the z ∼ 8 luminosity function is well described by a Schechter function over its full dynamic range with a characteristic magnitude M{sup ⋆}=−20.15{sub −0.38}{sup +0.29}, a faint-end slope of α=−1.87{sub −0.26}{sup +0.26}, and a number density of log{sub 10} ϕ{sup ⋆}[Mpc{sup −3}]=−3.24{sub −0.24}{sup +0.25}. Integrated down to M = –17.7, this luminosity function yields a luminosity density log{sub 10} ϵ[erg s{sup −1} Hz{sup −1} Mpc{sup −3}]=25.52{sub −0.05}{sup +0.05}. Our luminosity function analysis is consistent with previously published determinations within 1σ. The error analysis suggests that uncertainties on the faint-end slope are still too large to draw a firm conclusion about its evolution with redshift. We use our statistical framework to discuss the implication of our study for the physics of reionization. By assuming theoretically motivated priors on the clumping factor and the photon escape fraction we show that the UV luminosity density from galaxy samples down to M = –17.7 can ionize only 10%-50% of the neutral hydrogen at z ∼ 8. Full reionization would require extending the luminosity function down to M = –15. The data are consistent with a substantial fraction of neutral hydrogen at z > 7, in agreement with recent suggestions based on deep spectroscopy of z ∼ 8 LBGs.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Impact of spurious shear on cosmological parameter estimates from weak lensing observables

We research, residual errors in shear measurements, after corrections for instrument systematics and atmospheric effects, can impact cosmological parameters derived from weak lensing observations. Here we combine convergence maps from our suite of ray-tracing simulations with random realizations of spurious shear. This allows us to quantify the errors and biases of the triplet (Ω m ,w,σ 8 ) derived from the power spectrum (PS), as well as from three different sets of non-Gaussian statistics of the lensing convergence field: Minkowski functionals (MFs), low-order moments (LMs), and peak counts (PKs). Our main results are as follows: (i) We find an order of magnitude smaller biases from the PS than in previous work. (ii) The PS and LM yield biases much smaller than the morphological statistics (MF, PK). (iii) For strictly Gaussian spurious shear with integrated amplitude as low as its current estimate of σ sys 2 ≈ 10 -7 , biases from the PS and LM would be unimportant even for a survey with the statistical power of Large Synoptic Survey Telescope. However, we find that for surveys larger than ≈ 100 deg 2 , non-Gaussianity in the noise (not included in our analysis) will likely be important and must be quantified to assess the biases. (iv) The morphological statistics (MF, PK) introduce important biases even for Gaussian noise, which must be corrected in large surveys. The biases are in different directions in (Ωm,w,σ8) parameter space, allowing self-calibration by combining multiple statistics. Our results warrant follow-up studies with more extensive lensing simulations and more accurate spurious shear estimates.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

SU-E-J-177: A Computational Approach for Determination of Anisotropic PTV Margins Based On Statistical Shape Analysis for Prostate Cancer Radiotherapy

Purpose: Our aim of this study was to propose a computational approach for determination of anisotropic planning target volume (PTV) margins based on statistical shape analysis with taking into account time variations of clinical target volume (CTV) shapes for the prostate cancer radiation treatment planning (RTP). Methods: Systematic and random setup errors were measured using orthogonal projection and cone beam computed tomography (CBCT) images for data of 20 patients, who underwent the intensity modulated radiation therapy for prostate cancer. The low-risk, intermediate-risk, and high-risk CTVs were defined as only a prostate, a prostate plus proximal 1-cm seminal vesicles, and a prostate plus proximal 2-cm seminal vesicles, respectively. All CTV regions were registered with a reference CTV region with a median volume to remove the effect of the setup errors, and converted to a point distribution models. The systematic and random errors for translations of CTV regions were automatically evaluated by analyzing the movements of centroids of CTV regions. The random and systematic errors for shape variations of CTV regions were obtained from covariance matrices based on point distributions for the CTV contours on CBCT images of 72 fractions of 10 patients. Anisotropic PTV margins for 6 directions (right, left, anterior, posterior, superior and inferior) were derived by using Yoda’s PTV margin model. Results: PTV margins with and without shape variations were 5.75 to 8.03 mm and 5.23 to 7.67 mm for low-risk group, 5.87 to 8.33 mm and 5.23 to 7.67 mm for intermediate-risk group, and 5.88 to 8.25 mm and 5.29 to 7.82 mm for highrisk group, respectively. Conclusion: The proposed computational approach could be feasible for determination of the anisotropic PTV margins with taking into account CTV shape variations for the RTP.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Evaluating Tropical Precipitation Relations in CMIP6 Models with ARM Data

Abstract A set of diagnostics based on simple, statistical relationships between precipitation and the thermodynamic environment in observations is implemented to assess phase 6 of the Coupled Model Intercomparison Project (CMIP6) model behavior with respect to precipitation. Observational data from the Atmospheric Radiation Measurement (ARM) permanent field observational sites are augmented with satellite observations of precipitation and temperature as an observational baseline. A robust relationship across observational datasets between column water vapor (CWV) and precipitation, in which conditionally averaged precipitation exhibits a sharp pickup at some critical CWV value, provides a useful convective onset diagnostic for climate model comparison. While a few models reproduce an appropriate precipitation pickup, most models begin their pickup at too low CWV and the increase in precipitation with increasing CWV is too weak. Convective transition statistics compiled in column relative humidity (CRH) partially compensate for model temperature biases—although imperfectly since the temperature dependence is more complex than that of column saturation. Significant errors remain in individual models and weak pickups are generally not improved. The conditional-average precipitation as a function of CRH can be decomposed into the product of the probability of raining and mean precipitation during raining times (conditional intensity). The pickup behavior is primarily dependent on the probability of raining near the transition and on the conditional intensity at higher CRH. Most models roughly capture the CRH dependence of these two factors. However, compensating biases often occur: model conditional intensity that is too low at a given CRH is compensated in part by excessive probability of precipitation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Active Subspace Methods for Data-Intensive Inverse Problems (Final Report)

Fostered by advances in computing, simulation and data science have emerged as the third and fourth paradigms enabling scientific discovery. Many mathematical challenges arise when integrating these paradigms to produce new knowledge. One of the most important challenges is the inverse or calibration problem: find the inputs of a simulation such that its outputs agree with a given set of measurements. As both the cost of the complex simulations and the volume of measurement data sets increase, intuition-driven trial-and-error methods for calibration quickly become untenable; modern scientists need more computationally efficient and mathematically rigorous methods. When measurements and simulated predictions contain uncertainty, the Bayesian perspective on statistical inference provides a popular mathematical framework for developing computational methods to solve inverse problems. Markov chain Monte Carlo (MCMC) methods are a mainstay of this framework; they characterize a probability distribution on the space of model inputs that is conditioned on the given measurements. However, despite recent advances, MCMC methods remain impractical for expensive simulations with more than a handful of inputs. At its heart, MCMC searches the space of inputs for corresponding outputs that match measurements, and so it is afflicted with the curse of dimensionality. Additionally, MCMC methods become less efficient as the volume of the measurement data increases, particularly if the measurements are redundant. To make MCMC—and Bayesian methods, in general—amenable to today’s computer models with millions of inputs and terabytes of measurement data, one must devise methods to reduce the dimension of both the simulation inputs and the measurements. For many models used in practice, the outputs vary primarily on a low-dimensional manifold of the nominally high-dimensional input space. Recently developed active subspace methods discover this low-dimensional manifold and exploit it to enable analyses typically suited for low-dimensional settings (e.g., statistical inversion of complex systems). We hypothesize that active subspace methods can provide the necessary dimension reduction, enabling existing Bayesian methods to find solutions for a large class of otherwise intractable statistical inverse problems. We will test the hypothesis with a series of mathematical analyses and computational experiments that will proceed along three research veins: (i) integrating active subspace methods into methods for statistical inversion, (ii) addressing current challenges in active subspace methods, and (iii) incorporating methods for nonlinear dimensionality reduction into active subspace methods. We will apply the methods we develop to real inverse problems in chemical kinetics and turbulent flame modeling. Our team combines expertise in methods for inverse problems, dimension reduction, and reduced order modeling. With a history of active collaboration and thoughtful engagement, we bring enthusiasm and fresh perspectives to a well studied subject. We are excited by the potential opportunity to work together to extend and apply active subspace methods to data-intensive inverse problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Active Subspace Methods for Data-Intensive Inverse Problems

Fostered by advances in computing, simulation and data science have emerged as the third and fourth paradigms enabling scientific discovery. Many mathematical challenges arise when integrating these paradigms to produce new knowledge. One of the most important challenges is the inverse or calibration problem: find the inputs of a simulation such that its outputs agree with a given set of measurements. As both the cost of the complex simulations and the volume of measurement data sets increase, intuition-driven trial-and-error methods for calibration quickly become untenable; modern scientists need more computationally efficient and mathematically rigorous methods. When measurements and simulated predictions contain uncertainty, the Bayesian perspective on statistical inference provides a popular mathematical framework for developing computational methods to solve inverse problems. Markov chain Monte Carlo (MCMC) methods are a mainstay of this framework; they characterize a probability distribution on the space of model inputs that is conditioned on the given measurements. However, despite recent advances, MCMC methods remain impractical for expensive simulations with more than a handful of inputs. At its heart, MCMC searches the space of inputs for corresponding outputs that match measurements, and so it is afflicted with the curse of dimensionality. Additionally, MCMC methods become less efficient as the volume of the measurement data increases, particularly if the measurements are redundant. To make MCMC—and Bayesian methods, in general—amenable to today’s computer models with millions of inputs and terabytes of measurement data, one must devise methods to reduce the dimension of both the simulation inputs and the measurements. For many models used in practice, the outputs vary primarily on a low-dimensional manifold of the nominally high-dimensional input space. Recently developed active subspace methods discover this low-dimensional manifold and exploit it to enable analyses typically suited for low-dimensional settings (e.g., statistical inversion of complex systems). We hypothesize that active subspace methods can provide the necessary dimension reduction, enabling existing Bayesian methods to find solutions for a large class of otherwise intractable statistical inverse problems. We will test the hypothesis with a series of mathematical analyses and computational experiments that will proceed along three research veins: (i) integrating active subspace methods into methods for statistical inversion, (ii) addressing current challenges in active subspace methods, and (iii) incorporating methods for nonlinear dimensionality reduction into active subspace methods. We will apply the methods we develop to real inverse problems in chemical kinetics and turbulent flame modeling. Our team combines expertise in methods for inverse problems, dimension reduction, and reduced order modeling. With a history of active collaboration and thoughtful engagement, we bring enthusiasm and fresh perspectives to a well studied subject. We are excited by the potential opportunity to work together to extend and apply active subspace methods to data-intensive inverse problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

2025 Review and Revision of Federal Guidance Report 15

Federal Guidance Report No. 15 (FGR 15), External Exposure to Radionuclides in Air, Water and Soil, published in 2019 and referred to below as FGR 2019, provides age-specific effective dose rate coefficients for reference persons externally exposed to each of 1252 radionuclides homogenously distributed in environmental media. Soon after completion of FGR 2019, the International Commission on Radiological Protection (ICRP) published a similar report (ICRP Publication 144, 2020) addressing the same radionuclides and many of the external exposure scenarios addressed in FGR 2019. In 2023 Argonne National Laboratory (ANL) published a review of dose coefficients in FGR 2019, concluding that “The external effective dose from beta radiation is not appropriately accounted for in FGR 15 for both low- and high-energy beta emitters in air, in soil, and on soil surfaces.” That conclusion was based largely on comparisons of dose coefficients in FGR 2019 with values in ICRP Publication 144 but also on consideration of some unexpected patterns of equivalent dose rate coefficients across tissues and of effective dose rate coefficients across different soil depths, for a selected set of low-energy beta emitters. In response to the ANL report, the Center for Radiation Protection Knowledge (CRPK) at Oak Ridge National Laboratory (ORNL) performed an extensive reexamination of the methods and published values of FGR 2019. CRPK found that coding errors had resulted in inaccurate estimates, primarily overestimates, in many of the dose coefficients tabulated in FGR 2019 but that many of the differences between results in FGR 2019 and ICRP Publication 144 could be traced to differences in methodology. CRPK has corrected all errors in FGR 2019 associated with the coding errors and has taken the opportunity to improve a major portion of the remaining dose coefficients in FGR 2019, primarily through additional Monte Carlo calculations resulting in improved statistics for tissue equivalent dose rate coefficients for exposure to monoenergetic sources. This has eliminated the need for extrapolation of results from high and medium energies to low energies, as was done in FGR 2019. In addition, inconsistencies in the methodology identified in the review, such as computational representation of the adult female, were eliminated. The revised effective dose rate coefficients are consistent with values in ICRP Publication 144 except for differences in values clearly arising from differences in methodology; and differences in values for a relatively small set of very low-energy radionuclides with highly uncertain dose coefficients regardless of methodology.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

A Versatile and Accurate Method for Halo Mass Determination from Phase-space Distribution of Satellite Galaxies

We propose a versatile and accurate method to estimate the halo mass and concentration from the kinematics of satellite galaxies. We construct the 6D phase-space distribution function of satellites from a cosmological simulation based on the similarity of internal dynamics for different halos. Within the Bayesian statistical framework, not only can we efficiently infer the halo mass and concentration, we can also treat various observational effects, including the selection function, incomplete data, and measurement errors, in a rigorous and straightforward manner. Through tests with mock samples, we show that our method is valid and accurate, as well as more precise than pure steady-state methods. It can constrain the halo mass to within ~20% using only 20 tracers and has a small intrinsic uncertainty of ~10%. In addition to the clear application to the Milky Way and similar galaxies, our method can be extended to galaxy groups or clusters.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

High-fidelity Experiments of Turbulent Buoyant Jets from Simultaneous Particle Image Velocimetry and Laser Induced Fluorescence

Accurate models of turbulent buoyant flows are essential for the design of nuclear reactors thermal hydraulics and passive safety systems. However, available models fail to fully capture the physics of turbulent mixing when buoyancy becomes predominant with respect to momentum. Therefore, high-fidelity experiments of well-controlled fundamental flows are needed to develop and validate more accurate models. We performed experiments of positive and negative turbulent buoyant jets, both in uniform and stratified environments, with the aim of understanding the thermal hydraulics of turbulent mixing with variable density and providing high-fidelity data for the development and validation of turbulence models. Non-intrusive, simultaneous Particle Image Velocimetry and Laser Induced Fluorescence measurements were carried out to acquire instantaneous velocity and concentration fields on a vertical section parallel to the axis of a jet in the self-similar region. The Refractive Index Matching method was applied to measure high-resolution buoyant jets with up to 8.6% density difference. These data are free of the typical errors that characterize optical measurements of buoyancy driven flows (e.g., natural and mixed convection) where the refractive index of the fluid is inhomogeneous throughout the measurement domain. Turbulent statistics and entrainment of buoyant jets in uniform and stratified environments are presented. These data are compared with non-buoyant jets in uniform environment, as a reference to investigate the effects of buoyancy and stratification on turbulent mixing. The results will be used for the assessment of current turbulence models and as basis for the development of a new one that captures turbulent mixing.

42 ENGINEERING↗

DOE OSTI2025

Power and Limitations of Linear Programming Decoder for Quantum LDPC Codes

Decoding quantum error-correcting codes is a key challenge in enabling fault-tolerant quantum computation. In the classical setting, linear programming (LP) decoders offer provable performance guarantees and can leverage fast practical optimization algorithms. Although LP decoders have been proposed for quantum codes, their performance and limitations remain relatively underexplored. In this work, we uncover a key limitation of LP decoding for quantum low-density parity-check (LDPC) codes: certain constant-weight error patterns lead to ambiguous fractional solutions that cannot be resolved through independent rounding. To address this issue, we incorporate a post-processing technique known as ordered statistics decoding (OSD), which significantly enhances LP decoding performance in practice. Our results show that LP decoding, when augmented with OSD, can outperform belief propagation with the same post-processing for intermediate code sizes of up to hundreds of qubits. These findings suggest that LP-based decoders, equipped with effective post-processing, offer a promising approach for decoding near-term quantum LDPC codes.

Gu, Shouzhen [Yale U.]↗

DOE OSTI2025

Power and Limitations of Linear Programming Decoder for Quantum LDPC Codes

Decoding quantum error-correcting codes is a key challenge in enabling fault-tolerant quantum computation. In the classical setting, linear programming (LP) decoders offer provable performance guarantees and can leverage fast practical optimization algorithms. Although LP decoders have been proposed for quantum codes, their performance and limitations remain relatively underexplored. In this work, we uncover a key limitation of LP decoding for quantum low-density parity-check (LDPC) codes: certain constant-weight error patterns lead to ambiguous fractional solutions that cannot be resolved through independent rounding. To address this issue, we incorporate a post-processing technique known as ordered statistics decoding (OSD), which significantly enhances LP decoding performance in practice. Our results show that LP decoding, when augmented with OSD, can outperform belief propagation with the same post-processing for intermediate code sizes of up to hundreds of qubits. These findings suggest that LP-based decoders, equipped with effective post-processing, offer a promising approach for decoding near-term quantum LDPC codes.

Gu, Shouzhen [Yale U.]↗

DOE OSTI2025

Power and Limitations of Linear Programming Decoder for Quantum LDPC Codes

Decoding quantum error-correcting codes is a key challenge in enabling fault-tolerant quantum computation. In the classical setting, linear programming (LP) decoders offer provable performance guarantees and can leverage fast practical optimization algorithms. Although LP decoders have been proposed for quantum codes, their performance and limitations remain relatively underexplored. In this work, we uncover a key limitation of LP decoding for quantum low-density parity-check (LDPC) codes: certain constant-weight error patterns lead to ambiguous fractional solutions that cannot be resolved through independent rounding. To address this issue, we incorporate a post-processing technique known as ordered statistics decoding (OSD), which significantly enhances LP decoding performance in practice. Our results show that LP decoding, when augmented with OSD, can outperform belief propagation with the same post-processing for intermediate code sizes of up to hundreds of qubits. These findings suggest that LP-based decoders, equipped with effective post-processing, offer a promising approach for decoding near-term quantum LDPC codes.

Gu, Shouzhen [Yale U.]↗

DOE OSTI2025

DESI DR2 results. I. Baryon acoustic oscillations from the Lyman alpha forest

We present the baryon acoustic oscillation (BAO) measurements with the Lyman-𝛼 (Ly⁢𝛼) forest from the second data release (DR2) of the Dark Energy Spectroscopic Instrument (DESI) survey. Our BAO measurements include both the autocorrelation of the Ly⁢𝛼 forest absorption observed in the spectra of high-redshift quasars and the cross-correlation of the absorption with the quasar positions. The total sample size is approximately a factor of 2 larger than the DR1 dataset, with forest measurements in over 820,000 quasar spectra and the positions of over 1.2 million quasars. We describe several significant improvements to our analysis in this paper, and two supporting papers describe improvements to the synthetic datasets that we use for validation and how we identify damped Ly⁢𝛼 absorbers. Our main result is that we have measured the BAO scale with a statistical precision of 1.1% along and 1.3% transverse to the line of sight, for a combined precision of 0.65% on the isotropic BAO scale at 𝑧 eff =2.33. This excellent precision, combined with recent theoretical studies of the BAO shift due to nonlinear growth, motivated us to include a systematic error term in Ly⁢𝛼 BAO analysis for the first time. We measure the ratios 𝐷 𝐻 ⁡(𝑧 eff )/𝑟 𝑑 = 8.632 ± 0.098 ± 0.026 and 𝐷 𝑀 ⁡(𝑧 eff )/𝑟 𝑑 = 38.99 ± 0.52 ± 0.12, where 𝐷 𝐻 = 𝑐/𝐻⁡(𝑧) is the Hubble distance, 𝐷 𝑀 is the transverse comoving distance, 𝑟 𝑑 is the sound horizon at the drag epoch, and we quote both the statistical and the theoretical systematic uncertainty. The companion paper presents the BAO measurements at lower redshifts from the same dataset and the cosmological interpretation.

baryon acoustic oscillations↗

DOE OSTI2014

The coyote universe extended: Precision emulation of the matter power spectrum

Modern sky surveys are returning precision measurements of cosmological statistics such as weak lensing shear correlations, the distribution of galaxies, and cluster abundance. To fully exploit these observations, theorists must provide predictions that are at least as accurate as the measurements, as well as robust estimates of systematic errors that are inherent to the modeling process. In the nonlinear regime of structure formation, this challenge can only be overcome by developing a large-scale, multi-physics simulation capability covering a range of cosmological models and astrophysical processes. As a first step to achieving this goal, we have recently developed a prediction scheme for the matter power spectrum (a so-called emulator), accurate at the 1% level out to k ∼ 1 Mpc{sup –1} and z = 1 for wCDM cosmologies based on a set of high-accuracy N-body simulations. It is highly desirable to increase the range in both redshift and wavenumber and to extend the reach in cosmological parameter space. To make progress in this direction, while minimizing computational cost, we present a strategy that maximally reuses the original simulations. We demonstrate improvement over the original spatial dynamic range by an order of magnitude, reaching k ∼ 10 h Mpc{sup –1}, a four-fold increase in redshift coverage, to z = 4, and now include the Hubble parameter as a new independent variable. To further the range in k and z, a new set of nested simulations run at modest cost is added to the original set. The extension in h is performed by including perturbation theory results within a multi-scale procedure for building the emulator. This economical methodology still gives excellent error control, ∼5% near the edges of the domain of applicability of the emulator. A public domain code for the new emulator is released as part of the work presented in this paper.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

DESI DR2 Results I: Baryon Acoustic Oscillations from the Lyman Alpha Forest

We present the Baryon Acoustic Oscillation (BAO) measurements with the Lyman-alpha (LyA) forest from the second data release (DR2) of the Dark Energy Spectroscopic Instrument (DESI) survey. Our BAO measurements include both the auto-correlation of the LyA forest absorption observed in the spectra of high-redshift quasars and the cross-correlation of the absorption with the quasar positions. The total sample size is approximately a factor of two larger than the DR1 dataset, with forest measurements in over 820,000 quasar spectra and the positions of over 1.2 million quasars. We describe several significant improvements to our analysis in this paper, and two supporting papers describe improvements to the synthetic datasets that we use for validation and how we identify damped LyA absorbers. Our main result is that we have measured the BAO scale with a statistical precision of 1.1% along and 1.3% transverse to the line of sight, for a combined precision of 0.65% on the isotropic BAO scale at $z_{eff} = 2.33$. This excellent precision, combined with recent theoretical studies of the BAO shift due to nonlinear growth, motivated us to include a systematic error term in LyA BAO analysis for the first time. We measure the ratios $D_H(z_{eff})/r_d = 8.632 \pm 0.098 \pm 0.026$ and $D_M(z_{eff})/r_d = 38.99 \pm 0.52 \pm 0.12$, where $D_H = c/H(z)$ is the Hubble distance, $D_M$ is the transverse comoving distance, $r_d$ is the sound horizon at the drag epoch, and we quote both the statistical and the theoretical systematic uncertainty. The companion paper presents the BAO measurements at lower redshifts from the same dataset and the cosmological interpretation.

79 ASTRONOMY AND ASTROPHYSICS↗