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DOE OSTI2025

Machine Learning-Based Extreme Data Reduction for Prompt Supernova Pointing at DUNE

One of the goals of the Deep Underground Neutrino Experiment (DUNE) is to use the massive underground liquid argon time projection chamber (LArTPC) detectors at its far site for multimessenger astronomy (MMA), in the detection of neutrinos from core-collapse supernovae (SNe). Its current baseline trigger strategy detects activity in the detector that is consistent with supernova (SN) neutrinos and saves the raw data for further offline analysis but provides no prompt pointing information crucial for optical follow-ups by other observatories. This approach is based on the assumption that prompt pointing determination using raw data is computationally prohibitive. In this article, we demonstrate a proof-of-concept based on applying extreme data reduction on the buffered SN data in the DUNE data acquisition (DAQ) system’s front-end computers using a machine learning (ML) workflow. This reduces the data by ~5 orders of magnitude, allowing a full track reconstruction to be carried out quickly on a single server. The total time to perform the ML-based data reduction and the full track reconstruction is less than the time to transfer the SN data back to Fermilab or a high-performance computing (HPC) center. This shows that prompt processing of raw SN data is possible and, in fact, trivial once the data have been reduced to reject radiological backgrounds, paving the way to a high-quality SN pointing trigger that is based on fully reconstructed data instead of trigger primitives (TPs).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Cluster Structures with Machine Learning Support in Neutron Star M-R relations

Neutron stars (NS) are compact objects with strong gravitational fields, and a matter composition subject to extreme physical conditions. The properties of strongly interacting matter at ultra-high densities and temperatures impose a big challenge to our understanding and modelling tools. Some difficulties are critical, since one cannot reproduce such conditions in our laboratories or assess them purely from astronomical observations. The information we have about neutron star interiors are often extracted indirectly, e.g., from the star mass-radius relation. The mass and radius are global quantities and still have a significant uncertainty, which leads to great variability in studying the micro-physics of the neutron star interior. This leaves open many questions in nuclear astrophysics and the suitable equation of state (EoS) of NS. Recently, new observations appear to constrain the mass-radius and consequently has helped to close some open questions. In this work, utilizing modern machine learning techniques, we analyze the NS mass-radius (M-R) relationship for a set of EoS containing a variety of physical models. Our objective is to determine patterns through the M-R data analysis and develop tools to understand the EoS of neutron stars in forthcoming works.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Model-independent constraints on Ωm and H ( z ) from the link between geometry and growth

ABSTRACT We constrain the expansion history of the Universe and the cosmological matter density fraction in a model-independent way by exclusively making use of the relationship between background and perturbations under a minimal set of assumptions. We do so by employing a Gaussian process to model the expansion history of the Universe from present time to the recombination era. The expansion history and the cosmological matter density are then constrained using recent measurements from cosmic chronometers, Type-Ia supernovae, baryon acoustic oscillations, and redshift-space distortion data. Our results show that the evolution in the reconstructed expansion history is compatible with the Planck 2018 prediction at all redshifts. The current data considered in this study can constrain a Gaussian process on H(z) to an average $9.4 {{\ \rm per\ cent}}$ precision across redshift. We find Ωm = 0.224 ± 0.066, lower but statistically compatible with the Planck 2018 cosmology. Finally, the combination of future DESI measurements with the CMB measurement considered in this work holds the promise of $8 {{\ \rm per\ cent}}$ average constraints on a model-independent expansion history as well as a five-fold tighter Ωm constraint using the methodology developed in this work.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Heating and Acceleration of Solar Wind by Parametric Decay Instability Mediated Turbulence

One of the main goals of this project to understand the origin of density fluctuations in the solar winds, as revealed by the NASA’s Flagship satellite Parker Solar Probe (PSP) measurements, which motivates us to simulate and analyze magnetohydrodynamic (MHD) turbulence in the compressible regime. To achieve this goal, we have developed a set of sophisticated turbulence driving methods to study how MHD turbulence will develop under different time-correlated fluctuations at large-scales. Another key advance is that we have developed a new 3+1 (three spatial plus time domain) 4D FFT analysis of simulation data-cubes so that we can perform the spatio-temporal spectrum and mode decomposition studies on MHD turbulence.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Non-Gaussianity of optical emission lines in SDSS star-forming galaxies and its implications on galactic outflows

The shape of emission lines in the optical spectra of star-forming galaxies reveals the kinematics of the diffuse gaseous component. We analyse the shape of prominent emission lines in a sample of ~ 53000 star-forming galaxies from the Sloan Digital Sky Survey, focusing on departures from gaussianity. Departures from a single gaussian profile allow us to probe the motion of gas and to assess the role of outflows. The sample is divided into groups according to their stellar velocity dispersion and star formation rate (SFR). The spectra within each group are stacked to improve the signal-to-noise ratio of the emission lines, to remove individual signatures, and to enhance the effect of SFR on the shapes of the emission lines. The moments of the emission lines, including kurtosis and skewness, are determined. We find that most of the emission lines in strong star-forming systems unequivocally feature negative kurtosis. This signature is present in Hβ, Hα, [N ii], and [S ii] in massive galaxies with high SFRs. We attribute it as evidence of radial outflows of ionised gas driven by the star formation of the galaxies. Also, most of the emission lines in low-mass systems with high SFRs feature negative skewness, and we interpret it as evidence of dust obscuration in the galactic disk. These signatures are however absent in the [O iii] line, which is believed to trace a different gas component. The observed trend is significantly stronger in face-on galaxies, indicating that star formation drives the outflows along the galactic rotation axis, presumably the path of least resistance. The data suggest that outflows driven by star formation exert accumulated impacts on the interstellar medium, and the outflow signature is more evident in older galaxies as they have experienced a longer total duration of star formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers

We present a new class of AI models for the detection of quasi-circular, spinning, non-precessing binary black hole mergers whose waveforms include the higher order gravitational wave modes ($\ell$, |m|) = {(2,2), (2,1), (3,3), (3,2), (4,4)}, and mode mixing effects in the $\ell$ = 3, |m| = 2 harmonics. These AI models combine hybrid dilated convolution neural networks to accurately model both short- and long-range temporal sequential information of gravitational waves; and graph neural networks to capture spatial correlations among gravitational wave observatories to consistently describe and identify the presence of a signal in a three detector network encompassing the Advanced LIGO and Virgo detectors. We first trained these spatiotemporal-graph AI models using synthetic noise, using 1.2 million modeled waveforms to densely sample this signal manifold, within 1.7 h using 256 NVIDIA A100 GPUs in the Polaris supercomputer at the Argonne Leadership Computing Facility. This distributed training approach exhibited optimal classification performance, and strong scaling up to 512 NVIDIA A100 GPUs. With these AI ensembles we processed data from a three detector network, and found that an ensemble of 4 AI models achieves state-of-the-art performance for signal detection, and reports two misclassifications for every decade of searched data. We distributed AI inference over 128 GPUs in the Polaris supercomputer and 128 nodes in the Theta supercomputer, and completed the processing of a decade of gravitational wave data from a three detector network within 3.5 h. Finally, we fine-tuned these AI ensembles to process the entire month of February 2020, which is part of the O3b LIGO/Virgo observation run, and found 6 gravitational waves, concurrently identified in Advanced LIGO and Advanced Virgo data, and zero false positives. This analysis was completed in one hour using one NVIDIA A100 GPU.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Cosmic shear power spectra in practice

Cosmic shear is one of the most powerful probes of Dark Energy, targeted by several current and future galaxy surveys. Lensing shear, however, is only sampled at the positions of galaxies with measured shapes in the catalog, making its associated sky window function one of the most complicated amongst all projected cosmological probes of inhomogeneities, as well as giving rise to inhomogeneous noise. Partly for this reason, cosmic shear analyses have been mostly carried out in real-space, making use of correlation functions, as opposed to Fourier-space power spectra. Since the use of power spectra can yield complementary information and has numerical advantages over real-space pipelines, it is important to develop a complete formalism describing the standard unbiased power spectrum estimators as well as their associated uncertainties. Building on previous work, this paper contains a study of the main complications associated with estimating and interpreting shear power spectra, and presents fast and accurate methods to estimate two key quantities needed for their practical usage: the noise bias and the Gaussian covariance matrix, fully accounting for survey geometry, with some of these results also applicable to other cosmological probes. Here, we demonstrate the performance of these methods by applying them to the latest public data releases of the Hyper Suprime-Cam and the Dark Energy Survey collaborations, quantifying the presence of systematics in our measurements and the validity of the covariance matrix estimate. We make the resulting power spectra, covariance matrices, null tests and all associated data necessary for a full cosmological analysis publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A Hydro-particle-mesh Code for Efficient and Rapid Simulations of the Intracluster Medium

We introduce the cosmological HYPER code based on an innovative hydro-particle-mesh (HPM) algorithm for efficient and rapid simulations of gas and dark matter. For the HPM algorithm, we update the approach of Gnedin & Hui to expand the scope of its application from the lower-density intergalactic medium (IGM) to the higher-density intracluster medium (ICM). While the original algorithm tracks only one effective particle species, the updated version separately tracks the gas and dark matter particles, as they do not exactly trace each other on small scales. For the approximate hydrodynamics solver, the pressure term in the gas equations of motion is calculated using robust physical models. In particular, we use a dark matter halo model, ICM pressure profile, and IGM temperature–density relation, all of which can be systematically varied for parameter-space studies. We show that the HYPER simulation results are in good agreement with the halo model expectations for the density, temperature, and pressure radial profiles. Simulated galaxy cluster scaling relations for Sunyaev–Zel’dovich (SZ) and X-ray observables are also in good agreement with mean predictions, with scatter comparable to that found in hydrodynamic simulations. HYPER also produces lightcone catalogs of dark matter halos and full-sky tomographic maps of the lensing convergence, SZ effect, and X-ray emission. These simulation products are useful for testing data analysis pipelines, generating training data for machine learning, understanding selection and systematic effects, and for interpreting astrophysical and cosmological constraints.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Diverse data tighten constraints for neutron stars

Here analysis of data from astrophysical and terrestrial sources offers a promising way of narrowing the range of parameters that describe the extreme properties of neutron stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The Need for a System Science Approach to Global Magnetospheric Models

This perspective advocates for the need of a combined system science approach to global magnetospheric models and to spacecraft magnetospheric data to answer the question “Do simulations behave in the same manner as the magnetosphere does?” (instead of the standard validation question “How well do simulations reproduce spacecraft data?”). This approach will 1) validate global magnetospheric models statistically, without the need for a direct comparison against spacecraft data, 2) expose the deficiencies of the models, and 3) provide physics support to the system analysis performed on the magnetospheric system.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Domain Adaptation for Measurements of Strong Gravitational Lenses

Upcoming surveys are predicted to discover galaxy-scale strong lenses on the order of $10^5$, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but the alternative of training only on simulated data results in poor performance on real data. Domain Adaptation may be able to bridge the gap between simulated and real datasets. We utilize domain adaptation for the estimation of Einstein radius ($\Theta_E$) in simulated galaxy-scale gravitational lensing images with different levels of observational realism. We evaluate two domain adaptation techniques - Domain Adversarial Neural Networks (DANN) and Maximum Mean Discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain of lenses simulated to emulate noise conditions in the Dark Energy Survey (DES). We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain dataset. This work is the first application of domain adaptation for a regression task in strong lensing imaging analysis. Our results show the potential of using domain adaptation to perform analysis of future survey data with a deep neural network trained on simulated data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Detection of the large-scale tidal field with galaxy multiplet alignment in the DESI Y1 spectroscopic survey

We explore correlations between the orientations of small galaxy groups, or ‘multiplets’, and the large-scale gravitational tidal field. Using data from the Dark Energy Spectroscopic Instrument (DESI) Y1 survey, we detect the intrinsic alignment (IA) of multiplets to the galaxy-traced matter field out to separations of $100\,h^{-1}$ Mpc. Unlike traditional IA measurements of individual galaxies, this estimator is not limited by imaging of galaxy shapes and allows for direct IA detection beyond redshift $z=1$. Multiplet alignment is a form of higher order clustering, for which the scale-dependence traces the underlying tidal field and amplitude is a result of small-scale ($\lt 1h^{-1}$ Mpc) dynamics. Within samples of bright galaxies, luminous red galaxies (LRG) and emission-line galaxies, we find similar scale-dependence regardless of intrinsic luminosity or colour. This is promising for measuring tidal alignment in galaxy samples that typically display no IA. DESI’s LRG mock galaxy catalogues created from the A BACUS S UMMIT N -body simulations produce a similar alignment signal, though with a 33 per cent lower amplitude at all scales. An analytic model using a non-linear power spectrum (NLA) only matches the signal down to 20 $h^{-1}$ Mpc. Our detection demonstrates that galaxy clustering in the non-linear regime of structure formation preserves an interpretable memory of the large-scale tidal field. Multiplet alignment complements traditional two-point measurements by retaining directional information imprinted by tidal forces, and contains additional line-of-sight information compared to weak lensing. This is a more effective estimator than the alignment of individual galaxies in dense, blue, or faint galaxy samples.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Measurement of the B + /B 0 production ratio in e + e - collisions at the $\Upsilon$ (4S) resonance using B → J/Ψ($\ell\ell$)K decays at Belle

We measure the ratio of branching fractions for the $\Upsilon$ (4S) decays to B + B - and B 0 $\bar{B}$ 0 using B + → J/Ψ($\ell\ell$)K + and B 0 → J/Ψ($\ell\ell$)K 0 samples, where J/Ψ($\ell\ell$) stands for J/Ψ → $\ell$ + $\ell$ - ($\ell$ = e or μ), with 711 fb -1 of data collected at the $\Upsilon$ (4S) resonance with the Belle detector. We find the decay rate ratio of $\Upsilon$ (4S) → B + B - over $\Upsilon$ (4S) → B 0 $\bar{B}$ 0 to be 1.065 ± 0.012 ± 0.019 ± 0.047, which is the most precise measurement to date. The first and second uncertainties are statistical and systematic, respectively, and the third uncertainty is due to the assumption of isospin symmetry in B → J/Ψ($\ell\ell$) K.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Accuracy of power spectra in dissipationless cosmological simulations

Abstract We exploit a suite of large N-body simulations (up to N = 40963) performed with abacus, of scale-free models with a range of spectral indices n, to better understand and quantify convergence of the matter power spectrum. Using self-similarity to identify converged regions, we show that the maximal wavenumber resolved at a given level of accuracy increases monotonically as a function of time. At 1 per cent level it starts at early times from a fraction of $k_\Lambda$, the Nyquist wavenumber of the initial grid, and reaches at most, if the force softening is sufficiently small, ${\sim}2{-}3 k_\Lambda$ at the very latest times we evolve to. At the $5{{\ \rm per\ cent}}$ level, accuracy extends up to wavenumbers of order $5k_\Lambda$ at late times. Expressed as a suitable function of the scale-factor, accuracy shows a very simple n-dependence, allowing a extrapolation to place conservative bounds on the accuracy of N-body simulations of non-scale-free models like LCDM. We note that deviations due to discretization in the converged range are not well modelled by shot noise, and subtracting it in fact degrades accuracy. Quantitatively our findings are broadly in line with the conservative assumptions about resolution adopted by recent studies using large cosmological simulations (e.g. Euclid Flagship) aiming to constrain the mildly non-linear regime. On the other hand, we remark that conclusions about small-scale clustering (e.g. concerning the validity of stable clustering) obtained using PS data at wavenumbers larger than a few $k_\Lambda$ may need revision in light of our convergence analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Using Host Galaxy Photometric Redshifts to Improve Cosmological Constraints with Type Ia Supernovae in the LSST Era

We perform a rigorous cosmology analysis on simulated Type Ia supernovae (SNe Ia) and evaluate the improvement from including photometric host galaxy redshifts compared to using only the "z spec " subset with spectroscopic redshifts from the host or SN. We use the Deep Drilling Fields (~50 deg 2 ) from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) in combination with a low-z sample based on Data Challenge2. The analysis includes light-curve fitting to standardize the SN brightness, a high-statistics simulation to obtain a bias-corrected Hubble diagram, a statistical+systematics covariance matrix including calibration and photo-z uncertainties, and cosmology fitting with a prior from the cosmic microwave background. Compared to using the z spec subset, including events with SN+host photo-z results in (i) more precise distances for z > 0.5, (ii) a Hubble diagram that extends 0.3 further in redshift, and (iii) a 50% increase in the Dark Energy Task Force figure of merit (FoM) based on the w 0 w a CDM model. Analyzing 25 simulated data samples, the average bias on w 0 and w a is consistent with zero. The host photo-z systematic of 0.01 reduces FoM by only 2% because (i) most z < 0.5 events are in the z spec subset, (ii) the combined SN+host photo-z has ×2 smaller bias, and (iii) the anticorrelation between fitted redshift and color self-corrects distance errors. To prepare for analyzing real data, the next SN Ia cosmology analysis with photo-zs should include non–SN Ia contamination and host galaxy misassociations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Constraints on cosmological parameters from gamma-ray burst peak photon energy and bolometric fluence measurements and other data

ABSTRACT We use measurements of the peak photon energy and bolometric fluence of 119 gamma-ray bursts (GRBs) extending over the redshift range of 0.3399 ≤ z ≤ 8.2 to simultaneously determine cosmological and Amati relation parameters in six different cosmological models. The resulting Amati relation parameters are almost identical in all six cosmological models, thus validating the use of the Amati relation in standardizing these GRBs. The GRB data cosmological parameter constraints are consistent with, but significantly less restrictive than, those obtained from a joint analysis of baryon acoustic oscillation and Hubble parameter measurements.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

AbacusHOD : a highly efficient extended multitracer HOD framework and its application to BOSS and eBOSS data

ABSTRACT We introduce the AbacusHOD model and present two applications of AbacusHOD and the AbacusSummit simulations to observations. AbacusHOD is a Halo Occupation Distribution (HOD) framework written in Python that is particle-based, multitracer, highly generalized, and highly efficient. It is designed specifically with multitracer/cosmology analyses for next-generation large-scale structure surveys in mind, and takes advantage of the volume and precision offered by the new state-of-the-art AbacusSummit cosmological simulations. The model is also highly customizable and should be broadly applicable to any upcoming surveys and a diverse range of cosmological analyses. In this paper, we demonstrate the capabilities of the AbacusHOD framework through two example applications. The first example demonstrates the high efficiency and the large HOD extension feature set through an analysis of full-shape redshift-space clustering of BOSS galaxies at intermediate to small scales ($\lt 30\, h^{-1}$ Mpc), assessing the necessity of introducing secondary galaxy biases (assembly bias). We find strong evidence for using halo environment instead of concentration to trace secondary galaxy bias, a result which also leads to a moderate reduction in the ‘lensing is low’ tension. The second example demonstrates the multitracer capabilities of the AbacusHOD package through an analysis of the extended Baryon Oscillation Spectroscopic Survey cross-correlation measurements between three different galaxy tracers: luminous red galaxies, emission-line galaxies, and quasi-stellar objects. We expect the AbacusHOD framework, in combination with the AbacusSummit simulation suite, to play an important role in a simulation-based analysis of the upcoming Dark Energy Spectroscopic Instrument data sets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Measurement Uncertainty in One-of-a-kind Experiments

A golden standard in science is to repeat an experimental measurement multiple times and calculate the measured value with experimental uncertainty by following well developed statistical procedures. For various reasons – cost, technical difficulty, international treaties, ethics of dealing with human or animal subjects, ecology - many important experiments and observations can not be repeated. Astronomy, earthquakes, hurricanes produce data from one-of-a-kind events. When information is not available in any other way, it should not be dismissed as qualitative, anecdotal evidence only. Analyzed in a mathematically rigorous way, it produces quantitative experimental data. Analysis of data from one-of-a-kind event differs from analysis of repeated experiments’ data. For repeated experiments, the experimental error includes a range of true values generated by repetitions of the experiment, and measurement uncertainty caused by detectors. They are independent. Repetitions of any experiment, as similar as achievable, always have built-in differences resulting in a range of the true values rather than in a single value. Measurement uncertainty depends on the measurement system only. Digital measurements have very small uncertainty, frequently smaller than the range of true experimental values resulting from built-in differences in the experiment repetitions. When data from one–of–a kind experiment are analyzed, only the measurement uncertainty can be reported.

42 ENGINEERING↗