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At least 127 records · Page 7

Bridging the Gap between Cosmological Simulations with Graph Neural Networks and Domain Adaptation

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross dataset tasks (up to 28% better relative error and up to almost an order of magnitude better χ 2 ). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

97 MATHEMATICS AND COMPUTING↗

On the possibility of observing cosmic ray sources in high energy gamma rays

If cosmic rays are accelerated by strong shocks, then cosmic ray sources should be characterized by spectra, dN/dE alpha E exp -(2.0-2.2), reflecting the strength of those shocks. This is expected from the 'standard leaky box' model of cosmic ray propagation in which the source spectra are harder than the observed spectra because higher energy particles have shorter residence times in the galactic magnetic fields. Furthermore, data on cosmic ray nucleons suggest that these sources might be surrounded by material. If the latter is true, such sources should be observable in gamma rays at energies beyond 1 GeV where the angular resolution of gamma-ray telescopes is optimized and the background is significantly reduced. For identified sources, the source location accuracy can be shown to improve with increasing energy in spite of the decreasing statistics, as long as the gamma-ray spectrum is harder than dN/dE alpha E exp -gamma. A Monte Carlo model is used to predict the photon spectra which would be expected from cosmic ray sources under varying assumptions about the strength of the shocks in the acceleration region.

Ormes, J. F.↗

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks (up to $28\%$ better relative error and up to almost an order of magnitude better $\chi^2$). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

79 ASTRONOMY AND ASTROPHYSICS↗

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

State of the art astronomical simulations have provided datasets which enabled the training of novel deep learning techniques for constraining cosmological parameters. However, differences in subgrid physics implementation and numerical approximations among simulation suites lead to differences in simulated datasets, which pose a hard challenge when trying to generalize across diverse data domains and ultimately when applying models to observational data. Recent work reveals deep learning algorithms are able to extract more information from complex cosmological simulations than summary statistics like power spectra. We introduce Domain Adaptive Graph Neural Networks (DA-GNNs), trained on CAMELS data, inspired by CosmoGraphNet (Villanueva-Domingo et al 2023). By utilizing GNNs, we can capitalize on their capacity to capture both astrophysical and topological features of galaxy distributions. Mixing these capabilities with domain adaptation techniques such as Maximum Mean Discrepancy (MMD), which enable extraction of domain-invariant features, our framework demonstrates enhanced accuracy and robustness. We present experimental results, including the alignment of distributions across domains through data visualization. These findings suggest that DA-GNNs are an efficient way of extracting domain independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

79 ASTRONOMY AND ASTROPHYSICS↗

First Measurement of $π^+$-Ar and $p$-Ar Total Inelastic Cross Sections in the Sub-GeV Energy Regime with ProtoDUNE-SP Data

The ProtoDUNE-SP detector, a kiloton-scale prototype for the Deep Underground Neutrino Experiment (DUNE), is the largest liquid argon time projection chamber built to date. Operated at CERN from 2018 to 2020, it collected both cosmic-ray data and a beam consisting of positively-charged particles with discrete momentum settings across a range of 0.3 GeV/$c$ to 7 GeV/$c$. In this letter, we report the total inelastic cross section measurements for $π^+$-Ar and $p$-Ar interactions using selected $π^+$ and proton samples from the 1 GeV/$c$ beam data. These results provide the first measurement of the total inelastic cross sections for $π^+$-Ar in the 500-900 MeV kinetic energy range and for $p$-Ar below 450 MeV, both of which are directly relevant to the DUNE energy range. The measured cross sections are consistent with predictions and provide a dataset that was previously unavailable for argon targets. These measurements are essential for constraining neutrino-argon interaction models, which are crucial for the precision physics goals of the upcoming DUNE experiment.

Abbaslu, Saeed [IPM, Tehran] (ORCID:00000003356771↗

Constraints on the Galactic Halo Dark Matter from Fermi-LAT Diffuse Measurements

We have performed an analysis of the diffuse gamma-ray emission with the Fermi Large Area Telescope (LAT) in the Milky Way halo region, searching for a signal from dark matter annihilation or decay. In the absence of a robust dark matter signal, constraints are presented. We consider both gamma rays produced directly in the dark matter annihilation/decay and produced by inverse Compton scattering of the e+/e− produced in the annihilation/decay. Conservative limits are derived requiring that the dark matter signal does not exceed the observed diffuse gamma-ray emission. A second set of more stringent limits is derived based on modeling the foreground astrophysical diffuse emission using the GALPROP code. Uncertainties in the height of the diffusive cosmic-ray halo, the distribution of the cosmic-ray sources in the Galaxy, the index of the injection cosmic-ray electron spectrum, and the column density of the interstellar gas are taken into account using a profile likelihood formalism, while the parameters governing the cosmic-ray propagation have been derived from fits to local cosmic-ray data. The resulting limits impact the range of particle masses over which dark matter thermal production in the early universe is possible, and challenge the interpretation of the PAMELA/Fermi-LAT cosmic ray anomalies as the annihilation of dark matter.

DIFFUSE MEASUREMENTS↗

Measuring the Redshift Dependence of The Cosmic Microwave Background Monopole Temperature With Planck Data

We study the capability of Planck data to constrain deviations of the cosmic microwave background (CMB) blackbody temperature from adiabatic evolution using the thermal Sunyaev-Zeldovich anisotropy induced by clusters of galaxies. We consider two types of data sets depending on how the cosmological signal is removed: using a CMB template or using the 217 GHz map. We apply two different statistical estimators, based on the ratio of temperature anisotropies at two different frequencies and on a fit to the spectral variation of the cluster signal with frequency. The ratio method is biased if CMB residuals with amplitude approximately 1 microK or larger are present in the data, while residuals are not so critical for the fit method. To test for systematics, we construct a template from clusters drawn from a hydro-simulation included in the pre-launch Planck Sky Model. We demonstrate that, using a proprietary catalog of X-ray-selected clusters with measured redshifts, electron densities, and X-ray temperatures, we can constrain deviations of adiabatic evolution, measured by the parameter α in the redshift scaling T (z) = T0(1 + z)(sup 1−alpha), with an accuracy of sigma(sub alpha) = 0.011 in the most optimal case and with sigma alpha = 0.018 for a less optimal case. These results represent a factor of 2-3 improvement over similar measurements carried out using quasar spectral lines and a factor 6-20 with respect to earlier results using smaller cluster samples.

cosmic background radiation↗

Implications of cross section errors for cosmic ray propagation

Errors in nuclear interaction cross sections are the single most important limitation on the analysis of cosmic ray composition data. At the 18th International Cosmic Ray Conference, the potential importance of correlations in cross section errors in determining cosmic ray source abundances was demonstrated. In this paper the magnitude of cross section error correlation is estimated. Analysis suggests that cross section errors are essentially uncorrelated for nuclei with Z 29 and that the actual errors may be less than the nominal 35%.

Letaw, J. R.↗

Quiet time interplanetary cosmic ray anisotropies observed from Pioneer 10 and 11

Cerenkov counters on the Pioneer 10 and 11 spacecraft, capable of detecting alpha particles and protons with energies up to 480 MeV, and nucleons and electrons with energies up to 6 MeV, have yielded data on cosmic ray anisotropies during periods of low solar activity. Observations from Pioneer 11 place east-west anisotropy at 0.41 plus or minus 0.11%, and the north-south anisotropy at near zero; Pioneer 10 results show east-west anisotropy to be approximately 0.59 plus or minus 0.18%, and the north-south component at 0.25 plus or minus 0.08%. It is noted that the Pioneer 10 observations were obtained at the 6 AU range, while those from Pioneer 11 originated closer to the sun (1.1 to 2.7 AU). Attention is given to the ratio of the perpendicular to parallel components of the diffusion coefficient, and to the large north-south anisotropy reported by Pioneer 10, an effect due possibly to gradient drift, and to an additional streaming independent of the magnetic field polarity.

Ip, W.-H.↗

The ICEBERG Test Stand for DUNE Cold Electronics Development

ICEBERG is a liquid argon time projection chamber at Fermilab for the purpose of testing detector components and software for the Deep Underground Neutrino Experiment (DUNE). The detector features a 1.15m x 1m anode plane following the specifications of the DUNE horizontal drift far detector and a newly installed X-ARAPUCA photodetector. The status of ICEBERG is reported along with analysis of noise, pulser, and cosmic ray data from the ninth run beginning May 2024 with the goal of advising the DUNE collaboration on the optimal wire readout electronics configuration. In addition, development of an absolute energy scale calibration method is currently underway using known sources such as cosmic ray muon Michel electrons at the ~10 MeV scale and $^{39}$Ar decay electrons at the ~100keV scale. Research into AI-based identification of such events at the data acquisition level is introduced.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Radiation shielding estimates for manned Mars space flight

In the analysis of the required radiation shielding protection of spacecraft during a Mars flight, specific effects of solar activity (SA) on the intensity of galactic and solar cosmic rays were taken into consideration. Three spaceflight periods were considered: (1) maximum SA; (2) minimum SA; and (3) intermediate SA, when intensities of both galactic and solar cosmic rays are moderately high. Scenarios of spaceflights utilizing liquid-propellant rocket engines, low- and intermediate-thrust nuclear electrojet engines, and nuclear rocket engines, all of which have been designed in the Soviet Union, are reviewed. Calculations were performed on the basis of a set of standards for radiation protection approved by the U.S.S.R. State Committee for Standards. It was found that the lowest estimated mass of a Mars spacecraft, including the radiation shielding mass, obtained using a combination of a liquid propellant engine with low and intermediate thrust nuclear electrojet engines, would be 500-550 metric tons.

Non-NASA Center↗

Improving Detector Systematic Uncertainties Through Data-Driven Machine Learning

Detector simulation in liquid argon time projection chambers (LArTPCs) is a constant challenge. In particular the modeling of electrons response on wires is highly nontrivial. However, new machine learning techniques exist which can be leveraged to ameliorate these concerns. We present a novel methodology to attempt to learn from cosmic muon data in the ICARUS detector how reconstructed wire signals are influenced by features of the hits such as location, particle direction, angle relative to the wire plane, etc. A model can then be generated which can apply the learned mapping to Monte Carlo events to create a more data-like simulation sample. By creating such a sample we expect to reduce the systematic uncertainties at ICARUS due to our detector modeling.

Hausner, Harry [Fermilab] (ORCID:0000000188932280)↗

Improving Detector Systematic Uncertainties Through Data-Driven Machine Learning

Detector simulation in liquid argon time projection chambers (LArTPCs) is a constant challenge. In particular the modeling of electrons response on wires is highly nontrivial. However, new machine learning techniques exist which can be leveraged to ameliorate these concerns. We present a novel methodology to attempt to learn from cosmic muon data in the ICARUS detector how reconstructed wire signals are influenced by features of the hits such as location, particle direction, angle relative to the wire plane, etc. A model can then be generated which can apply the learned mapping to Monte Carlo events to create a more data-like simulation sample. By creating such a sample we expect to reduce the systematic uncertainties at ICARUS due to our detector modeling.

Hausner, Harry [Fermilab] (ORCID:0000000188932280)↗

Study of cosmic ray scintillations from 5-minute data of the scintillations telescope Izmran and world-wide network stations

During cosmic ray propagation in interplanetary space there appear characteristic cosmic-ray intensity scintillations which are due to charged particle scattering on random inhomogeneities of the interplanetary magnetic field. The power spectra of cosmic ray scintillations on the Earth during some intervals from 1977 to 1982 (for quiet periods, for solar flares and Forbush decreases due to power shock waves) have been calculated from five-minute, one and two-hour values of the cosmic-ray intensity measured by the scintillator supertelescope IZMIRAN. The spectra were estimated by the methods of spectral analysis and by autoregressive methods which mutually control each other and make it possible not only to analyze scintillation powers at distinguished frequencies, but also to determine the behavior of spectrum slopes in some frequency ranges.

Gulinsky, O. V.↗

The cosmic gamma-ray spectrum between 0.3 and 27 MeV measured on the Apollo 15

The spectrum of the total (diffuse and discrete sources) cosmic gamma-ray background over the 0.3 to 27 MeV range was measured with a 7.0 cm diameter x 7.0 cm long uncollimated NaI(Tl) scintillation counter located on a boom 7.6 m from the Apollo 15 service module. Data on cosmic gamma-rays were taken during transearth coast at various boom extensions, detector gains, and with the plastic anticoincidence scintillator enabled and disabled.

Trombka, J. I.↗

Estimating the change in asymptotic direction due to secular changes in the geomagnetic field

The concept of geomagnetic optics, as described by the asymptotic directions of approach, is extremely useful in the analysis of cosmic radiation data. However, when changes in cutoff occur as a result of evolution in the geomagnetic field, there are corresponding changes in the asymptotic cones of acceptance. A method is introduced of estimating the change in the asymptotic direction of approach for vertically incident cosmic ray particles from a reference set of directions at a specific epoch by considering the change in the geomagnetic cutoff.

Flueckiger, E. O.↗

Fermi LAT Search for Dark Matter in Gamma-Ray Lines and the Inclusive Photon Spectrum

Dark matter particle annihilation or decay can produce monochromatic gamma-ray lines and contribute to the diffuse gamma-ray background. Flux upper limits are presented for gamma-ray spectral lines from 7 to 200 GeV and for the diffuse gamma-ray background from 4.8 GeV to 264 GeV obtained from two years of Fermi Large Area Telescope data integrated over most of the sky. We give cross section upper limits and decay lifetime lower limits for dark matter models that produce gamma-ray lines or contribute to the diffuse spectrum, including models proposed as explanations of the PAMELA and Fermi cosmic-ray data.

Ackermann, M.↗