Probing L μ − L τ models with CE ν NS : A new look at the combined COHERENT CsI and Ar data
Not Available
79 ASTRONOMY AND ASTROPHYSICS↗
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Automatic Detection and Classification of Radio Galaxy Images by Deep Learning
Abstract Surveys conducted by radio astronomy observatories, such as SKA, MeerKAT, Very Large Array, and ASKAP, have generated massive astronomical images containing radio galaxies (RGs). This generation of massive RG images has imposed strict requirements on the detection and classification of RGs and makes manual classification and detection increasingly difficult, even impossible. Rapid classification and detection of images of different types of RGs help astronomers make full use of the observed astronomical image data for further processing and analysis. The classification of FRI and FRII is relatively easy, and there are more studies and literature on them at present, but FR0 and FRI are similar, so it is difficult to distinguish them. It poses a greater challenge to image processing. At present, deep learning has made breakthrough progress in the field of image analysis and processing and has preliminary applications in astronomical data processing. Compared with classification algorithms that can only classify galaxies, object detection algorithms that can locate and classify RGs simultaneously are preferred. In target detection algorithms, YOLOv5 has outstanding advantages in the classification and positioning of small targets. Therefore, we propose a deep-learning method based on an improved YOLOv5 object detection model that makes full use of multisource data, combining FIRST radio with SDSS optical image data, and realizes the automatic detection of FR0, FRI, and FRII RGs. The innovation of our work is that on the basis of the original YOLOv5 object detection model, we introduce the SE Net attention mechanism, increase the number of preset anchors, adjust the network structure of the feature pyramid, and modify the network structure, thereby allowing our model to demonstrate galaxy classification and position detection effects. Our improved model produces satisfactory results, as evidenced by experiments. Overall, the mean average precision (mAP@0.5) of our improved model on the test set reaches 89.4%, which can determine the position (R.A. and decl.) and automatically detect and classify FR0s, FRIs, and FRIIs. Our work contributes to astronomy because it allows astronomers to locate FR0, FRI, and FRII galaxies in a relatively short time and can be further combined with other astronomically generated data to study the properties of these galaxies. The target detection model can also help astronomers find FR0s, FRIs, and FRIIs in future surveys and build a large-scale star RG catalog. Moreover, our work is also useful for the detection of other types of galaxies.
Astronomy & Astrophysics↗
A High-level Design for Bidirectional Data Streaming to High-Performance Computing Systems from External Science Facilities
Cutting-edge science is increasingly data-driven due to the emergence of scientific machine learning models that can guide scientists toward fruitful areas of exploration. Experimental science facilities such as light and neutron sources, particle colliders, and radio astronomy telescopes are also producing raw measurement data at rates that exceed available data storage and computing capacity at those facilities. As a result, scientific workflows are being developed that concurrently couple experiments at science facilities with high-performance computing (HPC) facilities to enable analysis of experimental data while the experiment is ongoing, and where analysis results are potentially fed back to the experiment for use in experimental control and/or steering in a time-sensitive manner. Our goal is to design, prototype, and deploy a new capability for the Oak Ridge Leadership Computing Facility (OLCF) that enables such workflows through support for bidirectional, memory-based streaming of data from external experiments into and out of OLCF HPC systems. This high-level design document describes the related work and motivating use cases that inform our understanding of the technical requirements for this capability, and describes a proposed architectural solution that meets these requirements and our plans for demonstrating the capability.
97 MATHEMATICS AND COMPUTING↗
Fundamental Parameters of ∼30,000 M dwarfs in LAMOST DR1 Using Data-driven Spectral Modeling
M dwarfs are the most common type of star in the Galaxy, and because of their small size are favored targets for searches of Earth-sized transiting exoplanets. Current and upcoming all-sky spectroscopic surveys, such as the Large Sky Area Multi Fiber Spectroscopic Telescope (LAMOST), offer an opportunity to systematically determine physical properties of many more M dwarfs than has been previously possible. Here, we present new effective temperatures, radii, masses, and luminosities for 29,678 M dwarfs with spectral types M0–M6 in the first data release (DR1) of LAMOST. We derived these parameters from the supervised machine-learning code, The Cannon, trained with 1388 M dwarfs in the Transiting Exoplanet Survey Satellite Cool Dwarf Catalog that were also present in LAMOST with high signal-to-noise ratio (>250) spectra. Our validation tests show that the output parameter uncertainties are strongly correlated with the signal-to-noise of the LAMOST spectra, and we achieve typical uncertainties of 110 K in T{sub eff} (∼3%), 0.065 R{sub ⊙} (∼14%) in radius, 0.054 M{sub ⊙} (∼12%) in mass, and 0.012 L{sub ⊙} (∼20%) in luminosity. The model presented here can be rapidly applied to future LAMOST data releases, significantly extending the samples of well-characterized M dwarfs across the sky using new and exclusively data-based modeling methods.
79 ASTRONOMY AND ASTROPHYSICS↗
HYPHY: Deep Generative Conditional Posterior Mapping of Hydrodynamical Physics
Generating large-volume hydrodynamical simulations for cosmological observables is a computationally demanding task necessary for next-generation observations. In this work, we construct a novel fully convolutional variational autoencoder (VAE) to synthesize hydrodynamic fields conditioned on dark matter fields from N -body simulations. After training the model on a single hydrodynamical simulation, we are able to probabilistically map new dark-matter-only simulations to corresponding full hydrodynamical outputs. By sampling over the latent space of our VAE, we can generate posterior samples and study the variance of the mapping. We find that our reconstructed field provides an accurate representation of the target hydrodynamical fields as well as reasonable variance estimates. This approach has promise for the rapid generation of mocks as well as for implementation in a full inverse model of observed data.
79 ASTRONOMY AND ASTROPHYSICS↗
Using machine learning to identify extragalactic globular cluster candidates from ground-based photometric surveys of M87
Globular clusters (GCs) have been at the heart of many longstanding questions in many sub-fields of astronomy and, as such, systematic identification of GCs in external galaxies has immense impacts. In this study, we take advantage of M87’s well-studied GC system to implement supervised machine learning (ML) classification algorithms – specifically random forest and neural networks – to identify GCs from foreground stars and background galaxies, using ground-based photometry from the Canada–France–Hawaii Telescope (CFHT). We compare these two ML classification methods to studies of ‘human-selected’ GCs and find that the best-performing random forest model can reselect 61.2 per cent ± 8.0 per cent of GCs selected from HST data (ACSVCS) and the best-performing neural network model reselects 95.0 per cent ± 3.4 per cent. When compared to human-classified GCs and contaminants selected from CFHT data – independent of our training data – the best-performing random forest model can correctly classify 91.0 per cent ± 1.2 per cent and the best-performing neural network model can correctly classify 57.3 per cent ± 1.1 per cent. ML methods in astronomy have been receiving much interest as Vera C. Rubin Observatory prepares for first light. The observables in this study are selected to be directly comparable to early Rubin Observatory data and the prospects for running ML algorithms on the upcoming data set yields promising results.
79 ASTRONOMY AND ASTROPHYSICS↗
Robustness of deep learning algorithms in astronomy -- galaxy morphology studies
Deep learning models are being increasingly adopted in wide array of scientific domains, especially to handle high-dimensionality and volume of the scientific data. However, these models tend to be brittle due to their complexity and overparametrization, especially to the inadvertent adversarial perturbations that can appear due to common image processing such as compression or blurring that are often seen with real scientific data. It is crucial to understand this brittleness and develop models robust to these adversarial perturbations. To this end, we study the effect of observational noise from the exposure time, as well as the worst case scenario of a one-pixel attack as a proxy for compression or telescope errors on performance of ResNet18 trained to distinguish between galaxies of different morphologies in LSST mock data. We also explore how domain adaptation techniques can help improve model robustness in case of this type of naturally occurring attacks and help scientists build more trustworthy and stable models.
79 ASTRONOMY AND ASTROPHYSICS↗
AT2017gfo: Bayesian inference and model selection of multicomponent kilonovae and constraints on the neutron star equation of state
The joint detection of the gravitational wave GW170817, of the short γ-ray burst GRB170817A and of the kilonova AT2017gfo, generated by the the binary neutron star (NS) merger observed on 2017 August 17, is a milestone in multimessenger astronomy and provides new constraints on the NS equation of state. We perform Bayesian inference and model selection on AT2017gfo using semi-analytical, multicomponents models that also account for non-spherical ejecta. Observational data favour anisotropic geometries to spherically symmetric profiles, with a log-Bayes’ factor of ~104, and favour multicomponent models against single-component ones. The best-fitting model is an anisotropic three-component composed of dynamical ejecta plus neutrino and viscous winds. Using the dynamical ejecta parameters inferred from the best-fitting model and numerical–relativity relations connecting the ejecta properties to the binary properties, we constrain the binary mass ratio to q < 1.54 and the reduced tidal parameter to $120\lt \tilde{\Lambda }\lt 1110$. Finally, we combine the predictions from AT2017gfo with those from GW170817, constraining the radius of a NS of 1.4 M ⊙ to 12.2 ± 0.5 km (1σ level). This prediction could be further strengthened by improving kilonova models with numerical-relativity information.
79 ASTRONOMY AND ASTROPHYSICS↗
Old data, new forensics: The first second of SN 1987A neutrino emission
The next Milky Way supernova will be an epochal event in multimessenger astronomy, critical to tests of supernovae, neutrinos, and new physics. Realizing this potential depends on having realistic simulations of core collapse. Here, we investigate the neutrino predictions of modern models (1-, 2-, and 3-D) over the first ≃1 s, making the first detailed comparisons of these models to each other and to the SN 1987A neutrino data. Even with different methods and inputs, the models generally agree with each other. However, even considering the low neutrino counts, the models generally disagree with data. What can cause this? We show that neither neutrino oscillations nor different progenitor masses appear to be a sufficient solution. We outline urgently needed work.
79 ASTRONOMY AND ASTROPHYSICS↗
PreMevE‐MEO: Predicting Ultra‐Relativistic Electrons Using Observations From GPS Satellites
Abstract Ultra‐relativistic electrons with energies greater than or equal to two megaelectron‐volt (MeV) pose a major radiation threat to spaceborne electronics, and thus specifying those highly energetic electrons has a significant meaning to space weather communities. Here we report the latest progress in developing our predictive model for MeV electrons in the outer radiation belt. The new version, primarily driven by electron measurements made along medium‐Earth‐orbits (MEO), is called PREdictive MEV Electron (PreMevE)‐MEO model that nowcasts ultra‐relativistic electron flux distributions across the whole outer belt. Model inputs include >2 MeV electron fluxes observed in MEOs by a fleet of GPS satellites as well as electrons measured by one Los Alamos satellite in the geosynchronous orbit. We developed an innovative Sparse Multi‐Inputs Latent Ensemble NETwork (SmileNet) which combines convolutional neural networks with transformers, and we used long‐term in situ electron data from NASA's Van Allen Probes mission to train, validate, optimize, and test the model. It is shown that PreMevE‐MEO can provide hourly nowcasts with high model performance efficiency and high correlation with observations. This prototype PreMevE‐MEO model demonstrates the feasibility of making high‐fidelity predictions driven by observations from longstanding space infrastructure in MEO, thus has great potential of growing into an invaluable space weather operational warning tool.
79 ASTRONOMY AND ASTROPHYSICS↗
Laboratory Data on the EUV/Soft-X-Ray Flux Problem of Fe XVIII and Fe XIX
Observations by the spacecraft Extreme Ultraviolet Explorer (EUVE), Far Ultraviolet Spectroscopic Explorer (FUSE), Chandra, and XMM-Newton of Capella (α Aurigae) have encountered problems with the relative intensities of the Fe XVIII and Fe XIX line emission in the soft-X-ray (XUV) and extreme-ultraviolet spectral ranges versus various model predictions based on theoretical atomic data. The reason may either lie in astrophysical phenomena, e.g., at emission or because of absorption by the interstellar medium, in the theoretical atomic data, in one or more spectrometer calibrations, or in the spectral modeling. Here, by measurements using an electron beam ion trap we provide laboratory data obtained under conditions that are reasonably close to stellar emission regions. The laboratory data on Fe XVIII and Fe XIX show line ratios that are rather similar to the observations of Capella. The measurements, therefore, rule out astrophysical phenomena and calibration errors and point to issues with the modeling of the observed Capella emission.
79 ASTRONOMY AND ASTROPHYSICS↗
Constraining low redshift [C II ] emission by cross-correlating FIRAS and BOSS data
ABSTRACT We perform a tomographic cross-correlation analysis of archival FIRAS data and the BOSS galaxy redshift survey to constrain the amplitude of [C II] 2P3/2 → 2P1/2 fine structure emission. Our analysis employs spherical harmonic tomography (SHT), which is based on the angular cross-power spectrum between FIRAS maps and BOSS galaxy over-densities at each pair of redshift bins, over a redshift range of 0.24 < z < 0.69. We develop the SHT approach for intensity mapping, where it has several advantages over existing power spectral estimators. Our analysis constrains the product of the [C II] bias and [C II] specific intensity, $b_{\rm [C \small{\rm II}]}I_{\rm [C \small{\rm II}]}$, to be <0.31 MJy/sr at z ≈ 0.35 and <0.28 MJy/sr at z ≈ 0.57 at $95{{\ \rm per\ cent}}$ confidence. These limits are consistent with most current models of the [C II] signal, as well as with higher-redshift [C II] cross-power spectrum measurements from the Planck satellite and BOSS quasars. We also show that our analysis, if applied to data from a more sensitive instrument such as the proposed PIXIE satellite, can detect pessimistic [C II] models at high significance.
79 ASTRONOMY AND ASTROPHYSICS↗
Anomalies in the topology of the temperature fluctuations in the cosmic microwave background: An analysis of the NPIPE and FFP10 data releases
We present a topological analysis of the temperature fluctuation maps from the Planck 2020 Data Release 4 NPIPE dataset and the Planck 2018 Data Release 3 FFP10 dataset. We performed a multiscale analysis in terms of the homology characteristics of the maps, invoking relative homology to account for the analysis in the presence of masks. We performed our analysis for a range of smoothing scales spanning sub- and super-horizon scales corresponding to a full width at half maximum (FWHM) of 5',10',20',40',80',160',320', and 640', and employed simulations based on the standard model for comparison, which assumes the initial fluctuation field to be an isotropic and homogeneous Gaussian random field. Examining the behavior of topological components, represented by the 0D homology group, we find the observations to be approximately 2σ or less deviant from the simulations for all resolutions and scales for the NPIPE dataset. For the FFP10 dataset, we detect a 2.96σ deviation between the observations and simulations at N = 128, FWHM = 80'. For the topological loops, represented by the first homology group, the simulations and observations are consistent within 2σ for most resolutions and scales for both the datasets. However, for the NPIPE dataset, we observe a high deviation between the observation and simulations in the number of loops at FWHM = 320', but at a low dimensionless threshold ν = –2.5. Under a Gaussian assumption, this would amount to a deviation of ~4σ. However, the distribution in this bin is manifestly non-Gaussian and does not obey Poisson statistics either. In the absence of a true theoretical understanding, we simply note that the significance is higher than what may be resolved by 600 simulations, yielding an empirical p-value of at most 0.0016. Specifically in this case, our tests indicate that the numbers arise from a statistically stable regime, despite being based on small numbers. For the FFP10 dataset, the differences are not as strong as for the NPIPE dataset, indicating a 2.77σ deviation at this resolution and threshold. The Euler characteristic, which is the alternating sum of the ranks of relative homology groups, reflects the deviations in the components and loops. To assess the significance of combined levels for a given scale, we employed the empirical and theoretical versions of the χ 2 test as well as the nonparametric Tukey depth test. Although all statistics exhibit a stable distribution, we favor the empirical version of the χ 2 test in the final interpretation, as it indicates the most conservative differences. For the NPIPE dataset, we find that the components and loops differ at more than 95%, but agree within the 99% confidence level with respect to the base model at N = 32, FWHM = 320'. The Euler characteristic at this resolution displays a per mil deviation. In contrast, the FFP10 dataset shows that the observations are consistent with the base model within the 95% confidence level, at this and smaller scales. This is consistent with the observations of the Planck analysis pipeline via Minkowski functionals. For the largest smoothing scale, N = 16, FWHM = 640', both datasets exhibit an anomalous behavior of the loops, where FFP10 data exhibit a deviation that is larger by an order of magnitude than that of the NPIPE dataset. In contrast, the values for the topological components and the Euler characteristic agree between observations and model to within a confidence level of 99%. However, for the largest scales, the statistics are based on low numbers and may have to be regarded with caution. Even though both datasets exhibit mild to significant discrepancies, they also exhibit contrasting behaviors at various instances. Therefore, we do not find it feasible to convincingly accept or reject the null hypothesis. Disregarding the large-scale anomalies that persist at similar scales in WMAP and Planck, observations of the cosmic microwave background are largely consistent with the standard cosmological model within 2σ.
79 ASTRONOMY AND ASTROPHYSICS↗
Mirror twin Higgs cosmology: constraints and a possible resolution to the H$_{0}$ and S$_{8}$ tensions
The mirror twin Higgs model (MTH) is a solution to the Higgs hierarchy problem that provides well-predicted cosmological signatures with only three extra parameters: the temperature of the twin sector, the abundance of twin baryons, and the vacuum expectation value (VEV) of twin electroweak symmetry breaking. These parameters specify the behavior of twin radiation and the acoustic oscillations of twin baryons, which lead to testable effects on the cosmic microwave background (CMB) and large-scale structure (LSS). While collider searches can only probe the twin VEV, through a fit to cosmological data we show that the existing CMB (Planck18 TTTEEE+lowE+lowT+lensing) and LSS (KV450) data already provide useful constraints on the remaining MTH parameters. Additionally, we show that the presence of twin radiation in this model can raise the Hubble constant H$_{0}$ while the scattering twin baryons can reduce the matter fluctuations S$_{8}$, which helps to relax the observed H$_{0}$ and S$_{8}$ tensions simultaneously. This scenario is different from the typical ΛCDM + ΔN$_{eff}$ model, in which extra radiation helps with the Hubble tension but worsens the S$_{8}$ tension. For instance, when including the SH0ES and 2013 Planck SZ data in the fit, we find that a universe with ≳ 20% of the dark matter comprised of twin baryons is preferred over ΛCDM by ~ 4σ. If the twin sector is indeed responsible for resolving the H$_{0}$ and S$_{8}$ tensions, future measurements from the Euclid satellite and CMB Stage 4 experiment will further measure the twin parameters to O(1 - 10%)-level precision. Our study demonstrates how models with hidden naturalness can potentially be probed using precision cosmological data.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Using quasar X-ray and UV flux measurements to constrain cosmological model parameters
ABSTRACT Risaliti and Lusso have compiled X-ray and UV flux measurements of 1598 quasars (QSOs) in the redshift range 0.036 ≤ z ≤ 5.1003, part of which, z ∼ 2.4 − 5.1, is largely cosmologically unprobed. In this paper we use these QSO measurements, alone and in conjunction with baryon acoustic oscillation (BAO) and Hubble parameter [H(z)] measurements, to constrain cosmological parameters in six different cosmological models, each with two different Hubble constant priors. In most of these models, given the larger uncertainties, the QSO cosmological parameter constraints are mostly consistent with those from the BAO + H(z) data. A somewhat significant exception is the non-relativistic matter density parameter Ωm0 where QSO data favour Ωm0 ∼ 0.5 − 0.6 in most models. As a result, in joint analyses of QSO data with H(z) + BAO data the 1D Ωm0 distributions shift slightly towards larger values. A joint analysis of the QSO + BAO + H(z) data is consistent with the current standard model, spatially-flat ΛCDM, but mildly favours closed spatial hypersurfaces and dynamical dark energy. Since the higher Ωm0 values favoured by QSO data appear to be associated with the z ∼ 2 − 5 part of these data, and conflict somewhat with strong indications for Ωm0 ∼ 0.3 from most z < 2.5 data as well as from the cosmic microwave background anisotropy data at z ∼ 1100, in most models, the larger QSO data Ωm0 is possibly more indicative of an issue with the z ∼ 2 − 5 QSO data than of an inadequacy of the standard flat ΛCDM model.
79 ASTRONOMY AND ASTROPHYSICS↗
Can the Distance–Redshift Relation be Determined from Correlations between Luminosities?
We explore whether an independent determination of the distance–redshift relation, and hence cosmological model parameters, can be obtained from the apparent correlations between two different wave-band luminosities or fluxes, as has been claimed in recent works using the X-ray and ultraviolet luminosities and fluxes of quasars. We show that such an independent determination is possible only if the correlation between luminosities is obtained independently of the cosmological model and measured fluxes and redshifts, for example, based on sound theoretical models or unrelated observations. In particular, we show that if the correlation is determined empirically for two luminosities obtained from fluxes and redshifts, then the method suffers from circularity. In the case where the observed correlation between fluxes in very narrow redshift bins is used as a proxy for the luminosity correlation, we show that one is dealing with a pure tautology with no information on distances and cosmological model. We argue that the problem arises because of the incomplete treatment of the correlation, and we use numerical methods with a joint X-ray and ultraviolet quasar data set to demonstrate this shortcoming.
79 ASTRONOMY AND ASTROPHYSICS↗
Isomers and Dark Matter: 180m Ta and 178m Hf [Slides]
Dark matter has attractive gravitational interactions. It does not interact with light, or has very small electromagnetic coupling. It is stable, or has a lifetime much longer than the age of the Universe. No departure model from the law of gravity explains all data, whereas particle dark matter can.
79 ASTRONOMY AND ASTROPHYSICS↗
Hot QCD at finite isospin density: Confronting the SU(3) Nambu–Jona-Lasinio model with recent lattice data
Not Available
79 ASTRONOMY AND ASTROPHYSICS↗