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Perhaps the cosmological constant really is huge at the Planck scale, but is “hidden” by Planck scale quantum fluctuations of spacetime. I briefly review this proposal and provide some evidence, coming from a simplified midisuperspace model, that an appropriate “foamy” structure can do the job of hiding a large cosmological constant, and can persist under time evolution.
Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.
The accurate prediction of photovoltaic (PV) power generation is significant to ensure the economic and safe operation of power systems. To this end, the paper proposes a new digital twin (DT) empowered PV power prediction framework that is capable of ensuring reliable data transmission and employing the DT to achieve high accuracy of power prediction. With this framework, considering potential data contamination in the collected PV data, a generative adversarial network is employed to restore the historical data set, which offers a prerequisite to ensure accurate mapping from the physical space to the digital space. Further, a new DT empowered PV power prediction method is proposed. Therein, we model a DT that encompasses a digital physical model for reflecting the physical operation mechanism and a neural network model (i.e., a parallel network of convolution and bidirectional long-short-term memory model) for capturing the hidden spatial-temporal features. The proposed method enables the use of the DT to take advantages of the digital physical model and the neural network model, resulting in enhanced prediction accuracy. Finally, a real data set is conducted to access the effectiveness of the proposed method.
Dwarf early-type galaxies (ETGs) display a rich diversity in their photometric, structural, and dynamical properties. In this work, we address their structural complexity by studying with deep imaging a sample of nine dwarf ETGs from the Virgo galaxy cluster, characterized by having faint disk features such as bars and spiral arms, that lie mostly hidden within the bright diffuse light of the galaxies. We present a new, robust method that aims to identify and extract the disk substructure embedded in these dwarf ETGs. The method consists in an iterative procedure that gradually separates a galaxy image into two components: the bright, dominant, diffuse component, and the much fainter, underlying disk component. By applying it to the dwarf ETG sample, we quantify their disk substructure and find that its relative contribution to the total galaxy light ranges between 2.2% and 6.4% within two effective radii. We test the reliability of the method, and prove that it is accurate in recovering the disk substructure we introduce in mock galaxy images, even at low disk-to-total light fractions of a few percent. As a potential application of the method, we perform a Fourier analysis on the extracted disk substructures and measure the orientation, length, and strength of the bars, as well as the pitch angle and strength of the spiral arms. We also briefly discuss a scenario based on the numerical simulations presented in our companion paper, Brought to Light II: Smith et al., in which we investigate the origins of the substructure in such dwarf systems.
APOGEE is a high-resolution (R ~ 22,000), near-infrared, multi-epoch, spectroscopic survey of the Milky Way. The second generation of the APOGEE project, APOGEE-2, includes an expansion of the survey to the Southern Hemisphere called APOGEE-2S. This expansion enabled APOGEE to perform a fully panoramic mapping of all of the main regions of the Milky Way; in particular, by operating in the H band, APOGEE is uniquely able to probe the dust-hidden inner regions of the Milky Way that are best accessed from the Southern Hemisphere. In this paper we present the targeting strategy of APOGEE-2S, with special attention to documenting modifications to the original, previously published plan. The motivation for these changes is explained as well as an assessment of their effectiveness in achieving their intended scientific objective. In anticipation of this being the last paper detailing APOGEE targeting, we present an accounting of all such information complete through the end of the APOGEE-2S project; this includes several main survey programs dedicated to exploration of major stellar populations and regions of the Milky Way, as well as a full list of programs contributing to the APOGEE database through allocations of observing time by the Chilean National Time Allocation Committee and the Carnegie Institution for Science. This work was presented along with a companion article, Beaton et al. (2021), presenting the final target selection strategy adopted for APOGEE-2 in the Northern Hemisphere.
We report on the observations of gamma-ray burst (GRB) 190114C by the Fermi Gamma-ray Space Telescope and the Neil Gehrels Swift Observatory . The prompt gamma-ray emission was detected by the Fermi GRB Monitor (GBM), the Fermi Large Area Telescope (LAT), and the Swift Burst Alert Telescope (BAT) and the long-lived afterglow emission was subsequently observed by the GBM, LAT, Swift X-ray Telescope (XRT), and Swift UV Optical Telescope. The early-time observations reveal multiple emission components that evolve independently, with a delayed power-law component that exhibits significant spectral attenuation above 40 MeV in the first few seconds of the burst. This power-law component transitions to a harder spectrum that is consistent with the afterglow emission observed by the XRT at later times. This afterglow component is clearly identifiable in the GBM and BAT light curves as a slowly fading emission component on which the rest of the prompt emission is superimposed. As a result, we are able to observe the transition from internal-shock- to external-shock-dominated emission. We find that the temporal and spectral evolution of the broadband afterglow emission can be well modeled as synchrotron emission from a forward shock propagating into a wind-like circumstellar environment. We estimate the initial bulk Lorentz factor using the observed high-energy spectral cutoff. Considering the onset of the afterglow component, we constrain the deceleration radius at which this forward shock begins to radiate in order to estimate the maximum synchrotron energy as a function of time. We find that even in the LAT energy range, there exist high-energy photons that are in tension with the theoretical maximum energy that can be achieved through synchrotron emission from a shock. These violations of the maximum synchrotron energy are further compounded by the detection of very high-energy (VHE) emission above 300 GeV by MAGIC concurrent with our observations. We conclude that the observations of VHE photons from GRB 190114C necessitates either an additional emission mechanism at very high energies that is hidden in the synchrotron component in the LAT energy range, an acceleration mechanism that imparts energy to the particles at a rate that is faster than the electron synchrotron energy-loss rate, or revisions of the fundamental assumptions used in estimating the maximum photon energy attainable through the synchrotron process.
Some studies of stars' multielement abundance distributions suggest at least 5–7 significant dimensions, but others show that many elemental abundances can be predicted to high accuracy from [Fe/H] and [Mg/Fe] (or [Fe/H] and age) alone. We show that both propositions can be, and are, simultaneously true. We adopt a machine-learning technique known as normalizing flow to reconstruct the probability distribution of Milky Way disk stars in the space of 15 elemental abundances measured by APOGEE. Conditioning on T eff and $\mathrm{log}\,g$ minimizes the differential systematics. After further conditioning on [Fe/H] and [Mg/Fe], the residual scatter for most abundances is σ [X/H] ≲ 0.02 dex, consistent with APOGEE's reported statistical uncertainties of ~0.01–0.015 dex and intrinsic scatter of 0.01–0.02 dex. Despite the small scatter, residual abundances display clear correlations between elements, which we show are too large to be explained by measurement uncertainties or by the finite sampling noise. We must condition on at least seven elements to reduce the correlations to a level consistent with the observational uncertainties. Our results demonstrate that cross-element correlations are a much more sensitive probe of a hidden structure than dispersion, and they can be measured precisely in a large sample even if the star-by-star measurement noise is comparable to the intrinsic scatter. We conclude that many elements have an independent story to tell, even for the mundane disk stars and elements produced by the core-collapse and Type Ia supernovae. The only way to learn these lessons is to measure the abundances directly, and not merely infer them.
The search for Population III stars has fascinated and eluded astrophysicists for decades. One promising place for capturing evidence of their presence must be high-redshift objects; signatures should be recorded in their characteristic chemical abundances. We deduce the Fe and Mg abundances of the broadline region (BLR) from the intensities of ultraviolet Mg ii and Fe ii emission lines in the near-infrared spectrum of UKIDSS Large Area Survey (ULAS) J1342+0928 at z = 7.54, by advancing our novel flux-to-abundance conversion method developed for quasars up to z ~ 3. We find that the BLR of this quasar is extremely enriched, by a factor of 20 relative to the solar Fe abundance, together with a very low Mg/Fe abundance ratio: [Fe/H] = +1.36 ± 0.19 and [Mg/Fe] =-1.11 ± 0.12, only 700 million years after the Big Bang. We conclude that such an unusual abundance feature cannot be explained by the standard view of chemical evolution that considers only the contributions from canonical supernovae. While there remains uncertainty in the high-mass end of the Population III initial mass function, here we propose that the larger amount of iron in ULAS J1342+0928 was supplied by a pair-instability supernova (PISN) caused by the explosion of a massive Population III star in the high-mass end of the possible range of 150–300 M ⊙ . Chemical evolution models based on initial PISN enrichment well explain the trend in [Mg/Fe]-z all the way from z < 3 to z = 7.54. We predict that stars with very low [Mg/Fe] at all metallicities are hidden in the galaxy, and they will be efficiently discovered by ongoing new-generation photometric surveys.
We present continued analysis of a sample of low-redshift iron low-ionization broad-absorption-line quasars (FeLoBALQs). Choi et al. presented SimBAL spectral analysis of broad-absorption-line (BAL) outflows in 50 objects. Leighly et al. analyzed the optical emission lines of 30 of those 50 objects and found that they are characterized by either a high accretion rate (L Bol /L Edd > 0.3) or low accretion rate (0.03 < L Bol /L Edd < 0.3). We report that the outflow velocity is inversely correlated with the BAL location among the high-accretion-rate objects, with the highest velocities observed in parsec-scale outflows. In contrast, the low-Eddington-ratio objects showed the opposite trend. We confirmed the known relationship between the outflow velocity and L Bol /L Edd and found that the scatter plausibly originates in the force multiplier (launch radius) in the low(high)-accretion-rate objects. A log volume filling factor between –6 and –4 was found in most outflows but was as high as –1 for low-velocity compact outflows. We investigated the relationship between the observed [O III ] emission and that predicted from the BAL gas. We found that these could be reconciled if the emission-line covering fraction depends on the Seyfert type and BAL location. The difference between the predicted and observed [O III ] luminosity is correlated with the outflow velocity, suggesting that [O III ] emission in high-Eddington-ratio objects may be broad and hidden under Fe ii emission. We suggest that the physical differences in the outflow properties as a function of location in the quasar and accretion rate point to different formation, acceleration, and confinement mechanisms for the two FeLoBALQ types.
While it is difficult to observe the first black hole seeds in the early universe, we can study intermediate-mass black holes (IMBHs) in local dwarf galaxies for clues about their origins. In this paper we present a sample of variability-selected active galactic nuclei (AGN) in dwarf galaxies using optical photometry from the Zwicky Transient Facility (ZTF) and forward-modeled mid-IR photometry of time-resolved Wide-field Infrared Survey Explorer (WISE) co-added images. We found that 44 out of 25,714 dwarf galaxies had optically variable AGN candidates and 148 out of 79,879 dwarf galaxies had mid-IR variable AGN candidates, corresponding to active fractions of 0.17% ± 0.03% and 0.19% ± 0.02%, respectively. We found that spectroscopic approaches to AGN identification would have missed 81% of our ZTF IMBH candidates and 69% of our WISE IMBH candidates. Only nine candidates have been detected previously in radio, X-ray, and variability searches for dwarf galaxy AGN. The ZTF and WISE dwarf galaxy AGN with broad Balmer lines have virial masses of 10 5 M ⊙ < M BH < 10 7 M ⊙ , but for the rest of the sample, BH masses predicted from host galaxy mass range between 10 5.2 M ⊙ < M BH < 10 7.25 M ⊙ . We found that only 5 of 152 previously reported variability-selected AGN candidates from the Palomar Transient Factory in common with our parent sample were variable in ZTF. We also determined a nuclear supernova fraction of 0.05% ± 0.01% yr -1 for dwarf galaxies in ZTF. Our ZTF and WISE IMBH candidates show the promise of variability searches for the discovery of otherwise hidden low-mass AGN.
In this paper, the blue quasar SDSS J105816.19+544310.2 (=SDSS J1058+5443) at redshift 0.479 has been reported as the best true type 2 quasar candidate with the disappearance of central broad-line regions. There are no definite conclusions on the very existence of true type 2 active galactic nuclei (AGN), mainly due to detected optical broad emission lines in high-quality spectra of some previously classified true type 2 AGN candidates. Here, unlike previously reported true type 2 AGN candidates among narrow emission-line galaxies with weak AGN activities but strong stellar lights, the definitely blue quasar SDSS J1058+5443 can be well confirmed as a true type 2 quasar due to apparent quasar-shape blue continuum emissions but an apparent loss of both the optical broad Balmer emission lines and the near-UV (NUV) broad Mg II emission line. Based on different model functions and the F-test statistical technique, after considering blueshifted optical and UV Fe II emissions, there are no apparent broad optical Balmer emission lines and/or broad NUV Mg II lines, and the confidence level is smaller than 1σ in support of broad optical and NUV emission lines. Moreover, assuming the virialization assumption to broad-line emission clouds, the reconstructed broad emission lines strongly indicate that the probable intrinsic broad emission lines, if they exist, cannot be hidden or overwhelmed in the noise of the Sloan Digital Sky Survey spectrum of SDSS J1058+5443. Therefore, SDSS J1058+5443 is so far the best and most robust true type 2 quasar candidate, leading to the clear conclusion of the very existence of true type 2 AGN.
We investigate galactic winds in the HizEA galaxies, a collection of 46 late-stage galaxy mergers at z = 0.4–0.8, with stellar masses of $\mathrm{log}({M}_{* }/{M}_{\odot })=10.4\mbox{--}11.5$ , star formation rates (SFRs) of 20–500 M ⊙ yr -1 , and ultra-compact (a few 100 pc) central star-forming regions. We measure their gas kinematics using the Mg II λλ 2796,2803 absorption lines in optical spectra from MMT, Magellan, and Keck. We find evidence of outflows in 90% of targets, with maximum outflow velocities of 550–3200 km s -1 . We combine these data with ten samples from the literature to construct scaling relations for outflow velocity versus SFR, star formation surface density (Σ SFR ), M * , and SFR/M * . The HizEA galaxies extend the dynamic range of the scaling relations by a factor of ~2–4 in outflow velocity and an order of magnitude in SFR and Σ SFR . The ensemble scaling relations exhibit strong correlations between outflow velocity, SFR, SFR/R, and Σ SFR , and weaker correlations with M * and SFR/M * . The HizEA galaxies are mild outliers on the SFR and M* scaling relations, but they connect smoothly with more typical star-forming galaxies on plots of outflow velocity versus SFR/R and Σ SFR . These results provide further evidence that the HizEA galaxies' exceptional outflow velocities are a consequence of their extreme star formation conditions rather than hidden black hole activity, and they strengthen previous claims that Σ SFR is one of the most important properties governing the velocities of galactic winds.
The recent detection of high-energy neutrinos by IceCube in the direction of the nearby Seyfert/starburst galaxy NGC 1068 implies that radio-quiet active galactic nuclei can accelerate cosmic-ray ions. Dedicated multimessenger analyses suggest that the interaction of these high-energy ions with ambient gas or photons happens in a region of the galaxy that is highly opaque for GeV–TeV gamma rays. Otherwise, the GeV–TeV emission would violate existing constraints provided by the Fermi Large Area Telescope (LAT) and the Major Atmospheric Gamma Imaging Cherenkov. The conditions of high optical depth are realized near the central supermassive black hole (SMBH). At the same time, the GeV emission detected by the Fermi LAT is likely related to the galaxy’s sustained star formation activity. In this work, we derive a 20 MeV–1 TeV spectrum of NGC 1068 using 14 yr of Fermi LAT observations. We find that the starburst hadronic component is responsible for NGC 1068's emission above ~500 MeV. However, below this energy, an additional component is required. In the 20–500 MeV range, the Fermi LAT data are consistent with hadronic emission initiated by non-thermal ions interacting with gas or photons in the vicinity of the central SMBH. This highlights the importance of the MeV band to discover hidden cosmic-ray accelerators.
The orbital angular momentum (OAM) of electron states is an essential ingredient for topological and quantum geometric quantities in solids. For example, Dirac surface states with helical spin- and orbital-angular momenta are a hallmark of a 3D topological insulator. Angle-resolved photoemission spectroscopy (ARPES) with variable circular light polarization, known as circular dichroism (CD), has been assumed to be a direct probe of OAM and, by proxy, of the Berry curvature of electronic bands in energy- and momentum-space. Indeed, topological surface states have been shown to exhibit angle-dependent CD (CDAD), and more broadly, CD is often interpreted as evidence of spin-orbit coupling. Meanwhile, it is well-established that CD originates from the photoemission matrix elements, which can have extrinsic contributions related to the experimental geometry and the inherently broken inversion symmetry at the sample surface. Therefore, it is important to broadly examine CD-ARPES to determine the scenarios in which it provides a robust probe of intrinsic material physics. We performed CD-ARPES on the canonical topological insulator $\mathrm{Bi}_2\mathrm{Se}_3$ over a wide range of incident photon energies. Not only do we observe angle-dependent CD in the surface states, as expected, but we also find CD of a similar magnitude in virtually all bulk bands. Since OAM is forbidden by inversion symmetry in the bulk, we conclude this originates from symmetry-breaking in the photoemission process. Comparison with theoretical calculations supports this view and suggests that $\textit{hidden}$ OAM - localized to atomic sites within each unit cell - contributes significantly. Additional effects, including inter-atomic interference and final-state resonances, are responsible for the rapid variation of the CDAD signal with photon energy.
We propose a universal framework for a system of virtual accelerator controls (Twinac), a standard toolkit for research institutions to design, maintain, and use a real-time, end-to-end "digital twin" of their particle accelerator facility. This virtual counterpart will mirror any physical accelerator to provide (1) predictive maintenance; (2) surveillance of hidden environmental factors, such as seasonal temperature variations, which could impact performance of power supplies, magnets, and other instruments; and (3) a capability to model novel ways to operate the accelerator without risking equipment damage. Twinac is envisioned as accelerator facility agnostic, allowing institutions to share and reuse myriad simulation approaches (analytics-based, A.I.-driven, or combinations of the like) across facilities. The Twinac system lays the groundwork for a collaborative network of institutions to maintain and update this shared virtual accelerator technology.
Tantalum dioxide (TaO2) is a metastable tantalum compound. Here, we report the epitaxial stabilization of TaO2 on Al2O3 (1-102) (r-plane sapphire) substrates using suboxide molecular-beam epitaxy (MBE) and thermal laser epitaxy (TLE), demonstrating single-oriented, monodomain growth of anisotropically strained thin films. Microstructural investigation is performed using synchrotron X-ray diffraction and scanning transmission electron microscopy. The tetravalent oxidation state of tantalum is confirmed using X-ray absorption and photoemission spectroscopy as well as electron energy-loss spectroscopy. Optical properties are investigated via spectroscopic ellipsometry and reveal a 0.3 eV Mott gap of the tantalum 5d electrons. Density-functional theory and group theoretical arguments are used to evaluate the limited stability of the rutile phase and reveal the potential to unlock a hidden metal-insulator transition concomitant with a structural phase transition to a distorted rutile phase, akin to NbO2. Our work expands the understanding of tantalum oxides and paves the way for their integration into next-generation electronic and photonic devices.
Abstract. The planetary boundary layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, which can estimate PBLH by integrating the morning temperature profiles and surface meteorological observations. The DNN model is developed by leveraging a rich dataset of PBLH derived from long-standing radiosonde records augmented with high-resolution micro-pulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden-layer structures, which collectively yield a robust 27-year PBLH dataset over the southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micro-pulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (Green Ocean Amazon; tropical rainforest) and CACTI (Cloud, Aerosol, and Complex Terrain Interactions; middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary layer processes with implications for improving the representation of PBL in weather forecasting and climate modeling.
The planetary boundary-layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, designed to estimate PBLH by integrating morning temperature profiles with surface meteorological observations. The DNN model is developed by leveraging a rich data set of PBLH derived from long-standing radiosonde records and augmented with high-resolution micropulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden layer structures, which collectively yield a robust 27-year PBLH data set over the Southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote-sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micropulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote-sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (tropical rainforest) and CACTI (middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary-layer dynamics with implications for enhancing the representation of PBL in weather forecasting and climate modeling.