Dense, cold gas is the key ingredient for star formation. Over the last two decades, HCN(1-0) emission has been utilised as the most accessible dense gas tracer to study external galaxies. We present new measurements tracing the relationship between dense gas tracers, bulk molecular gas tracers, and star formation in the ALMA ALMOND survey, the largest sample of resolved (1-2 kpc resolution) HCN maps of galaxies in the local universe (d < 25 Mpc). We measure HCN/CO, a line ratio sensitive to the physical density distribution, and SFR/HCN, a proxy for the dense gas star formation efficiency, as a function of molecular gas surface density, stellar mass surface density, and dynamical equilibrium pressure across 31 galaxies, increasing the number of galaxies by a factor of > 3 over the previous largest such study (EMPIRE). HCN/CO increases (slope of ~ 0.5 and scatter of ~ 0.2 dex), while SFR/HCN decreases (slope of ~ -0.6 and scatter of ~ 0.4 dex) with increasing molecular gas surface density, stellar mass surface density and pressure. Galaxy centres with high stellar mass surface density show a factor of a few higher HCN/CO and lower SFR/HCN compared to the disc average, but both environments follow the same average trend. Our results emphasise that molecular gas properties vary systematically with the galactic environment and demonstrate that the scatter in the Gao-Solomon relation (SFR against HCN) is of physical origin.
To study the early Universe, it is essential to estimate cosmological parameters with high accuracy, which depends on the optimal reconstruction of Cosmic Microwave Background (CMB) maps and the measurement of their power spectrum. In this paper, we generalize the neural network developed for applying the Wiener Filter, initially presented for temperature maps in previous work, to polarization maps. Our neural network has a UNet architecture, including an extra channel for the noise variance map, to account for inhomogeneous noise, and a channel for the mask. In addition, we propose an iterative approach for reconstructing the E and B-mode fields, while addressing the E-to-B leakage present in the maps due to incomplete sky coverage. The accuracy achieved is satisfactory compared to the Wiener Filter solution computed with the standard Conjugate Gradient method, and it is highly efficient, enabling the computation of the power spectrum of an unknown signal using the optimal quadratic estimator. We further evaluate the quality of the reconstructed maps at the power spectrum level along with their corresponding errors, finding that these errors are smaller than those obtained using the well-known pseudo-$C_\ell$ approach. Our results show that increasing complexity in the applied mask presents a more significant challenge for B-mode reconstruction.
Stellar activity contamination of radial velocity (RV) data is one of the top challenges plaguing the field of extreme precision RV (EPRV) science. Previous work has shown that photometry can be very effective at removing such signals from RV data, especially stellar activity caused by rotating star spots and this http URL exact utility of photometry for removing RV activity contamination, and the best way to apply it, is not well known. We present a combination photometric and RV study of eight Kepler/K2 FGK stars with known stellar variability. We use NEID RVs acquired simultaneously with TESS photometry, and we perform injection recovery tests to quantify the efficacy of recent TESS photometry versus archival Kepler/K2 photometry for removing stellar variability from RVs. We additionally experiment with different TESS sectors when training our models in order to quantify the real benefit of simultaneously acquired RVs and photometry. We conclude that Kepler photometry typically performs better than TESS at removing noise from RV data when it is available, likely due to longer baseline and precision. In contrast, for targets with available K2 photometry, especially those most active, and with high precision ($\sigma_{NEID}$ $<$ 1 m s$^{-1}$) NEID RVs, TESS may be the more informative dataset. However, contrary to expectations, we have found that training on simultaneous photometry does not always achieve the best results.
The short-lived ionized emission lines in early spectroscopy of the nearby type II supernova SN 2024ggi signify the presence of dense circumstellar matter (CSM) close to its progenitor star. We proposed the Atacama Large Millimeter/submillimeter Array (ALMA) observations by its Director's Discretionary Time program to catch the potential synchrotron radiation associated with the ejecta-CSM interaction. Multi-epoch observations were conducted using ALMA band 6 at +8, +13, and +17 days after the discovery. The data show non-detections at the position of SN 2024ggi with a 3sigma upper limit of less than 0.15 mJy, corresponding to a luminosity of approximately 8*10^24 erg/s/Hz. In this paper, we leverage the non-detections to place constraints on the properties of CSM surrounding SN 2024ggi. We investigate both the Wind and Eruptive models for the radial distribution of CSM, assuming a constant mass-loss rate in the Wind model and a distance-variant mass-loss rate in the Eruptive model. The derived CSM distribution for the Wind model does not align with the early-time spectral features, while the ALMA observations suggest a mass-loss rate of ~ 5*10^-3 Msun/year for the Eruptive model. Conducting multi-epoch millimeter/submillimeter observations shortly after the explosion, with a cadence of a few days, could offer a promising opportunity to capture the observable signature of the Eruptive model.
Secondary cosmic ray fluxes are important probes of the propagation and interaction of high-energy particles in the Galaxy. Recent measurements of primary and secondary cosmic ray nuclei have revealed unexpected spectral features that demand a deeper understanding. In this work we report the direct measurement of the cosmic ray boron spectrum from 10 TeV/n to 8 TeV/n with eight years of data collected by the Dark Matter Particle Explorer (DAMPE) mission. The measured spectrum shows an evident hardening at $182\pm24$ GeV/n with a spectral power index of $\gamma_1 = 3.02 \pm 0.01$ before the break and an index change of $\Delta \gamma = 0.31 \pm 0.05$ after the break. A simple power law model is disfavored at a confidence level of 8$\sigma$. Compared with the hardenings measured in the DAMPE proton and helium spectra, the secondary boron spectrum hardens roughly twice as much as these primaries, which is consistent with a propagation related mechanism to interpret the spectral hardenings of cosmic rays observed at hundreds of GeV/n.
With the development of wide-field surveys, a large amount of data on short-period W UMa contact binaries have been obtained. Continuous and uninterrupted light curves as well as high-resolution spectroscopic data are crucial in determining the absolute physical parameters. Targets with both TMTS light curves and LAMOST medium-resolution spectra were selected. The absolute physical parameters were inferred with the W-D code for ten systems, all of them are W-type shallow or medium contact binaries. The O'Connell effect observed in the light curves can be explained by adding a spot on the primary or secondary component in the models. According to O-C analysis, the orbital periods exhibit a long-term increasing or decreasing trend, amongst which J0132, J1300, and J1402 show periodic variations that may be attributed to the presence of a third body or magnetic activity cycles. Spectral subtraction analysis revealed that the equivalent width of H$\alpha$ indicates strong magnetic activity in J0047, J0305, J0638, and J1402. Among the 10 selected binary systems, except for J0132 and J0913, the more massive components are found to be main-sequence stars while the less massive components have evolved off the main sequence. In J0132, both components are in the main sequence, whereas both components of J0913 lie above the terminal-age main sequence. Based on the relationship between orbital angular momentum and total mass for these two systems, as well as their low fill-out factors, it is possible that these two systems are newly formed contact binaries, having recently evolved from the detached configuration.
A primary target of the \Euclid space mission is to constrain early-universe physics by searching for deviations from a primordial Gaussian random field. A significant detection of primordial non-Gaussianity would rule out the simplest models of cosmic inflation and transform our understanding of the origin of the Universe. This paper forecasts how well field-level inference of galaxy redshift surveys can constrain the amplitude of local primordial non-Gaussianity ($f_{NL}$), within a Bayesian hierarchical framework, in the upcoming \Euclid data. We design and simulate mock data sets and perform Markov chain Monte Carlo analyses using a full-field forward modelling approach. By including the formation history of the cosmic matter field in the analysis, the method takes into account all available probes of primordial non-Gaussianity, and goes beyond statistical summary estimators of $f_{NL}$. Probes include, for example, two-point and higher-order statistics, peculiar velocity fields, and scale-dependent galaxy biases. Furthermore, the method simultaneously handles systematic survey effects, such as selection effects, survey geometries, and galaxy biases. The forecast shows that the method can reach precision levels of up to $\sigma (f_{NL}) = 2.3$ (68.3\% CI, and at the grid resolution $\Delta L = 62.5\,h^{-1}$Mpc) with \Euclid data. We also provide data products, including realistic $N$-body simulations with nonzero values of $f_{NL}$ and maps of adiabatic curvature fluctuations. The results underscore the feasibility and advantages of field-level inference to constrain $f_{NL}$ in galaxy redshift surveys. Our approach consistently captures all the information available in the large-scale structure to constrain $f_{NL}$, and resolves the degeneracy between early-universe physics and late-time gravitational effects, while mitigating the impact of systematic and observational effects.