We present a new dynamical measurement of the supermassive black hole mass and intrinsic shape of the stellar halo of the massive radio galaxy NGC 315 as part of the MASSIVE survey. High signal-to-noise ratio spectra from integral-field spectrographs at the Gemini and McDonald Observatories provide stellar kinematic measurements in $304$ spatial bins from the central ${\sim}0.3''$ out to $30''$. Using ${\sim} 2300$ kinematic constraints, we perform triaxial stellar orbit modeling with the TriOS code and search over ${\sim}$15,000 galaxy models with a Bayesian scheme to simultaneously measure six mass and intrinsic shape parameters. NGC 315 is triaxial and highly prolate, with middle-to-long and short-to-long axis ratios of $p=0.854$ and $q=0.833$ and a triaxiality parameter of $T=0.89$. The black hole mass inferred from our stellar kinematics is $M_\mathrm{BH} = \left(3.0 {\pm} 0.3\right) {\times} 10^{9}\ M_\odot$, which is higher than $M_\mathrm{BH}=(1.96^{+0.30}_{-0.13}) {\times} 10^{9} M_\odot$ inferred from CO kinematics (scaled to our distance). When the seven galaxies with $M_\mathrm{BH}$ measurements from both stellar and CO kinematics are compared, we find an intrinsic scatter of 0.28 dex in $M_\mathrm{BH}$ from the two tracers and do not detect statistically significant biases between the two methods in the current data. The implied black hole shadow size (${\approx} 4.7\, \mu{\rm as}$) and the relatively high millimeter flux of NGC 315 makes this galaxy a prime candidate for future horizon-size imaging studies.
We present a hybrid method for reconstructing the primordial density from late-time halos and galaxies. Our approach involves two steps: (1) apply standard Baryon Acoustic Oscillation (BAO) reconstruction to recover the large-scale features in the primordial density field and (2) train a deep learning model to learn small-scale corrections on partitioned subgrids of the full volume. At inference, this correction is then convolved across the full survey volume, enabling scaling to large survey volumes. We train our method on both mock halo catalogs and mock galaxy catalogs in both configuration and redshift space from the Quijote $1(h^{-1}\,\mathrm{Gpc})^3$ simulation suite. When evaluated on held-out simulations, our combined approach significantly improves the reconstruction cross-correlation coefficient with the true initial density field and remains robust to moderate model misspecification. Additionally, we show that models trained on $1(h^{-1}\,\mathrm{Gpc})^3$ can be applied to larger boxes--e.g., $(3h^{-1}\,\mathrm{Gpc})^3$--without retraining. Finally, we perform a Fisher analysis on our method's recovery of the BAO peak, and find that it significantly improves the error on the acoustic scale relative to standard BAO reconstruction. Ultimately, this method robustly captures nonlinearities and bias without sacrificing large-scale accuracy, and its flexibility to handle arbitrarily large volumes without escalating computational requirements makes it especially promising for large-volume surveys like DESI.
The SiTian Project represents a groundbreaking initiative in astronomy, aiming to deploy a global network of telescopes, each with a 1-meter aperture, for comprehensive time-domain sky surveys. The network's innovative architecture features multiple observational nodes, each comprising three strategically aligned telescopes equipped with filters. This design enables three-color (g, r, i) channel imaging within each node, facilitating precise and coordinated observations. As a pathfinder to the full-scale project, the Mini-SiTian Project serves as the scientific and technological validation platform, utilizing three 30-centimeter aperture telescopes to validate the methodologies and technologies planned for the broader SiTian network. This paper focuses on the development and implementation of the Master Control System (MCS),and the central command hub for the Mini-SiTian array. The MCS is designed to facilitate seamless communication with the SiTian Brain, the project's central processing and decision-making unit, while ensuring accurate task allocation, real-time status monitoring, and optimized observational workflows. The system adopts a robust architecture that separates front-end and back-end functionalities.A key innovation of the MCS is its ability to dynamically adjust observation plans in response to transient source alerts, enabling rapid and coordinated scans of target sky regions...(abridged)
The standard model of cosmology has provided a good phenomenological description of a wide range of observations both at astrophysical and cosmological scales for several decades. This concordance model is constructed by a universal cosmological constant and supported by a matter sector described by the standard model of particle physics and a cold dark matter contribution, as well as very early-time inflationary physics, and underpinned by gravitation through general relativity. There have always been open questions about the soundness of the foundations of the standard model. However, recent years have shown that there may also be questions from the observational sector with the emergence of differences between certain cosmological probes. In this White Paper, we identify the key objectives that need to be addressed over the coming decade together with the core science projects that aim to meet these challenges. These discordances primarily rest on the divergence in the measurement of core cosmological parameters with varying levels of statistical confidence. These possible statistical tensions may be partially accounted for by systematics in various measurements or cosmological probes but there is also a growing indication of potential new physics beyond the standard model. After reviewing the principal probes used in the measurement of cosmological parameters, as well as potential systematics, we discuss the most promising array of potential new physics that may be observable in upcoming surveys. We also discuss the growing set of novel data analysis approaches that go beyond traditional methods to test physical models. [Abridged]
https://doi.org/10.3847/2041-8213/acc32e ( arXiv:2303.06328 )