The Square Kilometre Array (SKA)

Abstract:

Abstract A tidal disruption event (TDE) occurs when a star travels too close to a supermassive black hole. In some cases, accretion of the disrupted material onto the black hole launches a relativistic jet. In this paper, we present a long-term observing campaign to study the radio and submillimeter emission associated with the fifth jetted/relativistic TDE: AT 2022cmc. Our campaign reveals a long-lived counterpart. We fit three different models to our data: a nonthermal jet, a spherical outflow consisting of both thermal and nonthermal electrons, and a jet with thermal and nonthermal electrons. We find that the data are best described by a relativistic spherical outflow propagating into an environment with a density profile following R −1.8. Comparison of AT 2022cmc to other TDEs finds agreement in the density profile of the environment but also that AT 2022cmc is twice as energetic as the other well-studied relativistic TDE, Swift J1644. Our observations of AT 2022cmc allow a thermal electron population to be inferred for the first time in a jetted transient, providing new insights into the microphysics of relativistic transients jets.

Abstract:

A tidal disruption event (TDE) occurs when a star travels too close to a supermassive black hole. In some cases, accretion of the disrupted material onto the black hole launches a relativistic jet. In this paper, we present a long-term observing campaign to study the radio and submillimeter emission associated with the fifth jetted/relativistic TDE: AT 2022cmc. Our campaign reveals a long-lived counterpart. We fit three different models to our data: a nonthermal jet, a spherical outflow consisting of both thermal and nonthermal electrons, and a jet with thermal and nonthermal electrons. We find that the data are best described by a relativistic spherical outflow propagating into an environment with a density profile following R−1.8. Comparison of AT 2022cmc to other TDEs finds agreement in the density profile of the environment but also that AT 2022cmc is twice as energetic as the other well-studied relativistic TDE, Swift J1644. Our observations of AT 2022cmc allow a thermal electron population to be inferred for the first time in a jetted transient, providing new insights into the microphysics of relativistic transients jets.

Abstract:

A key science goal of large sky surveys such as those conducted by the Vera C. Rubin Observatory and precursors to the Square Kilometre Array is the identification of variable and transient objects. One approach is analyzing time series of the changing brightness of sources, namely, light curves. However, finding adequate statistical representations of light curves is challenging because of the sparsity of observations, irregular sampling, and nuisance factors inherent in astronomical data collection. The wide diversity of objects that a large-scale survey will observe also means that making parametric assumptions about the shape of light curves is problematic. We present a Gaussian process (GP) regression approach for characterizing light-curve variability that addresses these challenges. Our approach makes no assumptions about the shape of a light curve and, therefore, is general enough to detect a range of variable and transient source types. In particular, we propose using the joint distribution of GP amplitude hyperparameters to distinguish variable and transient candidates from nominally stable ones and apply this approach to 6394 radio light curves from the ThunderKAT survey. We compare our results with two variability metrics commonly used in radio astronomy, namely ην and Vν, and show that our approach has better discriminatory power and interpretability. Finally, we conduct a rudimentary search for transient sources in the ThunderKAT data set to demonstrate how our approach might be used as an initial screening tool. Computational notebooks in Python and R are available to help deploy this framework to other surveys.

Abstract:

Abstract We investigate the clustering of Lyman-break galaxies at redshifts of 3 ≲ z ≲ 5 within the COSMOS field by measuring the angular two-point correlation function. Our robust sample of ~60,000 bright (mUV ≲ 27) Lyman-break galaxies was selected based on spectral energy distribution fitting across 14 photometric bands spanning optical and near-infrared wavelengths. We constrained both the 1- and 2-halo terms at separations up to 300 arcsec, finding an excess in the correlation function at scales corresponding to <20 kpc, consistent with enhancement due to clumps in the same galaxy or interactions on this scale. We then performed Bayesian model fits on the correlation functions to infer the Halo Occupation Distribution parameters, star formation duty cycle, and galaxy bias in three redshift bins. We examined several cases where different combinations of parameters were varied, showing that our data can constrain the slope of the satellite occupation function, which previous studies have fixed. For an MUV-limited sub-sample, we found galaxy bias values of $b_g=3.18^{+0.14}_{-0.14}$ at z ≃ 3, $b_g=3.58^{+0.27}_{-0.29}$ at z ≃ 4, $b_g=4.27^{+0.25}_{-0.26}$ at z ≃ 5. The duty cycle values are $0.62^{+0.25}_{-0.26}$, $0.40^{+0.34}_{-0.22}$, and $0.39^{+0.31}_{-0.20}$,respectively. These results suggest that, as the redshift increases, there is a slight decrease in the host halo masses and a shorter timescale for star formation in bright galaxies, at a fixed rest-frame UV luminosity threshold.

Abstract:

Abstract This study investigates the estimation of the neutral hydrogen (H i) mass function (HiMF) using a Bayesian stacking approach with simulated data for the Five-hundred-meter Aperture Spherical radio Telescope (FAST) H i intensity mapping (HiIM) drift-scan surveys. Using data from the IllustrisTNG simulation, we construct H i sky cubes at redshift z ∼ 0.1 and the corresponding optical galaxy catalogs, simulating FAST observations under various survey strategies, including pilot, deep-field, and ultradeep-field surveys. The HiMF is measured for distinct galaxy populations—classified by optical properties into red, blue, and bluer galaxies—and injected with systematic effects such as observational noise and flux confusion caused by the FAST beam. The results show that Bayesian stacking significantly enhances HiMF measurements. For red and blue galaxies, the HiMF can be well constrained with pilot surveys, while deeper surveys are required for the bluer galaxy population. Our analysis also reveals that sample variance dominates over observational noise, emphasizing the importance of wide-field surveys to improve constraints. Furthermore, flux confusion shifts the HiMF toward higher masses, which we address using a transfer function for correction. Finally, we explore the effects of intrinsic sample incompleteness and propose a framework to quantify its impact. This work lays the groundwork for future HiMF studies with FAST HiIM, addressing key challenges and enabling robust analyses of H i content across galaxy populations.