Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

We present a transfer function-based method to estimate angular power spectra from filtered maps for cosmic microwave background (CMB) surveys. This is especially relevant for experiments targeting the faint primordial gravitational wave signatures in CMB polarisation at large scales, such as the Simons Observatory (SO) small aperture telescopes. While timestreams can be filtered to mitigate the contamination from low-frequency noise, usual methods that calculate the mode coupling at individual multipoles can be challenging for experiments covering large sky areas or reaching few-arcminute resolution. The method we present here, although approximate, is more practical and faster for larger data volumes. We validate it through the use of simulated observations approximating the first year of SO data, going from half-wave plate-modulated timestreams to maps, and using simulations to estimate the mixing of polarisation modes induced by an example of time-domain filtering. We show its performance through an example null test and with an end-to-end pipeline that performs inference on cosmological parameters, including the tensor-to-scalar ratio r. The performance demonstration uses simulated observations at multiple frequency bands. We find that the method can recover unbiased parameters for our simulated noise levels.

Abstract:

This review is an up-to-date account of the use of numerical relativity to study dynamical, strong-gravity environments in a cosmological context. First, we provide a gentle introduction into the use of numerical relativity in solving cosmological spacetimes, aimed at both cosmologists and numerical relativists. Second, we survey the present body of work, focusing on general relativistic simulations, organised according to the cosmological history—from cosmogenesis, through the early hot Big Bang, to the late-time evolution of the universe. We discuss the present state-of-the-art, and suggest directions in which future work can be fruitfully pursued.

Abstract:

We present a sample of 341 “little red dots” (LRDs) spanning the redshift range z ∼ 2-11 using data from the CEERS, PRIMER, JADES, UNCOVER, and NGDEEP surveys. Unlike past use of color indices to identify LRDs, we employ continuum slope fitting using shifting bandpasses to sample the same rest-frame emission blueward and redward of the Balmer break. This enables the detection of LRDs over a wider redshift range and with less contamination from galaxies with strong breaks that otherwise lack a rising red continuum. The redshift distribution of our sample increases at z < 8 and then undergoes a rapid decline at z ∼ 4.5, which may tie the emergence of these sources to the inside-out growth that galaxies experience during this epoch. We find that LRDs are ∼1 dex more numerous than X-ray- and UV-selected active galactic nuclei (AGN) at z ∼ 5-7. Within our sample, we have identified the first two X-ray-detected LRDs. An X-ray spectral analysis confirms that these AGN are moderately obscured with log ( N H / cm 2 ) of 23 . 3 − 1.3 + 0.4 and 22.7 2 − 0.16 + 0.13 . Our analysis reveals that reddened AGN emission dominates their rest-optical light, while the rest-UV originates from their host galaxies. We also present NIRSpec observations from the RUBIES survey of 17 LRDs that show broad emission lines consistent with AGN activity. The confirmed AGN fraction of our sample is 71% for sources with F444W < 26.5. In addition, we find three LRDs with blueshifted Balmer absorption features in their spectra, suggesting an outflow of high-density, low-ionization gas from near the central engine of these faint, red AGN.

Abstract:

Automated planetary transit detection has become vital to identify and prioritize candidates for expert analysis and verification given the scale of modern telescopic surveys. Current methods for short-period exoplanet detection work effectively due to periodicity in the transit signals, but a robust approach for detecting single-transit events is lacking. However, volunteer-labeled transits collected by the Planet Hunters TESS (PHT) project now provide an unprecedented opportunity to investigate a data-driven approach to long-period exoplanet detection. In this work, we train a 1D convolutional neural network to classify planetary transits using PHT volunteer scores as training data. We find that this model recovers planet candidates (TESS objects of interest; TOIs) at a precision and recall rate exceeding those of volunteers, with a 20% improvement in the area under the precision-recall curve and 10% more TOIs identified in the top 500 predictions on average per sector. Importantly, the model also recovers almost all planet candidates found by volunteers but missed by current automated methods (PHT community TOIs). Finally we retrospectively utilise the model to simulate live deployment in PHT to reprioritize candidates for analysis. We also find that multiple promising planet candidates, originally missed by PHT, would have been found using our approach, showing promise for upcoming real-world deployment.