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In this paper, we present improvements to the pointing accuracy of the South Pole Telescope (SPT) using machine learning. The ability of the SPT to point accurately at the sky is limited by its structural imperfections, which are impacted by the extreme weather at the South Pole. Pointing accuracy is particularly important during SPT participation in observing campaigns with the Event Horizon Telescope (EHT), which requires stricter accuracy than typical observations with the SPT. We compile a training dataset of historical observations of astronomical sources made with the SPT-3G and EHT receivers on the SPT. We train two XGBoost models to learn a mapping from current weather conditions to two telescope drive control arguments — one which corrects for errors in azimuth and the other for errors in elevation. Our trained models achieve root mean squared errors on withheld test data of 2[Formula: see text]14 in cross-elevation and 3[Formula: see text]57 in elevation, well below our goal of 5[Formula: see text] along each axis. We deploy our models on the telescope control system and perform further in situ test observations during the EHT observing campaign in April 2024. Our models result in significantly improved pointing accuracy: for sources within the range of input variables where the models are best trained, average combined pointing error improved 33%, from 15[Formula: see text]9 to 10[Formula: see text]6. These improvements, while significant, fall shy of our ultimate goal, but they serve as a proof of concept for the development of future models. Planned upgrades to the EHT receiver on the SPT will necessitate even stricter pointing accuracy which will be achievable with our methods. 

Abstract We present the detection and characterization of fluctuations in linearly polarized emission from the atmosphere above the South Pole. These measurements make use of data from the SPT-3G receiver on the South Pole Telescope in three frequency bands centered at 95, 150, and 220 GHz. We use the cross-correlation between detectors to produce an unbiased estimate of the power in StokesI,Q, andUparameters on large angular scales. Our results are consistent with the polarized signal being produced by the combination of Rayleigh scattering of thermal radiation from the ground and thermal emission from a population of horizontally aligned ice crystals with an anisotropic distribution described by Kolmogorov turbulence. The measured spatial scaling, frequency scaling, and elevation dependence of the polarized emission are explained by this model. Polarized atmospheric emission has the potential to significantly impact observations on the large angular scales being targeted by searches for inflationary B-mode CMB polarization. We present the distribution of measured angular power spectrum amplitudes in StokesQandIfor 4 yr of Austral winter observations, which can be used to simulate the impact of atmospheric polarization and intensity fluctuations at the South Pole on a specified experiment and observation strategy. We present a mitigation strategy that involves both downweighting significantly contaminated observations and subtracting a polarized atmospheric signal from the 150 GHz band maps. In observations with the SPT-3G instrument, the polarized atmospheric signal is a well-understood and subdominant contribution to the measured noise after implementing the mitigation strategies described here. 

The detection of satellite thermal emission at millimeter wavelengths is presented using data from the 3rd-Generation receiver on the South Pole Telescope (SPT-3G). This represents the first reported detection of thermal emission from artificial satellites at millimeter wavelengths. Satellite thermal emission is shown to be detectable at high signal-to-noise on timescales as short as a few tens of milliseconds. An algorithm for downloading orbital information and tracking known satellites given observer constraints and time-ordered observatory pointing is described. Consequences for cosmological surveys and short-duration transient searches are discussed, revealing that the integrated thermal emission from all large satellites does not contribute significantly to the SPT-3G survey intensity map. Measured satellite positions are found to be discrepant from their two-line element (TLE) derived ephemerides up to several arcminutes which may present a difficulty in cross-checking or masking satellites from short-duration transient searches. 

Abstract Galaxy clusters identified via the Sunyaev-Zel’dovich effect (SZ) are a key ingredient in multi-wavelength cluster cosmology. We present and compare three methods of cluster identification: the standard Matched Filter (MF) method in SZ cluster finding, a Convolutional Neural Networks (CNN), and a ‘combined’ identifier. We apply the methods to simulated millimeter maps for several observing frequencies for a survey similar to SPT-3G, the third-generation camera for the South Pole Telescope. The MF requires image pre-processing to remove point sources and a model for the noise, while the CNN requires very little pre-processing of images. Additionally, the CNN requires tuning of hyperparameters in the model and takes cutout images of the sky as input, identifying the cutout as cluster-containing or not. We compare differences in purity and completeness. The MF signal-to-noise ratio depends on both mass and redshift. Our CNN, trained for a given mass threshold, captures a different set of clusters than the MF, some with SNR below the MF detection threshold. However, the CNN tends to mis-classify cutouts whose clusters are located near the edge of the cutout, which can be mitigated with staggered cutouts. We leverage the complementarity of the two methods, combining the scores from each method for identification. The purity and completeness are both 0.61 for MF, and 0.59 and 0.61 for CNN. The combined method yields 0.60 and 0.77, a significant increase for completeness with a modest decrease in purity. We advocate for combined methods that increase the confidence of many low signal-to-noise clusters. 

In everyday social environments, demands on attentional resources dynamically shift to balance our attention to targets of interest while alerting us to important objects in our surrounds.The current study uses electroencephalography to explore how the push-pull interaction between top-down and bottom-up attention manifests itself in dynamic auditory scenes. Using natural soundscapes as distractors while subjects attend to a controlled rhythmic sound sequence, we find that salient events in background scenes significantly suppress phase-locking and gamma responses to the attended sequence, countering enhancement effects observed for attended targets. In line with a hypothesis of limited attentional resources, the modulation of neural activity by bottom-up attention is graded by degree of salience of ambient events. The study also provides insights into the interplay between endogenous and exogenous attention during natural soundscapes, with both forms of attention engaging a common fronto-parietal network at different time lags. 

ABSTRACT We present an extension to a Sunyaev–Zel’dovich Effect (SZE) selected cluster catalogue based on observations from the South Pole Telescope (SPT); this catalogue extends to lower signal to noise than the previous SPT–SZ catalogue and therefore includes lower mass clusters. Optically derived redshifts, centres, richnesses, and morphological parameters together with catalogue contamination and completeness statistics are extracted using the multicomponent matched filter (MCMF) algorithm applied to the S/N > 4 SPT–SZ candidate list and the Dark Energy Survey (DES) photometric galaxy catalogue. The main catalogue contains 811 sources above S/N = 4, has 91  per cent purity, and is 95 per cent complete with respect to the original SZE selection. It contains in total 50 per cent more clusters and twice as many clusters above z = 0.8 in comparison to the original SPT-SZ sample. The MCMF algorithm allows us to define subsamples of the desired purity with traceable impact on catalogue completeness. As an example, we provide two subsamples with S/N > 4.25 and S/N > 4.5 for which the sample contamination and cleaning-induced incompleteness are both as low as the expected Poisson noise for samples of their size. The subsample with S/N > 4.5 has 98 per cent purity and 96 per cent completeness and is part of our new combined SPT cluster and DES weak-lensing cosmological analysis. We measure the number of false detections in the SPT-SZ candidate list as function of S/N, finding that it follows that expected from assuming Gaussian noise, but with a lower amplitude compared to previous estimates from simulations. 

Cosmic shear, galaxy clustering, and the abundance of massive halos each probe the large-scale structure of the Universe in complementary ways. We present cosmological constraints from the joint analysis of the three probes, building on the latest analyses of the lensing-informed abundance of clusters identified by the South Pole Telescope (SPT) and of the auto- and cross-correlation of galaxy position and weak lensing measurements ( $3\times 2\mathrm{pt}$) in the Dark Energy Survey (DES). We consider the cosmological correlation between the different tracers and we account for the systematic uncertainties that are shared between the large-scale lensing correlation functions and the small-scale lensing-based cluster mass calibration. Marginalized over the remaining $\mathrm{\Lambda }$cold dark matter ( $\mathrm{\Lambda }\mathrm{CDM}$) parameters (including the sum of neutrino masses) and 52 astrophysical modeling parameters, we measure ${\mathrm{\Omega }}_{\mathrm{m}}=0.300\pm 0.017$and ${\sigma }_8=0.797\pm 0.026$. Compared to constraints from primary cosmic microwave background (CMB) anisotropies, our constraints are only 15% wider with a probability to exceed of 0.22 ( $1.2\sigma$) for the two-parameter difference. We further obtain $S_8\equiv {\sigma }_8({\mathrm{\Omega }}_{\mathrm{m}}/0.3{)}^{0.5}=0.796\pm 0.013$which is lower than the measurement at the $1.6\sigma$level. The combined SPT cluster, DES $3\times 2\mathrm{pt}$, and datasets mildly prefer a nonzero positive neutrino mass, with a 95% upper limit $\sum m_{\nu }<0.25\mathrm{eV}$on the sum of neutrino masses. Assuming a $w\mathrm{CDM}$model, we constrain the dark energy equation of state parameter $w=-1.15_{-0.17}^{+0.23}$and when combining with primary CMB anisotropies, we recover $w=-1.20_{-0.09}^{+0.15}$, a $1.7\sigma$difference with a cosmological constant. The precision of our results highlights the benefits of multiwavelength multiprobe cosmology and our analysis paves the way for upcoming joint analyses of next-generation datasets. Published by the American Physical Society2025