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CoNi-based superalloys offer excellent high-temperature properties; yet, Co is also a strategic alloying element, and its content should only be as high as necessary. This study investigates Fe as a partial substitute for Co to reduce costs while evaluating its impact on mechanical properties. To evaluate this, we systematically investigate the effect of Fe substitutions on thermophysical properties, microstructure, partitioning behavior, lattice misfit, yield strength, and creep performance of three polycrystalline CoNi-based superalloys derived from CoWAlloy1 (Co–32Ni–12Cr–6Al–3W–2.5Ti–1.5Ta–0.4Si–0.1Hf–0.08B all in at. %). In these alloys, 4, 8, and 12 at. % Co is replaced with Fe. Increasing Fe content results in a gradual reduction in the solvus, solidus, and liquidus temperatures by 3.0, 1.9, and 1.4 °C per at. % Fe, respectively. The γ′ volume fraction and the lattice misfit decrease by about 0.7% and 0.01%, respectively, per at. % Fe substitution for Co. Fe predominantly partitions to the γ matrix, enhancing the partitioning of Co and Ni while reducing that of Al, Cr, and Ta, with no significant effect on Ti and W. Substituting Co with Fe moderately reduces yield and creep strength, primarily due to the decreasing γ′ volume fraction and a transition in the dominant deformation mechanisms from stacking fault shearing and microtwinning to matrix-based deformation as Fe content increases. Beneficial elemental segregation behaviors and localized phase transformations along creep-induced stacking faults remain active in alloys with high Fe content. These findings highlight the potential of Fe alloying to reduce costs while maintaining high-temperature strength in CoNi-based superalloys. 

Abstract—Simulating nonlinear systems featuring a dynamic DSP module is cumbersome, e.g., fiber transport with digital coherent receivers. Machine Learning is used to quickly and accurately estimate waveforms transported through multi-span fiber links over multiple launch powers and OSNRs. Replacing simulation techniques with ML is considered. 

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 analyze the cooling and feedback properties of 48 galaxy clusters at redshifts 0.4 < z < 1.3 selected from the South Pole Telescope (SPT) catalogs to evolve like the progenitors of massive and well-studied systems at z ∼ 0. We estimate the radio power at the brightest cluster galaxy (BCG) location of each cluster from an analysis of Australia Telescope Compact Array data. Assuming that the scaling relation between the radio power and active galactic nucleus (AGN) cavity power P cav observed at low redshift does not evolve with redshift, we use these measurements in order to estimate the expected AGN cavity power in the core of each system. We estimate the X-ray luminosity within the cooling radius L cool of each cluster from a joint analysis of the available Chandra X-ray and SPT Sunyaev–Zel’dovich (SZ) data. This allows us to characterize the redshift evolution of the P cav / L cool ratio. When combined with low-redshift results, these constraints enable investigations of the properties of the feedback–cooling cycle across 9 Gyr of cluster growth. We model the redshift evolution of this ratio measured for cool-core clusters by a log-normal distribution Log -  ( α + β z , σ 2 ) and constrain the slope of the mean evolution to β = −0.05 ± 0.47. This analysis improves the constraints on the slope of this relation by a factor of two. We find no evidence of redshift evolution of the feedback–cooling equilibrium in these clusters, which suggests that the onset of radio-mode feedback took place at an early stage of cluster formation. High values of P cav / L cool are found at the BCG location of noncool-core clusters, which might suggest that the timescales of the AGN feedback cycle and the cool core–noncool core transition are different. This work demonstrates that the joint analysis of radio, SZ, and X-ray data solidifies the investigation of AGN feedback at high redshifts. 

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 We present the average rest-frame spectrum of the final catalog of dusty star-forming galaxies (DSFGs) selected from the South Pole Telescope's SPT-SZ survey and measured with Band 3 of the Atacama Large Millimeter/submillimeter Array. This work builds on the previous average rest-frame spectrum, given in Spilker et al. (2014) for the first 22 sources, and is comprised of a total of 78 sources, normalized by their respective apparent dust masses. The spectrum spans 1.9 <z< 6.9 and covers rest-frame frequencies of 240–800 GHz. Combining this data with low-JCO observations from the Australia Telescope Compact Array, we detect multiple bright line features from¹²CO, [Ci], and H₂O, as well as fainter molecular transitions from¹³CO, HCN, HCO⁺, HNC, CN, H₂O⁺, and CH. We use these detections, along with limits from other molecules, to characterize the typical properties of the interstellar medium (ISM) for these high-redshift DSFGs. We are able to divide the large sample into subsets in order to explore how the average spectrum changes with various galaxy properties, such as effective dust temperature. We find that systems with hotter dust temperatures exhibit differences in the bright¹²CO emission lines, and contain either warmer and more excited dense gas tracers or larger dense gas reservoirs. These observations will serve as a reference point to studies of the ISM in distant luminous DSFGs (L_IR> 10¹²L_⊙), and will inform studies of chemical evolution before the peak epoch of star formation atz= 2–3. 

Weak lensing measurements suffer from well-known shear estimation biases, which can be partially corrected for with the use of image simulations. In this work we present an analysis of simulated images that mimic Hubble Space Telescope/Advance Camera for Surveys observations of high-redshift galaxy clusters, including cluster specific issues such as non-weak shear and increased blending. Our synthetic galaxies have been generated to have similar observed properties as the background-selected source samples studied in the real images. First, we used simulations with galaxies placed on a grid to determine a revised signal-to-noise-dependent ( S / N KSB ) correction for multiplicative shear measurement bias, and to quantify the sensitivity of our KSB+ bias calibration to mismatches of galaxy or PSF properties between the real data and the simulations. Next, we studied the impact of increased blending and light contamination from cluster and foreground galaxies, finding it to be negligible for high-redshift ( z  >  0.7) clusters, whereas shear measurements can be affected at the ∼1% level for lower redshift clusters given their brighter member galaxies. Finally, we studied the impact of fainter neighbours and selection bias using a set of simulated images that mimic the positions and magnitudes of galaxies in Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS) data, thereby including realistic clustering. While the initial SExtractor object detection causes a multiplicative shear selection bias of −0.028 ± 0.002, this is reduced to −0.016 ± 0.002 by further cuts applied in our pipeline. Given the limited depth of the CANDELS data, we compared our CANDELS-based estimate for the impact of faint neighbours on the multiplicative shear measurement bias to a grid-based analysis, to which we added clustered galaxies to even fainter magnitudes based on Hubble Ultra Deep Field data, yielding a refined estimate of ∼ − 0.013. Our sensitivity analysis suggests that our pipeline is calibrated to an accuracy of ∼0.015 once all corrections are applied, which is fully sufficient for current and near-future weak lensing studies of high-redshift clusters. As an application, we used it for a refined analysis of three highly relaxed clusters from the South Pole Telescope Sunyaev-Zeldovich survey, where we now included measurements down to the cluster core ( r  >  200 kpc) as enabled by our work. Compared to previously employed scales ( r  >  500 kpc), this tightens the cluster mass constraints by a factor 1.38 on average.