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SPT-3G+ is the next-generation camera for the South Pole Telescope (SPT). SPT is designed to measure the cosmic microwave background (CMB) and the mm/sub-mm sky. The planned focal plane consists of 34,000 microwave kinetic inductance detectors (MKIDs), divided among three observing bands centered at 220, 285, and 345 GHz. Each readout line is designed to measure 800 MKIDs over a 500 MHz bandwidth, which places stringent constraints on the accuracy of the frequency placement required to limit resonator collisions that reduce the overall detector yield. To meet this constraint, we are developing a two-step process that first optically maps the resonance to a physical pixel location, and then next trims the interdigitated capacitor (IDC) to adjust the resonator frequency. We present a cryogenic LED apparatus operable at 300 mK for the optical illumination of SPT-3G+ detector arrays. We demonstrate integration of the LED controls with the GHz readout electronics (RF-ICE) to take data on an array of prototype SPT-3G+ detectors. We show that this technique is useful for characterizing defects in the resonator frequency across the detector array and will allow for improvements in the detector yield. 

The constant and rapid evolution of technology has led to some amazing achievements. Normal people can communicate with others across the globe, relatively cheap Internet of Things (IoT) devices can be used to secure homes, track fitness and health, control appliances, etc., many people have access to a seemingly endless wealth of information in small devices in their pockets, organizations can provide high availability for important services by spinning up/down servers in minutes to scale with demand through cloud services, etc. However, not everyone who uses these technologies does so with a pure heart and good intentions, many people use them to commit or help commit crimes. A nefarious individual may use cloud services to host a highly available Command and Control (C2) server, a messaging app to form and communicate with a gang or hacking group, or IoT devices as part of a botnet designed to perform Distributed Denial of Service (DDoS) attacks. When these technologies are used in the commission of a crime, they hold valuable information that needs to be recovered forensically to use as evidence to convict the perpetrators. Unfortunately, that ever-evolving technology poses many challenges for digital forensics. This paper identifies and presents many of the challenges faced in digital forensics involving mobile devices, IoT devices, and cloud services in addition to proposing a framework for solving the IoT Forensic Data Analysis problem. 

Abstract The Summertime Line Intensity Mapper (SLIM) is a mm-wave line-intensity mapping (mm-LIM) experiment for the South Pole Telescope (SPT). The goal of SPT-SLIM is to serve as a technical and scientific pathfinder for the demonstration of the suitability and in-field performance of multi-pixel superconducting filterbank spectrometers for future mm-LIM experiments. Scheduled to deploy in the 2023-24 austral summer, the SPT-SLIM focal plane will include 18 dual-polarisation pixels, each coupled to an $$R = \lambda / \Delta \lambda = 300$$ R = λ / Δ λ = 300 thin-film microstrip filterbank spectrometer that spans the 2 mm atmospheric window (120–180 GHz). Each individual spectral channel feeds a microstrip-coupled lumped-element kinetic inductance detector, which provides the highly multiplexed readout for the 10k detectors needed for SPT-SLIM. Here, we present an overview of the preliminary design of key aspects of the SPT-SLIM focal plane array, a description of the detector architecture and predicted performance, and initial test results that will be used to inform the final design of the SPT-SLIM spectrometer array. 

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.