Data Interpretation and Management for an Atmospheric Probe Mission to Venus

After nearly 40 years without a dedicated U.S. mission to Venus, the Rocket Lab Mission to Venus is planning to launch a small probe to analyze the composition of Venus’ cloud layers. As the probe descends through the atmosphere, it will spend around five minutes in the cloud deck, from 66 km to 48 km above the surface, and roughly 20 minutes total in the atmosphere [French et al., 2022]. The probe’s primary scientific instrument, the Autofluorescence Nephelometer (AFN), will gather data by measuring the light scattering off particles, providing insight into their chemical composition based on refractive index and particle size [Baumgardner et al., 2022]. Unfortunately, the natural phenomena described by Mie scattering [Mie, 1908], the physics theory underpinning the AFN, holds that light scattering for a small solid angle is fundamentally degenerate: different combinations of refractive index and particle size can lead to identical light scattering. This degeneracy limits scientists’ ability to uniquely determine physical parameters of interest, leading some previous authors to rely upon helpful, but perhaps limiting, assumptions that mitigate this degeneracy. Complicating matters still further, the probe’s communication with Earth is subject to a strict data budget, limiting the amount of AFN measurements that may be used for analysis to begin with. This thesis addresses two important problems associated with the Rocket Lab Mission to Venus: 1) how to mitigate the light scattering degeneracy with minimal assumptions and 2) how to transmit valuable information within the limited data budget. To address the first problem, I introduce a data retrieval algorithm, based upon Bayesian statistical inference [Lindley, 1965], which combines a physical model of the instrument and a prior probability distribution describing each physical property. In some cases, this method can estimate the correct particle size and refractive index of a particle as the maximum likelihood value, from a single measurement even as it relaxes certain assumptions that were previously standard in the field, such as a small refractive index range. Using my data retrieval algorithm, I reanalyze the data collected by the Pioneer MultiProbe Mission to Venus’ nephelometers without the need for supplementary data from a different instrument [Ragent and Blamont, 1980]. I also provide new insight into the particle size and refractive index distributions seen by the Pioneer Mission’s small probes, which had not been possible with previous techniques. To address the second problem, I propose a data strategy for limited data missions like the Rocket Lab Mission to Venus. The method developed in this work relies upon Gaussian Mixture Models, which can efficiently represent multiple measurements asPh.D

Report to the President for year ended June 20, 2025, Arts Initiatives

This report contains the following sections: Artfinity arts festival, Center for Art, Science & Technology (CAST), Council for the Arts (CAMIT), Faculty Arts Grants and Visiting Artists, Eugene McDermott Award in the Arts at MIT, Personnel, and Student Arts Programs (Arts Incubator, Arts Scholars, Student Center Arts Studios, Wiesner Student Art Gallery)

Smartphone and Wearable Device-Based Digital Phenotyping to Understand Substance use and its Syndemics

Digital phenotyping is a process that allows researchers to leverage smartphone and wearable data to explore how technology use relates to behavioral health outcomes. In this Research Concepts article, we provide background on prior research that has employed digital phenotyping; the fundamentals of how digital phenotyping works, using examples from participant data; the application of digital phenotyping in the context of substance use and its syndemics; and the ethical, legal and social implications of digital phenotyping. We discuss applications for digital phenotyping in medical toxicology, as well as potential uses for digital phenotyping in future research. We also highlight the importance of obtaining ground truth annotation in order to identify and establish digital phenotypes of key behaviors of interest. Finally, there are many potential roles for medical toxicologists to leverage digital phenotyping both in research and in the future as a clinical tool to better understand the contextual features associated with drug poisoning and overdose. This article demonstrates how medical toxicologists and researchers can progress through phases of a research trajectory using digital phenotyping to better understand behavior and its association with smartphone usage

Report to the President for year ended June 30, 2025, Vice Provost for the Arts

This report contains the following sections: Artfinity arts festival, Arts Initiatives, Center for Art, Science & Technology (CAST), the List Visual Arts Center, the MIT Museum, Administrative Initiatives, Finances and Funding, and Personnel

The COVID-19 effect on the Paris agreement

The pandemic and efforts to control it are causing sharp reductions in global economic activity and associated fossil energy use, with unknown influence on longer-term efforts to limit greenhouse gas emissions under the Paris Climate Agreement. To explore this effect, estimates of economic recession and recovery in near-term months are extended to cover a return to full employment in future years, to be compared with an estimate of growth had COVID-19 not occurred. On the assumption that the Paris emissions pledges for 2020 will be met in any case, projection of global emissions with and without the pandemic show that, through its growth impact alone, it will yield only a small effect on emissions in 2030 and beyond. Other COVID legacies may include residual influences in patterns of consumption and travel, and the direction of recovery funds to low carbon investments. Most important, however, will be the effect of the economic shocks on the willingness of nations to meet (or augment) their existing Paris emissions pledges. The main effect of the pandemic on the threat of climate change, therefore, will be not its growth impact but its influence on national commitments to action

Neutronic Performance and Thermal Hydraulic Analysis of the MIT Reactor Fission Converter Experimental Facility Using High-Density U-10Mo Low-Enriched Uranium Fuel Elements

The MITR fission converter (FC) is a core-driven subcritical assembly at the MIT Nuclear Reactor Laboratory, located on the MIT campus in Cambridge, MA. The assembly is made of eleven partially-depleted MITR-II fuel elements in a separate cooling tank attached to the side of the core-tank graphite reflector. The FC serves to boost the thermal flux from the core and send a hardened neutron spectrum to an irradiation target, providing a fission energy flux spectrum without the need to put a sample inside the core tank. It was previously used for boron-neutron capture therapy clinical trials before its decommissioning in the 2010s. Recently, it has been modified from a medical beamline to a general-use engineering and materials testing facility. The new FC-based experimental facility has roughly one cubic meter of empty space downstream intended to contain large experiments, called the m³. This work is a safety and performance study aimed at quantifying the impact of modifying the facility’s geometry as part of the FC’s recommissioning, as well as the impact of changing its fuel from HEU to LEU fuel as part of the MITR LEU conversion project. Neutronics and thermal hydraulics analysis of the renovated facility have been performed using the codes MCNP5 and STAT7, respectively. This analysis quantified the FC’s k_eff, power distribution, multi-group neutron flux, and conditions which cause onset of nucleate boiling (ONB). It was determined that the FC assembly will remain subcritical (k _eff < 0.9) and low power (≤200 kW) under a wide range of performance conditions, including with both types of fuel and a variety of materials on the target-side of the FC tank. The HEU-fueled FC is expected to require no changes to the limiting safety system settings (LSSS) outlined in the original technical specifications document. The LEU fuel is expected to increase the FC performance, but as a tradeoff, will require minor changes to the LSSS setpoints to maintain margin to ONB under the most limiting thermal-hydraulic conditions. Additionally, this study evaluates the feasibility of using the FC for in-assembly fuel experiments, particularly as a pathway for testing the new LEU fuel elements at low power. This study indicated that this proposed FC configuration with one LEU and ten HEU elements is feasible and maintains wide safety margins.S.M

Characteristics of two polarized groups in online social networks’ controversial discourse

In today’s interconnected world, online social networks play a pivotal role in facilitating global communication. These platforms often host discussions on contentious topics such as climate change, vaccines, and war, leading to the formation of two distinct groups: deniers and believers. Understanding the characteristics of these groups is crucial for predicting information flow and managing the diffusion of information. Moreover, such understanding can enhance machine learning algorithms designed to automatically detect these groups, thereby contributing to the development of strategies to curb the spread of disinformation, including fake news and rumors. In this study, we employ social network analysis measures to extract the characteristics of these groups, conducting experiments on three large-scale datasets of over 22 million tweets. Our findings indicate that, based on network science measures, the denier (anti) group exhibits greater coherence than the believer (pro) group

What have we learned about artificial intelligence from studying the brain?

Neuroscience and artificial intelligence (AI) share a long, intertwined history. It has been argued that discoveries in neuroscience were (and continue to be) instrumental in driving the development of new AI technology. Scrutinizing these historical claims yields a more nuanced story, where AI researchers were loosely inspired by the brain, but ideas flowed mostly in the other direction

Exploring the Affordances of Sequence Mining in Educational Games

TEEM’20, October 21–23, 2020, Salamanca, SpainGames have become one of the most popular mediums across cultures and ages and the use of educational games is growing. There is ample evidence that supports the benefits of using games for learning and assessment. However, we do not usually find games incorporated into educational environments. One of the main problems that teachers face is to actually know how students are interacting with the game as they cannot analyze properly the effect of the activity on the students. To improve this issue, we can use the data generated by the interaction of students with such educational games to analyze the sequences and errors by transforming raw data into meaningful sequences that are interpretable and actionable for teachers. In this study we use a data collection from our game Shadowspect and implement learning analytics with process and sequence mining techniques to generate two metrics that aim to help teachers make proper assessment and better understand the process

Investigation of Multi-Z Impurity Transport in Tokamaks using Neural Networks

Achieving clean, sustainable energy at scale is a pressing global challenge. Fusion of light elements holds significant potential to address this critical need. While only experimental fusion reactors are currently operational, significant progress is being made in the research and design of near-future tokamak fusion power plants. Reactor success will depend on a comprehensive understanding of heat and particle transport, including the role of impurities. This thesis focuses on the development of machine-agnostic neural network surrogates for TGLF, designed to predict impurity transport coefficients alongside heat and electron particle fluxes in DD plasmas. Training data are derived from synthetic fluxes generated for L, H, and I confinement modes in Alcator C-Mod, DIII-D, and ASDEX-Upgrade. To reduce training complexity, shot data are discretized by radius, and networks are developed at six ρ coordinates: 0.2, 0.4, 0.6, 0.7, 0.8, and 0.9. Fifteen plasma parameters are selected as inputs to the neural networks after examining TGLF flux sensitivities across all five output channels. Predicted impurity fluxes for arbitrary charge states and masses, ranging from 4He to 184W, are used to derive diffusive and convective transport coefficients. Three types of synthetic TGLF data are created and applied to network training to produce accurate models. The primary synthetic data type approximates experimental data by sampling within a perturbation range of ±10% around a given shot. Supporting data types enhance network performance by improving trends in single-parameter (1D) scans and addressing areas of highest network uncertainty. Hyperparameter optimization and testing resulted in highly accurate networks. Testing set relative errors averaged over ρ = 0.4–0.7 and 0.9 show approximate deviations of 0.12 ± 0.029 for heat flux and 0.42 ± 0.095 for particle flux channels. However, error metrics at ρ = 0.2 and 0.8 require location-specific tuning and potentially more data to match the accuracy achieved at other radii. The networks are used to analyze boron and carbon impurity peaking within machinespecific H-modes. Their predictions are then compared to published results. Qualitative results for boron peaking correlations in ASDEX-Upgrade are clearly reproduced, while carbon peaking trends in DIII-D are weaker. Sparse DIII-D data, which also includes atypical advanced modes, is believed to have contributed to reduced accuracy in these cases. Using H-mode shots spanning low to high local collisionality, impurity diffusion trends with charge state (Z) in ITG and TEM dominated plasmas were examined, showing good agreement with published studies. Additionally, analysis of network-derived convective transport shows that Z-sensitivity increases with collisionality. Network scans of the ion and electron heat flux responses to temperature gradients also reveal the clear presence of a critical gradient at all radii. These results demonstrate that the neural networks developed in this work can reliably reproduce TGLF results and deliver fast predictions of heat, electron particle, and impurity transport in tokamaks.S.M