With the direct detection of gravitational waves (GWs) from LIGO in 2016, and recent evidence from the NANOGrav collaboration for a stochastic GW background, GW astronomy is becoming an important tool for understanding the universe. Recently it has been shown that axion dark matter (DM) experiments can extend the search for GWs to much higher frequencies, kHz < f < GHz. In this talk we'll discuss how light DM detectors utilizing single phonon excitations in crystal targets, previously shown to be sensitive to a wide variety of DM candidates, are also sensitive to GWs in the frequency range, THz < f < 100 THz, corresponding to the range of optical phonon energies, meV < \omega < 100 meV. We'll discuss the mechanism by which high frequency GWs can generate single phonons, and consider the detector sensitivity of different target materials. Lastly, we'll discuss how these high frequency GWs may be produced in processes such as black hole inspirals and superradiance.

]]>MPSDS Seminar Series

January 17, 2024

12:00 - 1:00 pm

In person, room 1070 Institute for Social Research, and via Zoom. The Zoom call will be locked 10 minutes after the start of the presentation.

Using Synergies Between Survey Statistics and Causal Inference to Improve Transportability of Clinical Trials

Medical researchers have understood for many years that treatment effect estimates obtained from a randomized clinical trial (RCT) -- termed efficacy'' -- can differ from those obtained in a general population -- termed effectiveness''. Only in the past decade has extensive work begun in the statistical literature to bridge this gap using formal quantitative methods. As noted by Rod Little in a letter to the editor in the New Yorker ...randomization in randomized clinical trials concerns the allocation of the treatment, not the selection of individuals for the study. The latter can have an important impact on the average size of a treatment effect,'' with RCT samples often designed, sometimes explicitly, to be more likely to include individuals for whom the treatment may be more effective.

This issue has been various termed generalizability'' or transportability." Why do we care about transportability? In RCTs we are in the happy situation were treatment assignment is randomized, so confounding due to either observed or unobserved (pre-treatment) covariates is not an issue. But while randomization of treatment eliminates the effect of unobserved confounders, at least net of non-compliance, it does not eliminate the effect of unobserved effect modifiers, which can impact the causal effect of treatment in a population that differs from the RCT sample population. The impact of these interactions on the marginal effect of treatment thus can differ between the RCT population and the final population of interest.

Concurrent with research into transportability has been research into making population inference from non-probability samples. There is a close overlap between these two approaches, particularly with respect to the non-probability inference methods that rely on information from a relevant probability sample of the target population to reduce selection bias effects. When there are relevant censuses or probability samples of the target patient population of interest, these methods can be adapted to transport information from the RCT to the patient population. Because the RCT setting focuses on causal inference, this adaptation involves extensions to estimate counterfactuals. Thus approaches that treat population inference as a missing data problem are a natural fit to connect these two strands of methodological innovation.

In particular, we propose to extend a pseudo-weighting'' methodology from other non-probability settings to a doubly robust'' estimator that treats sampling probabilities or weights as regression covariates to achieve consistent estimation of population quantities. We explore our proposed approach and compare with some standard existing methods in a simulation study to assess the effectiveness of the approach under differing degrees of selection bias and model misspecification, and compare it with results obtained using the RT data only and with existing methods that use inverse probability weights. We apply it to a study of pulmonary artery catheterization in critically ill patients where we believe differences between the trial sample and the larger population might impact overall estimates of treatment effects.

MPSDS JPSM Seminar Series

January 24, 2024

12:00 - 1:00 EST

In person, Room 1070, Institute for Social Research and via Zoom. The Zoom call will be locked 10 minutes after the start of the presentation.

A Novel Methodology for Improving Applications of Modern Predictive Modeling Tools to Linked Data Sets Subject to Mismatch Error

In recent years, the rise of social media platforms such as Twitter/X has provided social scientists with a wealth of user-content data, and there has been renewed interest in the utility of administrative records for increasing survey efficiency. Combining social media data, administrative records, and survey data has the potential to produce a comprehensive source of information for social research. These data are often collected from multiple sources and combined by probabilistic record linkage. For the analysis of these linked data files, advanced machine learning techniques, such as random forests, boosting, and related ensemble methods, have become essential tools for survey methodologists and data scientists. There is, however, a potential pitfall in the widespread application of these techniques to linked data sets that needs more attention. Linkage errors such as mismatch and missed-match errors can distort the true relationships between variables and adversely alter the performance metrics routinely output by predictive modeling techniques, such as variable importance, confusion matrices, RMSE, etc. Thus, the actual predictive performance of these machine-learning techniques may not be realized. In this presentation, I will describe a new general methodology designed to adjust modern predictive modeling techniques for the presence of mismatch errors in linked data sets. The proposed approach, based on mixture modeling, is general enough to accommodate various predictive modeling techniques in a unified fashion. I evaluate the performance of the new methodology with simulations implemented in R. I will conclude with recommendations for future work in this area.

Brady T. West is a Research Professor in the Survey Methodology Program, located within the Survey Research Center at the Institute for Social Research on the University of Michigan-Ann Arbor (U-M) campus. He earned his PhD from the Michigan Program in Survey and Data Science in 2011. Before that, he received an MA in Applied Statistics from the U-M Statistics Department in 2002, being recognized as an Outstanding First-year Applied Masters student, and a BS in Statistics with Highest Honors and Highest Distinction from the U-M Statistics Department in 2001. His current research interests include the implications of measurement error in auxiliary variables and survey paradata for survey estimation, selection bias in surveys, responsive/adaptive survey design, interviewer effects, and multilevel regression models for clustered and longitudinal data. He is the lead author of a book comparing different statistical software packages in terms of their mixed-effects modeling procedures (Linear Mixed Models: A Practical Guide using Statistical Software, Third Edition, Chapman Hall/CRC Press, 2022), and he is a co-author of a second book entitled Applied Survey Data Analysis (with Steven Heeringa and Pat Berglund), the second edition of which was published by CRC Press in June 2017. He was elected as a Fellow of the American Statistical Association in 2022.

When do two different looking quantum field theories describe the same physics? This is essentially asking when the quantum field theories are isomorphic. In the case of topological quantum field theories, there are sometimes a way to determine them via topological invariants. For a superconformal field theory, what would be the minimal set of “invariants” to determine when they are isomorphic? I will discuss some approaches to this question in the context of superconformal field theories in four and six dimensions. Utilizing 4d class S theories that also admits 6d (1,0) SCFT origins, I will explain how a certain class of 4d N=2 SCFTs, which a priori look like distinct theories, can be shown to describe the same physics. I will further explain how the 6d (1,0) origin sheds light on the 3d duality.

]]>Locality and unitary forces scattering amplitudes to factorize when taking the momentum of one of the external particles to zero. This factorization has proven very useful for recursion relations for amplitudes at high multplicities. The recursion can break down, however, when the amplitude contains a pole at infinity. In this talk we are going to make modest step towards a prescription of “unitarity at infinity”. We do this by studying on-shell diagrams, which are on-shell gauge invariant objects that appear as cuts of loop integrands in the context of generalized unitarity and serve as building blocks for amplitudes in recursion relations. In the dual formulation, they are associated with cells of the positive Grassmannian. We will describe on-shell diagrams in N<4 supersymmetric Yang-Mills (SYM) theory and show that there exists a diagrammatic operation that corresponds to sending one of the momenta to infinity.

]]>Given that axions are both a promising candidate to solve problems in the Standard Model and are ubiquitous in quantum gravity, it is crucial to accurately determine their signatures. In this talk, we discuss how the axion's compact field space leads to interesting interactions with topological defects, specifically monopoles and strings. In the case of monopoles, we show that, due to the Witten effect, axions interacting with abelian gauge fields generate a potential for the axion from loops of magnetic monopoles, and discuss a simple phenomenological example where this potential is the dominant contribution to the axion mass. In the case of strings, we discuss superconductivity from massless chiral excitations along the string. We show that bulk fermions do not need to become massless in the core of the string for there to be trapped massless excitations, and explore the counterintuitive phase structure of these zero modes, which become less localized to the string as the mass is increased.

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]]>MPSDS JPSM Seminar Series

February 7, 2024

12:00 - 1:00

In person, room 1070 Institute for Social Research, and via Zoom.

The Zoom call will be locked 10 minutes after the start of the presentation.

Recent Developments and Open Problems in Post-Linkage Data Analysis

Record linkage and subsequent data analysis of the linked file with suitable propagation of uncertainty can be performed if the analyst also happens to be the linker or at least has comprehensive information about how the data were linked. However, it is rather common that the two processes are considered in a separate fashion, with the analyst being handed a linked file that is possibly subject to substantial linkage error (false matches and missed matches). Ignoring such error can render statistical analysis invalid. At the same time, accounting for linkage error with limited information about the linkage process poses a variety of challenges. This talk will outline a framework based on a mixture model for addressing mismatch error in the secondary analysis of linked files. Its use will be demonstrated in several case studies. Finally, we will present recent extensions, future directions and open problems.

Martin Slawski is an Associate Professor in the Department of Statistics at George

Mason University. His research on data analysis after record linkage is currently

supported by NSF. His research interests concern topics in computational statistics and applications in various domains. He serves as an associate editor of the Electronic Journal of Statistics. He received his Ph.D. in Computer Science from Saarland University, Germany, and was a postdoctoral associate in Statistics and Computer

Science at Rutgers University prior to joining his current institution.

I will describe ongoing work on the thermodynamics of quantum fields in far-from-equilibrium states. The key tool is modular flow, a nonstandard time-evolution map defined relative to a choice of state, which makes that state "look thermal." Famously, the modular flow for the Minkowski vacuum in the Rindler wedge is a geometric boost, which is one way of stating the Unruh effect. In this talk, I will outline a characterization of the settings in which modular flow is geometrically local, i.e., a complete list of "generalized Unruh effects" in arbitrary spacetimes and for arbitrary quantum field theories. The arguments involve analytic manipulations of position-space correlators, which may be of independent interest to those of you working on amplitudes.

]]>MPSDS JPSM Seminar Series

March 13, 2024

12:00 - 1:00 EDT

In person, room 1070, Institute for Social Research, and via Zoom. The Zoom call will be locked 10 minutes after the start of the presentation.

When “representative” surveys fail: Can a non-ignorable missingness mechanism explain bias in estimates of COVID-19 vaccine uptake?

Recently, attention was drawn to the failure of two very large internet-based probability surveys to correctly estimate COVID-19 vaccine uptake in the U.S. in early 2021. Both the Delphi-Facebook COVID-19 Trends and Impact Survey (CTIS) and Census Household Pulse Survey (HPS) overestimated vaccine uptake substantially (14 and 17 points in May 2021) compared to retroactively available CDC benchmark data. These surveys had large numbers of respondents but very low response rates (<10%), and thus non-ignorable nonresponse could have substantially impacted estimates. Specifically, it is plausible that “anti-vaccine” individuals were less likely to complete a survey about COVID-19; we might also hypothesize that “anti-vaccine” individuals could be suspicious of the government and thus less likely to respond to an official government-sponsored survey. In this talk we use proxy pattern-mixture models (PPMMs) to retrospectively estimate the proportion of adults (18+) who received at least one dose of a COVID-19 vaccine, using data from the CTIS and HPS, under a non-ignorable nonresponse assumption. We compare these estimates to the true benchmark uptake numbers and show that the PPMM could have detected the direction of the bias and have provided meaningful bias bounds. We also use the PPMM to estimate vaccine hesitancy, a measure without a benchmark truth, and compare to the direct survey estimates. We conclude with discussion of how the PPMM could be prospectively as part of an assessment of nonresponse and/or selection bias, factors that would facilitate such analyses in the future, and ongoing work to extend the PPMM to novel areas.

Rebecca Andridge is an Associate Professor of Biostatistics at The Ohio State University College of Public Health. She conducts methodologic work in imputation methods for missing data, primarily in large-scale probability samples, and measures of selection bias for nonprobability samples. In particular, she works on methods for imputing data when missingness is driven by the missing values themselves (missing not at random). She teaches introductory graduate and undergraduate biostatistics and won the College's Outstanding Teaching Award in 2011 and is a Fellow of the American Statistical Association.

MPSDS JPSM Seminar Series

March 13, 2024

12:00 - 1:00 EDT

In person, room 1070, Institute for Social Research, and via Zoom. The Zoom call will be locked 10 minutes after the start of the presentation.

When “representative” surveys fail: Can a non-ignorable missingness mechanism explain bias in estimates of COVID-19 vaccine uptake?

Recently, attention was drawn to the failure of two very large internet-based probability surveys to correctly estimate COVID-19 vaccine uptake in the U.S. in early 2021. Both the Delphi-Facebook COVID-19 Trends and Impact Survey (CTIS) and Census Household Pulse Survey (HPS) overestimated vaccine uptake substantially (14 and 17 points in May 2021) compared to retroactively available CDC benchmark data. These surveys had large numbers of respondents but very low response rates (<10%), and thus non-ignorable nonresponse could have substantially impacted estimates. Specifically, it is plausible that “anti-vaccine” individuals were less likely to complete a survey about COVID-19; we might also hypothesize that “anti-vaccine” individuals could be suspicious of the government and thus less likely to respond to an official government-sponsored survey. In this talk we use proxy pattern-mixture models (PPMMs) to retrospectively estimate the proportion of adults (18+) who received at least one dose of a COVID-19 vaccine, using data from the CTIS and HPS, under a non-ignorable nonresponse assumption. We compare these estimates to the true benchmark uptake numbers and show that the PPMM could have detected the direction of the bias and have provided meaningful bias bounds. We also use the PPMM to estimate vaccine hesitancy, a measure without a benchmark truth, and compare to the direct survey estimates. We conclude with discussion of how the PPMM could be prospectively as part of an assessment of nonresponse and/or selection bias, factors that would facilitate such analyses in the future, and ongoing work to extend the PPMM to novel areas.

Rebecca Andridge is an Associate Professor of Biostatistics at The Ohio State University College of Public Health. She conducts methodologic work in imputation methods for missing data, primarily in large-scale probability samples, and measures of selection bias for nonprobability samples. In particular, she works on methods for imputing data when missingness is driven by the missing values themselves (missing not at random). She teaches introductory graduate and undergraduate biostatistics and won the College's Outstanding Teaching Award in 2011 and is a Fellow of the American Statistical Association.

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