Stellar Wakes from Dark Matter Subhalos

We propose a novel method utilizing stellar kinematic data to detect low-mass substructure in the Milky Way's dark matter halo. By probing characteristic wakes that a passing dark matter subhalo leaves in the phase space distribution of ambient halo stars, we estimate sensitivities down to subhalo masses $\sim 10^7\,M_\odot$ or below. The detection of such subhalos would have implications for dark-matter and cosmological models that predict modifications to the halo-mass function at low halo masses. We develop an analytic formalism for describing the perturbed stellar phase-space distributions, and we demonstrate through simulations the ability to detect subhalos using the phase-space model and a likelihood framework. Our method complements existing methods for low-mass subhalo searches, such as searches for gaps in stellar streams, in that we can localize the positions and velocities of the subhalos today.Comment: 6 + 3 pages, 1 + 2 figures, code available at: https://github.com/bsafdi/stellarWake

Randomization Inference When N Equals One

N-of-1 experiments, where a unit serves as its own control and treatment in different time windows, have been used in certain medical contexts for decades. However, due to effects that accumulate over long time windows and interventions that have complex evolution, a lack of robust inference tools has limited the widespread applicability of such N-of-1 designs. This work combines techniques from experiment design in causal inference and system identification from control theory to provide such an inference framework. We derive a model of the dynamic interference effect that arises in linear time-invariant dynamical systems. We show that a family of causal estimands analogous to those studied in potential outcomes are estimable via a standard estimator derived from the method of moments. We derive formulae for higher moments of this estimator and describe conditions under which N-of-1 designs may provide faster ways to estimate the effects of interventions in dynamical systems. We also provide conditions under which our estimator is asymptotically normal and derive valid confidence intervals for this setting.Comment: 27 pages, 2 figure

Single-qubit rotations in two-dimensional optical lattices with multiqubit addressing

Published versio

\u3ci\u3eCamelina sativa\u3c/i\u3e promoters of seed development genes resemble \u3ci\u3eArabidopsis thaliana\u3c/i\u3e orthologs

Camelina sativa, a relative of the model plant Arabidopsis thaliana, has gained commercial interest in recent years for its seed oil. Camelina oil is a desirable ingredient in animal feed and for cooking due to its rich concentration of omega-3 fatty acids. It is also emerging as an eco-friendly jet biofuel. However, since C. sativa has only recently entered the public eye, regulation of its seed oil synthesis remains largely unexplored. A. thaliana, on the other hand, has been extensively studied. Promoters directly upstream of genes harbor hundreds of cis-acting regulatory DNA elements. These motifs are often tissue-specific and associated with specific biological processes. Many motifs are also induced by hormones, which regulate development. Comparing promoter motifs of four prominent seed development genes, FUS3, LEC1, LEC2, and ABI3, in A. thaliana to their orthologs in C. sativa revealed significant similarity between the two. This suggests that regulation of seed development in C. sativa may be similar to that of A. thaliana. Notably, seed-specific motifs associated with storage proteins and carbon metabolism are most highly represented in both species. However, C. sativa seed-specific promoters were found to harbor a greater composition of motifs induced by abscisic acid, which has been implicated in seed maturation and dormancy. Moving forward, expression analysis of these genes in various C. sativa tissue types will further validate their function. Understanding specific regulatory factors modulating seed development will reveal molecular targets to improve camelina oil yield for industrial applications