Tootfinder

Aptly: Making Mobile Apps from Natural Language
Evan W. Patton, David Y. J. Kim, Ashley Granquist, Robin Liu, Arianna Scott, Jennet Zamanova, Harold Abelson
https://arxiv.org/abs/2405.00229

Aptly: Making Mobile Apps from Natural Language
We present Aptly, an extension of the MIT App Inventor platform enabling mobile app development via natural language powered by code-generating large language models (LLMs). Aptly complements App Inventor's block language with a text language designed to allow visual code generation via text-based LLMs. We detail the technical aspects of how the Aptly server integrates LLMs with a realtime collaboration function to facilitate the automated creation and editing of mobile apps given user instruct…

LAMOST J1010 2358 is not a Pair-Instability Supernova Relic
Pierre N. Thibodeaux, Alexander P. Ji, William Cerny, Evan N. Kirby, Joshua D. Simon
https://arxiv.org/abs/2404.17078 <…

LAMOST J1010+2358 is not a Pair-Instability Supernova Relic
The discovery of a star formed out of pair-instability supernova ejecta would have massive implications for the Population III star initial mass function and the existence of stars over 100 Msun, but none have yet been found. Recently, the star LAMOST J1010+2358 was claimed to be a star that formed out of gas enriched by a pair-instability supernova. We present a non-LTE abundance analysis of a new high-resolution Keck/HIRES spectrum of J1010+2358. We determined the carbon and aluminum abundanc…

Attention-based Neural Network Emulators for Multi-Probe Data Vectors Part I: Forecasting the Growth-Geometry split
Kunhao Zhong, Evan Saraivanov, James Caputi, Vivian Miranda, Supranta S. Boruah, Tim Eifler, Elisabeth Krause
https://arxiv.org/abs/2402.17716

Attention-based Neural Network Emulators for Multi-Probe Data Vectors Part I: Forecasting the Growth-Geometry split
We present a new class of machine-learning emulators that accurately model the cosmic shear, galaxy-galaxy lensing, and galaxy clustering real space correlation functions in the context of Rubin Observatory year one simulated data. To illustrate its capabilities in forecasting models beyond the standard $Λ$CDM, we forecast how well LSST Year 1 data will be able to probe the consistency between geometry $Ω^{\rm geo}_\mathrm{m}$ and growth $Ω^{\rm growth}_\mathrm{m}$ dark matter densities in t…

Feature Density Estimation for Out-of-Distribution Detection via Normalizing Flows
Evan D. Cook, Marc-Antoine Lavoie, Steven L. Waslander
https://arxiv.org/abs/2402.06537

Feature Density Estimation for Out-of-Distribution Detection via Normalizing Flows
Out-of-distribution (OOD) detection is a critical task for safe deployment of learning systems in the open world setting. In this work, we investigate the use of feature density estimation via normalizing flows for OOD detection and present a fully unsupervised approach which requires no exposure to OOD data, avoiding researcher bias in OOD sample selection. This is a post-hoc method which can be applied to any pretrained model, and involves training a lightweight auxiliary normalizing flow mod…

CGOLS V: Disk-wide Stellar Feedback and Observational Implications of the Cholla Galactic Wind Model
Evan E. Schneider, S. Alwin Mao
https://arxiv.org/abs/2402.12474

CGOLS V: Disk-wide Stellar Feedback and Observational Implications of the Cholla Galactic Wind Model
We present the fifth simulation in the CGOLS project -- a set of isolated starburst galaxy simulations modeled over large scales ($10\kpc$) at uniformly high resolution ($Δx \approx 5\pc$). Supernova feedback in this simulation is implemented as a disk-wide distribution of clusters, and we assess the impact of this geometry on several features of the resulting outflow, including radial profiles of various phases; mass, momentum, and energy outflow rates; covering fraction of cool gas; mock abs…

Supervised Reconstruction for Silhouette Tomography
Evan Bell, Michael T. McCann, Marc Klasky
https://arxiv.org/abs/2402.07298 https://

Supervised Reconstruction for Silhouette Tomography
In this paper, we introduce silhouette tomography, a novel formulation of X-ray computed tomography that relies only on the geometry of the imaging system. We formulate silhouette tomography mathematically and provide a simple method for obtaining a particular solution to the problem, assuming that any solution exists. We then propose a supervised reconstruction approach that uses a deep neural network to solve the silhouette tomography problem. We present experimental results on a synthetic da…

A Compositional Framework for First-Order Optimization
Tyler Hanks, Matthew Klawonn, Evan Patterson, Matthew Hale, James Fairbanks
https://arxiv.org/abs/2403.05711

A Compositional Framework for First-Order Optimization
Optimization decomposition methods are a fundamental tool to develop distributed solution algorithms for large scale optimization problems arising in fields such as machine learning and optimal control. In this paper, we present an algebraic framework for hierarchically composing optimization problems defined on hypergraphs and automatically generating distributed solution algorithms that respect the given hierarchical structure. The central abstractions of our framework are operads, operad alg…

Keck Primary Mirror Closed-Loop Segment Control using a Vector-Zernike Wavefront Sensor
Maissa Salama, Charlotte Guthery, Vincent Chambouleyron, Rebecca Jensen-Clem, J. Kent Wallace, Jacques-Robert Delorme, Mitchell Troy, Tobias Wenger, Daniel Echeverri, Luke Finnerty, Nemanja Jovanovic, Joshua Liberman, Ronald A. Lopez, Dimitri Mawet, Evan C. Morris, Maaike van Kooten, Jason J. Wang, Peter Wizinowich, Yinzi Xin, Jerry Xuan

Keck Primary Mirror Closed-Loop Segment Control using a Vector-Zernike Wavefront Sensor
We present the first on-sky segmented primary mirror closed-loop piston control using a Zernike wavefront sensor (ZWFS) installed on the Keck II telescope. Segment co-phasing errors are a primary contributor to contrast limits on Keck and will be necessary to correct for the next generation of space missions and ground-based extremely large telescopes (ELTs), which will all have segmented primary mirrors. The goal of the ZWFS installed on Keck is to monitor and correct primary mirror co-phasing…

RG-CAT: Detection Pipeline and Catalogue of Radio Galaxies in the EMU Pilot Survey
Nikhel Gupta, Ray P. Norris, Zeeshan Hayder, Minh Huynh, Lars Petersson, X. Rosalind Wang, Andrew M. Hopkins, Heinz Andernach, Yjan Gordon, Simone Riggi, Miranda Yew, Evan J. Crawford, B\"arbel Koribalski, Miroslav D. Filipovi\'c, Anna D. Kapin\'ska, Stanislav Shabala, Tessa Vernstrom, Joshua R. Marvil

RG-CAT: Detection Pipeline and Catalogue of Radio Galaxies in the EMU Pilot Survey
We present source detection and catalogue construction pipelines to build the first catalogue of radio galaxies from the 270 $\rm deg^2$ pilot survey of the Evolutionary Map of the Universe (EMU-PS) conducted with the Australian Square Kilometre Array Pathfinder (ASKAP) telescope. The detection pipeline uses Gal-DINO computer-vision networks (Gupta et al., 2024) to predict the categories of radio morphology and bounding boxes for radio sources, as well as their potential infrared host positions…

RG-CAT: Detection Pipeline and Catalogue of Radio Galaxies in the EMU Pilot Survey
Nikhel Gupta, Ray P. Norris, Zeeshan Hayder, Minh Huynh, Lars Petersson, X. Rosalind Wang, Andrew M. Hopkins, Heinz Andernach, Yjan Gordon, Simone Riggi, Miranda Yew, Evan J. Crawford, B\"arbel Koribalski, Miroslav D. Filipovi\'c, Anna D. Kapin\'ska, Stanislav Shabala, Tessa Vernstrom, Joshua R. Marvil

RG-CAT: Detection Pipeline and Catalogue of Radio Galaxies in the EMU Pilot Survey
We present source detection and catalogue construction pipelines to build the first catalogue of radio galaxies from the 270 $\rm deg^2$ pilot survey of the Evolutionary Map of the Universe (EMU-PS) conducted with the Australian Square Kilometre Array Pathfinder (ASKAP) telescope. The detection pipeline uses Gal-DINO computer-vision networks (Gupta et al., 2024) to predict the categories of radio morphology and bounding boxes for radio sources, as well as their potential infrared host positions…

Kinematic and metallicity of the dwarf spheroidal galaxy Andromeda XVIII
Kateryna Kvasova, Evan N. Kirby, Rachael L. Beaton
https://arxiv.org/abs/2404.11804

Kinematic and metallicity of the dwarf spheroidal galaxy Andromeda XVIII
Andromeda XVIII is an isolated dwarf galaxy 579 kpc away from the nearest large galaxy, M31. It is a candidate ``backsplash galaxy'' that might have been affected by a close passage to M31. We present new Keck/DEIMOS spectroscopy of Andromeda XVIII to assess the likelihood that it is a backsplash galaxy. We estimated the velocities, metallicities ([Fe/H]), and $α$-enhancements ([$α$/Fe]) for 56 probable members. We estimated Andromeda XVIII's mean heliocentric velocity, rotation velocity, pos…

Dust Survival in Galactic Winds
Helena M. Richie, Evan E. Schneider, Matthew W. Abruzzo, Paul Torrey
https://arxiv.org/abs/2403.03711 https://

Dust Survival in Galactic Winds
We present a suite of high-resolution numerical simulations to study the evolution and survival of dust in hot galactic winds. We implement a novel dust framework in the Cholla hydrodynamics code and use wind tunnel simulations of cool, dusty clouds to understand how thermal sputtering affects the dust content of galactic winds. Our simulations illustrate how various regimes of cloud evolution impact dust survival, dependent on cloud size, wind properties, and dust grain size. We find that sign…