arXiv subjects

Andrew Householder

Publications and source records attributed to Andrew Householder.

4 recordsLinked to original sources

arXiv2026

On the Detectability of Volcanic Exo-Ios That May Fuel Auroras on Super-Jupiters

Studies suggest Jupiter's aurorae are supplied with plasma from volcanic outflows on the planet's innermost moon, Io. Repeating bursts of radio emission thought to trace massively scaled-up analogs of Jupiter's aurorae have been detected around nearly a dozen isolated substellar worlds, yet the source of the electrons fueling the aurorae remains unknown. Volcanism from tidally heated exosatellites may provide the plasma that fuel the aurora on these worlds. We assess whether transit observations provide a viable means of detecting exosatellites around aurorally active substellar worlds, thereby enabling future tests of this hypothesis. Specifically, we analyze JWST near- and mid-infrared light curves of SIMP 0136+0933, a $12.7 M_J$ "super-Jupiter", known to exhibit auroral emission. We demonstrate the capability to detect exosatellites in the SIMP 0136+0933 system with satellite-to-host mass ratios comparable to those of Jupiter's Galilean moons, achieving detection success rates of 66% for Io-to-Jupiter mass ratio satellites and 93% for Ganymede-to-Jupiter mass ratio satellites. Although the existing light curve is sufficient to demonstrate that this technique is capable of detecting transiting exosatellites, the available archival data are too short in duration to place meaningful constraints on the presence of a transiting satellite in this system. We conclude that JWST light curves spanning $\sim$1.5 days for 4-12 known aurorally active super-Jupiters would be sufficient to yield evidence for or against this hypothesis. A small target sample may suffice, as short satellite periods boost transit probabilities and aurorally active worlds may be preferentially observed near edge-on inclinations.

astro-ph.EP↗

arXiv2025

A Deep Search for Exomoons Around WISE 0855 With JWST

JWST is collecting time-series observations of many free-floating planets (FFPs) to study their weather, but these light curves are the ideal datasets to search for exomoons that transit the FFP during observations. In this paper, we present observations of the planetary-mass Y dwarf ($T=250-285K$, $M = 6.5\pm3.5 M_{Jup}$, d = 2.3$\,$pc) WISE J085510.83-071442.5 (WISE 0855), whose proximity and brightness make it ideal for a transiting exomoon search. We examine 11 hours of time-series spectra from the JWST Near-Infrared Spectrograph (NIRSpec) whose sensitivity, in combination with Gaussian process (GP) modeling, allows for the disentanglement of exomoon transits from WISE 0855's variability. We do not find statistically significant evidence of an exomoon transit in this dataset. Using injection and recovery tests of artificial transits for depths ranging between 0.1-1% (0.35-1.12 $R_{\oplus}$) we explore the exomoon parameter space where we could successfully detect transits. For transit depths $\geq 0.5\%$ (1.96$\,R_{\text{Titan}}$), our detection rate is 96%, which, for WISE 0855, corresponds to a moon with a companion-to-host mass ratio similar to that of Titan and Saturn. Given our sensitivity, transit probabilities, and our observational duration, we determine a $\sim$91% probability of detecting a Titan mass analog exomoon after 18 such observations if every observed system hosts a Titan mass analog exomoon in a Galilean-like system. This suggests that JWST observations of dozens of FFPs could yield meaningful constraints on the occurrence rate of exomoons. This paper is the first demonstration that JWST is sensitive to Galilean moon mass analogs around FFPs.

astro-ph.EP↗

arXiv2025

Sensitivity to Sub-Io-sized Exosatellite Transits in the MIRI LRS Lightcurve of the Nearest Substellar Worlds

JWST's unprecedented sensitivity enables precise spectrophotometric monitoring of substellar worlds, revealing atmospheric variability driven by mechanisms operating across different pressure levels. This same precision now permits exceptionally sensitive searches for transiting exosatellites, small terrestrial companions to these worlds. Using a novel simultaneous dual-band search method to address host variability, we present a search for transiting exosatellites in an 8-hour JWST/MIRI LRS lightcurve of the nearby ($2.0\,pc$) substellar binary WISE J1049-5319AB, composed of two $\sim30 M_{\rm Jup}$ brown dwarfs separated by $3.5\,au$ and viewed near edge-on. Although we detect no statistically significant transits, our injection-recovery tests demonstrate sensitivity to satellites as small as $0.275\,R_{\oplus}$ ($0.96\,R_{\rm Io}$ or $\sim$1 lunar radius), corresponding to 300ppm transit depths, and satellite-to-host mass ratios $>$$10^{-6}$. This approach paves the way for detecting Galilean-moon analogs around directly imaged brown dwarfs, free-floating planets, and wide-orbit exoplanets, dozens of which are already scheduled for JWST lightcurve monitoring. In our Solar System, each giant planet hosts on average 3.5 moons above this threshold, suggesting that JWST now probes a regime where such companions are expected to be abundant. The technique and sensitivities demonstrated here mark a critical step toward detecting exosatellites and ultimately enabling constraints on the occurrence rates of small terrestrial worlds orbiting $1\text{-}70$$M_{\rm Jup}$ hosts.

astro-ph.EP↗

arXiv2021

Clustering COVID-19 Lung Scans

With the ongoing COVID-19 pandemic, understanding the characteristics of the virus has become an important and challenging task in the scientific community. While tests do exist for COVID-19, the goal of our research is to explore other methods of identifying infected individuals. Our group applied unsupervised clustering techniques to explore a dataset of lungscans of COVID-19 infected, Viral Pneumonia infected, and healthy individuals. This is an important area to explore as COVID-19 is a novel disease that is currently being studied in detail. Our methodology explores the potential that unsupervised clustering algorithms have to reveal important hidden differences between COVID-19 and other respiratory illnesses. Our experiments use: Principal Component Analysis (PCA), K-Means++ (KM++) and the recently developed Robust Continuous Clustering algorithm (RCC). We evaluate the performance of KM++ and RCC in clustering COVID-19 lung scans using the Adjusted Mutual Information (AMI) score.

cs.CV↗