arXiv subjects

Alex Davis

Publications and source records attributed to Alex Davis.

5 recordsLinked to original sources

arXiv2026

Finding Habitable Exoplanets with Binary Relative Astrometry: Planet Detection and Characterization with the Microarcsecond Astrometric Retrieval Algorithm (MARA)

Binary relative astrometry is a technique to search for rocky planets in the habitable zone of nearby binary stars using 1D relative astrometry at the microarcsecond level. This unprecedented precision would allow a custom-designed space telescope to directly measure the occurrence rate of these planets. The success of such a mission depends on our ability to recover and characterize planets from the unique format of extreme precision binary relative astrometry data. We present MARA, the Microarcsecond Astrometric Retrieval Algorithm, specifically designed for these data. We describe the design and format of the MARA pipeline, and demonstrate its accuracy and performance with a series of validation tests on simulated data, using the SHERA SMEx mission concept as an example. Our injection/recovery tests show that with these data, MARA is able to detect and characterize rocky planets in the habitable zone of alpha Cen A, down to a coplanar mass of about 1 Earth mass in 1 year orbits. Expanding to a range of input planet masses and periods for the same example mission, we find that the results from these injection/recovery tests generally agree with the analytic predictions of binary relative astrometry sensitivity. We use MARA to map out the expected completeness as a function of planet mass and period, which in this case reaches down to about 0.5 M Earth masses at 3 year orbits around alpha Cen A. These depth-of-search calculations will be a vital ingredient in demographics calculations from the final data from a binary relative astrometry mission.

astro-ph.EP↗

arXiv2026

Searching for Habitable Exoplanets with Relative Astrometry (SHERA). I. The Case for Searching for Planets in Binary Star Systems

Discovering Earth-like planets orbiting Sun-like stars was identified as a priority science goal of the Astronomy 2020 Decadal Survey. It is confounded by many factors, one of which is the high multiplicity of Sun-like stars in the local neighborhood - half of nearby Sun-like stars are in binary or higher-order stellar systems, which are less amenable to the detection of small planets with almost all of the currently productive exoplanet detection techniques. Here we describe the SHERA (Searching for Habitable Exoplanets with Relative Astrometry) NASA Small Explorer mission concept. SHERA utilizes diffractive-pupil technology on a small, simple optical space telescope to achieve microarcsecond precision relative astrometry on 14 Sun-like stars in seven nearby multi-star systems, combining the pupil and stellar binarity to provide a precise reference in the image plane. With this precision, SHERA would enable: (i) a search for rocky planets in the habitable zones of the closest Sun-like stars; (ii) an investigation of the impact of binary star formation on small, widely separated planets; and (iii) the performance of crucial precursor observations on a number of high-priority targets of NASA's future missions to characterize Earth-like planets, such as the Habitable Worlds Observatory. When combined with radial velocity measurements, SHERA relative astrometry will also enable exploration of the three-dimensional orbital structure of planets in binary systems.

astro-ph.EP↗

arXiv2026

An analysis of machine learning approaches for enhancing decision-making in complex discrete choice tasks

Discrete choice modeling is a common tool used for preference elicitation during policy-making, but this is typically done through parametric models. Machine learning can push the boundaries of discrete choice modeling for policy-based preference elicitation by adopting a data-driven approach or learning individual preferences. However, there is limited knowledge of how well machine learning methods can estimate individual discrete choice rules under individual heterogeneity, especially in the context of challenges often experienced during preference elicitation. This study evaluates four machine learning models (multinomial logistic regression, generalized additive model, twinned neural network, and Gaussian process) with respect to their capacity to learn and predict five choice rules that are important in the behavioral and social sciences (linear strong utility, monotonic strong utility, ideal point, lexicographic semiorder, and multiattribute linear ballistic accumulator). Monte Carlo experiments were performed to assess model performance when increasing a) the number of attributes in the choice alternatives, b) the number of training choice sets, and c) the choice rule's determinism. The simulation results demonstrated that semi-parametric and non-parametric models generally outperform parametric models across all choice rules and experimental contexts. Model performance also generally improves by 6% to 96% and 0% to 55%, respectively, with an increase in training choice sets and choice rule determinism. A case study using real energy policy preference data was also conducted, where TNN performed best with a BIC of 13.351. This work demonstrated the viability and limitations of semi-parametric and non-parametric models in the context of policy-centric discrete choice modeling and showed how the choice task context should drive model selection.

cs.LG↗

arXiv2026

A Century of Radial Velocity and Astrometric Monitoring of 70 Oph AB: New PFS Data and Constraints on Planetary Companions

At a distance of 5.1 pc, the 70 Oph AB binary star system is one of the most favorable targets for future direct imaging and astrometry missions surveying mature, terrestrial planets. We present new radial velocities (RVs) obtained with the Planet Finder Spectrograph (PFS) on the 6.5\,m Magellan II Clay Telescope in Chile. We collected 499 measurements of 70 Oph A and 334 measurements of 70 Oph B during 2023--2025. Combining these data with decades of archival RVs and astrometry, we derive an updated orbital solution for the binary and dynamical masses of $0.88 \pm 0.004\,M_\odot$ and $0.73 \pm 0.003\,M_\odot$ for the primary and secondary components, respectively. We find that the long-term RV variability of both components is consistent with stellar activity modulated by rotation periods, and we detect no coherent planetary signals in either component. We place upper limits on any planets orbiting in the plane of the binary. The 27 yr RV baseline for 70 Oph A excludes Jupiter-mass planets interior to 5 au and reaches a sensitivity of $0.3\,M_{\rm Jup}$ at 1 au or $0.5\,M_{\rm Jup}$ at 2 au. For 70 Oph B, with PFS data we rule out planets more massive than $0.25$--$0.3\,M_{\rm Jup}$ inside 0.5 au. We show that stable S-type orbits around 70 Oph A extend to $\sim2.5$ au, covering the habitable zone. Thus, Saturn-mass planets or smaller on stable orbits in the habitable zone of 70 Oph A are allowed. Overall, our results provide important guidance for future planet searches around this stellar system.

astro-ph.EP↗

arXiv2020

Indirect Identification of Psychosocial Risks from Natural Language

During the perinatal period, psychosocial health risks, including depression and intimate partner violence, are associated with serious adverse health outcomes for parents and children. To appropriately intervene, healthcare professionals must first identify those at risk, yet stigma often prevents people from directly disclosing the information needed to prompt an assessment. We examine indirect methods of eliciting and analyzing information that could indicate psychosocial risks. Short diary entries by peripartum women exhibit thematic patterns, extracted by topic modeling, and emotional perspective, drawn from dictionary-informed sentiment features. Using these features, we use regularized regression to predict screening measures of depression and psychological aggression by an intimate partner. Journal text entries quantified through topic models and sentiment features show promise for depression prediction, with performance almost as good as closed-form questions. Text-based features were less useful for prediction of intimate partner violence, but moderately indirect multiple-choice questioning allowed for detection without explicit disclosure. Both methods may serve as an initial or complementary screening approach to detecting stigmatized risks.

cs.CL↗