BACK TO BASICS: PROBING UNIVERSITY STUDENTS’ FOUNDATIONAL KNOWLEDGE OF ASTRONOMICAL ANATOMY

There is an enduring problem in astronomy education of students knowing far less than lecturers expect about the nature of astronomical objects. In previous work of ours, using the Introductory Astronomy Questionnaire (IAQ), we have looked at students’ knowledge of relative scale of astronomical objects—essentially what is bigger or further away than something else. We have previously identified, for example, that among 922 Norwegian middle school students, 41% believed planets were bigger than stars, and for 211 undergraduate students at the University of New Mexico, 29% of students had the same misconception before commencing an introductory astronomy course. To explore the origins of these misconceptions, we also asked students at the University of New Mexico to provide basic definitions of a planet, star, galaxy, universe and solar system. Responses were coded for categories informed by object definitions as used by astrophysicists, such as knowing that planets orbit stars. In this presentation, I will discuss our coding, analysis and results. For example, only 30% of students identified that planets orbit a star in their definition of planets before taking the course. This research has elucidated that basic knowledge of astronomical anatomy cannot be assumed of students entering the tertiary education sector

Equality, diversity and inclusion perspectives

An overview of the developments arising from equality, diversity and inclusion events at this year’s National Astronomy Meeting, by Vinesh Maguire-Rajpaul on behalf of the organizers

The EXPRES Stellar Signals Project II. State of the Field in Disentangling Photospheric Velocities

Measured spectral shifts due to intrinsic stellar variability (e.g., pulsations, granulation) and activity (e.g., spots, plages) are the largest source of error for extreme-precision radial-velocity (EPRV) exoplanet detection. Several methods are designed to disentangle stellar signals from true center-of-mass shifts due to planets. The Extreme-precision Spectrograph (EXPRES) Stellar Signals Project (ESSP) presents a self-consistent comparison of 22 different methods tested on the same extreme-precision spectroscopic data from EXPRES. Methods derived new activity indicators, constructed models for mapping an indicator to the needed radial-velocity (RV) correction, or separated out shape- and shift-driven RV components. Since no ground truth is known when using real data, relative method performance is assessed using the total and nightly scatter of returned RVs and agreement between the results of different methods. Nearly all submitted methods return a lower RV rms than classic linear decorrelation, but no method is yet consistently reducing the RV rms to sub-meter-per-second levels. There is a concerning lack of agreement between the RVs returned by different methods. These results suggest that continued progress in this field necessitates increased interpretability of methods, high-cadence data to capture stellar signals at all timescales, and continued tests like the ESSP using consistent data sets with more advanced metrics for method performance. Future comparisons should make use of various well-characterized data sets—such as solar data or data with known injected planetary and/or stellar signals—to better understand method performance and whether planetary signals are preserved

Gaussian process tools for modelling stellar signals and studying exoplanets

The discovery of exoplanets represents one of the greatest scientific revolutions in history, and exoplanetary science has rapidly become uniquely positioned to address profound questions about the origins of life, and about humanity's place (and future) in the cosmos. Since the discovery of the first exoplanet over two decades ago, the radial velocity (RV) method has been one of the most productive techniques for discovering new planets. It has also become indispensable for characterising exoplanets detected via other techniques, notably transit photometry. Unfortunately, signals intrinsic to stars themselves - especially magnetic activity signals - can induce RV variations that can drown out or even mimic planetary signals. Modelling and thus mitigating these signals is notoriously difficult, which represents a major obstacle to using next-generation instruments to detect lower mass planets, planets with longer periods, and planets around more magnetically-active stars. Enter Gaussian processes (GPs), which have a number of features that make them very well suited to the joint modelling of stochastic activity processes and dynamical (e.g. planetary) signals. In this thesis, I leverage GPs to enable the study of smaller planets around a wider variety of stars than has previously been possible. In particular, I develop a principled and sophisticated Bayesian framework, based on GPs, for modelling RV time series jointly with ancillary activity-sensitive proxies, thus allowing activity signals to be constrained and disentangled from genuine planetary signals. I show that my framework succeeds even in cases where existing techniques would fail to detect planets, e.g. the case of a weak planetary signal with period identical to its host star's rotation period. In a first application of the framework, I demonstrate that Alpha Centauri Bb - until 2016, thought to be the closest exoplanet to Earth, and also the lowest minimum-mass exoplanet around a Sun-like star - was, in fact, an astrophysical false positive. Next, I use the framework to re-characterise the well-studied Kepler-10 system, thereby resolving a mystery surrounding the mass of planet Kepler-10c. I also use the framework to help discover or characterise various exoplanets. Finally, the activity modelling framework aside, I also present in outline form a few promising applications of GPs in the context of modelling stellar signals and studying exoplanets, viz. GPs for (i) enhanced characterisation of stellar rotation; (ii) generating realistic synthetic observations, and modelling in a systematic way the effects of an observing window function; and (iii) ultra-precise extraction of RV shifts directly from observed spectra, without requiring template cross-correlation.</p

Gaussian process tools for modelling stellar signals and studying exoplanets

The discovery of exoplanets represents one of the greatest scientific revolutions in history, and exoplanetary science has rapidly become uniquely positioned to address profound questions about the origins of life, and about humanity's place (and future) in the cosmos. Since the discovery of the first exoplanet over two decades ago, the radial velocity (RV) method has been one of the most productive techniques for discovering new planets. It has also become indispensable for characterising exoplanets detected via other techniques, notably transit photometry. Unfortunately, signals intrinsic to stars themselves - especially magnetic activity signals - can induce RV variations that can drown out or even mimic planetary signals. Modelling and thus mitigating these signals is notoriously difficult, which represents a major obstacle to using next-generation instruments to detect lower mass planets, planets with longer periods, and planets around more magnetically-active stars. Enter Gaussian processes (GPs), which have a number of features that make them very well suited to the joint modelling of stochastic activity processes and dynamical (e.g. planetary) signals. In this thesis, I leverage GPs to enable the study of smaller planets around a wider variety of stars than has previously been possible. In particular, I develop a principled and sophisticated Bayesian framework, based on GPs, for modelling RV time series jointly with ancillary activity-sensitive proxies, thus allowing activity signals to be constrained and disentangled from genuine planetary signals. I show that my framework succeeds even in cases where existing techniques would fail to detect planets, e.g. the case of a weak planetary signal with period identical to its host star's rotation period. In a first application of the framework, I demonstrate that Alpha Centauri Bb - until 2016, thought to be the closest exoplanet to Earth, and also the lowest minimum-mass exoplanet around a Sun-like star - was, in fact, an astrophysical false positive. Next, I use the framework to re-characterise the well-studied Kepler-10 system, thereby resolving a mystery surrounding the mass of planet Kepler-10c. I also use the framework to help discover or characterise various exoplanets. Finally, the activity modelling framework aside, I also present in outline form a few promising applications of GPs in the context of modelling stellar signals and studying exoplanets, viz. GPs for (i) enhanced characterisation of stellar rotation; (ii) generating realistic synthetic observations, and modelling in a systematic way the effects of an observing window function; and (iii) ultra-precise extraction of RV shifts directly from observed spectra, without requiring template cross-correlation.</p

The EXPRES Stellar Signals Project II. State of the Field in Disentangling Photospheric Velocities

Abstract: Measured spectral shifts due to intrinsic stellar variability (e.g., pulsations, granulation) and activity (e.g., spots, plages) are the largest source of error for extreme-precision radial-velocity (EPRV) exoplanet detection. Several methods are designed to disentangle stellar signals from true center-of-mass shifts due to planets. The Extreme-precision Spectrograph (EXPRES) Stellar Signals Project (ESSP) presents a self-consistent comparison of 22 different methods tested on the same extreme-precision spectroscopic data from EXPRES. Methods derived new activity indicators, constructed models for mapping an indicator to the needed radial-velocity (RV) correction, or separated out shape- and shift-driven RV components. Since no ground truth is known when using real data, relative method performance is assessed using the total and nightly scatter of returned RVs and agreement between the results of different methods. Nearly all submitted methods return a lower RV rms than classic linear decorrelation, but no method is yet consistently reducing the RV rms to sub-meter-per-second levels. There is a concerning lack of agreement between the RVs returned by different methods. These results suggest that continued progress in this field necessitates increased interpretability of methods, high-cadence data to capture stellar signals at all timescales, and continued tests like the ESSP using consistent data sets with more advanced metrics for method performance. Future comparisons should make use of various well-characterized data sets—such as solar data or data with known injected planetary and/or stellar signals—to better understand method performance and whether planetary signals are preserved