Assessing the performance of filtering algorithms for Earth-analogues observed with PLATO

openContext: Detecting Earth-like planets presents a formidable challenge, primarily due to the influence of stellar activity, granulation and systematic errors on transit signals observed through photometric techniques. Stellar phenomena, including flares, spots, and convection, as well as the instrumental errors introduce complexities that obscure the identification of genuine planetary transits. Goal: This thesis aims to assess and compare the effectiveness of various filtering algorithms, with a specific focus on detecting Earth-like planets within the Habitable Zone, with orbital periods extending up to 2 years. The studied algorithms include the one developed by the research groups specifically to detrend the light curves from the stellar activity: Young Stars Detrending (YSD), as well as the all-purpose algorithms: biweight and Huber spline methods employed with 3 different window lengths (0.7, 1.4 and 2.0 days). The results hold particular relevance for the upcoming PLATO mission, as synthetic light curves were generated using the PLATO Solar-like Light-curve Simulator (PSLS). A series of injection-retrieval tests were conducted on these synthetic light curves to evaluate the performance of the selected filtering algorithms. Results: The biweight method and YSD Lowess regression emerge as the most effective algorithms for conducting a blind search for Earth-analogous planets. However, the precise retrieval of planetary parameters from the recovered transit signals remains challenging, as filtering algorithms distort the original signal. For the P1 sample representing the target stars during the initial two years of the mission, these algorithms fail to recover the planetary signal when applied to F5 spectral type stars. This is primarily due to the larger radii of such stars, which complicates detection by extending the period duration and reducing the planet-to-star radius ratio.Context: Detecting Earth-like planets presents a formidable challenge, primarily due to the influence of stellar activity, granulation and systematic errors on transit signals observed through photometric techniques. Stellar phenomena, including flares, spots, and convection, as well as the instrumental errors introduce complexities that obscure the identification of genuine planetary transits. Goal: This thesis aims to assess and compare the effectiveness of various filtering algorithms, with a specific focus on detecting Earth-like planets within the Habitable Zone, with orbital periods extending up to 2 years. The studied algorithms include the one developed by the research groups specifically to detrend the light curves from the stellar activity: Young Stars Detrending (YSD), as well as the all-purpose algorithms: biweight and Huber spline methods employed with 3 different window lengths (0.7, 1.4 and 2.0 days). The results hold particular relevance for the upcoming PLATO mission, as synthetic light curves were generated using the PLATO Solar-like Light-curve Simulator (PSLS). A series of injection-retrieval tests were conducted on these synthetic light curves to evaluate the performance of the selected filtering algorithms. Results: The biweight method and YSD Lowess regression emerge as the most effective algorithms for conducting a blind search for Earth-analogous planets. However, the precise retrieval of planetary parameters from the recovered transit signals remains challenging, as filtering algorithms distort the original signal. For the P1 sample representing the target stars during the initial two years of the mission, these algorithms fail to recover the planetary signal when applied to F5 spectral type stars. This is primarily due to the larger radii of such stars, which complicates detection by extending the period duration and reducing the planet-to-star radius ratio

Small businesses and the use of a market information system: an experimental approach

The ongoing digital revolution is redefining not only certain industries but also the wider society and economy. One of the major promises of the digital transformation for businesses is the increased capability of evidence-based decision-making, thus increasing the effectiveness of decisions and reducing associated costs. Data is one of the most important business assets, and the ability to incorporate it into decision-making is an essential ingredient for success. However, small businesses are inherently at a disadvantage due to their scarce resources and informal, often intuitive, management style. Not only do they lack strategic management capability and processes that facilitate evidence-based decision making but they also struggle with adopting and using information technology that is a necessary component of this. Nonetheless, they are the backbone of the economy and their survival is key for preserving thousands of jobs and the healthy functioning of the fabric of society. This study investigates this general problem in the specific context of small food and drink producers supplying a major UK supermarket. The focus is on marketing decision-making and the use of a custom-built market information system. A behavioural lens was applied to the design of a theory-based intervention to increase system use. Environmental restructuring, which involved a change to the data presentation format, was identified as a viable intervention with a scope to make the system more adjusted to the specific context of this study. Two experiments were conducted to test the effectiveness of the intervention. First, a laboratory experiment with 154 students tested the impact of different data presentation formats on decision-making performance. Second, a 9-month long field experiment with 113 small food producers built on the findings from the laboratory experiment and investigated the scope for the change in data presentation format to influence actual system use behaviour. The results of this study make a number of contributions to theory, method and practice. Broadly, the study demonstrated how behavioural analysis combined with design science and experimental methods can deliver impactful interventions amongst small businesses. Specifically, it revealed the causal effect between the data presentation format and actual system use behaviour. The importance of incorporating contextually relevant variables is also highlighted. Methodologically, the study highlighted the shortcomings in previous studies treating system use as a dichotomous variable and the reliance on reported usage instead of objective measures. Finally, the study resulted in an improved version of the market information system, which is now used by over 120 small food businesses to inform their marketing decisions. In this way this study has improved the usage of invaluable market information, which will help small businesses to become more competitive and better prepared for the digital revolution

Overweight People Have Low Levels of Implicit Weight Bias, but Overweight Nations Have High Levels of Implicit Weight Bias

Although a greater degree of personal obesity is associated with weaker negativity toward overweight people on both explicit (i.e., self-report) and implicit (i.e., indirect behavioral) measures, overweight people still prefer thin people on average. We investigated whether the national and cultural context - particularly the national prevalence of obesity predicts attitudes toward overweight people independent of personal identity and weight status. Data were collected from a total sample of 338,121 citizens from 71 nations in 22 different languages on the Project Implicit website (https://implicit.harvard.edu/) between May 2006 and October 2010. We investigated the relationship of the explicit and implicit weight bias with the obesity both at the individual (i.e., across individuals) and national (i.e., across nations) level. Explicit weight bias was assessed with self-reported preference between overweight and thin people; implicit weight bias was measured with the Implicit Association Test (IAT). The national estimates of explicit and implicit weight bias were obtained by averaging the individual scores for each nation. Obesity at the individual level was defined as Body Mass Index (BMI) scores, whereas obesity at the national level was defined as three national weight indicators (national BMI, national percentage of overweight and underweight people) obtained from publicly available databases. Across individuals, greater degree of obesity was associated with weaker implicit negativity toward overweight people compared to thin people. Across nations, in contrast, a greater degree of national obesity was associated with stronger implicit negativity toward overweight people compared to thin people. This result indicates a different relationship between obesity and implicit weight bias at the individual and national levels

MOCCA-SURVEY data base II – Properties of intermediate mass black holes escaping from star clusters

In this work, we investigate properties of intermediate-mass black holes (IMBHs) that escape from star clusters due to dynamical interactions. The studied models were simulated as part of the preliminary second survey carried out using the MOCCA code (MOCCA-SURVEY Database II), which is based on the Monte Carlo N-body method and does not include gravitational wave recoil kick prescriptions of the binary black hole merger product. We have found that IMBHs are more likely to be formed and ejected in models where both initial central density and central escape velocities have high values. Most of our studied objects escape in a binary with another black hole (BH) as their companion and have masses between 100 and 140 M⊙⁠. Escaping IMBHs tend to build-up mass most effectively through repeated mergers in a binary with BHs due to gravitational wave emission. Binaries play a key role in their ejection from the system as they allow these massive objects to gather energy needed for escape. The binaries in which IMBHs escape tend to have very high binding energy at the time of escape and the last interaction is strong but does not involve a massive intruder. These IMBHs gain energy needed to escape the cluster gradually in successive dynamical interactions. We present specific examples of the history of IMBH formation and escape from star cluster models. We also discuss the observational implications of our findings as well as the potential influence of the gravitational wave recoil kicks on the process

Overweight People Have Low Levels of Implicit Weight Bias, but Overweight Nations Have High Levels of Implicit Weight Bias

Although a greater degree of personal obesity is associated with weaker negativity toward overweight people on both explicit (i.e., self-report) and implicit (i.e., indirect behavioral) measures, overweight people still prefer thin people on average. We investigated whether the national and cultural context \u2013 particularly the national prevalence of obesity \u2013 predicts attitudes toward overweight people independent of personal identity and weight status. Data were collected from a total sample of 338,121 citizens from 71 nations in 22 different languages on the Project Implicit website (https://implicit.harvard.edu/) between May 2006 and October 2010. We investigated the relationship of the explicit and implicit weight bias with the obesity both at the individual (i.e., across individuals) and national (i.e., across nations) level. Explicit weight bias was assessed with self-reported preference between overweight and thin people; implicit weight bias was measured with the Implicit Association Test (IAT). The national estimates of explicit and implicit weight bias were obtained by averaging the individual scores for each nation. Obesity at the individual level was defined as Body Mass Index (BMI) scores, whereas obesity at the national level was defined as three national weight indicators (national BMI, national percentage of overweight and underweight people) obtained from publicly available databases. Across individuals, greater degree of obesity was associated with weaker implicit negativity toward overweight people compared to thin people. Across nations, in contrast, a greater degree of national obesity was associated with stronger implicit negativity toward overweight people compared to thin people. This result indicates a different relationship between obesity and implicit weight bias at the individual and national levels

National differences in gender–science stereotypes predict national sex differences in science and math achievement

About 70% of more than half a million Implicit Association Tests completed by citizens of 34 countries revealed expected implicit stereotypes associating science with males more than with females. We discovered that nation-level implicit stereotypes predicted nation-level sex differences in 8th-grade science and mathematics achievement. Self-reported stereotypes did not provide additional predictive validity of the achievement gap. We suggest that implicit stereotypes and sex differences in science participation and performance are mutually reinforcing, contributing to the persistent gender gap in science engagement

National differences in gender–science stereotypes predict national sex differences in science and math achievement

About 70% of more than half a million Implicit Association Tests completed by citizens of 34 countries revealed expected implicit stereotypes associating science with males more than with females. We discovered that nation-level implicit stereotypes predicted nation-level sex differences in 8th-grade science and mathematics achievement. Self-reported stereotypes did not provide additional predictive validity of the achievement gap. We suggest that implicit stereotypes and sex differences in science participation and performance are mutually reinforcing, contributing to the persistent gender gap in science engagement

National differences in gender-science stereotypes predict national sex differences in science and math achievement

About 70% of more than half a million Implicit Association Tests completed by citizens of 34 countries revealed expected implicit stereotypes associating science with males more than with females. We discovered that nation-level implicit stereotypes predicted nation-level sex differences in 8th-grade science and mathematics achievement. Self-reported stereotypes did not provide additional predictive validity of the achievement gap. We suggest that implicit stereotypes and sex differences in science participation and performance are mutually reinforcing, contributing to the persistent gender gap in science engagement.Psycholog

Scatter plots of relations of implicit (IAT) and explicit weight bias with BMI at the individual level.

<p>Note. Each point in the plots represents the average preference of participants as a function of their BMI. The weight bias scores ranges from +2 to -2 for the IAT and from +3 to -3 for the explicit, with 0 indicating no relative preference between thin people over overweight people. More positive scores indicate a preference for thin people over overweight people, while more negative scores indicate a preference for overweight people over thin people. Vertical bars signify standard error. Data for participants with BMI greater than 60 were not included in the plot (0.15%). The regression line was computed on the original and not on the average data.</p

Scatter plots of relations of implicit (IAT) and explicit weight bias as a function of three weight indicators (BMI, percentage of overweight and underweight people) at the national level.

<p>Note. The weight bias scores ranges from +2 to -2 for the IAT and from +3 to -3 for the explicit, with 0 indicating no relative preference between thin people over overweight people. More positive scores indicate a preference for thin people over overweight people, while more negative scores indicate a preference for overweight people over thin people.</p