X-ray Polarization of the Eastern Lobe of SS 433

How astrophysical systems translate the kinetic energy of bulk motion into the acceleration of particles to very high energies is a pressing question. SS 433 is a microquasar that emits TeV gamma-rays indicating the presence of high-energy particles. A region of hard X-ray emission in the eastern lobe of SS 433 was recently identified as an acceleration site. We observed this region with the Imaging X-ray Polarimetry Explorer and measured a polarization degree in the range 38% to 77%. The high polarization degree indicates the magnetic field has a well ordered component if the X-rays are due to synchrotron emission. The polarization angle is in the range -12 to +10 degrees (east of north) which indicates that the magnetic field is parallel to the jet. Magnetic fields parallel to the bulk flow have also been found in supernova remnants and the jets of powerful radio galaxies. This may be caused by interaction of the flow with the ambient medium.Comment: 8 pages, accepted in the Astrophysical Journal Letter

First X-ray polarization measurement confirms the low black-hole spin in LMC X-3

X-ray polarization is a powerful tool to investigate the geometry of accreting material around black holes, allowing independent measurements of the black hole spin and orientation of the innermost parts of the accretion disk. We perform the X-ray spectro-polarimetric analysis of an X-ray binary system in the Large Magellanic Cloud, LMC X-3, that hosts a stellar-mass black hole, known to be persistently accreting since its discovery. We report the first detection of the X-ray polarization in LMC X-3 with the Imaging X-ray Polarimetry Explorer, and find the average polarization degree of 3.2% +- 0.6% and a constant polarization angle -42 deg +- 6 deg over the 2-8 keV range. Using accompanying spectroscopic observations by NICER, NuSTAR, and the Neil Gehrels Swift observatories, we confirm previous measurements of the black hole spin via the X-ray continuum method, a ~ 0.2. From polarization analysis only, we found consistent results with low black-hole spin, with an upper limit of a < 0.7 at a 90% confidence level. A slight increase of the polarization degree with energy, similar to other black-hole X-ray binaries in the soft state, is suggested from the data but with a low statistical significance.Comment: 14 pages, 8 figures, submitted to Ap

The first X-ray polarimetric observation of the black hole binary LMC X-1

We report on an X-ray polarimetric observation of the high-mass X-ray binary LMC X-1 in the high/soft state, obtained by the Imaging X-ray Polarimetry Explorer (IXPE) in October 2022. The measured polarization is below the minimum detectable polarization of 1.1 per cent (at the 99 per cent confidence level). Simultaneously, the source was observed with the NICER, NuSTAR and SRG/ART-XC instruments, which enabled spectral decomposition into a dominant thermal component and a Comptonized one. The low 2-8 keV polarization of the source did not allow for strong constraints on the black-hole spin and inclination of the accretion disc. However, if the orbital inclination of about 36 degrees is assumed, then the upper limit is consistent with predictions for pure thermal emission from geometrically thin and optically thick discs. Assuming the polarization degree of the Comptonization component to be 0, 4, or 10 per cent, and oriented perpendicular to the polarization of the disc emission (in turn assumed to be perpendicular to the large scale ionization cone orientation detected in the optical band), an upper limit to the polarization of the disc emission of 1.0, 0.9 or 0.9 per cent, respectively, is found (at the 99 per cent confidence level).Comment: 12 pages, 9 figures, 4 tables. Accepted for publication in MNRA

Tracking the X-ray Polarization of the Black Hole Transient Swift J1727.8-1613 during a State Transition

We report on a campaign on the bright black hole X-ray binary Swift J1727.8$-$1613 centered around five observations by the Imaging X-ray Polarimetry Explorer (IXPE). This is the first time it has been possible to trace the evolution of the X-ray polarization of a black hole X-ray binary across a hard to soft state transition. The 2--8 keV polarization degree slowly decreased from $\sim$4\% to $\sim$3\% across the five observations, but remained in the North-South direction throughout. Using the Australia Telescope Compact Array (ATCA), we measure the intrinsic 7.25 GHz radio polarization to align in the same direction. Assuming the radio polarization aligns with the jet direction (which can be tested in the future with resolved jet images), this implies that the X-ray corona is extended in the disk plane, rather than along the jet axis, for the entire hard intermediate state. This in turn implies that the long ($\gtrsim$10 ms) soft lags that we measure with the Neutron star Interior Composition ExploreR (NICER) are dominated by processes other than pure light-crossing delays. Moreover, we find that the evolution of the soft lag amplitude with spectral state differs from the common trend seen for other sources, implying that Swift J1727.8$-$1613 is a member of a hitherto under-sampled sub-population.Comment: Submitted to ApJ. 20 pages, 8 figure

Recreating the Relationship between Subjective Wellbeing and Personality Using Machine Learning: An Investigation into Facebook Online Behaviours

The twenty-first century has delivered technological advances that allow researchers to utilise social media to predict personal traits and psychological constructs. This article aims to further our understanding of the relationship between subjective wellbeing (SWB) and the Five Factor Model (FFM) of personality by attempting to replicate the relationship using machine learning prediction models. Data from the myPersonality Project was used; with observed SWB scores derived from the Satisfaction With Life Scale (SWLS) and Five Factor Model (FFM) personality profiles generated using responses on the 100-item IPIP proxy of the NEO-PI-R. After data cleaning, FFM personality traits and SWB scores were predicted by reducing Facebook Likes into 50 dimensions using SVD and then running the data through six multiple regressions (fitting the model via least squares and splitting the data via k-folds validation) with the Likes dimensions as predictors and each of the FFM traits and the SWB score as response variables. Standard multiple regression analyses were conducted for the observed and machine learning predicted variables to compare the relationships in the context of previous literature. The results revealed that in the observed model, high SWB was predicted by high extraversion, conscientiousness, and agreeableness, and low openness to experience and neuroticism as per previous research. For the machine learning model, high SWB was predicted by high extraversion, openness to experience, conscientiousness, and agreeableness, and low neuroticism. The relationships between SWB and extraversion, neuroticism, and conscientiousness were successfully replicated in the machine learning model. Openness to experience changed direction in its relationship with SWB from the observed to machine learning-derived variables due to failure to accurately recreate the variable, and agreeableness was multicollinear with SWB in the machine learning model due to the unknowing use of identical digital behaviours to replicate each construct. Implications of the results and directions for future research are discussed

Australian Youth Resilience and Help-Seeking during COVID-19: A Cross-Sectional Study

The COVID-19 pandemic has seriously impacted youth mental health. Their resilience, defined as the ability to respond to adversity, has also been impaired. Help-seeking refers to the activity of addressing oneself to others when facing trouble. The objective of this study was to understand the levels of youth resilience and help-seeking during COVID-19 in 2021. Data were collected online from 181 Australian adolescents aged 12&ndash;17 years. The General Help-Seeking Questionnaire, the Actual Help-Seeking Questionnaire, and the Resilience Scale were used. Mean and frequency analysis and independent samples t-tests were performed. The Pearson correlation coefficient was calculated. Resilience was in the low range (mean = 66.56, SD 15.74) and associated with no help-seeking. For a personal problem and suicidal ideation, participants were most likely to contact a mental health professional, with means of 4.97 (SD 1.75) and 4.88 (SD 1.97), respectively. The majority did not seek help (n = 47) for challenges with anxiety or depression. This study corroborates previous findings on limited help-seeking in youth because of self-reliance and low confidence in others. Resilience decreased during COVID-19 in parallel with help-seeking. Strategies aiming to increase resilience and help-seeking, such as school-based programs, are needed given their decrease in Australian youths due to the COVID-19 pandemic

Recreating the Relationship between Subjective Wellbeing and Personality Using Machine Learning: An Investigation into Facebook Online Behaviours

The twenty-first century has delivered technological advances that allow researchers to utilise social media to predict personal traits and psychological constructs. This article aims to further our understanding of the relationship between subjective wellbeing (SWB) and the Five Factor Model (FFM) of personality by attempting to replicate the relationship using machine learning prediction models. Data from the myPersonality Project was used; with observed SWB scores derived from the Satisfaction With Life Scale (SWLS) and Five Factor Model (FFM) personality profiles generated using responses on the 100-item IPIP proxy of the NEO-PI-R. After data cleaning, FFM personality traits and SWB scores were predicted by reducing Facebook Likes into 50 dimensions using SVD and then running the data through six multiple regressions (fitting the model via least squares and splitting the data via k-folds validation) with the Likes dimensions as predictors and each of the FFM traits and the SWB score as response variables. Standard multiple regression analyses were conducted for the observed and machine learning predicted variables to compare the relationships in the context of previous literature. The results revealed that in the observed model, high SWB was predicted by high extraversion, conscientiousness, and agreeableness, and low openness to experience and neuroticism as per previous research. For the machine learning model, high SWB was predicted by high extraversion, openness to experience, conscientiousness, and agreeableness, and low neuroticism. The relationships between SWB and extraversion, neuroticism, and conscientiousness were successfully replicated in the machine learning model. Openness to experience changed direction in its relationship with SWB from the observed to machine learning-derived variables due to failure to accurately recreate the variable, and agreeableness was multicollinear with SWB in the machine learning model due to the unknowing use of identical digital behaviours to replicate each construct. Implications of the results and directions for future research are discussed

Australian Youth Resilience and Help-Seeking during COVID-19: A Cross-Sectional Study

The COVID-19 pandemic has seriously impacted youth mental health. Their resilience, defined as the ability to respond to adversity, has also been impaired. Help-seeking refers to the activity of addressing oneself to others when facing trouble. The objective of this study was to understand the levels of youth resilience and help-seeking during COVID-19 in 2021. Data were collected online from 181 Australian adolescents aged 12–17 years. The General Help-Seeking Questionnaire, the Actual Help-Seeking Questionnaire, and the Resilience Scale were used. Mean and frequency analysis and independent samples t-tests were performed. The Pearson correlation coefficient was calculated. Resilience was in the low range (mean = 66.56, SD 15.74) and associated with no help-seeking. For a personal problem and suicidal ideation, participants were most likely to contact a mental health professional, with means of 4.97 (SD 1.75) and 4.88 (SD 1.97), respectively. The majority did not seek help (n = 47) for challenges with anxiety or depression. This study corroborates previous findings on limited help-seeking in youth because of self-reliance and low confidence in others. Resilience decreased during COVID-19 in parallel with help-seeking. Strategies aiming to increase resilience and help-seeking, such as school-based programs, are needed given their decrease in Australian youths due to the COVID-19 pandemic