Photometric Observations of the Eta Carinae 2009.0 Spectroscopic Event

We have observed Eta Carinae over 34 nights between 4th January 2009 and 27th March 2009 covering the estimated timeframe for a predicted spectroscopic event related to a suspected binary system concealed within the homunculus nebula. A photometric minimum feature was confirmed to be periodic and comparison to a previous event indicated that the period to within our error at 2022.6 +/-1.0 d. Using the E-region standard star system, the apparent V magnitudes determined for the local comparison stars were HD303308 8.14+/-0.02, HD 93205 7.77 +/-0.03 and HD93162 8.22 +/-0.05. The latter star was found to be dimmer than previously reported.Comment: 5 pages,4 figures, 1 tabl

Accretion onto the Companion of Eta Carinae During the Spectroscopic Event: III. the He II 4686 Line

We continue to explore the accretion model of the massive binary system eta Carinae by studying the anomalously high He II 4686 line. The line appears just before periastron and disappears immediately thereafter. Based on the He II 4686 line emission from O-stars and their modeling in the literature, we postulate that the He II 4686 line comes from the acceleration zone of the secondary stellar wind. We attribute the large increase in the line intensity to a slight increase in the density of the secondary stellar wind in its acceleration zone. The increase in density could be due to the ionization and subsequent deceleration of the wind by the enhanced X-ray emission arising from the shocked secondary wind further downstream or to accretion of the primary stellar wind. Accretion around the secondary equatorial plane gives rise to collimation of the secondary wind, which increases its density, hence enhancing the He II 4686 emission line. In contrast with previous explanations, the presently proposed model does not require a prohibitively high X-ray flux to directly photoionize the He.Comment: ApJ, in pres

Accretion onto the Companion of Eta Carinae During the Spectroscopic Event. IV. the Disappearance of Highly Ionized Lines

We show that the rapid and large decrease in the intensity of high-ionization emission lines from the Eta Carinae massive binary system can be explained by the accretion model. These emission lines are emitted by material in the nebula around the binary system that is being ionized by radiation from the hot secondary star. The emission lines suffer three months long deep fading every 5.54 year, assumed to be the orbital period of the binary system. In the accretion model, for ~70 day the less massive secondary star is accreting mass from the primary wind instead of blowing its fast wind. The accretion event has two effects that substantially reduce the high-energy ionizing radiation flux from the secondary star. (1) The accreted mass absorbs a larger fraction of the ionizing flux. (2) The accreted mass forms a temporarily blanked around the secondary star that increases its effective radius, hence lowering its effective temperature and the flux of high energy photons. This explanation is compatible with the fading of the emission lines at the same time the X-ray is declining to its minimum, and with the fading being less pronounced in the polar directions.Comment: ApJ, in pres

On the photometric variability of blue supergiants in NGC 300 and its impact on the Flux-weighted Gravity-Luminosity Relationship

We present a study of the photometric variability of spectroscopically confirmed supergiants in NGC 300, comprising 28 epochs extending over a period of five months. We find 15 clearly photometrically variable blue supergiants in a sample of nearly 70 such stars, showing maximum light amplitudes ranging from 0.08 to 0.23 magnitudes in the V band, and one variable red supergiant. We show their light curves, and determine semi-periods for two A2 Ia stars. Assuming that the observed changes correspond to similar variations in the bolometric luminosity, we test for the influence of this variability on the Flux-weighted Gravity--Luminosity Relationship and find a negligible effect, showing that the calibration of this relationship, which has the potential to measure extragalactic distances at the Cepheid accuracy level, is not affected by the stellar photometric variability in any significant way.Comment: 9 pages, 3 figures, 3 tables. Accepted for publication in the Astrophysical Journa

The Purple Haze of Eta Carinae: Binary-Induced Variability?

Asymmetric variability in ultraviolet images of the Homunculus obtained with the Advanced Camera for Surveys/High Resolution Camera on the Hubble Space Telescope suggests that Eta Carinae is indeed a binary system. Images obtained before, during, and after the recent ``spectroscopic event'' in 2003.5 show alternating patterns of bright spots and shadows on opposite sides of the star before and after the event, providing a strong geometric argument for an azimuthally-evolving, asymmetric UV radiation field as one might predict in some binary models. The simplest interpretation of these UV images, where excess UV escapes from the secondary star in the direction away from the primary, places the major axis of the eccentric orbit roughly perpendicular to our line of sight, sharing the same equatorial plane as the Homunculus, and with apastron for the hot secondary star oriented toward the southwest of the primary. However, other orbital orientations may be allowed with more complicated geometries. Selective UV illumination of the wind and ejecta may be partly responsible for line profile variations seen in spectra. The brightness asymmetries cannot be explained plausibly with delays due to light travel time alone, so a single-star model would require a seriously asymmetric shell ejection.Comment: 8 pages, fig 1 in color, accepted by ApJ Letter

Using Artificial Intelligence to Predict Intracranial Hypertension in Patients After Traumatic Brain Injury:A Systematic Review

Intracranial hypertension (IH) is a key driver of secondary brain injury in patients with traumatic brain injury. Lowering intracranial pressure (ICP) as soon as IH occurs is important, but a preemptive approach would be more beneficial. We systematically reviewed the artificial intelligence (AI) models, variables, performances, risks of bias, and clinical machine learning (ML) readiness levels of IH prediction models using AI. We conducted a systematic search until 12-03-2023 in three databases. Only studies predicting IH or ICP in patients with traumatic brain injury with a validation of the AI model were included. We extracted type of AI model, prediction variables, model performance, validation type, and prediction window length. Risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool, and we determined the clinical ML readiness level. Eleven out of 399 nonduplicate publications were included. A gaussian processes model using ICP and mean arterial pressure was most common. The maximum reported area under the receiver operating characteristic curve was 0.94. Four studies conducted external validation, and one study a prospective clinical validation. The prediction window length preceding IH varied between 30 and 60 min. Most studies (73%) had high risk of bias. The highest clinical ML readiness level was 6 of 9, indicating “real-time model testing” stage in one study. Several IH prediction models using AI performed well, were externally validated, and appeared ready to be tested in the clinical workflow (clinical ML readiness level 5 of 9). A Gaussian processes model was most used, and ICP and mean arterial pressure were frequently used variables. However, most studies showed a high risk of bias. Our findings may help position AI for IH prediction on the path to ultimate clinical integration and thereby guide researchers plan and design future studies.</p

Using Artificial Intelligence to Predict Intracranial Hypertension in Patients After Traumatic Brain Injury:A Systematic Review

Intracranial hypertension (IH) is a key driver of secondary brain injury in patients with traumatic brain injury. Lowering intracranial pressure (ICP) as soon as IH occurs is important, but a preemptive approach would be more beneficial. We systematically reviewed the artificial intelligence (AI) models, variables, performances, risks of bias, and clinical machine learning (ML) readiness levels of IH prediction models using AI. We conducted a systematic search until 12-03-2023 in three databases. Only studies predicting IH or ICP in patients with traumatic brain injury with a validation of the AI model were included. We extracted type of AI model, prediction variables, model performance, validation type, and prediction window length. Risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool, and we determined the clinical ML readiness level. Eleven out of 399 nonduplicate publications were included. A gaussian processes model using ICP and mean arterial pressure was most common. The maximum reported area under the receiver operating characteristic curve was 0.94. Four studies conducted external validation, and one study a prospective clinical validation. The prediction window length preceding IH varied between 30 and 60 min. Most studies (73%) had high risk of bias. The highest clinical ML readiness level was 6 of 9, indicating “real-time model testing” stage in one study. Several IH prediction models using AI performed well, were externally validated, and appeared ready to be tested in the clinical workflow (clinical ML readiness level 5 of 9). A Gaussian processes model was most used, and ICP and mean arterial pressure were frequently used variables. However, most studies showed a high risk of bias. Our findings may help position AI for IH prediction on the path to ultimate clinical integration and thereby guide researchers plan and design future studies.</p