Dynamics of cold circumstellar gas in debris disks

Mounting observational evidence indicates that cold circumstellar gas is present in debris disk systems. This work focuses on various dynamical processes that debris-disk gas may undergo. We review five mechanisms that can transport angular momentum and their applications to debris disks. These include molecular viscosity, hydrodynamic turbulence, magnetohydrodynamic turbulence, magnetized disk winds, and laminar magnetic stress. We find that molecular viscosity can result in $\alpha$ as high as $\lesssim 0.1$ for sufficiently low densities, while the Rossby wave instability is a possible source of hydrodynamic turbulence and structure formation. We argue that the vertical shear instability is unlikely due to the long cooling times. The onset of the magnetorotational instability (MRI) is dichotomous: for low density disks the MRI can be excited at the midplane, while for high mass disks it may only be operating at $z>2-3H$, if at all. The MHD wind and laminar magnetic stress mechanisms rely on the configuration and strength of any background large-scale magnetic field, the existence of which is uncertain and possibly unlikely. We conclude that the dominant mechanism and its efficiency in transporting angular momentum varies from one system to the other, depending especially closely on the gas density. More detailed analyses shall be performed in the future focusing on representative, nearby debris disks.Comment: 16 pages, 9 figures, submitted to MNRAS and revise

Survey of planetesimal belts with ALMA: gas detected around the Sun-like star HD 129590

Gas detection around main sequence stars is becoming more common with around 20 systems showing the presence of CO. However, more detections are needed, especially around later spectral type stars to better understand the origin of this gas and refine our models. To do so, we carried out a survey of 10 stars with predicted high likelihoods of secondary CO detection using ALMA in band 6. We looked for continuum emission of mm-dust as well as gas emission (CO and CN transitions). The continuum emission was detected in 9/10 systems for which we derived the discs' dust masses and geometrical properties, providing the first mm-wave detection of the disc around HD 106906, the first mm-wave radius for HD 114082, 117214, HD 15745, HD 191089 and the first radius at all for HD 121191. A crucial finding of our paper is that we detect CO for the first time around the young 10-16 Myr old G1V star HD 129590, similar to our early Sun. The gas seems colocated with its planetesimal belt and its total mass is likely between $2-10 \times 10^{-5}$ M$_\oplus$. This first gas detection around a G-type main-sequence star raises questions as to whether gas may have been released in the Solar System as well in its youth, which could potentially have affected planet formation. We also detected CO gas around HD 121191 at a higher S/N than previously and find that the CO lies much closer-in than the planetesimals in the system, which could be evidence for the previously suspected CO viscous spreading owing to shielding preventing its photodissociation. Finally, we make estimates for the CO content in planetesimals and the HCN/CO outgassing rate (from CN upper limits), which we find are below the level seen in Solar System comets in some systems.Comment: Accepted for publication in MNRAS. 22 pages, 13 figure

Secondary gas in debris discs released following the decay of long-lived radioactive nuclides, catastrophic or resurfacing collisions

Kuiper-like belts of planetesimals orbiting stars other than the Sun are most commonly detected from the thermal emission of small dust produced in collisions. Emission from gas, most notably CO, highlights the cometary nature of these planetesimals. Here we present models for the release of gas from comet-like bodies in these belts, both due to their thermophysical evolution, most notably the decay of long-lived radioactive nuclides and collisional evolution, including catastrophic and gentler resurfacing collisions. We show that the rate of gas release is not proportional to the rate of dust release, if non-catastrophic collisions or thermal evolution dominate the release of CO gas. In this case, care must be taken when inferring the composition of comets. Non-catastrophic collisions dominate the gas production at earlier times than catastrophic collisions, depending on the properties of the planetesimal belt. We highlight the importance of the thermal evolution of comets, including crucially the decay of long-lived radioactive nuclides, as a source of CO gas around young (<50Myr) planetary systems, if large (10-100s kms) planetesimals are present.Comment: Submitted to MNRAS, 16 page

Classification of Drivers' Workload Using Physiological Signals in Conditional Automation

The use of automation in cars is increasing. In future vehicles, drivers will no longer be in charge of the main driving task and may be allowed to perform a secondary task. However, they might be requested to regain control of the car if a hazardous situation occurs (i.e., conditionally automated driving). Performing a secondary task might increase drivers' mental workload and consequently decrease the takeover performance if the workload level exceeds a certain threshold. Knowledge about the driver's mental state might hence be useful for increasing safety in conditionally automated vehicles. Measuring drivers' workload continuously is essential to support the driver and hence limit the number of accidents in takeover situations. This goal can be achieved using machine learning techniques to evaluate and classify the drivers' workload in real-time. To evaluate the usefulness of physiological data as an indicator for workload in conditionally automated driving, three physiological signals from 90 subjects were collected during 25 min of automated driving in a fixed-base simulator. Half of the participants performed a verbal cognitive task to induce mental workload while the other half only had to monitor the environment of the car. Three classifiers, sensor fusion and levels of data segmentation were compared. Results show that the best model was able to successfully classify the condition of the driver with an accuracy of 95%. In some cases, the model benefited from sensors' fusion. Increasing the segmentation level (e.g., size of the time window to compute physiological indicators) increased the performance of the model for windows smaller than 4 min, but decreased for windows larger than 4 min. In conclusion, the study showed that a high level of drivers' mental workload can be accurately detected while driving in conditional automation based on 4-min recordings of respiration and skin conductance

Imaging using quantum noise properties of light

We show that it is possible to estimate the shape of an object by measuring only the fluctuations of a probing field, allowing us to expose the object to a minimal light intensity. This scheme, based on noise measurements through homodyne detection, is useful in the regime where the number of photons is low enough that direct detection with a photodiode is difficult but high enough such that photon counting is not an option. We generate a few-photon state of multi-spatial-mode vacuum-squeezed twin beams using four-wave mixing and direct one of these twin fields through a binary intensity mask whose shape is to be imaged. Exploiting either the classical fluctuations in a single beam or quantum correlations between the twin beams, we demonstrate that under some conditions quantum correlations can provide an enhancement in sensitivity when estimating the shape of the object

Relevant Physiological Indicators for Assessing Workload in Conditionally Automated Driving, Through Three-Class Classification and Regression

In future conditionally automated driving, drivers may be asked to take over control of the car while it is driving autonomously. Performing a non-driving-related task could degrade their takeover performance, which could be detected by continuous assessment of drivers' mental load. In this regard, three physiological signals from 80 subjects were collected during 1 h of conditionally automated driving in a simulator. Participants were asked to perform a non-driving cognitive task (N-back) for 90 s, 15 times during driving. The modality and difficulty of the task were experimentally manipulated. The experiment yielded a dataset of drivers' physiological indicators during the task sequences, which was used to predict drivers' workload. This was done by classifying task difficulty (three classes) and regressing participants' reported level of subjective workload after each task (on a 0–20 scale). Classification of task modality was also studied. For each task, the effect of sensor fusion and task performance were studied. The implemented pipeline consisted of a repeated cross validation approach with grid search applied to three machine learning algorithms. The results showed that three different levels of mental load could be classified with a f1-score of 0.713 using the skin conductance and respiration signals as inputs of a random forest classifier. The best regression model predicted the subjective level of workload with a mean absolute error of 3.195 using the three signals. The accuracy of the model increased with participants' task performance. However, classification of task modality (visual or auditory) was not successful. Some physiological indicators such as estimates of respiratory sinus arrhythmia, respiratory amplitude, and temporal indices of heart rate variability were found to be relevant measures of mental workload. Their use should be preferred for ongoing assessment of driver workload in automated driving

An ALMA Survey of M-dwarfs in the Beta Pictoris Moving Group with Two New Debris Disc Detections

Previous surveys in the far-infrared have found very few, if any, M-dwarf debris discs among their samples. It has been questioned whether M-dwarf discs are simply less common than earlier types, or whether the low detection rate derives from the wavelengths and sensitivities available to those studies. The highly sensitive, long wavelength Atacama Large Millimetre/submillimetre Array can shed light on the problem. This paper presents a survey of M-dwarf stars in the young and nearby Beta Pictoris Moving Group with ALMA at Band 7 (880\,$\mu$m). From the observational sample we detect two new sub-mm excesses that likely constitute unresolved debris discs around GJ\,2006\,A and AT\,Mic\,A and model distributions of the disc fractional luminosities and temperatures. From the science sample of 36 M-dwarfs including AU\,Mic we find a disc detection rate of 4/36 or 11.1$^{+7.4}_{-3.3}$\% that rises to 23.1$^{+8.3}_{-5.5}$\% when adjusted for completeness. We conclude that this detection rate is consistent with the detection rate of discs around G and K type stars and that the disc properties are also likely consistent with earlier type stars. We additionally conclude that M-dwarf stars are not less likely to host debris discs, but instead their detection requires longer wavelength and higher sensitivity observations than have previously been employed.Comment: Accepted to MNRA

A dataset on the physiological state and behavior of drivers in conditionally automated driving

This dataset contains data of 346 drivers collected during six experiments conducted in a fixed-base driving simulator. Five studies simulated conditionally automated driving (L3 SAE), and the other one simulated manual driving (L0-SAE). The dataset includes physiological data (electrocardiogram (ECG), electrodermal activity (EDA), and respiration (RESP)), driving and behavioral data (reaction time, steering wheel angle, …), performance data of non-driving-related tasks, and questionnaire responses. Among them, measures from standardized questionnaires were collected, either to control the experimental manipulation of the driver's state, or to measure constructs related to human factors and driving safety (drowsiness, mental workload, affective state, situation awareness, situational trust, user experience). In the provided dataset, some raw data have been processed, notably physiological data from which physiological indicators (or features) have been calculated. The latter can be used as input for machine learning models to predict various states (sleep deprivation, high mental workload, ...) that may be critical for driver safety. Subjective self-reported measures can also be used as ground truth to apply regression techniques. Besides that, statistical analyses can be performed using the dataset, in particular to analyze the situational awareness or the takeover quality of drivers, in different states and different driving scenarios. Overall, this dataset contributes to better understanding and consideration of the driver's state and behavior in conditionally automated driving. In addition, this dataset stimulates and inspires research in the fields of physiological/affective computing and human factors in transportation, and allows companies from the automotive industry to better design adapted human-vehicle interfaces for safe use of automated vehicles on the roads