Search

JWST has revealed diverse new populations of high-redshift ($z\sim4-11$) AGN and extreme star-forming galaxies that challenge current models. In this paper, we use rest-frame UV emission-line diagnostics to identify AGN candidates and other exceptional ionizing sources, complementing previous studies predominantly focused on broad-line AGN. In this paper, we use rest-frame UV emission-line diagnostics to identify AGN candidates and other exceptional ionizing sources, complementing previous studies predominantly focused on broad-line AGN. From a parent sample of 205 $\mathrm{z_{spec}}>3$ UNCOVER galaxies with NIRSpec/PRISM follow-up, we identify 12 C IV, He II, and C III] emitters. Leveraging the combined rest-optical and UV coverage of PRISM, we limit the emission-line model space using the sample's [O III]/H$\beta$ distribution, significantly decreasing the overlap between AGN and star-formation models in the UV diagnostics. We then find that the five He II emitters are the strongest AGN candidates, with further support from two [Ne V] detections and one X-ray detection from Chandra. We cannot robustly quantify the AGN fraction in this sample, but we note that close to 20% of $\mathrm{M_{*}>2\times10^{9}\,M_{\odot}}$ parent sample galaxies are AGN candidates. The lower-mass line emitters, which are consistent with both AGN and star-forming photoionization models, have more compact sizes and higher specific star formation rates than the parent sample. Higher-resolution and deeper data on these UV line emitters should provide much stronger constraints on the obscured AGN fraction at $z > 3$., Comment: Submitted to ApJ

We explore the physical properties of five massive quiescent galaxies at $z\sim2.5$, revealing the presence of non-negligible dust reservoirs. JWST NIRSpec observations were obtained for each target, finding no significant line emission; multiple star formation tracers independently place upper limits between $0.1-10~M_\odot / \mathrm{yr}$. Spectral energy distribution modeling with Prospector infers stellar masses between $\log_{10}[M / M_\odot] \sim 10-11$ and stellar mass-weighted ages between $1-2$ Gyr. The inferred mass-weighted effective radii ($r_{eff}\sim 0.4-1.4$ kpc) and inner $1$ kpc stellar surface densities ($\log_{10}[\Sigma / M_\odot \mathrm{kpc}^2 ]\gtrsim 9$) are typical of quiescent galaxies at $z \gtrsim 2$. The galaxies display negative color gradients (redder core and bluer outskirts); for one galaxy, this effect results from a dusty core, while for the others it may be evidence of an "inside-out" growth process. Unlike local quiescent galaxies, we identify significant reddening in these typical cosmic noon passive galaxies; all but one require $A_V \gtrsim 0.4$. This finding is in qualitative agreement with previous studies but our deep 20-band NIRCam imaging is able to significantly suppress the dust-age degeneracy and confidently determine that these galaxies are reddened. We speculate about the physical effects that may drive the decline in dust content in quiescent galaxies over cosmic time., Comment: 18 pages, 8 figures, submitted to ApJ

Most car-following models were originally developed for lane-based traffic. Over the past two decades, efforts have been made to calibrate car-following models for non-lane-based traffic. However, traffic conditions with varying vehicle dimensions, intermittent following, and multiple leaders often occur and make subjective Leader-Follower (LF) pair identification challenging. In this study, we analyze Vehicle Following (VF) behavior in traffic with a lack of lane discipline using high-resolution microscopic trajectory data collected in Chennai, India. The paper's main contributions are threefold. Firstly, three criteria are used to identify LF pairs from the driver's perspective, taking into account the intermittent following, lack of lane discipline due to consideration of lateral separation, and the presence of in-between vehicles. Second, the psycho-physical concept of the regime in the Wiedemann-99 model is leveraged to determine the traffic-dependent "influence zone" for LF identification. Third, a joint and consistent framework is proposed for identifying LF pairs and estimating VF parameters. The proposed methodology outperforms other heuristic-based LF identification methods from the literature in terms of quantitative and qualitative performance measures. The proposed approach can enable robust and more realistic LF identification and VF parameter calibration with practical applications such as LOS analysis, capacity, and travel time estimation.

While macroscopic models for single or multi-lane traffic flow are well established, these models are not applicable to the dynamics and characteristics of disordered traffic which is characterized by widely different types of vehicles and no lane discipline. We propose a first-order two-dimensional Lighthill-Whitham-Richards (LWR) model for the continuous macroscopic longitudinal and lateral dynamics of this type of traffic flow. The continuity equation is extended into two dimensions and the equation is closed by assuming a longitudinal flow-density relationship as in traditional one-dimensional models while the lateral dynamics is based on boundary repulsion and a desire of a majority of the drivers to go to less dense regions. This is equivalent to Fick's law giving rise to a lateral diffusion term. Using the proposed model, several numerical tests were conducted under different traffic scenarios representing a wide range of traffic conditions. Even for extreme initial conditions, the model's outcome turned out to be plausible and consistent with observed traffic flow dynamics. Moreover, the numerical convergence test is performed using an analytical solution for lateral steady-state conditions. The model was applied for bicycle simulation and reproduced the evolution of lateral density profile with asymmetric behavior.

Recently, a fully two-dimensional microscopic traffic flow model for lane-free vehicular traffic flow has been proposed [Physica A, 509, pp. 1-11 (2018)]. In this contribution, we generalize this model to describe any kind of human-driven directed flow including lane-based vehicular flow, lane-free mixed traffic, bicycle traffic, and pedestrian flow. The proposed intelligent-agent model (IAM) has the same philosophy as the well-known social-force model (SFM) for pedestrians but the interaction and boundary forces are based on car-following models making this model suitable for higher speeds. Depending on the underlying car-following model, the IAM includes anticipation, response to relative velocities, and accident-free driving. When adding a suitable floor field, the IAM reverts to an integrated car-following and lane-changing model with continuous lane changes. We simulate this model in several lane-based and lane-free environments in various geometries with and without obstacles. We observe that the model produces accident-free traffic flow reproducing the observed self-organisation phenomena., Comment: Accepted by Transportation Letters: the International Journal of Transportation Research

Recent experimental and empirical observations have demonstrated that stochasticity plays a critical role in car following (CF) dynamics. To reproduce the observations, quite a few stochastic CF models have been proposed. However, while calibrating the deterministic CF models is well investigated, studies on how to calibrate the stochastic models are lacking. Motivated by this fact, this paper aims to address this fundamental research gap. Firstly, the CF experiment under the same driving environment is conducted and analyzed. Based on the experimental results, we test two previous calibration methods, i.e., the method to minimize the Multiple Runs Mean (MRMean) error and the method of maximum likelihood estimation (MLE). Deficiencies of the two methods have been identified. Next, we propose a new method to minimize the Multiple Runs Minimum (MRMin) error. Calibration based on the experimental data and the synthetic data demonstrates that the new method outperforms the two previous methods. Furthermore, the mechanisms of different methods are explored from the perspective of error analysis. The analysis indicates that the new method can be regarded as a nested optimization model. The method separates the aleatoric errors caused by stochasticity from the epistemic error caused by parameters, and it is able to deal with the two kinds of errors effectively. Finally, we find that under the calibration framework of stochastic CF models, the calibrated parameter set using spacing as MoP may not always outperform that using velocity as MoP. These findings are expected to enhance the understanding of the role of stochasticity in CF dynamics where the new calibration framework for stochastic CF models is established., Comment: 34 pages, 13 figures, 17 tables

High-contrast imaging has afforded astronomers the opportunity to study light directly emitted by adolescent (tens of Myr) and ``proto" ($<$10Myr) planets still undergoing formation. Direct detection of these planets is enabled by empirical Point Spread Function (PSF) modeling and removal algorithms. The computational intensity of such algorithms, and their multiplicity of tunable input parameters, has led to the prevalence of ad-hoc optimization approaches to high-contrast imaging results. In this work, we present a new, systematic approach to optimization vetted using data of the high-contrast stellar companion HD 142527 B from the Magellan Adaptive Optics (MagAO) Giant Accreting Protoplanet Survey (GAPlanetS). More specifically, we present a grid search technique designed to explore three influential parameters of the PSF-subtraction algorithm pyKLIP -- annuli, movement, and KL modes. We consider multiple metrics for post-processed image quality in order to optimally recover at H$\alpha$ (656nm) synthetic planets injected into contemporaneous continuum (643nm) images. These metrics include: peak (single-pixel) SNR, average (multi-pixel average) SNR, 5$\sigma$ contrast, and false-positive fraction. We apply continuum-optimized KLIP reduction parameters to six H$\alpha$ direct detections of the low-mass stellar companion HD142527 B, and recover the companion at a range of separations. Relative to a single-informed, non-optimized set of KLIP parameters applied to all datasets uniformly, our multi-metric grid search optimization led to improvements in companion SNR of up to 1.2$\sigma$, with an average improvement of 0.6$\sigma$. Since many direct imaging detections lie close to the canonical 5$\sigma$ threshold, even such modest improvements may result in higher yields in future imaging surveys., Comment: 18 pages, 7 figures, Accepted to AJ

During the third all-sky survey (eRASS3), eROSITA, the soft X-ray instrument aboard Spectrum-Roentgen-Gamma, detected a new hard X-ray transient, eRASSt J040515.6-745202, in the direction of the Magellanic Bridge. We arranged follow-up observations and searched for archival data to reveal the nature of the transient. Using X-ray observations with XMM-Newton, NICER, and Swift, we investigated the temporal and spectral behaviour of the source for over about 10 days. The X-ray light curve obtained from the XMM-Newton observation with an 28 ks exposure revealed a type-I X-ray burst with a peak bolometric luminosity of at least 1.4e37 erg/s. The burst energetics are consistent with a location of the burster at the distance of the Magellanic Bridge. The relatively long exponential decay time of the burst of 70 s indicates that it ignited in a H-rich environment. The non-detection of the source during the other eROSITA surveys, twelve and six months before and six months after eRASS3, suggests that the burst was discovered during a moderate outburst which reached 2.6e36 erg/s in persistent emission. During the NICER observations, the source showed alternating flux states with the high level at a similar brightness as during the XMM-Newton observation. This behaviour is likely caused by dips as also seen during the last hour of the XMM-Newton observation. Evidence for a recurrence of the dips with a period of 21.8 hr suggests eRASSt J040515.6-745202 is a low-mass X-ray binary (LMXB) system with an accretion disk seen nearly edge on. We identify a multi-wavelength counterpart to the X-ray source in UVW1 and g, r, i, and z images obtained by the optical/UV monitor on XMM-Newton and the Dark Energy Camera at the Cerro Tololo Inter-American Observatory. (abbreviated), Comment: Accepted for publication in Astronomy & Astrophysics. 10 pages, 13 figures

Accreting protoplanets represent a window into planet formation processes. We report H{\alpha} differential imaging results from the deepest and most comprehensive accreting protoplanet survey to date, acquired with the Magellan Adaptive Optics (MagAO) system's VisAO camera. The fourteen transitional disks targeted are ideal candidates for protoplanet discovery due to their wide, heavily depleted central cavities, wealth of non-axisymmetric circumstellar disk features evocative of ongoing planet formation, and ongoing stellar accretion. To address the twin challenges of morphological complexity in the target systems and PSF instability, we develop novel approaches for frame selection and optimization of the Karhounen-Loeve Image Processing algorithm pyKLIP. We detect one new candidate protoplanet, CS Cha "c", at a separation of 75mas and a Delta mag of 5.1 and robustly recover the HD142527 B and HD100453 B low mass stellar companions across multiple epochs. Though we cannot rule out a substantial scattered light contribution to its emission, we also recover LkCa 15 b. Its presence inside of the cleared disk cavity and consistency with a forward-modeled point source suggest that it remains a viable protoplanet candidate. The protoplanet PDS 70 c was marginally recovered under our conservative general methodology. However, through targeted optimization in H-alpha} imagery, we tentatively recover PDS 70 c in three epochs and PDS 70 b in one epoch. Of the many other previously-reported companions and companion candidates around objects in the sample, we do not recover any additional robust candidates. However, lack of recovery at moderate H-alpha contrast does not rule out the presence of protoplanets at these locations, and we report limiting H-alpha contrasts in such cases., Comment: Final version, in press at AJ

We used Transiting Exoplanet Survey Satellite (TESS) data to identify 29 candidate active galactic nuclei (AGNs) through their optical variability. The high-cadence, high-precision TESS light curves present a unique opportunity for the identification of AGNs, including those not selected through other methods. Of the candidates, we found that 18 have either previously been identified as AGNs in the literature or could have been selected based on emission-line diagnostics, mid-IR colors, or X-ray luminosity. AGNs in low-mass galaxies offer a window into supermassive black hole (SMBH) and galaxy co-evolution and 8 of the 29 candidates have estimated black hole masses $\mathrm{\lesssim 10^{6} M_{\odot}}$. The low-mass galaxies NGC 4395 and NGC 4449 are two of our five "high-confidence" candidates. By applying our methodology to the entire TESS main and extended mission datasets, we expect to identify $\sim$45 more AGN candidates, of which $\sim$26 will be new and $\sim$8 will be in low-mass galaxies., Comment: 21 pages, 17 figures, 6 tables. Will be submitted to AAS journals. Comments welcome

While deep reinforcement learning (RL) has been increasingly applied in designing car-following models in the last years, this study aims at investigating the feasibility of RL-based vehicle-following for complex vehicle dynamics and strong environmental disturbances. As a use case, we developed an inland waterways vessel-following model based on realistic vessel dynamics, which considers environmental influences, such as varying stream velocity and river profile. We extracted natural vessel behavior from anonymized AIS data to formulate a reward function that reflects a realistic driving style next to comfortable and safe navigation. Aiming at high generalization capabilities, we propose an RL training environment that uses stochastic processes to model leading trajectory and river dynamics. To validate the trained model, we defined different scenarios that have not been seen in training, including realistic vessel-following on the Middle Rhine. Our model demonstrated safe and comfortable driving in all scenarios, proving excellent generalization abilities. Furthermore, traffic oscillations could effectively be dampened by deploying the trained model on a sequence of following vessels.

We analyze high-cadence data from the Transiting Exoplanet Survey Satellite (TESS) of the ambiguous nuclear transient (ANT) ASASSN-18el. The optical changing-look phenomenon in ASASSN-18el has been argued to be due to either a drastic change in the accretion rate of the existing active galactic nucleus (AGN) or the result of a tidal disruption event (TDE). Throughout the TESS observations, short-timescale stochastic variability is seen, consistent with an AGN. We are able to fit the TESS light curve with a damped-random-walk (DRW) model and recover a rest-frame variability amplitude of $\hat{\sigma} = 0.93 \pm 0.02$ mJy and a rest-frame timescale of $\tau_{DRW} = 20^{+15}_{-6}$ days. We find that the estimated $\tau_{DRW}$ for ASASSN-18el is broadly consistent with an apparent relationship between the DRW timescale and central supermassive black hole mass. The large-amplitude stochastic variability of ASASSN-18el, particularly during late stages of the flare, suggests that the origin of this ANT is likely due to extreme AGN activity rather than a TDE., Comment: Updated to reflect the accepted version in MNRAS

We propose and validate a novel car following model based on deep reinforcement learning. Our model is trained to maximize externally given reward functions for the free and car-following regimes rather than reproducing existing follower trajectories. The parameters of these reward functions such as desired speed, time gap, or accelerations resemble that of traditional models such as the Intelligent Driver Model (IDM) and allow for explicitly implementing different driving styles. Moreover, they partially lift the black-box nature of conventional neural network models. The model is trained on leading speed profiles governed by a truncated Ornstein-Uhlenbeck process reflecting a realistic leader's kinematics. This allows for arbitrary driving situations and an infinite supply of training data. For various parameterizations of the reward functions, and for a wide variety of artificial and real leader data, the model turned out to be unconditionally string stable, comfortable, and crash-free. String stability has been tested with a platoon of five followers following an artificial and a real leading trajectory. A cross-comparison with the IDM calibrated to the goodness-of-fit of the relative gaps showed a higher reward compared to the traditional model and a better goodness-of-fit.