Single Epoch Research Articles

Mesiri: Mephisto Early Supernovae Ia Rapid Identifier

The early time observations of Type Ia supernovae (SNe Ia) play a crucial role in investigating and resolving longstanding questions about progenitor stars and the explosion mechanisms of these events. Colors of supernovae (SNe) in the initial days after the explosion can help differentiate between different types of SNe. However, the use of true color information to identify SNe Ia at the early-time explosion is still in its infancy. The Multi-channel Photometric Survey Telescope (Mephisto) is a photometric survey telescope equipped with three CCD cameras, capable of simultaneously imaging the same patch of sky in three bands (u, g, i or v, r, z), yielding real-time colors of astronomical objects. In this paper, we introduce a new time-series classification tool named Mephisto Early Supernovae Ia Rapid Identifier (Mesiri), which, for the first time, utilizes real-time color information to distinguish early-time SNe Ia from core-collapse supernovae. Mesiri is based on the deep learning approach and can achieve an accuracy of 96.75% ± 0.79%, and AUC of 98.87% ± 0.53% in case of single epoch random observation before the peak brightness. These values reach towards perfectness if additional data points on several night observations are considered. The classification with real-time color significantly outperforms that with pseudo-color, especially at the early time, i.e., with only a few points of observations. The BiLSTM architecture shows the best performance compared to others that have been tested in this work.

Automated quantification of periodic discharges in human electroencephalogram

Periodic discharges (PDs) are pathologic patterns of epileptiform discharges repeating at regular intervals, commonly detected in the human electroencephalogram (EEG) signals in patients who are critically ill. The frequency and spatial extent of PDs are associated with the tendency of PDs to cause brain injury, existing automated algorithms do not quantify the frequency and spatial extent of PDs. The present study presents an algorithm for quantifying frequency and spatial extent of PDs. The algorithm quantifies the evolution of these parameters within a short (10–14 second) window, with a focus on lateralized and generalized periodic discharges. We test our algorithm on 300 ‘easy’, 300 ‘medium’, and 240 ‘hard’ examples (840 total epochs) of periodic discharges as quantified by interrater consensus from human experts when analyzing the given EEG epochs. We observe 95.0% agreement with a 95% confidence interval (CI) of [94.9%, 95.1%] between algorithm outputs with reviewer clincal judgement for easy examples, 92.0% agreement (95% CI [91.9%, 92.2%]) for medium examples, and 90.4% agreement (95% CI [90.3%, 90.6%]) for hard examples. The algorithm is also computationally efficient and is able to run in 0.385 ± 0.038 seconds for a single epoch using our provided implementation of the algorithm. The results demonstrate the algorithm’s effectiveness in quantifying these discharges and provide a standardized and efficient approach for PD quantification as compared to existing manual approaches.

An analytic formulation of convolutional neural network learning for pattern recognition

Training convolutional neural networks (CNNs) using back-propagation (BP) is a time-consuming and resource-intensive process, primarily due to the need to iterate over the dataset multiple times. In contrast, analytic learning aims to train neural networks in a single epoch, offering a potential solution to these challenges. However, existing studies of analytic learning have been limited to multilayer perceptrons (MLPs). In this article, we propose an analytic formulation for convolutional neural network learning (ACnnL), which represents a significant advancement towards non-iterative learning paradigms for CNNs. Our formulation demonstrates that ACnnL extends the principles of MLP regularization constraints. From the implicit regularization and network interpretability viewpoints, we provide insights into why CNNs often exhibit superior generalization capabilities. The ACnnL is validated by conducting classification tasks on benchmark datasets such as MNIST, FashionMNIST, CIFAR10, CIFAR100 and Tiny-ImageNet. It is encouraging that the ACnnL trains CNNs in a significantly fast manner with reasonably close prediction accuracies to those using BP. In particular, a 5-layer vanilla CNN trained by ACnnL gave an accuracy of 0.9931, 0.9155, 0.7049 and 0.4628 for these datasets. The ACnnL achieves training speeds that are approximately 17 times faster than BP on GPU and 113 times faster than BP on CPU, while maintaining competitive prediction accuracies. Moreover, our experiments disclose a unique advantage of ACnnL under the small-sample scenario when training data are scarce or expensive. In a nutshell, an analytic method which deals well with small-sample size data has been put forward for the first time for fast CNN training with inherent network interpretability.

Size and kinematics of the C IV broad emission line region from microlensing-induced line profile distortions in two gravitationally lensed quasars

Microlensing of the broad emission line region (BLR) in gravitationally lensed quasars produces line profile distortions that can be used to probe the BLR size, geometry, and kinematics. Based on single-epoch spectroscopic data, we analyzed the C IV line profile distortions due to microlensing in two quasars, SDSS J133907.13+131039.6 (J1339) and SDSS J113803.73+031457.7 (J1138), complementing previous studies of microlensing in the quasars Q2237+0305 and J1004+4112. J1339 shows a strong, asymmetric line profile deformation, while J1138 shows a more modest, symmetric deformation, confirming the rich diversity of microlensing-induced spectral line deformations. To probe the C IV BLR, we compared the observed line profile deformations to simulated ones. The simulations are based on three simple BLR models, a Keplerian disk (KD), an equatorial wind (EW), and a polar wind (PW), of various sizes, inclinations, and emissivities. These models were convolved with microlensing magnification maps specific to the microlensed quasar images, which produced a large number of distorted line profiles. The models that best reproduce the observed line profile deformations were then identified using a Bayesian probabilistic approach. We find that the line profile deformations can be reproduced with the simple BLR models under consideration, with no need for more complex geometries or kinematics. The models with disk geometries (KD and EW) are preferred, while the PW model is definitely less likely. In J1339, the EW model is favored, while the KD model is preferred in Q2237+0305, suggesting that various kinematical models can dominate the C IV BLR. For J1339, we find the C IV BLR half-light radii to be r1/2 = 5.1−2.9+4.6 light-days and r1/2 = 6.7−3.8+6.0 light-days from spectra obtained in 2014 and 2017, respectively. They do agree within uncertainties. For J1138, the amplitude of microlensing is smaller and more dependent on the macro-magnification factor. From spectra obtained in 2005 (single epoch), we find r1/2 = 4.9−2.7+4.9 light-days and r1/2 = 12−8+13 light-days for two extreme values of the macro-magnification factor. Combining these new measurements with those previously obtained for the quasars Q2237+0305 and J1004+4112, we show that the BLR radii estimated from microlensing do follow the C IV radius–luminosity relation obtained from reverberation mapping, although the microlensing radii seem to be systematically smaller, which could indicate either a selection bias or a real offset.

Detection of moisture of flowing grain with a novel deep learning structure using 2D spectrogram data

In order to preserve the stored grain for a long time without deterioration, the moisture content must be accurately known. In this study, the moisture content of flowing grain was determined using radar spectrogram data and CNN structure. A free-space measurement technique-based experimental environment was established for the purpose of collecting the necessary signals to measure the moisture content. The measurement plant is composed of two horn antennas, a vector network analyzer (VNA), and a flowing grain mechanism. In the experimental environment, the S11 and S21 parameter values are recorded from the VNA. The short-time Fourier transforms (STFT) of the recorded signals were then taken, and 2D spectrogram images were created. The dataset, comprising 22,780 images, was split into 80 % for training and 20 % for testing; then, they were subjected to regression analysis using CNNs. First, the mean absolute error (MAE) values of the 27 pre-trained CNN architectures in a single epoch are calculated. Subsequently, the architecture with the best four regression results is automatically selected. The training and testing steps are performed independently for each selected architecture, and the results are recorded. The MAE, root mean square error (RMSE), mean square error (MSE) and mean absolute percentage error (MAPE) metrics are employed to assess the efficacy of the CNN architectures. Among these, ResNet50 is the architecture that yields the most favorable results. Subsequently, a subsequent architecture with fewer parameters and a more expeditious processing time is proposed. The novel deep-learning CNN architecture demonstrated superior performance compared to the pre-trained architectures. The results are as follows: MAE = 0.0411, MSE = 0.0149, RMSE = 0.122, and MAPE = 0.0397. When comparing the time spent on training and testing, the least time-consuming architecture required approximately 72 min, whereas this study was completed in approximately 325 s. The pronounced disparity is readily apparent. The results demonstrate that when the CNN is appropriately modeled and trained, the combination of CNN and appropriate signal processing can effectively determine the moisture content of grains.

The Sloan Digital Sky Survey Reverberation Mapping Project: Key Results

We present the final data from the Sloan Digital Sky Survey (SDSS) Reverberation Mapping (RM) project, a precursor to the SDSS-V Black Hole Mapper RM program. This data set includes 11 yr photometric and 7 yr spectroscopic light curves for 849 broad-line quasars over a redshift range of 0.1 < z < 4.5 and a luminosity range of L bol = 1044−47.5 erg s−1, along with spectral and variability measurements. We report 23, 81, 125, and 110 RM lags (relative to optical continuum variability) for broad Hα, Hβ, Mg ii, and C iv using the SDSS-RM sample, spanning much of the luminosity and redshift ranges of the sample. Using 30 low-redshift RM active galactic nuclei with dynamical-modeling black hole masses, we derive a new estimate of the average virial factor of logf=0.62±0.07 for the line dispersion measured from the rms spectrum. The intrinsic scatter of individual virial factors is 0.31 ± 0.07 dex, indicating a factor of 2 systematic uncertainty in RM black hole masses. Our lag measurements reveal significant R–L relations for Hβ and Mg ii at high redshift, consistent with the latest measurements based on heterogeneous samples. While we are unable to robustly constrain the slope of the R–L relation for C iv given the limited dynamic range in luminosity, we found substantially larger scatter in C iv lags at fixed L 1350. Using the SDSS-RM lag sample, we derive improved single-epoch (SE) mass recipes for Hβ, Mg ii, and C iv, which are consistent with their respective RM masses as well as between the SE recipes from two different lines, over the luminosity range probed by our sample. The new Hβ and Mg ii recipes are approximately unbiased estimators at given RM masses, but there are systematic biases in the C iv recipe. The intrinsic scatter of SE masses around RM masses is ∼0.45 dex for Hβ and Mg ii, increasing to ∼0.58 dex for C iv.

DEW: A wavelet approach of rare sound event detection.

This paper presents a novel sound event detection (SED) system for rare events occurring in an open environment. Wavelet multiresolution analysis (MRA) is used to decompose the input audio clip of 30 seconds into five levels. Wavelet denoising is then applied on the third and fifth levels of MRA to filter out the background. Significant transitions, which may represent the onset of a rare event, are then estimated in these two levels by combining the peak-finding algorithm with the K-medoids clustering algorithm. The small portions of one-second duration, called 'chunks' are cropped from the input audio signal corresponding to the estimated locations of the significant transitions. Features from these chunks are extracted by the wavelet scattering network (WSN) and are given as input to a support vector machine (SVM) classifier, which classifies them. The proposed SED framework produces an error rate comparable to the SED systems based on convolutional neural network (CNN) architecture. Also, the proposed algorithm is computationally efficient and lightweight as compared to deep learning models, as it has no learnable parameter. It requires only a single epoch of training, which is 5, 10, 200, and 600 times lesser than the models based on CNNs and deep neural networks (DNNs), CNN with long short-term memory (LSTM) network, convolutional recurrent neural network (CRNN), and CNN respectively. The proposed model neither requires concatenation with previous frames for anomaly detection nor any additional training data creation needed for other comparative deep learning models. It needs to check almost 360 times fewer chunks for the presence of rare events than the other baseline systems used for comparison in this paper. All these characteristics make the proposed system suitable for real-time applications on resource-limited devices.

Generalizable Sleep Staging via Multi-Level Domain Alignment

Automatic sleep staging is essential for sleep assessment and disorder diagnosis. Most existing methods depend on one specific dataset and are limited to be generalized to other unseen datasets, for which the training data and testing data are from the same dataset. In this paper, we introduce domain generalization into automatic sleep staging and propose the task of generalizable sleep staging which aims to improve the model generalization ability to unseen datasets. Inspired by existing domain generalization methods, we adopt the feature alignment idea and propose a framework called SleepDG to solve it. Considering both of local salient features and sequential features are important for sleep staging, we propose a Multi-level Feature Alignment combining epoch-level and sequence-level feature alignment to learn domain-invariant feature representations. Specifically, we design an Epoch-level Feature Alignment to align the feature distribution of each single sleep epoch among different domains, and a Sequence-level Feature Alignment to minimize the discrepancy of sequential features among different domains. SleepDG is validated on five public datasets, achieving the state-of-the-art performance.

Cosmological Distance Measurement of Twelve Nearby Supernovae IIP with ROTSE-IIIb

We present cosmological analysis of 12 nearby (z < 0.06) Type IIP supernovae (SNe IIP) observed with the ROTSE-IIIb telescope. To achieve precise photometry, we present a new image-differencing technique that is implemented for the first time on the ROTSE SN photometry pipeline. With this method, we find up to a 20% increase in the detection efficiency and significant reduction in residual rms scatter of the SN lightcurves when compared to the previous pipeline performance. We use the published optical spectra and broadband photometry of well-studied SNe IIP to establish temporal models for ejecta velocity and photospheric temperature evolution for our SNe IIP population. This study yields measurements that are competitive with other methods even when the data are limited to a single epoch during the photospheric phase of SNe IIP. Using the fully reduced ROTSE photometry and optical spectra, we apply these models to the respective photometric epochs for each SN in the ROTSE IIP sample. This facilitates the use of the Expanding Photosphere Method (EPM) to obtain distance estimates to their respective host galaxies. We then perform cosmological parameter fitting using these EPM distances, from which we measure the Hubble constant to be 72.9−4.3+5.7kms−1Mpc−1 , which is consistent with the standard ΛCDM model values derived using other independent techniques.

Active Galactic Nuclei and Host Galaxies in COSMOS-Web. I. NIRCam Images, Point-spread-function Models and Initial Results on X-Ray-selected Broad-line AGNs at 0.35 ≲ z ≲ 3.5

We present detailed and comprehensive data reduction and point-spread-function (PSF) model construction for all public JWST NIRCam imaging data from the COSMOS-Web treasury program (up to 2023 June, totaling 0.28 deg2). We show that the NIRCam PSF has significant short-timescale temporal variations and random spatial variations in all four filters (F115W, F150W, F277W, and F444W). Combining NIRCam with archival Hubble Space Telescope imaging, we perform multiwavelength active galactic nucleus (AGN)+host image decomposition to study the properties of 143 X-ray-selected (L bol = 1043.6–47.2 erg s−1) broad-line AGNs at 0.35 ≲ z ≲ 3.5. Leveraging the superb resolution, wavelength coverage, and sensitivity of NIRCam, we successfully detect host stellar emission after decomposing the central AGN point source in 142 objects. ∼2/3 AGNs are in star-forming galaxies based on the UVJ diagram, suggesting that there is no instantaneous negative AGN feedback. X-ray-selected broad-line AGN hosts follow a similar stellar mass–size relation as inactive galaxies, albeit with slightly smaller galaxy sizes. We find that although major mergers are rare (∼7%–22%) among the sample, more subtle nonaxisymmetric features from stellar bars, spiral arms, and minor mergers are ubiquitous, highlighting the importance of secular processes and minor mergers in triggering AGN activity. For a subsample of 30 AGNs at 1 < z < 2.5 with black hole mass measurements from single epoch spectra, they follow a similar black hole mass-stellar mass relation as local inactive early-type galaxies but reside preferentially near the upper envelope of nearby AGNs. We caution that selection biases and the intrinsic differences of AGN populations at different redshifts may significantly affect their location on the black hole mass-stellar mass plane.

The California-Kepler Survey. XI. A Survey of Chromospheric Activity through the Lens of Precise Stellar Properties

Surveys of exoplanet host stars are valuable tools for assessing population level trends in exoplanets, and their outputs can include stellar ages, activity, and rotation periods. We extracted chromospheric activity measurements from the California-Kepler Survey Gaia survey spectra in order to probe connections between stellar activity and fundamental stellar properties. Building on the California Kepler Survey's legacy of 1189 planet host star stellar properties including temperature, surface gravity metallicity, and isochronal age, we add measurements of the Ca ii H and K lines as a proxy for chromospheric activity for 879 planet hosting stars. We used these chromospheric activity measurements to derive stellar rotation periods. We find a discrepancy between photometrically derived and activity-derived rotation periods for stars on the Rossby Ridge. These results support the theory of weakened magnetic braking. We find no evidence for metallicity-dependent activity relations, within the metallicity range of −0.2 to +0.3 dex. With our single epoch spectra we identify stars that are potentially in Maunder minimum–like state using a combination of log( RHK′ ) and position below the main sequence. We do not yet have the multiyear time series needed to verify stars in Maunder minimum–like states. These results can help inform future theoretical studies that explore the relationship between stellar activity, stellar rotation, and magnetic dynamos.

Efficient ELM Model With Parameter Optimization Using Pso Algorithms in the Prediction of Combustion Pressure Parameters of Dsi Engine Using Ethanol- Gasoline Blends

The present study focused mainly on developing PSO based ELM model to predict cylinder pressure associated parameters. Performance of PSO-ELM model then compared with ELM model to obtain its credential. For training and testing the models, data has been acquired through experiments on a Twin Spark Ignition (TSI) gasoline engine using EGB as fuel. The various operating variables are treated as input data whereas cylinder pressure associated parameters are treated as output data for the model. The result of the proposed modelling study indicated that PSO-ELM model has obtained the best performance with lowest value of MSE, MAPE (%) and hidden layer size as compared to ELM model. Hence PSO-ELM results in an efficient model structure with great generalization performance. Further, it is also observed that PSO-ELM takes more time as it calls for an iterative procedure for searching the optimal solution as compared to ELM, which takes only a single epoch.

The convergence mechanism of Low Earth Orbit enhanced GNSS (LeGNSS) Precise Point Positioning (PPP)

ABSTRACT The recruitment of the Low Earth Orbit (LEO) constellation is recognized as an effective way to augment Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) in the near future. Its potential to accelerate PPP convergence has been proved with simulated data. However, the mechanism of how the geometric change of LEO accelerates the convergence of GNSS PPP has not been studied from a theoretical perspective, which hampers the understanding and exploitation of the enhancement of LEO. In this article, the convergence mechanism of LEO enhanced GNSS PPP is investigated in terms of theoretical analysis and simulated verification. To show the characteristics of the ambiguities during convergence, eigenvalue decomposition is used to divide the ambiguities into orthogonal components, named geometric-related component, clock-error-related component, and independent component. The results show that the precision of geometric-related components of ambiguities, which correlates with position parameters, is low at a single epoch, while the precision can be greatly improved with the fast geometric change of LEO. On the other hand, the precision of clock-error-related components of ambiguities, which correlates with clock errors, cannot be improved by fast geometric change of LEO constellation due to its irrelevance to geometry, which causes the precision of each ambiguity to be low. Further investigations show that single-differenced ambiguities could overcome this drawback and are beneficial to ambiguity resolution.

FibroVit-Vision transformer-based framework for detection and classification of pulmonary fibrosis from chest CT images.

Pulmonary Fibrosis (PF) is an immedicable respiratory condition distinguished by permanent fibrotic alterations in the pulmonary tissue for which there is no cure. Hence, it is crucial to diagnose PF swiftly and precisely. The existing research on deep learning-based pulmonary fibrosis detection methods has limitations, including dataset sample sizes and a lack of standardization in data preprocessing and evaluation metrics. This study presents a comparative analysis of four vision transformers regarding their efficacy in accurately detecting and classifying patients with Pulmonary Fibrosis and their ability to localize abnormalities within Images obtained from Computerized Tomography (CT) scans. The dataset consisted of 13,486 samples selected out of 24647 from the Pulmonary Fibrosis dataset, which included both PF-positive CT and normal images that underwent preprocessing. The preprocessed images were divided into three sets: the training set, which accounted for 80% of the total pictures; the validation set, which comprised 10%; and the test set, which also consisted of 10%. The vision transformer models, including ViT, MobileViT2, ViTMSN, and BEiT were subjected to training and validation procedures, during which hyperparameters like the learning rate and batch size were fine-tuned. The overall performance of the optimized architectures has been assessed using various performance metrics to showcase the consistent performance of the fine-tuned model. Regarding performance, ViT has shown superior performance in validation and testing accuracy and loss minimization, specifically for CT images when trained at a single epoch with a tuned learning rate of 0.0001. The results were as follows: validation accuracy of 99.85%, testing accuracy of 100%, training loss of 0.0075, and validation loss of 0.0047. The experimental evaluation of the independently collected data gives empirical evidence that the optimized Vision Transformer (ViT) architecture exhibited superior performance compared to all other optimized architectures. It achieved a flawless score of 1.0 in various standard performance metrics, including Sensitivity, Specificity, Accuracy, F1-score, Precision, Recall, Mathew Correlation Coefficient (MCC), Precision-Recall Area under the Curve (AUC PR), Receiver Operating Characteristic and Area Under the Curve (ROC-AUC). Therefore, the optimized Vision Transformer (ViT) functions as a reliable diagnostic tool for the automated categorization of individuals with pulmonary fibrosis (PF) using chest computed tomography (CT) scans.

Octofitter: Fast, Flexible, and Accurate Orbit Modeling to Detect Exoplanets

As next-generation imaging instruments and interferometers search for planets closer to their stars, they must contend with increasing orbital motion and longer integration times. These compounding effects make it difficult to detect faint planets but also present an opportunity. Increased orbital motion makes it possible to move the search for planets into the orbital domain, where direct images can be freely combined with the radial velocity and proper motion anomaly, even without a confirmed detection in any single epoch. In this paper, we present a fast and differentiable multimethod orbit-modeling and planet detection code called Octofitter. This code is designed to be highly modular and allows users to easily adjust priors, change parameterizations, and specify arbitrary function relations between the parameters of one or more planets. Octofitter further supplies tools for examining model outputs including prior and posterior predictive checks and simulation-based calibration. We demonstrate the capabilities of Octofitter on real and simulated data from different instruments and methods, including HD 91312, simulated JWST/NIRISS aperture masking interferometry observations, radial velocity curves, and grids of images from the Gemini Planet Imager. We show that Octofitter can reliably recover faint planets in long sequences of images with arbitrary orbital motion. This publicly available tool will enable the broad application of multiepoch and multimethod exoplanet detection, which could improve how future targeted ground- and space-based surveys are performed. Finally, its rapid convergence makes it a useful addition to the existing ecosystem of tools for modeling the orbits of directly imaged planets.

The role of tidal interactions in the formation of slowly rotating early-type stars in young star clusters

ABSTRACT The split main sequences found in the colour–magnitude diagrams of star clusters younger than ∼600 Myr are suggested to be caused by the dichotomy of stellar rotation rates of upper main-sequence stars. Tidal interactions have been suggested as a possible explanation of the dichotomy of the stellar rotation rates. This hypothesis proposes that the slow rotation rates of stars along the split main sequences are caused by tidal interactions in binaries. To test this scenario, we measured the variations in the radial velocities of slowly rotating stars along the split main sequence of the young Galactic cluster NGC 2422 (∼90 Myr) using spectra obtained at multiple epochs with the Canada–France–Hawai’i Telescope. Our results show that most slowly rotating stars are not radial velocity variables. Using the theory of dynamical tides, we find that the binary separations necessary to fully or partially synchronize our spectroscopic targets, on time-scales shorter than the cluster age, predict much larger radial velocity variations across multiple-epoch observations, or a much larger radial velocity dispersion at a single epoch, than the observed values. This indicates that tidal interactions are not the dominant mechanism to form slowly rotating stars along the split main sequences. As the observations of the rotation velocity distribution among B- and A-type stars in binaries of larger separations hint at a much stronger effect of braking with age, we discuss the consequences of relaxing the constraints of the dynamical tides theory.

FRB 20121102A: images of the bursts and the varying radio counterpart

ABSTRACT As more Fast Radio Bursts (FRBs) are being localized, we are learning that some fraction have persistent radio sources (PRSs). Such a discovery motivates an improvement in our understanding of the nature of those counterparts, the relation to the bursts themselves and why only some FRBs have PRSs. We report on observations made of FRB 20121102A with the MeerKAT radio telescope. Across five epochs, we detect the PRS associated with FRB 20121102A. Our observations are split into a cluster of four epochs (MJD 58732–58764) and a separate single epoch about 1000 d later. The measured flux density is constant across the first four observations but then decays by more than one-third in the final observation. Our observations on MJD 58736 coincided with the detections of 11 bursts from FRB 20121102A by the MeerTRAP backend, seven of which we detected in the image plane. We discuss the importance of image plane detections when considering the commensal transient searches being performed with MeerKAT and other radio facilities. We find that MeerKAT is so sensitive that within a 2-s image, we can detect any FRB with a flux density above 2.4 mJy at 1.3 GHz and so could localize every FRB that has been detected by CHIME to date.

MultiCAM: a multivariable framework for connecting the mass accretion history of haloes with their properties

ABSTRACT Models that connect galaxy and halo properties often summarize a halo’s mass accretion history (MAH) with a single value and use this value as the basis for predictions. However, a single-value summary fails to capture the complexity of MAHs and information can be lost in the process. We present MultiCAM, a generalization of traditional abundance matching frameworks, which can simultaneously connect the full MAH of a halo with multiple halo and/or galaxy properties. As a first case study, we apply MultiCAM to the problem of connecting dark matter halo properties to their MAHs in the context of a dark matter-only simulation. While some halo properties, such as concentration, are more strongly correlated to the early-time mass growth of a halo, others, like the virial ratio, have stronger correlations with late-time mass growth. This highlights the necessity of considering the impact of the entire MAH on halo properties. For most of the halo properties we consider, we find that MultiCAM models that use the full MAH achieve higher accuracy than conditional abundance matching models that use a single epoch. We also demonstrate an extension of MultiCAM that captures the covariance between predicted halo properties. This extension provides a baseline model for applications where the covariance between predicted properties is important.

Spectroscopy of nine eruptive young variables using TANSPEC

In recent times, 3.6m Devasthal Optical Telescope (DOT) has installed an optical to near infra-red spectrograph, TANSPEC, which provides spectral coverage from 0.55-2.5 microns. Using TANSPEC, we have obtained a single epoch spectrum of a set 9 FUors and EXors. We have analysed line profiles of the sources and compared with the previously published spectra of these objects. Comparing the line profile shapes with the existing theoretical predictions, we have tried to interpret the physical processes that are responsible for the current disc evolution and the present accretion dynamics. Our study has shown the importance for time evolved spectroscopic studies for better understanding the evolution of the accretion mechanisms. This in turn can help in better categorisation of the young stars displaying episodic accretion behaviour.

Sequential Generation of Multi-GNSS Multi-Frequency PPP-RTK Products and Their Performance Using the EUREF Permanent GNSS Network

In the classic Precise Point Positioning (PPP) approach, observations of Global Navigation Satellite Systems (GNSS) are processed at the network level to generate satellite clocks and positions. This information can be used to enable accurate point positioning for a single GNSS receiver. In the PPP Real-Time Kinematic (PPP-RTK) approach, satellite phase biases are considered as well, enabling ambiguity resolution at the network and user levels. In this research, 30 s multi-frequency raw GPS, Galileo, and BDS-2/3 observations are processed at the network and user levels in a sequential Kalman filter. PPP-RTK enabling products are generated for up to five frequencies, and ambiguity resolution is performed at the network and user levels using a flexible ambiguity reparameterization approach, comparable to wide- and narrow-laning, which has shown to yield a significantly improved single epoch coordinate solution when multi-frequency observations are available. Different assumptions regarding the time stability of receiver and satellite phase biases have been made and compared. The availability of a precise user coordinate solution when multi-frequency and dual-frequency observations are processed is assessed and compared. A precise ambiguity-fixed solution is available in three epochs or fewer in 77% of all cases with an average of 24 visible satellites for static and kinematic receivers when multi-frequency observations are processed. When only dual-frequency observations are considered, a fixed solution is available in seven epochs or fewer in 71% of all cases. The fastest fixed solution was found in two epochs with multi-frequency observations and in six epochs with dual-frequency observations. Estimating a reference phase clock did not lead to an improvement in the coordinate solution. The findings indicate that a fixed solution can potentially be found faster than often suggested, with potential for further improvement when more satellites or regional atmospheric corrections are considered.