Variability in Low Ionization Broad Absorption Line outflows

We present results of our time variability studies of Mg ii and Al iii absorption lines in a sample of 22 Low Ionization Broad Absorption Line QSOs (LoBAL QSOs) at 0.2 ≤ z_(em) ≤ 2.1 using the 2-m telescope at IUCAA Girawali Observatory over a time-scale of 10 d to 7.69 years in the QSO's rest frame. Spectra are analysed in conjunction with photometric light curves from Catalina Real-Time Transient Survey. Long time-scale (i.e. ≥1 year) absorption line variability is seen in eight cases (36 per cent systems) while only four of them (i.e. 18 per cent systems) show variability over short time-scales (i.e. <1 year). We notice a tendency of highly variable LoBAL QSOs to have high ejection velocity, low equivalent width and low redshift. The detection rate of variability in LoBAL QSOs showing Fe fine-structure lines (FeLoBAL QSOs) is less than that seen in non-Fe LoBAL QSOs. Absorption line variability is more frequently detected in QSOs having continuum dominated by Fe emission lines compared to rest of the QSOs. Confirming these trends with a bigger sample will give vital clues for understanding the physical distinction between different BAL QSO sub-classes. We correlate the absorption line variability with various parameters derived from continuum light curves and find no clear correlation between continuum flux and absorption line variabilities. However, sources with large absorption line variability also show large variability in their light curves. We also see appearance/disappearance of absorption components in two cases and clear indications for profile variations in four cases. The observed variability can be best explained by a combination of process driven by continuum variations and clouds transiting across the line of sight

Weighing neutrinos using high redshift galaxy luminosity functions

Laboratory experiments measuring neutrino oscillations, indicate small mass differences between different mass eigenstates of neutrinos. The absolute mass scale is however not determined, with at present the strongest upper limits coming from astronomical observations rather than terrestrial experiments. The presence of massive neutrinos suppresses the growth of perturbations below a characteristic mass scale, thereby leading to a decreased abundance of collapsed dark matter halos. Here we show that this effect can significantly alter the predicted luminosity function (LF) of high redshift galaxies. In particular we demonstrate that a stringent constraint on the neutrino mass can be obtained using the well measured galaxy LF and our semi-analytic structure formation models. Combining the constraints from the Wilkinson Microwave Anisotropy Probe 7 year (WMAP7) data with the LF data at z = 4, we get a limit on the sum of the masses of 3 degenerate neutrinos \Sigma m_\nu < 0.52 eV at the 95 % CL. The additional constraints using the prior on Hubble constant strengthens this limit to \Sigma m_\nu < 0.29 eV at the 95 % CL. This neutrino mass limit is a factor of order 4 improvement compared to the constraint based on the WMAP7 data alone, and as stringent as known limits based on other astronomical observations. As different astronomical measurements may suffer from different set of biases, the method presented here provides a complementary probe of \Sigma m_\nu . We suggest that repeating this exercise on well measured luminosity functions over different redshift ranges can provide independent and tighter constraints on \Sigma m_\nu .Comment: 14 pages, 7 figures, submitted to PR

Discovery of a compact gas-rich DLA galaxy at z = 2.2: evidences for a starburst-driven outflow

We present the detection of Ly-alpha, [OIII] and H-alpha emission associated with an extremely strong DLA system (N(HI) = 10^22.10 cm^-2) at z=2.207 towards the quasar SDSS J113520-001053. This is the largest HI column density ever measured along a QSO line of sight, though typical of what is seen in GRB-DLAs. This absorption system also classifies as ultrastrong MgII system with W2796_r=3.6 A. The mean metallicity of the gas ([Zn/H]=-1.1) and dust depletion factors ([Zn/Fe]=0.72, [Zn/Cr]=0.49) are consistent with (and only marginally larger than) the mean values found in the general QSO-DLA population. The [OIII]-Ha emitting region has a very small impact parameter with respect to the QSO line of sight, b=0.1", and is unresolved. From the Ha line, we measure SFR=25 Msun/yr. The Ly-a line is double-peaked and is spatially extended. More strikingly, the blue and red Ly-a peaks arise from distinct regions extended over a few kpc on either side of the star-forming region. We propose that this is the consequence of Ly-a transfer in outflowing gas. The presence of starburst-driven outflows is also in agreement with the large SFR together with a small size and low mass of the galaxy (Mvir~10^10 Msun). From the stellar UV continuum luminosity of the galaxy, we estimate an age of at most a few 10^7 yr, again consistent with a recent starburst scenario. We interpret the data as the observation of a young, gas rich, compact starburst galaxy, from which material is expelled through collimated winds powered by the vigorous star formation activity. We substantiate this picture by modelling the radiative transfer of Ly-a photons in the galactic counterpart. Though our model (a spherical galaxy with bipolar outflowing jets) is a simplistic representation of the true gas distribution and velocity field, the agreement between the observed and simulated properties is particularly good. [abridged]Comment: 15 pages, 18 figures, 4 tables, accepted for publication in Astronomy and Astrophysic

Herschel ATLAS : the cosmic star formation history of quasar host galaxies

We present a derivation of the star formation rate per comoving volume of quasar host galaxies, derived from stacking analyses of far-infrared to mm-wave photometry of quasars with redshifts 0 z 6 and absolute I-band magnitudes -22 > I-AB > -32 We use the science demonstration observations of the first similar to 16 deg(2) from the Herschel Astrophysical Terahertz Large Area Survey (H-ATLAS) in which there are 240 quasars from the Sloan Digital Sky Survey (SDSS) and a further 171 from the 2dF-SDSS LRG and QSO (2SLAQ) survey. We supplement this data with a compilation of data from IRAS, ISO, Spitzer, SCUBA and MAMBO. H-ATLAS alone statistically detects the quasars in its survey area at > 5 sigma at 250, 350 and 500 mu m. From the compilation as a whole we find striking evidence of downsizing in quasar host galaxy formation: low-luminosity quasars with absolute magnitudes in the range -22 > I-AB > -24 have a comoving star formation rate (derived from 100 mu m rest-frame luminosities) peaking between redshifts of 1 and 2, while high-luminosity quasars with I-AB -26 have a maximum contribution to the star formation density at z similar to 3. The volume-averaged star formation rate of -22 > IAB > -24 quasars evolves as (1 + z)(2.3 +/- 0.7) at z 2, but the evolution at higher luminosities is much faster reaching (1 + z)(10 +/- 1) at -26 > I-AB > -28. We tentatively interpret this as a combination of a declining major merger rate with time and gas consumption reducing fuel for both black hole accretion and star formation

Characterization of shape and dimensional accuracy of incrementally formed titanium sheet parts with intermediate curvatures between two feature types

Single point incremental forming (SPIF) is a relatively new manufacturing process that has been recently used to form medical grade titanium sheets for implant devices. However, one limitation of the SPIF process may be characterized by dimensional inaccuracies of the final part as compared with the original designed part model. Elimination of these inaccuracies is critical to forming medical implants to meet required tolerances. Prior work on accuracy characterization has shown that feature behavior is important in predicting accuracy. In this study, a set of basic geometric shapes consisting of ruled and freeform features were formed using SPIF to characterize the dimensional inaccuracies of grade 1 titanium sheet parts. Response surface functions using multivariate adaptive regression splines (MARS) are then generated to model the deviations at individual vertices of the STL model of the part as a function of geometric shape parameters such as curvature, depth, distance to feature borders, wall angle, etc. The generated response functions are further used to predict dimensional deviations in a specific clinical implant case where the curvatures in the part lie between that of ruled features and freeform features. It is shown that a mixed-MARS response surface model using a weighted average of the ruled and freeform surface models can be used for such a case to improve the mean prediction accuracy within ±0.5 mm. The predicted deviations show a reasonable match with the actual formed shape for the implant case and are used to generate optimized tool paths for minimized shape and dimensional inaccuracy. Further, an implant part is then made using the accuracy characterization functions for improved accuracy. The results show an improvement in shape and dimensional accuracy of incrementally formed titanium medical implants

High-Ion Absorption in Seven GRB Host Galaxies at z=2-4: Evidence for both Circumburst Plasma and Outflowing Interstellar Gas

(Abridged) We use VLT/UVES high-resolution optical spectroscopy of seven GRB afterglows at z_GRB>2 to investigate circumburst and interstellar plasma in the host galaxies. Our sample consists of GRBs 021004, 050730, 050820, 050922C, 060607, 071031, and 080310. Four of these spectra were taken in rapid-response mode, within 30 minutes of the Swift GRB detection. We identify several distinct categories of high-ion absorption at velocities close to z_GRB: (i) Strong high-ion components at z_GRB itself are always seen in OVI, CIV, and SiIV, and usually (in 6 of 7 cases) in NV. We discuss circumburst and interstellar models for the origin of this absorption. Using the non-detection of SIV* toward GRB 050730 together with a UV photo-excitation model, we place a lower limit of 400 pc on the distance of the SIV-bearing gas from the GRB. (ii) Complex, multi-component CIV and SiIV profiles extending over 100-400 km/s around z_GRB are observed in each spectrum; these velocity fields are similar to those measured in damped Lyman-alpha systems at similar redshifts, suggesting a galactic origin. (iii) Asymmetric, blueshifted, absorption-line wings covering 65-140 km/s are seen in the CIV, SiIV, and OVI profiles in 4 of the 7 spectra. The wing kinematics together with the observation that two wings show "Galactic" CIV/SiIV ratios suggest these features trace outflowing ISM gas in the GRB host galaxies. (iv) High-velocity (HV; 500-5000 km/s) components are detected in 6 of the 7 spectra. The HV components show diverse properties. In the cases of GRBs 071031 and 080310, both the ionization level (very high CIV/SiIV ratios and absence of neutral-phase absorption) and the kinematics of the HV components can be explained by Wolf-Rayet winds from the GRB progenitors.Comment: 20 pages, 9 figures (7 in color), accepted by A&A, updated with proof corrections including changes to Table

Multivariate Adaptive Regression Splines Model to Predict Fracture Characteristics of High Strength and Ultra High Strength Concrete Beams

This paper presents Multivariate Adaptive Regression Splines (MARS) model to predict the fracture characteristics of high strength and ultra high strength concrete beams. Fracture characteristics include fracture energy (GF), critical stress intensity factor (KIC) and critical crack tip opening displacement (CTODc). This paper also presents the details of development of MARS model to predict failure load (Pmax) of high strength concrete (HSC) and ultra high strength concrete (UHSC) beam specimens. Characterization of mix and testing of beams of high strength and ultra strength concrete have been described. Methodologies for evaluation of fracture energy, critical stress intensity factor and critical crack tip opening displacement have been outlined. MARS model has been developed by establishing a relationship between a set of predicators and dependent variables. MARS is based on a divide and conquers strategy partitioning the training data sets into separate regions; each gets its own regression line. Four MARS models have been developed by using MATLAB software for training and prediction of fracture parameters and failure load.MARS has been trained with about 70% of the total 87 data sets and tested with about 30% of the total data sets. It is observed from the studies that the predicted values of Pmax, GF, KIC and CTODC are in good agreement with those of the experimental values

ANN Model to Predict Fracture Characteristics of High Strength and Ultra High Strength Concrete Beams

This paper presents fracture mechanics based Artificial Neural Network (ANN) model to predict the fracture characteristics of high strength and ultra high strength concrete beams. Fracture characteristics include fracture energy (Gf), critical stress intensity factor (KIC) and critical crack tip opening displacement (CTODc). Failure load of the beam (Pmax) is also predicated by using ANN model. Characterization of mix and testing of beams of high strength and ultra strength concrete have been described. Methodologies for evaluation of fracture energy, critical stress intensity factor and critical crack tip opening displacement have been outlined. Back-propagation training technique has been employed for updating the weights of each layer based on the error in the network output. Levenberg- Marquardt algorithm has been used for feed-forward back-propagation. Four ANN models have been developed by using MATLAB software for training and prediction of fracture parameters and failure load. ANN has been trained with about 70% of the total 87 data sets and tested with about 30% of the total data sets. It is observed from the studies that the predicted values of Pmax, Gf, failure load, KIc and CTODc are in good agreement with those of the experimental values

A novel hybrid swarm optimized multilayer neural network for spatial prediction of flash floods in tropical areas using sentinel-1 SAR imagery and geospatial data

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg–Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility

Hateful Sentiment Detection in Real-Time Tweets: An LSTM-Based Comparative Approach

It is undeniable that social media has improved our lives in many ways, like allowing interactions with others all over the world and network expansion for businesses. However, there are detrimental effects of such accessibility, including the rapid spread of hate through offensive messages typically directed toward gender, religion, race, and disability, which can cause psychological harm. To address this problem of social media, many researchers have recently proposed various algorithms powered by machine learning (ML) and deep learning for the detection of hate speech. This work proposes a hate speech detection model based on long-short term memory (LSTM), using term frequency inverse document frequency (TF-IDF) vectorization, and makes comparisons with support vector machine (SVM), Naïve Bayes (NB), logistic regression (LR), XGBoost (XGB), random forest (RF), $K$ -nearest neighbor ( $k$ -NN), artificial neural network (ANN), and bidirectional encoder representations from transformers (BERT) models. To validate and authenticate our proposed work, we obtained and classified a real-time Twitter data stream of a trending topic using Twitter API into two classes: hate speech and nonhate speech. The precision, recall, and $F$ 1 score achieved by LSTM are 0.98, 0.99, and 0.98, respectively. The accuracy of LSTM for detecting hateful sentiment was found to be 97%, surpassing the accuracy of other models