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Podcasts offer a unique tool in making science learning informative, engaging, and entertaining. They provide an excellent way to incorporate storytelling in science instruction. Podcasts can be accessed easily and free of cost on any internet-enabled device. Recording podcasts is also easy and inexpensive, making them useful to record lesson summaries and other content for revision. This article outlines different ways of using podcasts in the teaching and learning process. To support informal learning, podcasts typically cover cross-disciplinary issues that are relevant to present times as well as those important to local communities. They also feature interviews with researchers who walk through the scientific process of their discoveries. All of these things add value to a physics unit beyond textbook content. In a subject where assessments are dominated by mathematical equations, student-recorded podcasts offer an opportunity for students to string together spoken-word narratives of physics phenomena. In this article, we outline an example lesson centered around the NPR Short Wave podcast.

The LIGO-Virgo gravitational wave detectors have confidently observed 4 events involving neutron stars: two binary neutron star (BNS) mergers (GW170817 and GW190425), and two neutron star-black hole mergers (GW200105 and GW200115). However, our theoretical understanding of the remnant properties of such systems is incomplete due to the complexities related to the modeling of matter effects and the very high computational cost of corresponding numerical relativity simulations. An important such property is the recoil velocity, which is imparted onto the remnant due to the anisotropic emission of gravitational radiation and the dynamical ejection of matter in the kilonova. In this work, we combine gravitational radiation as well as dynamical ejecta distributions, computed by the Computational Relativity numerical simulations, to get accurate estimates for BNS remnant recoil velocities. We find that recoils due to ejection of matter dominate those caused by gravitational wave emission. Knowledge of BNS remnant recoil velocities is important in determining if the remnant is retained by its environment for future hierarchical mergers which, in turn, can form binaries with black holes in the so-called lower mass gap of 3 to 5 solar masses., Comment: 5 pages, 2 figures, submitted to Proc. of IAU Symp. 363

We present the design and performance of the GROWTH-India telescope, a 0.7 m robotic telescope dedicated to time-domain astronomy. The telescope is equipped with a 4k back-illuminated camera giving a 0.82-degree field of view and sensitivity of m_g ~20.5 in 5-min exposures. Custom software handles observatory operations: attaining high on-sky observing efficiencies (>~ 80%) and allowing rapid response to targets of opportunity. The data processing pipelines are capable of performing PSF photometry as well as image subtraction for transient searches. We also present an overview of the GROWTH-India telescope's contributions to the studies of Gamma-ray Bursts, the electromagnetic counterparts to gravitational wave sources, supernovae, novae and solar system objects., 17 pages, 8 figures, Accepted for publication in The Astronomical Journal

A number of synoptic sky surveys are underway or being planned. Typically they are done with small telescopes and relatively short exposure times. A search for transient or variable sources involves comparison with deeper baseline images, ideally obtained through the same telescope and camera. With that in mind we have stacked images from the 0.68~m Schmidt telescope on Mt. Bigelow taken over ten years as part of the Catalina Sky Survey. In order to generate deep reference images for the Catalina Real-time Transient Survey, close to 0.8 million images over 8000 fields and covering over 27000~sq.~deg. have gone into the deep stack that goes up to 3 magnitudes deeper than individual images. CRTS system does not use a filter in imaging, hence there is no standard passband in which the optical magnitude is measured. We estimate depth by comparing these wide-band unfiltered co-added images with images in the $g$-band and find that the image depth ranges from 22.0--24.2 across the sky, with a 200-image stack attaining an equivalent AB magnitude sensitivity of 22.8. We compared various state-of-the-art software packages for co-adding astronomical images and have used SWarp for the stacking. We describe here the details of the process adopted. This methodology may be useful in other panoramic imaging applications, and to other surveys as well. The stacked images are available through a server at Inter-University Centre for Astronomy and Astrophysics (IUCAA)., 16 pages, 15 figures

Instrumental and environmental transient noise bursts in gravitational-wave detectors, or glitches, may impair astrophysical observations by adversely affecting the sky localization and the parameter estimation of gravitational-wave signals. Denoising of detector data is especially relevant during low-latency operations because electromagnetic follow-up of candidate detections requires accurate, rapid sky localization and inference of astrophysical sources. NNETFIX is a machine learning-based algorithm designed to remove glitches detected in coincidence with transient gravitational-wave signals. NNETFIX uses artificial neural networks to estimate the portion of the data lost due to the presence of the glitch, which allows the recalculation of the sky localization of the astrophysical signal. The sky localization of the denoised data may be significantly more accurate than the sky localization obtained from the original data or by removing the portion of the data impacted by the glitch. We test NNETFIX in simulated scenarios of binary black hole coalescence signals and discuss the potential for its use in future low-latency LIGO-Virgo-KAGRA searches. In the majority of cases for signals with a high signal-to-noise ratio, we find that the overlap of the sky maps obtained with the denoised data and the original data is better than the overlap of the sky maps obtained with the original data and the data with the glitch removed., 26 pages, 10 figures, 10 tables

Two important parameters inferred from the gravitational wave signals of binaries of precessing black holes are the spin tilt angles, i.e., the angles at which the black holes' spin axes are inclined with respect to the binary's orbital angular momentum. The LIGO-Virgo parameter estimation analyses currently provide spin tilts at a fiducial reference frequency, often the lowest frequency used in the data analysis. However, the most astrophysically interesting quantities are the spin tilts when the binary was formed, which can be significantly different from those at the reference frequency for strongly precessing binaries. The spin tilts at formally infinite separation are a good approximation to the tilts at formation in many formation channels and can be computed efficiently for binary black holes using precession-averaged evolution. Here, we present a new code for computing the tilts at infinity that combines the precession-averaged evolution with orbit-averaged evolution at high frequencies and illustrate its application to GW190521 and other binary black hole detections from O3a. We have empirically determined the transition frequency between the orbit-averaged and precession-averaged evolution to produce tilts at infinity with a given accuracy. We also have regularized the precession-averaged equations in order to obtain good accuracy for the very close-to-equal-mass binary parameters encountered in practice. This additionally allows us to investigate the singular equal-mass limit of the precession-averaged expressions, where we find an approximate scaling of $1/(1 - q)$ with the mass ratio $q$., Comment: 25 pages, 16 figures

This study is oriented towards quantification of the skill addition by regional climate models (RCMs) in the parent general circulation models (GCMs) while simulating wind speed and wind potential with particular reference to the Indian offshore region. To arrive at a suitable reference dataset, the performance of wind outputs from three different reanalysis datasets is evaluated. The comparison across the RCMs and their corresponding parent GCMs is done on the basis of annual/seasonal wind statistics, intermodel bias, wind climatology, and classes of wind potential. It was observed that while the RCMs could simulate spatial variability of winds, well for certain subregions, they generally failed to replicate the overall spatial pattern, especially in monsoon and winter. Various causes of biases in RCMs were determined by assessing corresponding maps of wind vectors, surface temperature, and sea-level pressure. The results highlight the necessity to carefully assess the RCM-yielded winds before using them for sensitive applications such as coastal vulnerability and hazard assessment. A supplementary outcome of this study is in form of wind potential atlas, based on spatial distribution of wind classes. This could be beneficial in suitably identifying viable subregions for developing offshore wind farms by intercomparing both the RCM and GCM outcomes. It is encouraging that most of the RCMs and GCMs indicate that around 70% of the Indian offshore locations in monsoon would experience mean wind potential greater than 200 W/m2.

A probabilistic framework for evaluating changes in offshore wind potential under a changing climate is presented considering three general circulation models (GCMs), two representative concentration pathways and two statistical downscaling techniques. The analysis was done with respect to three specific locations along the Indian coastline, identified earlier for future offshore energy extraction. The uncertainty among the GCMs was addressed and the choice of an appropriate GCM was made by combining analysis of GCM uncertainty and model reliability. The regional scale wind was derived from the selected GCM using one hard computing method, namely bilinear interpolation and quantile mapping, and one soft method, an artificial neural network. The efficiency of these methods was assessed using certain performance criteria for the historical period and a convergence criterion over the future period. To evaluate wind potential, the GCM output was downscaled from daily to hourly, taking into account the removal of gusts because of long interval averaging. The wind potential for past (1979–2005) and future (2006–2032) time slices was determined using both hard and soft methods as well as two representative concentration pathways. The resulting four outcomes at annual and seasonal scale were compared with the extrapolated trend of past wind potential derived from both GCM and reanalysis data. Finally, the most efficient strategy was evaluated and the actual extractable power was estimated considering a standard power curve. It is concluded that at all the three locations the annual average wind potential will substantially increase, benefitting the offshore industry.

Random projection (RP) is a powerful dimension reduction technique widely used in the analysis of high dimensional data. We demonstrate how this technique can be used to improve the computational efficiency of gravitational wave searches from compact binaries of neutron stars or black holes. Improvements in low-frequency response and bandwidth due to detector hardware upgrades pose a data analysis challenge in the advanced LIGO era as they result in increased redundancy in template databases and longer templates due to the higher number of signal cycles in-band. The RP-based methods presented here address both these issues within the same broad framework. We first use RP for an efficient, singular value decomposition inspired template matrix factorization and develop a geometric intuition for why this approach works. We then use RP to calculate approximate time-domain match correlations in a lower dimensional vector space. For searches over parameters corresponding to non-spinning binaries with a neutron star and a black hole, a combination of the two methods can reduce the total on-line computational cost by an order of magnitude over a nominal baseline. This can, in turn, help free-up computational resources needed to go beyond current spin-aligned searches to more complex ones involving generically spinning waveforms., Matches the published version in Physical Review D (Rapid Communication) Phys. Rev. D 99, 101503(R) (2019)

With an installed capacity of over 19,000 MW, the wind power currently accounts for almost 70% of the total installed capacity among the renewable energy sector in India. The extraction of wind power mainly depends on prevailing meteorology which is strongly influenced by monsoon variability. The monsoon season is characterized by significant fluctuations in between periods of wet and dry spells. During the dry spells, the demand for power from agriculture and cooling equipment increases, whereas during the wet periods, such demand reduces, although, at the same time, the power supply increases because of strong westerly winds contributing to an enhanced production of wind energy. At this backdrop, we aim to assess the impact of intra-seasonal wind variability on the balance of energy supply and demand during monsoon seasons in India. Further, we explore the probable cause of wind variability by relating it to El Nino events. It is observed that the active and break phases in wind significantly impact the overall wind potential output. Although the dry spells are generally found to reduce the overall wind potential, their impact on the potential seems to have declined after the year 2000. The impact of meteorological changes on variations in wind power studied in this work should find applications typically in taking investment decisions on conventional generation facilities, like thermal, which are currently used to maintain the balance of power supply and demand.

The offshore wind energy is slated to meet world's energy demand significantlyin future. In India three offshore regions along the eastern, southern and western parts of the coastline, namely, Rameshwaram, Kanyakumari and Jakhau, have been identified by the Government of India for offshore wind energy extraction. This is based on the wind power potential assessed with the help of historical data. One of the wind parameters that plays an important role in evaluating the energy resource is the persistence of mean wind, often known through the positive serial correlation in a given wind time series. The persistence observed through historical data however may not remain same in future due to the effect of climate change induced by global warming.In this paper, the wind speed persistence at the above mentioned three locations has been analyzed in the light of climate change. The daily wind speeds for the future time slice of 2006-2032 are derived from the Canadian General Circulation Model: CGCM4run with RCP 4.5 warming scenario. Such projected wind information is compared with the historical wind over the time slice of 1979-2005 and belonging to the ‘CGCM: 20C3M’ model. The GCM data suffer from bias and this is removed using the quantile mapping method. The stationarity test of ‘unit-root’ indicates that the daily wind speed for each stationis stationarity. In order to quantify the persistence at the turbine cut-in speed of 4 m/s, the wind speed duration curve (WSDC) isdrawn. The results demonstrate a decrease in the persistence in future of the order of 3% and 8% at Rameshwaram and Jakhau, respectively and an increase in it by around 3% at Kanyakumari. These trends are further confirmed by the alternate autocorrelation function approach. From the wind persistence point of view alone therefore the power extraction at Kanyakumari may go up in future, which may not be the case with the two other sites.The variances across the three datasets are analyzed using the Levene's test, which showed that the cross-variance across the three locations might decrease in future, but the individual variance at Jakhau might increase and the same at Rameshwaram and Kanyakumari might remain the same in future, not much affecting the energy extraction over the latter two sites.

The increasing global warming is most likely to affect the magnitude and pattern of wind at a regional level and such an effect may or not follow the trend predicted at the global scale. Regional level exercises are therefore necessary while making decisions related to engineering infrastructure. In this paper an attempt is made to know the extent of change in design as well as operational wind conditions at two offshore locations along the west coast of India. The design wind speeds with return periods of 10, 50 and 100 years derived for two 30-year time slices in the past and future are compared. In two separate exercises the past and future wind at the local level is simulated by empirical downscaling as well as by interpolation of the general circulation model (GCM) output. Both sets of past and future data are fitted to the Generalized Pareto as well as Weibull distributions using the peak over threshold method to extract long term wind speeds with a specified return. It is noticed that at the given locations the operational and design wind may undergo an increase of around 11%-14% when no downscaling is adopted and 14%-17% when the GCM data are downscaled. Although these figures may suffer from a certain level of statistical uncertainty the study points out to take a relook into the safety margins kept in the design and operation of ocean structures in the light of global warming. (C) 2014 Elsevier Ltd. All rights reserved.

In this paper, design of an optical sensor is proposed which can be used for detection of bacteria in contaminated water. We have considered a couple of 2D photonic crystal structures (A and B). The analysis of both the structures is done with the help of Finite Difference Time Domain i.e. FDTD technique and modelling of these structures is done using OptiFDTD simulation tool. Optical properties of different bacteria in water are studied, and the refractive index values are given as input to OptiFDTD. Final response of different bacteria for the two structures is observed.