arXiv subjects

Suryarao Bethapudi

Publications and source records attributed to Suryarao Bethapudi.

5 recordsLinked to original sources

arXiv2024

Multiwavelength Constraints on the Origin of a Nearby Repeating Fast Radio Burst Source in a Globular Cluster

The precise origins of fast radio bursts (FRBs) remain unknown. Multiwavelength observations of nearby FRB sources can provide important insights into the enigmatic FRB phenomenon. Here, we present results from a sensitive, broadband X-ray and radio observational campaign of FRB 20200120E, the closest known extragalactic repeating FRB source (located 3.63 Mpc away in an ~10-Gyr-old globular cluster). We place deep limits on the persistent and prompt X-ray emission from FRB 20200120E, which we use to constrain possible origins for the source. We compare our results with various classes of X-ray sources, transients, and FRB models. We find that FRB 20200120E is unlikely to be associated with ultraluminous X-ray bursts, magnetar-like giant flares, or an SGR 1935+2154-like intermediate flare. Although other types of bright magnetar-like intermediate flares and short X-ray bursts would have been detectable from FRB 20200120E during our observations, we cannot entirely rule them out as a class. We show that FRB 20200120E is unlikely to be powered by an ultraluminous X-ray source or a young extragalactic pulsar embedded in a Crab-like nebula. We also provide new constraints on the compatibility of FRB 20200120E with accretion-based FRB models involving X-ray binaries. These results highlight the power of multiwavelength observations of nearby FRBs for discriminating between FRB models.

astro-ph.HE↗

arXiv2023

DM-power: an algorithm for high precision dispersion measure with application to fast radio bursts

We present DM-power, a new method for precisely determining the dispersion measure (DM) of radio bursts, and apply it to the Fast Radio Burst (FRB) source FRB~20180916B. Motivated by the complex structure on multiple time scales seen in FRBs, DM-power optimizes the DM by combining measurements at multiple Fourier frequencies in the power spectrum of the burst. By optimally weighting the measurements at each Fourier frequency, DM-power finds a burst DM that effectively incorporates information on many different burst timescales. We validate this technique on simulated Gaussian pulse profiles with a precision down to $σ_{\rm DM} \sim 0.001~{\rm pc~cm}^{-3}$, and then apply it to bursts from pulsar B0329+54 and FRB~20180916B. The precision of these DM measurements are sufficient to measure a statistically significant variation in DM over a $\approx 2$ hr span. While this variation could be the result of electron density variations along the line of sight, it is more like that the observed variation is the result of intrinsic frequency-dependent burst structure that can mimic a dispersive delay.

astro-ph.HE↗

arXiv2021

pinta: The uGMRT Data Processing Pipeline for the Indian Pulsar Timing Array

We introduce pinta, a pipeline for reducing the upgraded Giant Metre-wave Radio Telescope (uGMRT) raw pulsar timing data, developed for the Indian Pulsar Timing Array experiment. We provide a detailed description of the workflow and usage of pinta, as well as its computational performance and RFI mitigation characteristics. We also discuss a novel and independent determination of the relative time offsets between the different back-end modes of uGMRT and the interpretation of the uGMRT observation frequency settings, and their agreement with results obtained from engineering tests. Further, we demonstrate the capability of pinta to generate data products which can produce high-precision TOAs using PSR J1909-3744 as an example. These results are crucial for performing precision pulsar timing with the uGMRT.

astro-ph.IM↗

arXiv2018

Separation of pulsar signals from noise with supervised machine learning algorithms

We evaluate the performance of four different machine learning (ML) algorithms: an Artificial Neural Network Multi-Layer Perceptron (ANN MLP ), Adaboost, Gradient Boosting Classifier (GBC), XGBoost, for the separation of pulsars from radio frequency interference (RFI) and other sources of noise, using a dataset obtained from the post-processing of a pulsar search pi peline. This dataset was previously used for cross-validation of the SPINN-based machine learning engine, used for the reprocessing of HTRU-S survey data arXiv:1406.3627. We have used Synthetic Minority Over-sampling Technique (SMOTE) to deal with high class imbalance in the dataset. We report a variety of quality scores from all four of these algorithms on both the non-SMOTE and SMOTE datasets. For all the above ML methods, we report high accuracy and G-mean in both the non-SMOTE and SMOTE cases. We study the feature importances using Adaboost, GBC, and XGBoost and also from the minimum Redundancy Maximum Relevance approach to report algorithm-agnostic feature ranking. From these methods, we find that the signal to noise of the folded profile to be the best feature. We find that all the ML algorithms report FPRs about an order of magnitude lower than the corresponding FPRs obtained in arXiv:1406.3627, for the same recall value.

astro-ph.IM↗

arXiv2017

Median statistics estimates of Hubble and Newton's Constant

Robustness of any statistics depends upon the number of assumptions it makes about the measured data. We point out the advantages of median statistics using toy numerical experiments and demonstrate its robustness, when the number of assumptions we can make about the data are limited. We then apply the median statistics technique to obtain estimates of two constants of nature, Hubble Constant ($H_0$) and Newton's Gravitational Constant($G$), both of which show significant differences between different measurements. For $H_0$, we update the analysis done by Chen and Ratra (2011) and Gott et al. (2001) using $576$ measurements. We find after grouping the different results according to their primary type of measurement, the median estimates are given by $H_0=72.5^{+2.5}_{-8}$ km/sec/Mpc with errors corresponding to 95% c.l. (2$σ$) and $G=6.674702^{+0.0014}_{-0.0009} \times 10^{-11} \mathrm{N m^{2}kg^{-2}}$ corresponding to 68% c.l. (1$σ$).

astro-ph.CO↗