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Ninan Sajeeth Philip

Publications and source records attributed to Ninan Sajeeth Philip.

15 recordsLinked to original sources
arXiv2025

Computational Astrophysics, Data Science & AI/ML in Astronomy: A Perspective from Indian Community

In contemporary astronomy and astrophysics (A&A), the integration of high-performance computing (HPC), big data analytics, and artificial intelligence/machine learning (AI/ML) has become essential for advancing research across a wide range of scientific domains. These tools are playing an increasingly pivotal role in accelerating discoveries, simulating complex astrophysical phenomena, and analyzing vast amounts of observational data. For India to maintain and enhance its competitive edge in the global landscape of computational astrophysics and data science, it is crucial for the Indian A&A community to fully embrace these transformative technologies. Despite limited resources, the expanding Indian community has already made significant scientific contributions. However, to remain globally competitive in the coming years, it is vital to establish a robust national framework that provides researchers with reliable access to state-of-the-art computational resources. This system should involve the regular solicitation of computational proposals, which can be assessed by domain experts and HPC specialists, ensuring that high-impact research receives the necessary support. By building such a system, India can cultivate the talent, infrastructure, and collaborative environment necessary to foster world-class research in computational astrophysics and data science.

astro-ph.IM↗
arXiv2020

Correlation between relativistic reflection fraction and photon index in NuSTAR sample of Seyfert 1 AGN

The primary X-ray emission from AGN, described by a power law, irradiates the accretion disc producing reflection features in the spectrum. The reflection features arising from the inner regions of the disc can be significantly modified by the relativistic effects near the black hole. We investigate the relationship between the relativistic reflection fraction $R_f$, defined as the ratio of the coronal intensity that illuminates the accretion disc to the coronal intensity observed directly, and the hard X-ray photon index $Γ$ of a NuSTAR sample of Seyfert 1 galaxies. The X-ray spectra are modelled using relxill code which helps to directly obtain the reflection fraction of a relativistically smeared reflection component. The parameter $R_f$ depends on the amount of Comptonised X-ray emission intercepted by the inner accretion disc. We found a positive correlation between $Γ$ and $R_f$ in our sample. Seed photons from a larger area of an accretion disc entering the corona will result in increased cooling of the coronal plasma, giving rise to steeper X-ray spectrum. The corona irradiating the larger area of the disc will result in higher reflection fraction. Thus, the observed $R_f-Γ$ relation is most likely related to the variations in the disc-corona geometry of AGN.

astro-ph.HE↗
arXiv2017

Information Retrieval and Recommendation System for Astronomical Observatories

We present a machine learning based information retrieval system for astronomical observatories that tries to address user defined queries related to an instrument. In the modern instrumentation scenario where heterogeneous systems and talents are simultaneously at work, the ability to supply with the right information helps speeding up the detector maintenance operations. Enhancing the detector uptime leads to increased coincidence observation and improves the likelihood for the detection of astrophysical signals. Besides, such efforts will efficiently disseminate technical knowledge to a wider audience and will help the ongoing efforts to build upcoming detectors like the LIGO-India etc even at the design phase to foresee possible challenges. The proposed method analyses existing documented efforts at the site to intelligently group together related information to a query and to present it on-line to the user. The user in response can further go into interesting links and find already developed solutions or probable ways to address the present situation optimally. A web application that incorporates the above idea has been implemented and tested for LIGO Livingston, LIGO Hanford and Virgo observatories.

astro-ph.IM↗
arXiv2017

Determining the torus covering factors for a sample of type 1 AGN in the local Universe

In the unified scheme of active galactic nuclei, a dusty torus absorbs and then reprocesses a fraction of the intrinsic luminosity which is emitted at longer wavelengths. Thus, subject to radiative transfer corrections, the fraction of the sky covered by the torus as seen from the central source (known as the covering factor $f_c$) can be estimated from the ratio of the infrared to the bolometric luminosities of the source as $f_c=L_{\rm torus}/L_{\rm Bol}$. However, the uncertainty in determining $L_{\rm Bol}$ has made the estimation of covering factors by this technique difficult, especially for AGN in the local Universe where the peak of the observed SEDs lies in the UV (ultraviolet). Here, we determine the covering factors of an X-ray/optically selected sample of 51 type~1 AGN. The bolometric luminosities of these sources are derived using a self-consistent, energy-conserving model that estimates the contribution in the unobservable far-UV region, using multi-frequency data obtained from SDSS, \textit{XMM-Newton}, \textit{WISE}, 2MASS and UKIDSS. We derive a mean value of $f_c\sim$0.30 with a dispersion of 0.17. Sample correlations, combined with simulations, show that $f_c$ is more strongly anti-correlated with $λ_{\rm Edd}$ than with $L_{\rm Bol}$. This points to large-scale torus geometry changes associated with the Eddington-dependent accretion flow, rather than a receding torus, with its inner sublimation radius determined solely by heating from the central source. Furthermore, we do not see any significant change in the distribution of $f_c$ for sub-samples of radio-loud sources or Narrow Line Seyfert~1 galaxies (NLS1s), though these sub-samples are small.

astro-ph.HE↗
arXiv2017

Transient Classification in LIGO data using Difference Boosting Neural Network

Detection and classification of transients in data from gravitational wave detectors are crucial for efficient searches for true astrophysical events and identification of noise sources. We present a hybrid method for classification of short duration transients seen in gravitational wave data using both supervised and unsupervised machine learning techniques. To train the classifiers we use the relative wavelet energy and the corresponding entropy obtained by applying one-dimensional wavelet decomposition on the data. The prediction accuracy of the trained classifier on 9 simulated classes of gravitational wave transients and also LIGO's sixth science run hardware injections are reported. Targeted searches for a couple of known classes of non-astrophysical signals in the first observational run of Advanced LIGO data are also presented. The ability to accurately identify transient classes using minimal training samples makes the proposed method a useful tool for LIGO detector characterization as well as searches for short duration gravitational wave signals.

astro-ph.IM↗
arXiv2016

UV and X-Ray Variability of the Narrow-Line Seyfert 1 Galaxy Ark 564

We analyse eight XMM-Newton observations of the bright Narrow-Line Seyfert 1 galaxy Arakelian 564 (Ark 564). These observations, separated by ~6 days, allow us to look for correlations between the simultaneous UV emission (from the Optical Monitor) with not only the X-ray flux but also with the different X-ray spectral parameters. The X-ray spectra from all the observations are found to be adequately fitted by a double Comptonization model where the soft excess and the hard X-ray power law are represented by thermal Comptonization in a low temperature plasma and hot corona, respectively. Apart from the fluxes of each component, the hard X-ray power law index is found to be variable. These results suggest that the variability is associated with changes in the geometry of the inner region. The UV emission is found to be variable and well correlated with the high energy index while the correlations with the fluxes of each component are found to be weaker. Using viscous time-scale arguments we rule out the possibility that the UV variation is due to fluctuating accretion rate in the outer disc. If the UV variation is driven by X-ray reprocessing, then our results indicate that the strength of the X-ray reprocessing depends more on the geometry of the X-ray producing inner region rather than on the X-ray luminosity alone.

astro-ph.HE↗
arXiv2014

A Wavelet Based Algorithm for the Identification of Oscillatory Event-Related Potential Components

Event Related Potentials (ERPs) are very feeble alterations in the ongoing Electroencephalogram (EEG) and their detection is a challenging problem. Based on the unique time-based parameters derived from wavelet coefficients and the asymmetry property of wavelets a novel algorithm to separate ERP components in single-trial EEG data is described. Though illustrated as a specific application to N170 ERP detection, the algorithm is a generalized approach that can be easily adapted to isolate different kinds of ERP components. The algorithm detected the N170 ERP component with a high level of accuracy. We demonstrate that the asymmetry method is more accurate than the matching wavelet algorithm and t-CWT method by 48.67 and 8.03 percent respectively. This paper provides an off-line demonstration of the algorithm and considers issues related to the extension of the algorithm to real-time applications.

cs.OH↗
arXiv2011

Photometric Catalogue of Quasars and Other Point Sources in the Sloan Digital Sky Survey

We present a catalogue of about 6 million unresolved photometric detections in the Sloan Digital Sky Survey Seventh Data Release classifying them into stars, galaxies and quasars. We use a machine learning classifier trained on a subset of spectroscopically confirmed objects from 14th to 22nd magnitude in the SDSS {\it i}-band. Our catalogue consists of 2,430,625 quasars, 3,544,036 stars and 63,586 unresolved galaxies from 14th to 24th magnitude in the SDSS {\it i}-band. Our algorithm recovers 99.96% of spectroscopically confirmed quasars and 99.51% of stars to i $\sim$21.3 in the colour window that we study. The level of contamination due to data artefacts for objects beyond $i=21.3$ is highly uncertain and all mention of completeness and contamination in the paper are valid only for objects brighter than this magnitude. However, a comparison of the predicted number of quasars with the theoretical number counts shows reasonable agreement.

astro-ph.IM↗
arXiv2010

A Learning Algorithm based on High School Teaching Wisdom

A learning algorithm based on primary school teaching and learning is presented. The methodology is to continuously evaluate a student and to give them training on the examples for which they repeatedly fail, until, they can correctly answer all types of questions. This incremental learning procedure produces better learning curves by demanding the student to optimally dedicate their learning time on the failed examples. When used in machine learning, the algorithm is found to train a machine on a data with maximum variance in the feature space so that the generalization ability of the network improves. The algorithm has interesting applications in data mining, model evaluations and rare objects discovery.

cs.AI↗
arXiv2010

Results from the Supernova Photometric Classification Challenge

We report results from the Supernova Photometric Classification Challenge (SNPCC), a publicly released mix of simulated supernovae (SNe), with types (Ia, Ibc, and II) selected in proportion to their expected rate. The simulation was realized in the griz filters of the Dark Energy Survey (DES) with realistic observing conditions (sky noise, point-spread function and atmospheric transparency) based on years of recorded conditions at the DES site. Simulations of non-Ia type SNe are based on spectroscopically confirmed light curves that include unpublished non-Ia samples donated from the Carnegie Supernova Project (CSP), the Supernova Legacy Survey (SNLS), and the Sloan Digital Sky Survey-II (SDSS-II). A spectroscopically confirmed subset was provided for training. We challenged scientists to run their classification algorithms and report a type and photo-z for each SN. Participants from 10 groups contributed 13 entries for the sample that included a host-galaxy photo-z for each SN, and 9 entries for the sample that had no redshift information. Several different classification strategies resulted in similar performance, and for all entries the performance was significantly better for the training subset than for the unconfirmed sample. For the spectroscopically unconfirmed subset, the entry with the highest average figure of merit for classifying SNe~Ia has an efficiency of 0.96 and an SN~Ia purity of 0.79. As a public resource for the future development of photometric SN classification and photo-z estimators, we have released updated simulations with improvements based on our experience from the SNPCC, added samples corresponding to the Large Synoptic Survey Telescope (LSST) and the SDSS, and provided the answer keys so that developers can evaluate their own analysis.

astro-ph.CO↗
arXiv2002

A difference boosting neural network for automated star-galaxy classification

In this paper we describe the use of a new artificial neural network, called the difference boosting neural network (DBNN), for automated classification problems in astronomical data analysis. We illustrate the capabilities of the network by applying it to star galaxy classification using recently released, deep imaging data. We have compared our results with classification made by the widely used Source Extractor (SExtractor) package. We show that while the performance of the DBNN in star-galaxy classification is comparable to that of SExtractor, it has the advantage of significantly higher speed and flexibility during training as well as classification.

astro-ph↗
arXiv2001

On the predictability of Rainfall in Kerala- An application of ABF Neural Network

Rainfall in Kerala State, the southern part of Indian Peninsula in particular is caused by the two monsoons and the two cyclones every year. In general, climate and rainfall are highly nonlinear phenomena in nature giving rise to what is known as the `butterfly effect'. We however attempt to train an ABF neural network on the time series rainfall data and show for the first time that in spite of the fluctuations resulting from the nonlinearity in the system, the trends in the rainfall pattern in this corner of the globe have remained unaffected over the past 87 years from 1893 to 1980. We also successfully filter out the chaotic part of the system and illustrate that its effects are marginal over long term predictions.

cs.NE↗
arXiv2001

Chaos for Stream Cipher

This paper discusses mixing of chaotic systems as a dependable method for secure communication. Distribution of the entropy function for steady state as well as plaintext input sequences are analyzed. It is shown that the mixing of chaotic sequences results in a sequence that does not have any state dependence on the information encrypted by them. The generated output states of such a cipher approach the theoretical maximum for both complexity measures and cycle length. These features are then compared with some popular ciphers.

cs.CR↗