arXiv subjects

Supratik Bose

Publications and source records attributed to Supratik Bose.

3 recordsLinked to original sources

arXiv2026

Shaping SHAPE - A spectro-polarimeter onboard Chandrayaan-3 to observe Earth as an Exoplanet

Spectro-polarimetry of HAbitable Planet Earth (SHAPE) is an experimental instrument onboard the Propulsion Module (Orbiter) of the Chandrayaan-3 mission, designed to perform disc-integrated spectro-polarimetric observations of Earth from lunar and highly elliptical Earth orbits. SHAPE is a compact, lightweight spectro-polarimeter comprising three subsystems: the Electro-Optical Detector System (EODS)-Optics, EODS-Electronics, and Radio Frequency Source (RFS). An Acousto-Optic Tunable Filter (AOTF), driven by an in-house-developed 80$-$135 MHz RF source, provides spectral filtering in the near-infrared (NIR) wavelength range of 1.0$-$1.7 $\mu$m and produces two narrow-band beams with mutually perpendicular linear polarization states. The instrument optics, with a field of view of approximately 2.6{\deg}, focus the two beams onto InGaAs detectors. A spectral resolution of 2$-$4 nm is achieved using in-house-designed low-noise front-end electronics. The instrument also incorporates processing and power electronics for signal processing, detector biasing, and subsystem control. We present the overall instrument design, results from pre-launch ground-based testing, and in-orbit operational performance. The current configuration enables SHAPE to measure disc-integrated signatures of Earth over a range of phase angles, providing a test bed for characterizing Earth-like exoplanets and benchmarking future exoplanet observations.

astro-ph.IM↗

arXiv2024

SHAPE -- A Spectro-Polarimeter Onboard Propulsion Module of Chandrayaan-3 Mission

SHAPE (Spectro-polarimetry of HAbitable Planet Earth) is an experiment onboard the Chandrayaan-3 Mission, designed to study the spectro-polarimetric signatures of the habitable planet Earth in the near-infrared (NIR) wavelength range (1.0 - 1.7 $\mu$m). The spectro-polarimeter is the only scientific payload (experimental in nature) on the Propulsion Module (PM) of the Chandrayaan-3 mission. The instrument is a compact and lightweight spectro-polarimeter with an Acousto-Optic Tunable Filter (AOTF) at its core. The AOTF operates in the frequency range of 80 MHz to 135 MHz with a power of 0.5 - 2.0 Watts. The two output beams (e-beam and o-beam) from the AOTF are focused onto two InGaAs detectors (pixelated, 1D linear array) with the help of focusing optics. The primary (aperture) optics, with a diameter of $\sim$2 mm, collects the NIR light for input to the AOTF, defining the field of view (FOV) of 2.6$^\circ$. The payload has a mass of 4.8 kg and operates at a power of 25 Watts. This manuscript highlights some of the ground-based results, including the post-launch initial performance of the payload while orbiting around the Moon to observe Earth.

astro-ph.IM↗

arXiv2022

Machine learning based surrogate modeling with SVD enabled training for nonlinear civil structures subject to dynamic loading

The computationally expensive estimation of engineering demand parameters (EDPs) via finite element (FE) models, while considering earthquake and parameter uncertainty limits the use of the Performance Based Earthquake Engineering framework. Attempts have been made to substitute FE models with surrogate models, however, most of these models are a function of building parameters only. This necessitates re-training for earthquakes not previously seen by the surrogate. In this paper, the authors propose a machine learning based surrogate model framework, which considers both these uncertainties in order to predict for unseen earthquakes. Accordingly,earthquakes are characterized by their projections on an orthonormal basis, computed using SVD of a representative ground motion suite. This enables one to generate large varieties of earthquakes by randomly sampling these weights and multiplying them with the basis. The weights along with the constitutive parameters serve as inputs to a machine learning model with EDPs as the desired output. Four competing machine learning models were tested and it was observed that a deep neural network (DNN) gave the most accurate prediction. The framework is validated by using it to successfully predict the peak response of one-story and three-story buildings represented using stick models, subjected to unseen far-field ground motions.

stat.ML↗