arXiv subjects

Sayan Saha

Publications and source records attributed to Sayan Saha.

14 recordsLinked to original sources

arXiv2026

Sensitivity of Next-Generation CMB Surveys to Neutrinos and Other Light Relics

Neutrinos and other light relics leave characteristic imprints in the cosmic microwave background anisotropies, making their observation a sensitive probe of the particle content and thermal history of the early universe. The energy density in these relativistic species is parameterized by their effective number $N_\mathrm{eff}$. Measuring this parameter at the percent level, which is a long-standing science goal of CMB-S4 and other experiments, would test a wide range of well-motivated physics within and beyond the Standard Model of particle physics. In this paper, we present Fisher-matrix forecasts of the projected sensitivity to $N_\mathrm{eff}$ of several CMB-S4 survey configurations considered during its extensive design phase. The conceptual design reaches $\sigma(N_\mathrm{eff}) < 0.03$ over its seven-year observing period, while the revised configuration achieves the same precision over a longer timescale. We complement these results with a cosmic-variance-limited survey over the same multipole range to quantify the room for improvement accessible with additional instrumental, observational, and theoretical efforts. Finally, we discuss the broad implications of precise $N_\mathrm{eff}$ measurements for the radiation sector, big bang nucleosynthesis, light thermal relics, and other early-universe physics. The forecasts presented in this work are performed with the publicly released DRAFT (Dark Radiation Anisotropy Flowdown Team) tool. It provides an end-to-end pipeline from simulated foreground maps and component separation to delensing and projected sensitivities for any cosmological parameter, and it can be directly applied to other cosmic microwave background survey designs.

astro-ph.CO↗

arXiv2026

Multi-sensor fusion for fine-guidance and milliarcsecond-level attitude estimation of balloon-borne telescope

Balloon-borne telescopes rely on fine-guidance systems to achieve milliarcsecond image stability despite residual disturbances from the balloon environment. In these systems, the Fast Steering Mirror (FSM) stabilizes the image in two focal-plane axes, but leaves systematic, field-dependent residual motion induced by boresight roll. This effect, referred to as roll leakage, becomes more important for wider fields of view. In this work, roll leakage is characterized using data from the 2023 Superpressure Balloon-borne Imaging Telescope (SuperBIT) science flight. SuperBIT is a 0.5-m near-ultraviolet to near-infrared telescope that demonstrated milliarcsecond-level image stability during its 45-night science flight. We find that passive focal-plane star-camera measurements correlate strongly with independent roll measurements across a large set of science exposures, showing that boresight roll frequently drives residual focal-plane motion. We then develop a simulation framework combining optical ray tracing, asynchronous guide-star measurements, estimation, and FSM control to study this behavior. The framework is used to compare single-star and multi-star guidance architectures under realistic flight disturbances. For the SuperBIT geometry, we find that multi-star estimation reduces average roll-induced science-field image motion by 31.8%, increasing to 77.4% for a representative geometry of GigaBIT, SuperBIT's planned larger-aperture successor. These results motivate further investigation of multi-star fine-guidance architectures for GigaBIT.

astro-ph.IM↗

arXiv2026

Lensing-Reconstructed Dark Matter-Intracluster Medium Coherence as a Probe of Cluster Dynamical State: Application to HSTFF, RELICS, and CLASH Clusters

We present the first application of Fourier-space coherence analysis between the lensing-reconstructed projected mass distribution and the X-ray-emitting intracluster medium to a sample of 49 observed galaxy clusters. Using publicly available HST convergence maps from the Hubble Frontier Fields, CLASH, and RELICS programs, together with Chandra X-ray imaging, we measure the scale-dependent coherence between the dark-matter-dominated surface mass density and the hot baryonic gas. We use the coherence length, l_CR, defined as the scale above which the two maps remain at least 90% coherent, as a diagnostic of cluster dynamical state. Across the sample, dynamically relaxed systems exhibit high coherence over a broad range of scales and small l_CR/r500, while disturbed and merging systems show a loss of coherence on intermediate and small scales, yielding larger l_CR/r500. The inferred coherence lengths show sensitivity to lens-model assumptions and to the heterogeneous extent of the available convergence maps. Nevertheless, the coherence signal remains physically interpretable and provides a stringent measure of dark-matter-gas alignment. Applying a conservative threshold, l_CR/r500 < 0.2, we find that only 16% of the sample is relaxed; this fraction rises to 41% for a more permissive threshold of l_CR/r500 < 0.4. Relative to previous X-ray and morphological classifications, we find a 24% disagreement, with the coherence method identifying more systems as dynamically disturbed. These results demonstrate that lensing-X-ray coherence provides a complementary, scale-resolved probe of cluster dynamical state, while highlighting the need for homogeneous, wide-field weak-lensing maps to control reconstruction and field-of-view systematics.

astro-ph.CO↗

arXiv2026

Convolution Operators on Weighted Hahn Spaces

This paper studies the convolution operator on weighted Hahn sequence spaces. The boundedness and compactness of these operators, together with the multiplier algebras of the weighted Hahn space and its dual, are investigated. A complete characterization of the spectrum and fine spectrum is obtained, with illustrative examples. The introduction of the weighted framework leads to the emergence of new multiplier and spectral properties.

math.FA↗

arXiv2026

Lensing in the Blue III: Weak Lensing Shape Catalogs of 30 Merging Galaxy Clusters

We present the weak gravitational lensing dataset from the Super-pressure Balloon-Borne Imaging Telescope (SuperBIT), which imaged 30 galaxy clusters during its 45 night flight in April to May 2023. SuperBIT is a first-of-its-kind balloon-borne imaging telescope that achieved near diffraction-limited observations in near-space conditions above 98% of the Earth's atmosphere. We use the metacalibration algorithm to obtain calibrated galaxy shapes for our target clusters and several calibration fields, enabling unbiased reconstruction of the weak-lensing signal. We employ several diagnostics throughout the pipeline, including assessments of point-spread function (PSF) modeling residuals and their impact on weak-lensing measurements, as well as tests for correlations between galaxy shapes and measured galaxy and PSF properties. To assess the multiplicative shear bias of the pipeline, we analyze a parallel set of simulated images that incorporate the real observing conditions from the flight, including measured SuperBIT PSFs, observed sky backgrounds, and detector noise, yielding a bias of $(1.1 \pm 7.8)$~per~cent.

astro-ph.CO↗

arXiv2026

Analysis of the Ventriloquism Aftereffect Using Network Theory Techniques

Ventriloquism After-Effect is the phenomenon where sustained exposure to the ventriloquist illusion causes a change in unisensory auditory localization towards the location where the visual stimulus was present. We investigate the recalibration in EEG networks that causes this change and the track the timeline of changes in the auditory processing pathway. Our results obtained using network analysis, non-stationary time series analysis and multivariate pattern classification show that recalibration takes place early in the auditory processing pathway and the after-effect decays with time after exposure to the illusion.

q-bio.NC↗

arXiv2025

Facile Salt-Assisted Hydrothermal Synthesis of Nanodiamonds from CHO Precursors: Atomic-Scale Mechanistic Insights

Hydrothermal synthesis offers an economical and scalable way to produce nanodiamonds under relatively mild, low-pressure and low-temperature conditions. However,its sustainability and the detailed mechanisms behind diamond formation in such environments are still not fully understood. In this work, we designed ten hydrothermal synthesis protocols using different CHO-based molecular precursors containing COOH and OH groups, such as organic acids, polyols, sugars, and polysaccharides.The reactions were carried out at 190 degrees Centigrade in chlorinated, strongly alkaline aqueous solutions with alkali and alkaline-earth metal ions. Using high-resolution transmission electron microscopy and X-ray photoelectron spectroscopy, we confirmed the presence of diamond-specific lattice planes and sp3-hybridized carbon structures. Our results show that the type of precursor, its molecular size, and the ionic composition of the solution play key roles in determining the defect patterns and polymorph distribution in the resulting nanodiamonds. Atomic-scale imaging showed both coherent and incoherent transitions from graphite to diamond, along with gradual lattice compression and complex twinning patterns. These observations provide direct insight into how interfacial crystallography and defect dynamics drive diamond formation in aqueous systems. Overall, the study positions hydrothermal synthesis as a sustainable, chemistry-driven, and tunable approach for creating nanodiamonds tailored for applications in quantum technologies, biomedicine, catalysis, and advanced materials.

cond-mat.mes-hall↗

arXiv2023

Cluster profiles from beyond-the-QE CMB lensing mass maps

Clusters of galaxies, being the largest collapsed structures in the universe, offer valuable insights into the nature of cosmic evolution. Precise calibration of the mass of clusters can be obtained by extracting their gravitational lensing signal on the Cosmic Microwave Background (CMB) fluctuations. We extend and test here the performance achieved on cluster scales by the parameter-free, maximum a posteriori (MAP) CMB lensing reconstruction method, which has been shown to be optimal in the broader context of CMB lensing mass map and power spectrum estimation. In the context of cluster lensing, the lensing signal of other large-scale structures acts as an additional source of noise. We show here that by delensing the CMB fluctuations around each and every cluster, this noise variance is reduced according to expectations. We also demonstrate that the well-known bias in the temperature quadratic estimator in this regime, sourced by the strong non-Gaussianity of the signal, is almost entirely mitigated without any scale cuts. Being statistically speaking an optimal and blind lensing mass map reconstruction, the MAP estimator is a promising tool for the calibration of the masses of clusters.

astro-ph.CO↗

arXiv2021

Bayesian estimation of our local motion from the Planck-2018 CMB temperature map

The largest fluctuation in the CMB sky is the CMB dipole, which is believed to be caused by the motion of our observation frame with respect to the CMB rest frame. This motion accounts for the known motion of the Solar System barycentre with a best-fit amplitude of $369$ km/s, in the direction ($\ell= 264^\circ$, $b=48^\circ$) in galactic coordinates. Along with the CMB dipole signal, this motion also causes an inevitable signature of statistical anisotropy in the higher multipoles due to the modulation and aberration of the CMB temperature and polarization fields. This leads to a correlation between adjacent CMB multipoles causing a non-zero value of the off-diagonal terms in the covariance matrix which can be captured in terms of the dipolar spectra of the bipolar spherical harmonics (BipoSH). In our work, we jointly infer the CMB power spectrum and the BipoSH spectrum in a Bayesian framework using the $\textit{Planck}$-2018 $\texttt{SMICA}$ temperature map. We detect amplitude and direction of the local motion consistent with the canonical value $v=369$ km/s inferred from CMB dipole with a statistical significance of $4.54\sigma$, $4.97\sigma$ and $5.23\sigma$ respectively from the masked temperature map with the available sky fraction $40.1\%$, $59.1\%$, and $72.2\%$, confirming the common origin of both the signals. The Bayes factor in favor of the canonical value is between $7$ to $8$ depending on the choice of mask. But it strongly disagrees (by a value of the Bayes factor about $10^{-10}-10^{-11}$) with a higher value of local motion which one can infer from the amplitude of the dipole signal obtained from the CatWISE2020 quasar catalog using the WISE and NEOWISE data set.

astro-ph.CO↗

arXiv2016

An MILP Approach for Real-time Optimal Controller Synthesis with Metric Temporal Logic Specifications

The fundamental idea of this work is to synthesize reactive controllers such that closed-loop execution trajectories of the system satisfy desired specifications that ensure correct system behaviors, while optimizing a desired performance criteria. In our approach, the correctness of a system's behavior can be defined according to the system's relation to the environment, for example, the output trajectories of the system terminate in a goal set without entering an unsafe set. Using Metric Temporal Logic (MTL) specifications we can further capture complex system behaviors and timing requirements, such as the output trajectories must pass through a number of way-points within a certain time frame before terminating in the goal set. Given a Mixed Logical Dynamical (MLD) system and system specifications in terms of MTL formula or simpler reach-avoid specifications, our goal is to find a closed-loop trajectory that satisfies the specifications, in non-deterministic environments. Using an MILP framework we search over the space of input signals to obtain such valid trajectories of the system, by adding constraints to satisfy the MTL formula only when necessary, to avoid the exponential complexity of solving MILP problems. We also present experimental results for planning a path for a mobile robot through a dynamically changing environment with a desired task specification.

eess.SY↗

arXiv2013

Comparative Studies on Decentralized Multiloop PID Controller Design Using Evolutionary Algorithms

Decentralized PID controllers have been designed in this paper for simultaneous tracking of individual process variables in multivariable systems under step reference input. The controller design framework takes into account the minimization of a weighted sum of Integral of Time multiplied Squared Error (ITSE) and Integral of Squared Controller Output (ISCO) so as to balance the overall tracking errors for the process variables and required variation in the corresponding manipulated variables. Decentralized PID gains are tuned using three popular Evolutionary Algorithms (EAs) viz. Genetic Algorithm (GA), Evolutionary Strategy (ES) and Cultural Algorithm (CA). Credible simulation comparisons have been reported for four benchmark 2x2 multivariable processes.

eess.SY↗

arXiv2012

Identification of Nonlinear Systems From the Knowledge Around Different Operating Conditions: A Feed-Forward Multi-Layer ANN Based Approach

The paper investigates nonlinear system identification using system output data at various linearized operating points. A feed-forward multi-layer Artificial Neural Network (ANN) based approach is used for this purpose and tested for two target applications i.e. nuclear reactor power level monitoring and an AC servo position control system. Various configurations of ANN using different activation functions, number of hidden layers and neurons in each layer are trained and tested to find out the best configuration. The training is carried out multiple times to check for consistency and the mean and standard deviation of the root mean square errors (RMSE) are reported for each configuration.

eess.SY↗

arXiv2012

Estimation, Analysis and Smoothing of Self-Similar Network Induced Delays in Feedback Control of Nuclear Reactors

This paper analyzes a nuclear reactor power signal that suffers from network induced random delays in the shared data network while being fed-back to the Reactor Regulating System (RRS). A detailed study is carried out to investigate the self similarity of random delay dynamics due to the network traffic in shared medium. The fractionality or selfsimilarity in the network induced delay that corrupts the measured power signal coming from Self Powered Neutron Detectors (SPND) is estimated and analyzed. As any fractional order randomness is intrinsically different from conventional Gaussian kind of randomness, these delay dynamics need to be handled efficiently, before reaching the controller within the RRS. An attempt has been made to minimize the effect of the randomness in the reactor power transient data with few classes of smoothing filters. The performance measure of the smoothers with fractional order noise consideration is also investigated into.

eess.SY↗

arXiv2012

Adaptive Gain and Order Scheduling of Optimal Fractional Order PI{\lambda}D{\mu} Controllers with Radial Basis Function Neural-Network

Gain and order scheduling of fractional order (FO) PI{\lambda}D{\mu} controllers are studied in this paper considering four different classes of higher order processes. The mapping between the optimum PID/FOPID controller parameters and the reduced order process models are done using Radial Basis Function (RBF) type Artificial Neural Network (ANN). Simulation studies have been done to show the effectiveness of the RBFNN for online scheduling of such controllers with random change in set-point and process parameters.

eess.SY↗