**Finalflash** is a Python package designed for primary beam corrections of uGMRT radio interferometric images. The software uses frequency-dependent beam models and FITS file handling to improve the accuracy of radio astronomical data. It is open source and available under the MIT License. The code is hosted at https://github.com/arpan-52/Finalflash.

Upcoming surveys with new radio observatories such as the Square Kilometre Array will gen-
erate a wealth of imaging data containing large numbers of radio galaxies. Different classes
of radio galaxies can be used as tracers of the cosmic environment, including the dark matter
density field, to address key cosmological questions. Classifying these galaxies based on mor-
phology is thus an important step towards achieving the science goals of next generation radio
surveys. Radio galaxies have been traditionally classified as Fanaroff–Riley (FR) I and II,
although some exhibit more complex ‘bent’ morphologies arising from environmental factors
or intrinsic properties. In this work, we present the FIRST Classifier, an online system for
automated classification of Compact and Extended radio sources. We developed the FIRST
Classifier based on a trained deep Convolutional Neural Network model to automate the mor-
phological classification of compact and extended radio sources observed in the FIRST radio
survey. Our model achieved an overall accuracy of 97 per cent and a recall of 98 per cent,
100 per cent, 98 per cent, and 93 per cent for Compact, BENT, FRI, and FRII galaxies, re-
spectively. The current version of the FIRST classifier is able to predict the morphological
class for a single source or for a list of sources as Compact or Extended (FRI, FRII, and
BENT).

We present PyMerger, a Python tool for detecting binary black hole (BBH) mergers from the Einstein Telescope (ET), based on a Deep Residual Neural Network model (ResNet). ResNet was trained on data combined from all three proposed sub-detectors of ET (TSDCD) to detect BBH mergers. Five different lower frequency cutoffs (Flow): 5 Hz, 10 Hz, 15 Hz, 20 Hz, and 30 Hz, with match-filter Signal-to-Noise Ratio (MSNR) ranges: 4-5, 5-6, 6-7, 7-8, and >8, were employed in the data simulation. Compared to previous work that utilized data from single sub-detector data (SSDD), the detection accuracy from TSDCD has shown substantial improvements, increasing from 60%, 60.5%, 84.5%, 94.5% to 78.5%, 84%, 99.5%, 100%, and 100% for sources with MSNR of 4-5, 5-6, 6-7, 7-8, and >8, respectively. The ResNet model was evaluated on the first Einstein Telescope mock Data Challenge (ET-MDC1) dataset, where the model demonstrated strong performance in detecting BBH mergers, identifying 5,566 out of 6,578 BBH events, with optimal SNR starting from 1.2, and a minimum and maximum DL of 0.5 Gpc and 148.95 Gpc, respectively. Despite being trained only on BBH mergers without overlapping sources, the model achieved high BBH detection rates. Notably, even though the model was not trained on BNS and BHNS mergers, it successfully detected 11,477 BNS and 323 BHNS mergers in ET-MDC1, with optimal SNR starting from 0.2 and 1, respectively, indicating its potential for broader applicability.

flashcurve is a novel, powerful, deep-learning-based approach to estimate the necessary time windows for adaptive binning light curves in Fermi-LAT data using raw photon data. Gamma rays measured by the Fermi-LAT satellite tell us a lot about the processes taking place in high-energetic astrophysical objects. The fluxes coming from these objects are, however, extremely variable. Hence, gamma-ray light curves produced by flashcurve optimally use adaptive bin sizes in order to retrieve most information about the source dynamics and to combine gamma-ray observations in a multi-messenger perspective.

Extensible spacetime agnostic general relativistic ray-tracing (GRRT): Gradus.jl is a suite of tools related to tracing geodesics and calculating observational signatures of accreting compact objects. Gradus.jl requires only a specification of the non-zero metric components of a chosen spacetime in order to solve the geodesic equation and compute a wide variety of trajectories and orbits. Various algorithms for calculating physical quantities are implemented generically, so they may be used with different classes of spacetime with minimal effort.

Mosaic is a software package that consists of an interferometric pattern simulator and characterizer, an optimized tiling generator and a beamforming weights calculator. It is being used in the filter-banking beamformer in MeerKAT telescope and more than 200 pulsars have been discovered from the multiple beam observations supported by Mosaic.

Falcon-DM simulates intermediate mass ratio inspirals in DM spikes. This lightweight N-body code is written in C++ and is specifically tuned for simulating IMRIs embedded in dark matter (DM) spikes. It features a 2nd order Drift-Kick-Drift integrator using the symplectic HOLD scheme and symmetrized, individual, time-steps for accurate time-integration. Falcon-DM also offers post-Newtonian (PN) effects up to PN2.5 using the auxiliary velocity algorithm.

Heracles manages harmonic-space statistics on the sphere. It takes catalogs of positions and function values on the sphere and turns them into angular power spectra and mixing matrices. Heracles is both a Python library, to be used in notebooks or data processing pipelines, and a tool for running measurements from the command line using a configuration file.

fastPTA forecasts the sensitivity of future Pulsar Timing Array (PTA) configurations and assesses constraints on Stochastic Gravitational Wave Background (SGWB) parameters. The code can generate mock PTA catalogs with noise levels compatible with current and future PTA experiments. These catalogs can then be used to perform Fisher forecasts of MCMC simulations.

StellarSpectraObservationFitting (SSOF) measures radial velocities and creates data-driven models (with fast, physically-motivated Gaussian Process regularization) for the time-variable spectral features for both the telluric transmission and stellar spectrum measured by Extremely Precise Radial Velocity (EPRV) spectrographs (while accounting for the wavelength-dependent instrumental line-spread function). Written in Julia, SSOF provides two methods for estimating the uncertainties on the RVs and model scores based on the photon uncertainties in the original data. For quick estimates of the uncertainties, the code looks at the local curvature of the likelihood space; the second method for estimating errors is via bootstrap resampling.