SDSS/BOSS-RCA is a Python command-line and guided-menu tool for post-pipeline residual-covariance advisory analysis of selected SDSS/BOSS spectral samples. It builds auditable RCA matrices from SDSS/BOSS DR12 spec-lite or plate-MJD-fiber selections, applies conservative mask/inverse-variance handling, computes residual-correlation diagnostics, evaluates alignment-sensitive diagnostic controls, and produces conservative advisor decisions focused on naive stacking, averaging, independence, and uncertainty assumptions.

The software automates the end-to-end diagnostic workflow, from selected-spectrum ingestion and RCA matrix construction to residual-correlation diagnostics, diagnostic controls, advisor decisions, report generation, and traceability packaging. It generates author- and reviewer-facing PDF reports, visual diagnostic summaries, JSON manifests, SHA-256 fingerprints, QR/fingerprint verification blocks, and reproducible audit artifacts, reducing the manual time required to assemble, document, and review residual-covariance evidence for a selected SDSS/BOSS spectral matrix.
SDSS/BOSS-RCA belongs to the broader FRANJAMAR-RCA ecosystem of post-pipeline residual-covariance advisor tools. Within this ecosystem, several survey- and mission-specific RCA branches share a common diagnostic philosophy, partially aligned RCA command-line grammar, guided-menu design, traceability model, report-generation approach, and reviewer-facing interpretation boundary, while each branch retains its own data-ingestion layer, adapters, validation examples, and instrument- or survey-specific limits.

SDSS/BOSS-RCA does not replace the official SDSS/BOSS pipeline, recalibrate spectra, validate redshifts, certify data quality, or validate physical or cosmological conclusions. Its purpose is to make residual-covariance assumptions visible, reproducible, auditable, and easier to inspect for selected SDSS/BOSS spectral matrices.

This code is a work on improving the simulation of integrated pulse profiles of pulsars. Pulsars, highly magnetized rotating neutron stars, serve as precise cosmic clocks useful for studying gravity and the interstellar medium. Although periodic pulses from many pulsars are observed and modeled at different radio frequencies, a robust realistic simulation of their profiles utilizing their parameters remain underdeveloped. The present code is developed to focus on building a physically consistent model to reproduce observed pulse shapes across radio frequency range. It is developed by using Band 5 data from InPTA and EPN database to generate a profile parameter library as an input to the simulator. The simulations of the integrated pulse profiles from this code will be useful in coherently understanding the various aspects of the pulsar emission mechanism. By refining simulation techniques and incorporating the propagation effects of the interstellar medium, the current code proves to be helpful in generating synthetic, yet realistic models. The results are expected to contribute to the interpretation of observational data and to the development of improved timing and emission models for pulsars.

DESI-RCA is a Python command-line and guided-menu tool for post-pipeline residual-covariance advisory analysis and traceable reporting for DESI spectral products. It prepares controlled spectral matrices from DESI coadd products, constructs residual diagnostic products, computes spectrum-by-spectrum residual-correlation diagnostics, evaluates null controls, and produces conservative advisory decisions focused on stacking caution and residual-covariance risk.

The tool builds on the residual-covariance diagnostic methodology demonstrated in the peer-reviewed Astronomy and Computing article by F. Javier Martinez Sanchez, "Forensic Characterization of Correlated Residuals in Large Spectroscopic Pipelines: A Case Study of SDSS/BOSS Data" (Astronomy and Computing, 57, 101155, 2026; DOI: https://doi.org/10.1016/j.ascom.2026.101155), and adapts that RCA-style approach to DESI spectral products.

DESI-RCA preserves DESI metadata when available, including fiber, petal, spectrograph, tile, night, arm, survey, observing programme, and target-class information. This enables metadata-aware and fiber-aware residual-covariance inspection while avoiding causal claims about instrumental origin.
DESI-RCA is part of the broader FRANJAMAR-RCA ecosystem of Residual Covariant Advisor tools, which also includes JWST-RCA, EUCLID-RCA, and RUBIN-RCA. The common RCA philosophy is to provide post-pipeline residual-covariance diagnostics, null controls, stacking-caution advisories, traceable reports, and reproducible verification packages, while adapting ingestion and metadata handling to each survey or instrument.

Outputs include PDF reports, visual diagnostic summaries, machine-readable manifests, SHA-256 fingerprints, report packages, and package-verification results. DESI-RCA does not replace the DESI pipeline, certify DESI data products, validate cosmological conclusions, or claim pipeline failure. Its purpose is to make residual-covariance structure visible, reproducible, and auditable for selected DESI spectral subsets.

Browser-based suite of 26 interactive calculators for the Omega
Centauri (NGC 5139) intermediate-mass black hole debate, Fermi
Paradox analysis, and Macro Transcension Hypothesis. Tools cover
stellar kinematics (M-σ estimator, velocity dispersion), pulsar
acceleration constraints using TRAPUM 2026 data, Gaia DR4 precision
forecasting, ADAF/RIAF accretion spectral energy distributions with
JWST cross-checks, gravitational-wave horizon plotting (LISA, ET,
CE, PTA), Bayesian evidence aggregation, SETI sensitivity (radio
and IR excess), cosmological distances, and Kardashev-scale
engineering tools. All tools are hash-addressable (permalink-stable
state), browser-only (no backend, no PII), and 23 are exposed via a
Model Context Protocol (MCP) server. Curated measurement data is
CC0; code is MIT; prose is CC BY 4.0.

idl_emcee is an IDL/GDL implementation of the affine-invariant Markov chain Monte Carlo ensemble sampler, also known as the MCMC Hammer. The package is based on the S-Lang/ISIS implementation isis_emcee by M. Nowak (2016) and follows the sampler proposed by Goodman & Weare (2010), implemented in Python as emcee (ascl:1303.002). idl_emcee provides routines for generating MCMC samples, propagating uncertainties in input parameters through user-defined functions, and estimating confidence intervals. It supports both IDL and GDL and is intended for Bayesian uncertainty analysis and error propagation.

slmpi_emcee is an MPI-parallelized implementation of the S-Lang MCMC Hammer for the Interactive Spectral Interpretation System (ISIS) (ascl:1302.002). It extends the S-Lang isis_emcee implementation of the affine-invariant ensemble sampler by M. Nowak (2016), originally proposed by Goodman & Weare (2010) and popularized by the Python emcee package (ascl:1303.002), by distributing MCMC walkers across MPI processes through the S-Lang MPI interface. The package is designed to accelerate Bayesian parameter estimation, uncertainty analysis, and spectral-model fitting workflows in ISIS. It is intended for astronomers performing spectral model fitting in X-ray high-energy astrophysics.

pyEQUIB performs plasma diagnostics, abundance analysis, and extinction analysis of ionized nebulae from measured emission-line fluxes. Written in pure Python, the package implements proEQUIB functionality and provides routines for collisionally excited lines, recombination lines, Balmer-line extinction analysis, electron-temperature and electron-density diagnostics, ionic abundances, atomic level populations, line emissivities, and critical densities. The package includes routines for collisionally excited lines that use the multi-level-atom statistical-equilibrium solver based on the Fortran program EQUIB (ascl:1603.005), along with recombination-line routines for selected heavy-element ions. pyEQUIB uses AtomNeb to read atomic data such as collision strengths, transition probabilities, and recombination coefficients, relies on NumPy and SciPy, and is intended for astronomers studying ionized nebulae with the Python programming language.

proEQUIB performs plasma diagnostics and abundance analysis of ionized gaseous nebulae from measured emission-line fluxes. Written in IDL/GDL, the library determines interstellar extinction, dereddens observed fluxes, and derives electron temperatures, electron densities, ionic abundances, atomic level populations, line emissivities, and critical densities. The package includes routines for collisionally excited lines based on the statistical-equilibrium approach for multi-level atoms of the Fortran program EQUIB (ascl:1603.005), and recombination-line routines for selected heavy-element ions. proEQUIB uses AtomNeb to read atomic data such as collision strengths, transition probabilities, and recombination coefficients, and is suitable for both integrated spectra and spatially resolved integral-field spectroscopy studies.

Earth-like Planet Calculator estimates how astrophysical and planetary assumptions affect counts of potentially Earth-like candidate environments in the Milky Way and, by scaling, the observable universe. The browser-based code applies a transparent multiplicative scenario model conceptually related to Drake-equation-style reasoning, with literature-informed presets, calibration-status indicators, source-linked parameters, Monte Carlo uncertainty propagation, Galactic Habitable Zone assumptions, atmospheric-retention and planetary-property filters, nearest-candidate distance estimates, and observable-universe scaling. The code can support exploratory investigations of questions associated with the Fermi paradox by quantifying how different assumptions influence candidate-environment counts. It reports deterministic and Monte Carlo outputs as conditional scenario estimates derived from explicit assumptions, rather than as observational detections, empirical occurrence-rate determinations, or claims about confirmed biospheres, inhabited worlds, or technologically detectable activity.

pydive identifies and characterizes cosmic voids in large-scale structure catalogs using Delaunay triangulation. A Python implementation and extension of the Delaunay TrIangulation Void findEr (ascl:2605.019), it replaces the original SciPy-based backend with a CGAL-based implementation. The code computes simplex- and void-related quantities such as areas, volumes, sphericity, circumsphere radii, and Delaunay-based density estimates. pydive also provides routines to construct extended void catalogs and to split the void sample into central and satellite voids.