Astronomical data generally consists of 2 or more high-resolution axes, e.g., X,Y position on the sky or wavelength and position-along-one-axis (long-slit spectrometer). Analyzing these multi-dimension observations requires combining 3D source models (including velocity effects), instrument models, and multi-dimensional data comparison and fitting. A prototype of such a "Beyond-XSPEC" (Noble & Nowak, 2008) system is presented here using Chandra imag- ing and dispersed HETG grating data. Techniques used include: Monte Carlo event generation, chi-squared comparison, conjugate gradient fitting adapted to the Monte Carlo characteristics, and informative visualizations at each step. These simple baby steps of progress only scratch the surface of the computational potential that is available these days for astronomical analysis.
X-ray spectral fitting of astronomical sources requires convolving the intrinsic spectrum or model with the instrumental response. Standard forward modeling techniques have proven success in recovering the underlying physical parameters in moderate to high signal-to-noise regimes; however, they struggle to achieve the same level of accuracy in low signal-to-noise regimes. Additionally, the use of machine learning techniques on X-ray spectra requires access to the intrinsic spectrum. Therefore, the measured spectrum must be effectively deconvolved from the instrumental response. In this note, w…
Transition edge sensor microcalorimeters can measure x-ray and gamma-ray energies with very high energy resolution and high photon-collection efficiency. For this technology to reach its full potential in future x-ray observatories, each sensor must be able to measure hundreds or even thousands of photon energies per second. Current "optimal filtering" approaches to achieve the best possible energy resolution work only for photons well isolated in time, a requirement in direct conflict with the need for high-rate measurements. We describe a new analysis procedure to allow fitting for the pulse…
We present the SPEX software package for modeling and fitting X-ray spectra. Our group has developed spectral models, atomic data and code for X-ray applications since the 1970's. Since the 1990's these are further developed in the public SPEX package. In the last decades, X-ray spectroscopy has been revolutionized by the high-resolution grating spectrometers aboard XMM-Newton and Chandra. Currently, new high-resolution detectors aboard the Hitomi mission, and future missions, like XRISM and Athena, will provide another major step forward in spectral resolution. This poses challenges for us to…
Simulation-based inference with neural posterior estimation applied to X-ray spectral fitting -- III Deriving exact posteriors with dimension reduction and importance sampling
Institut de Recherche en Astrophysique et Planétologie（天体物理学与行星学研究所以）
CIGALE is a powerful multiwavelength spectral energy distribution (SED) fitting code for extragalactic studies. However, the current version of CIGALE is not able to fit X-ray data, which often provide unique insights into AGN intrinsic power. We develop a new X-ray module for CIGALE, allowing it to fit SEDs from the X-ray to infrared (IR). We also improve the AGN fitting of CIGALE from UV-to-IR wavelengths. We implement a modern clumpy two-phase torus model, SKIRTOR. To account for moderately extincted type 1 AGNs, we implement polar-dust extinction. We publicly release the source code (named…
High frequency quasi-periodic oscillations (QPOs) from weakly magnetized neutron stars display rapid frequency variability and high coherence with quality factors up to at least 200 at frequencies around 850 Hz. Their parameters have been estimated so far from standard min(chi2) fitting techniques, after combining a large number of Power Density Spectra (PDS), as to have the powers normally distributed. Accounting for the statistical properties of PDS, we apply a maximum likelihood method to derive the QPO parameters in the non Gaussian regime. The method presented is general, easy to implemen…
Multilayer X-ray mirrors consist of a coating of a large number of alternate layers of high Z and low Z materials with a typical thickness of 10-100 Angstrom, on a suitable substrate. Such coatings play an important role in enhancing the reflectivity of X-ray mirrors by allowing reflections at angles much larger than the critical angle of X-ray reflection for the given materials. Coating with an equal thickness of each bilayer enhances the reflectivity at discrete energies, satisfying Bragg condition. However, by systematically varying the bilayer thickness in the multilayer stack, it is possi…
Simulation-Based Inference with Neural Posterior Estimation applied to X-ray spectral fitting: Demonstration of working principles down to the Poisson regime
We present a powerful new tool for fitting broadband gamma-ray burst afterglow data, which can be used to determine the burst explosion parameters and the synchrotron radiation parameters. By making use of scale invariance between relativistic jets of different energies and different circumburst medium densities, and by capturing the output of high-resolution two-dimensional relativistic hydrodynamical (RHD) jet simulations in a concise summary, the jet dynamics are generated quickly. Our method calculates the full light curves and spectra using linear radiative transfer sufficiently fast to a…
The Sunyaev-Zel'dovich Array (SZA), an eight element interferometer designed to probe the Sunyaev-Zel'dovich effect (SZE) from galaxy clusters, which I helped construct and operate, is described here (Part I). I then use SZA observations to investigate the utility of a new, self-similar pressure profile for fitting SZE observations of galaxy clusters (Part II). The SZA 30-GHz receiver system probes angular scales ~1-5'. A model that can accurately describe a cluster's pressure profile over a correspondingly broad range of radii is therefore required. In the analysis presented here, I fit a 2-p…
Modern and future surveys effectively provide a panchromatic view for large numbers of extragalactic objects. Consistently modeling these multiwavelength survey data is a critical but challenging task for extragalactic studies. The Code Investigating GALaxy Emission (CIGALE) is an efficient PYTHON code for spectral energy distribution (SED) fitting of galaxies and active galactic nuclei (AGNs). Recently, a major extension of CIGALE (named X-CIGALE) has been developed to account for AGN/galaxy X-ray emission and improve AGN modeling at UV-to-IR wavelengths. Here, we apply X-CIGALE to different …
Simulation-based inference with neural posterior estimation applied to X-ray spectral fitting II -- High-resolution spectroscopy with the X-ray Integral Field Unit
While considerable advance has been made to account for statistical uncertainties in astronomical analyses, systematic instrumental uncertainties have been generally ignored. This can be crucial to a proper interpretation of analysis results because instrumental calibration uncertainty is a form of systematic uncertainty. Ignoring it can underestimate error bars and introduce bias into the fitted values of model parameters. Accounting for such uncertainties currently requires extensive case-specific simulations if using existing analysis packages. Here we present general statistical methods th…
We present NPTFit, an open-source code package, written in python and cython, for performing non-Poissonian template fits (NPTFs). The NPTF is a recently-developed statistical procedure for characterizing the contribution of unresolved point sources (PSs) to astrophysical data sets. The NPTF was first applied to Fermi gamma-ray data to give evidence that the excess of ~GeV gamma-rays observed in the inner regions of the Milky Way likely arises from a population of sub-threshold point sources, and the NPTF has since found additional applications studying sub-threshold extragalactic sources at h…
ELISA: A Bayesian X/$γ$-ray Spectral Analysis Toolkit
Institute of High Energy Physics, Chinese Academy of Sciences（中国科学院高能物理研究所）；Jinggangshan University（井冈山大学）；TIANFU Cosmic Ray Research Center（天府宇宙线研究中心）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）
The thermal Sunyaev-Zeldovich (SZ) effect and the X-ray emission offer separate and highly complementary probes of the thermodynamics of the intracluster medium. We present JoXSZ, the first publicly available code designed to jointly fit SZ and X-ray data coming from various instruments to derive the thermodynamic profiles of galaxy clusters. JoXSZ follows a fully Bayesian forward-modelling approach, accounts for the SZ calibration uncertainty and X-ray background level systematic. It improves upon most state-of-the-art, and not publicly available, analyses because it adopts the correct Poisso…
We present an improved method for the precise reconstruction of cosmic ray air showers above $10^{17}$ eV with sparse radio arrays. The method is based on the comparison of predictions for radio pulse shapes by CoREAS simulations to measured pulses. We applied our method to the data of Tunka-Rex, a 1 km$^2$ radio array in Siberia operating in the frequency band of 30-80 MHz. Tunka-Rex is triggered by the air-Cherenkov detector Tunka-133 and by scintillators (Tunka-Grande). The instrument collects air-shower data since 2012. The present paper describes updated data and signal analyses of Tunka-…
Parallel computing and steadily increasing computation speed have led to a new tool for analyzing multiple datasets and datatypes: fitting several datasets simultaneously. With this technique, physically connected parameters of individual data can be treated as a single parameter by implementing this connection into the fit directly. We discuss the terminology, implementation, and possible issues of simultaneous fits based on the X-ray data analysis tool Interactive Spectral Interpretation System (ISIS). While all data modeling tools in X-ray astronomy allow in principle fitting data from mult…
Fitting models to data using Bayesian inference is quite common, but when each point in parameter space gives a curve, fitting the curve to a data set requires new nuisance parameters, which specify the metric embedding the one-dimensional curve into the higher-dimensional space occupied by the data. A generic formalism for curve fitting in the context of Bayesian inference is developed which shows how the aforementioned metric arises. The result is a natural generalization of previous works, and is compared to oft-used frequentist approaches and similar Bayesian techniques.
