arXiv subjects

Daniela Huppenkothen

Publications and source records attributed to Daniela Huppenkothen.

At least 19 recordsLinked to original sources

arXiv2026

High performance Stingray

X-ray astrophysical objects show variability on a wide range of timescales, i.e. from fractions of second to years. The open-source Python library stingray is able to perform time series analyses with a focus on high-energy astrophysics. Comprising the most commonly used Fourier analyses techniques, it also supports a range of additional extensions able to analyse pulsar data, simulate data sets and perform statistical modelling. With the latest release of the code, new Fourier methods and support for additional missions have been implemented, making Stingray more easily adaptable and extendable to other use cases. In this paper we focus on testing the performance and robustness of the latest version of stingray . We consider the possible different behaviour of the code when dealing with data sets smaller or larger than the RAM. We address the problem of dealing with large data sets and implemented parallel versions of the slowest methods in this regime. For small data sets, we investigate the possibility of a porting in GPU of key functions of the code.

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arXiv2026

A NICER view of PSR J1614$-$2230: a massive and compact millisecond pulsar

Using pulse profile modeling, we obtain the mass-radius measurement of a millisecond pulsar (MSP) with data from the Neutron Star Interior Composition ExploreR, XMM-Newton and the Chandra X-ray Observatory. We report here the radius of PSR J1614$-$2230, the second most massive MSP confirmed by radio timing. All of the data sets are well described by a simple model composed of two circular hot spots. The final result yields an equatorial radius of $R_{\rm eq}=10.06 ^{+1.25}_{-0.87}\,$km, and a gravitational mass of $M=1.937^{+0.012}_{-0.013}\,M_{\odot}$ (equally tailed 68% credible intervals). Although a non-thermal component was previously reported at higher energies, we find no sign of it in either our phase-averaged or phase-resolved spectral analyses. Using new relations linking the compactness to oblateness or surface gravity, and tailored to the spin frequency of PSR J1614$-$2230, we infer a configuration with one hot spot near the pole, and another near the equator. The tight mass posterior is essentially informed by radio timing, while the radius constraint is not as tight due to the low source signal (8.5$σ$ X-ray pulse significance). However, over all geometries and atmosphere models tested, the radius posterior tends toward low values ($\lesssim12.08\,$km, 90th percentile in all cases).

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arXiv2026

Fast Inference on Astronomical Time Series with Trans-Dimensional Flow Matching Posterior Estimation

The analysis of time series plays an important part in the study of (fast) transient events, including gamma-ray bursts, magnetar bursts, fast radio bursts, and solar flares. A common approach is to decompose the time series into pulses and study the pulse characteristics, such as location and amplitude, in order to constrain physical models of the source and its environment. However, estimating both the number and characteristics of these pulses presents a trans-dimensional inference problem that traditional sampling methods such as Markov Chain Monte Carlo (MCMC) and Nested Sampling struggle to solve efficiently. Simulation-based inference methods, often incorporating machine learning techniques, provide an alternative approach when traditional approaches are insufficient. Here, we introduce trans-dimensional Flow Matching Posterior Estimation (t-FMPE) implemented on a transformer architecture capable of efficient, amortized trans-dimensional inference on uniformly sampled univariate time series data. In this initial study, we apply the method to three test cases: simulated time series with known ground-truth parameters, observational data of Fast Radio Bursts and observations of X-ray bursts from magnetars. We show that t-FMPE achieves qualitative agreement with MCMC reference posteriors, successfully reproducing parameter correlations, as quantified through classifier two-sample tests. The trained network performs inference several orders of magnitude faster than MCMC and nested sampling, reaching sampling rates of 100 posterior samples per second for an 80-dimensional parameter space. The results demonstrate potential of t-FMPE for large-scale analysis of time series datasets when traditional sampling methods become infeasible, and also enable inferring unbiased posteriors in the presence of observational biases such as dead time.

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arXiv2026

nDspec: a new Python library for modelling multi-dimensional datasets in X-ray astronomy

The current fleet of X-ray telescopes produces a wealth of multi-dimensional data, allowing us to study sources in time, photon energy, and polarisation. At the same time, it has become increasingly clear that progress in our physical understanding will only come from studying these sources in multiple dimensions simultaneously. Enabling multi-dimensional studies of X-ray sources requires new theoretical models predicting these datasets, new methods to analyse them, and, crucially, a software framework to combine data, models, and methods efficiently. However, the current ecosystem of software packages developed for X-ray data analysis does not provide the flexibility for advanced modelling of multi-dimensional datasets. In this paper, we introduce nDspec, a new python-based library designed to allow users to seamlessly model one- and multi-dimensional datasets common to X-ray astronomy. Unlike most other libraries, it is designed as a flexible, modular, and extensible framework capable of accommodating multi-dimensional data and able to connect to a range of different inference tools and algorithms. Here we focus on modelling timing and spectral-timing data as a function of both Fourier frequency and energy, in addition to limited support for time-averaged spectra. We discuss design philosophy and current features, and showcase an example use case by characterising a NICER observation of a black hole X-ray binary. We also highlight plans for extensions to other dimensions and new features, such as the inclusion of polarimetry and the improved statistical methods for Bayesian inference.

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arXiv2026

Emulation of non-linear 1D spectral models: relativistic X-ray reflection

The use of machine learning techniques to approximate computationally expensive models has become increasingly prevalent in a wide variety of fields within astronomy. We discuss the implementation of emulators for 1-dimensional models in the context of the astrophysical numerical model reltrans, a black hole X-ray spectral model that models the effects of relativistically smeared emission from an accretion disk. We argue that the decision of whether and how to emulate should follow from a systematic characterisation of the target model, and we demonstrate a diagnostic workflow: examining how the spectrum varies with individual parameters. We adopt a modular strategy, emulating only the relativistically convolved reflection spectrum (1-10% of the total flux) rather than the full model. Using an operator-learning architecture with Fourier feature embeddings and FiLM conditioning, we reproduce the reflection spectrum to O(0.1)% precision across 0.1-100 keV with a 4-10x speed-up that scales considerably better under vectorised evaluation. This emulator, RTFAST2, recovers the true parameters of simulated observations without the systematic posterior biases of our previous work. We conclude that no architecture is universally transferable and bespoke emulators motivated by a model's specific structure are required. The modular approach taken in this work presents a promising strategy for future emulators of numerical models.

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arXiv2026

A NICER view of the millisecond pulsar PSR J2124$-$3358: evidence for a helium atmosphere

Pulse profile modeling has proven to be a powerful technique for determining the mass and radius of neutron stars. To date, this method has been applied to a handful of millisecond pulsars observed by the Neutron Star Interior Composition Explorer (NICER). However, analyses of more millisecond pulsars are necessary to determine tight constraints on the equation of state of superdense matter. In this study, we present an analysis of the isolated, rotation-powered millisecond pulsar PSR J2124$-$3358 using the X-ray Pulse Simulation and Inference (X-PSI) package, a publicly available state-of-the-art code for neutron-star relativistic ray tracing and Bayesian parameter inference. We use NICER and Chandra observations of this pulsar, exploring different neutron star atmospheric compositions and different configurations of the hot polar caps responsible for the pulsed X-ray emission. Our analyses favor a helium atmospheric composition, plausibly originating from accretion and subsequent evaporation of a former hydrogen-depleted binary companion. For this composition, and given the faint nature of the source and the low signal-to-noise of the data sets, we obtain broad posterior distributions yielding a mass $M = 1.8\pm0.5\,M_\odot$ and an equatorial radius $R_{\mathrm{eq}} = 11.7^{+2.6}_{-3.0}$ km (medians and $68\%$ credible intervals), and infer a configuration consisting of two slightly non-antipodal hot spots. By contrast, when using a hydrogen atmosphere model, the mass and radius decrease by $\sim 0.5\,M_\odot$ and $\sim 1$ km, respectively. Future multiwavelength studies, particularly those incorporating radio and gamma-ray pulse-emission, may provide tighter constraints on the geometry and physical properties of this source.

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arXiv2026

Equation-of-state-informed pulse profile modeling

NICER has enabled mass-radius inferences for pulsars using pulse profile modeling (PPM), providing constraints on the equation of state (EOS) of cold, dense matter. To date, PPM and EOS inference have been carried out as two separate steps, with the former using EOS-agnostic priors. This approach has several drawbacks. Ideally, one would perform a fully hierarchical Bayesian inference where the pulse profile and EOS model parameters are jointly fit, but implementing such a framework is complex and computationally demanding. Here, we present an intermediate solution introducing an EOS-informed prior on mass-radius into the existing PPM pipeline using normalizing flows. By focusing on the parameter space consistent with certain EOSs, this approach both tightens constraints on neutron star parameters while reducing computational costs and requiring minimal additional implementation effort. We test this approach on two pulsars, PSR J0740+6620 and PSR J0437-4715, and with two EOS model families: a model based on the speed of sound inside the neutron star interior (CS) and a piecewise-polytropic (PP) model. Both EOS models implement constraints from chiral effective field theory calculations of dense matter. For both pulsar datasets, the inferred radius credible intervals are narrower than in the EOS-agnostic case, with CS favoring smaller radii and PP favoring larger radii. For PSR J0437-4715, the EOS-informed priors reveal a new, more extreme geometric mode that is statistically favored but physically questionable. Including the PPM posteriors in the subsequent EOS inference further tightens the mass-radius posteriors through the chiral effective field theory constraints. However, there is also a sensitivity to the high-density extensions, where the PP (CS) model produces a shift towards larger (smaller) radii and corresponding stiffening (softening) of the pressure-energy density relation.

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arXiv2026

The statistical properties of the cross spectrum

The cross spectrum encodes the correlated variability between two time signals. In recent years, the cross spectrum has been used to study astronomical sources, particularly in the field of X-ray timing. In the literature, it has been common to either simultaneously fit the real and imaginary components of the cross spectrum, or fit the phase and magnitude. Until now, a full discussion of the statistical distribution of the cross spectrum has been missing from the astronomical literature. In this paper, we present a derivation of the full statistical distribution of a cross spectrum between two time series, showing that it follows an asymmetric Laplace distribution. We further provide the probability distribution function for a cross spectrum random variable, along with the marginal distributions for many quantities. We also relate the cross spectrum to the power spectra of the constituent time series. This work will enable the cross spectrum to be used more accurately as a probe of physical processes such as accretion onto black holes and neutron stars.

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arXiv2026

A NICER View of PSR J0030+0451: Updated Constraints from Six Years of NICER Observations

Pulse-profile modeling of rotation-powered millisecond pulsars targeted by NICER has enabled mass--radius constraints of several neutron star sources, with implications for the dense-matter equation of state. For the bright isolated pulsar PSR J0030+0451, the inferred mass--radius was previously found to depend strongly on the assumed hot spot model. These hot-spot models yielded different mass--radius constraints, with the statistically preferred model exhibiting some mild tension with results inferred for PSR J0437$-$4715, PSR~J0614$-$3329, and GW170817. We present an updated pulse-profile analysis of PSR J0030+0451 using new NICER observations obtained between 2017 July to 2023 January, increasing the number of X-ray counts by about 50% compared to previous analyses. We jointly analyze the NICER data with archival XMM-Newton observations to better constrain the source spectrum and background. The new analysis significantly reduces the discrepancy between the hot spot models. The inferred mass and radius are $M = 1.43^{+0.20}_{-0.17}\,M_\odot$ and $R_{\rm eq} = 12.68^{+1.31}_{-1.04}$ km (68% credible intervals), reducing the tension with the results from other sources. In addition, the inferred hot spot configurations suggest the presence of intra-spot temperature gradients.

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arXiv2025

A NICER view of the 1.4 solar-mass edge-on pulsar PSR J0614-3329

Four neutron star radius measurements have already been obtained by modeling the X-ray pulses of rotation-powered millisecond pulsars observed by the Neutron Star Interior Composition ExploreR (NICER). We report here the radius measurement of PSR J0614-3329 employing the same method with NICER and XMM-Newton data using Bayesian Inference. For all different models tested, including one with unrestricted inclination prior, we retrieve very similar non-antipodal hot regions geometries and radii. For the preferred model, we infer an equatorial radius of $R_{\rm eq}=10.29^{+1.01}_{-0.86}\,$km for a mass of $M=1.44^{+0.06}_{-0.07} \, M_{\odot}$ (median values with equal-tailed $68\%$ credible interval), the latter being essentially constrained from radio timing priors obtained by MeerKAT. A more complex model, fitting the data equally well, resulted in a consistent inferred radius. We find that, for all different models, the pulse emission originates from two hot regions, one at the pole and the other at the equator. The resulting radius constraint is consistent with previous X-ray and gravitational wave measurements of neutron stars in the same mass range. Equation of state inferences, including previous NICER and gravitational wave results, slightly soften the equation of state with PSR J0614$-$3329 included and shift the allowed mass-radius region toward lower radii by $\sim 300\,$m, which is compatible with previous analyses to within less than one standard deviation.

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arXiv2025

Modeling X-ray photon pile-up with a normalizing flow

The dynamic range of imaging detectors flown on-board X-ray observatories often only covers a limited flux range of extrasolar X-ray sources. The analysis of bright X-ray sources is complicated by so-called pile-up, which results from high incident photon flux. This nonlinear effect distorts the measured spectrum, resulting in biases in the inferred physical parameters, and can even lead to a complete signal loss in extreme cases. Piled-up data are commonly discarded due to resulting intractability of the likelihood. As a result, a large number of archival observations remain underexplored. We present a machine learning solution to this problem, using a simulation-based inference framework that allows us to estimate posterior distributions of physical source parameters from piled-up eROSITA data. We show that a normalizing flow produces better-constrained posterior densities than traditional mitigation techniques, as more data can be leveraged. We consider model- and calibration-dependent uncertainties and the applicability of such an algorithm to real data in the eROSITA archive.

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arXiv2025

The variability of active galaxies: I. Broad-band noise X-ray power spectra from XMM-Newton and Swift

Accreting supermassive black holes at the centres of galaxies are the engine of active galactic nuclei (AGN). X-ray light curves of unabsorbed AGN show dramatic random variability on timescales ranging from seconds to years. The power spectrum of the fluctuations is usually well-modelled with a power law that decays as $1/f$ at low frequencies, and which bends to $1/f^{2-3}$ at high frequencies. The timescale associated with the bend correlates well with the mass of the black hole and may also correlate with bolometric luminosity in the `X-ray variability plane'. Because AGN light curves are usually irregularly sampled, the estimation of AGN power spectra is challenging. In a previous paper, we introduced a new method to estimate the parameters of bending power law power spectra from AGN light curves. We apply this method to a sample of 56 variable and unabsorbed AGN, observed with XMM-Newton and Swift in the $0.3-1.5$ keV band over the past two decades. We obtain estimates of the bends in 50 sources, which is the largest sample of X-ray bends in the soft band. We also find that the high-frequency power spectrum is often steeper than 2. We update the X-ray variability plane with new bend timescale measurements spanning from 7 min to 62 days. We report the detections of low-frequency bends in the power spectra of five AGN, three of which are previously unpublished: 1H 1934-063, Mkn 766 and Mkn 279.

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arXiv2025

Searching for quasi-periodicities in short transients: the curious case of GRB 230307A

Gamma-ray bursts (GRBs) are the most powerful explosions in the Universe; their energy release reache s us from the end of the re-ionization era, making them invaluable cosmological probes. GRB 230307A i s the second-brightest GRB ever observed in the 56 years of observations since the discovery of the phenomenon in 1967. Follow-up observations of the event at longer wavelengths revealed a lanthanide-ri ch kilonova with long-lasting X-ray emission immediately following the prompt gamma-rays. Moreover, t he gamma-ray light curve of GRB 230307A collected with INTEGRAL's SPectrometer of INTEGRAL AntiCoincidence Shield (SPI-ACS) and Fermi's Gamma-Ray Burst Monitor (GBM). We use Fourier analysis, wavelets and Gaussian Processes to search for periodic and quasi-periodic oscillations (QPOs) in the prompt gamma-ray emission of GRB 230307A. We critically assess all three methods in terms of their robustness for detections of QPOs in fast transients such as GRBs. Our analyses reveal QPOs at a frequency of $\sim 1.2$ Hz (0.82s period) near the burst's peak emission phase, consistent across instruments and detection methods. We also identify a second, less significant QPO at $\sim 2.9$ Hz (0.34s) nearly simultaneously. We hypothesise that the two QPOs originate from the transition epoch at the end of the jet acceleration phase. These QPOs re present plasma circulation periods in vorticity about the jet axis carried outwards to the prompt radiation zone at much larger radii. They are sampled by colliding structures (e.g., shocks) in the spinning jet, possibly marking the evolution of plasma rotation during the final stages of the progenitor neutron star coalescence event.

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arXiv2025

Modelling variability power spectra of active galaxies from irregular time series

A common feature of Active Galactic Nuclei (AGN) is their random variations in brightness across the whole emission spectrum, from radio to $γ$-rays. Studying the nature and origin of these fluctuations is critical to characterising the underlying variability process of the accretion flow that powers AGN. Random timing fluctuations are often studied with the power spectrum; this quantifies how the amplitude of variations is distributed over temporal frequencies. Red noise variability -- when the power spectrum increases smoothly towards low frequencies -- is ubiquitous in AGN. The commonly used Fourier analysis methods, have significant challenges when applied to arbitrarily sampled light curves of red noise variability. Several time-domain methods exist to infer the power spectral shape in the case of irregular sampling but they suffer from biases which can be difficult to mitigate, or are computationally expensive. In this paper, we demonstrate a method infer the shape of broad-band power spectra for irregular time series, using a Gaussian process regression method scalable to large datasets. The power spectrum is modelled as a power-law model with one or two bends with flexible slopes. The method is fully Bayesian and we demonstrate its utility using simulated light curves. Finally, Ark 564, a well-known variable Seyfert 1 galaxy, is used as a test case and we find consistent results with the literature using independent X-ray data from XMM-Newton and Swift. We provide publicly available, documented and tested implementations in Python and Julia.

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arXiv2025

The Galactic population of magnetars : a simulation-based inference study

Population synthesis modeling of the observed dynamical and physical properties of a population is a highly effective method for constraining the underlying birth parameters and evolutionary tracks. In this work, we apply a population synthesis model to the canonical magnetar population to gain insight into the parent population. We utilize simulation-based inference to reproduce the observed magnetar population with a model which takes into account the secular evolution of the force-free magnetosphere and magnetic field decay simultaneously and self-consistently. Our observational constraints are such that no magnetar is detected through their persistent emission when convolving the simulated populations with the XMM-Newton EPIC-pn Galactic plane observations, and that all of the $\sim$30 known magnetars are discovered through their bursting activity in the last $\sim50$ years. Under these constraints, we find that, within 95 % credible intervals, the birth rate of magnetars to be $1.8^{+2.6}_{-0.6}$ kyr$^{-1}$, and lead to having $10.7^{+18.8}_{-4.4}$ % of neutron stars born as magnetars. We also find a mean magnetic field at birth ($μ_b$ is in T) $\log\left(μ_b\right) = 10.2^{+0.1}_{-0.2}$, a magnetic field decay slope $α_d = 1.9 ^{+0.9}_{-1.3}$, and timescale $τ_d = 17.9^{+24.1}_{-14.5}$ kyr, in broad agreement with previous estimates. We conclude this study by exploring detection prospects: an all-sky survey with XMM-Newton would potentially allow to get around 7 periodic detections of magnetars, with approximately 150 magnetars exceeding XMM-Newton's flux threshold, and the upcoming AXIS experiment should allow to double these detections.

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arXiv2025

RTFAST-Spectra: Emulation of X-ray reverberation mapping for active galactic nuclei

Bayesian analysis has begun to be more widely adopted in X-ray spectroscopy, but it has largely been constrained to relatively simple physical models due to limitations in X-ray modelling software and computation time. As a result, Bayesian analysis of numerical models with high physics complexity have remained out of reach. This is a challenge, for example when modelling the X-ray emission of accreting black hole X-ray binaries, where the slow model computations severely limit explorations of parameter space and may bias the inference of astrophysical parameters. Here, we present RTFAST-Spectra: a neural network emulator that acts as a drop in replacement for the spectral portion of the black hole X-ray reverberation model RTDIST. This is the first emulator for the reltrans model suite and the first emulator for a state-of-the-art x-ray reflection model incorporating relativistic effects with 17 physically meaningful model parameters. We use Principal Component Analysis to create a light-weight neural network that is able to preserve correlations between complex atomic lines and simple continuum, enabling consistent modelling of key parameters of scientific interest. We achieve a $\mathcal{O}(10^2)$ times speed up over the original model in the most conservative conditions with $\mathcal{O}(1\%)$ precision over all 17 free parameters in the original numerical model, taking full posterior fits from months to hours. We employ Markov Chain Monte Carlo sampling to show how we can better explore the posteriors of model parameters in simulated data and discuss the complexities in interpreting the model when fitting real data.

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arXiv2025

Representation learning for fast radio burst dynamic spectra

Fast radio bursts (FRBs) are millisecond-duration radio transients of extragalactic origin, with diverse time-frequency patterns and emission properties that require explanation. With one possible exception, FRBs are detected only in the radio, so analyzing their dynamic spectra is therefore crucial to disentangling the physical processes governing their generation and propagation. Furthermore, comparing FRB morphologies provides insights into possible differences among their progenitors and environments. This study applies unsupervised learning and deep learning techniques to investigate FRB dynamic spectra, focusing on two approaches: Principal Component Analysis (PCA) and a Convolutional Autoencoder (CAE) enhanced by an Information-Ordered Bottleneck (IOB) layer. PCA served as a computationally efficient baseline, capturing broad trends, identifying outliers, and providing valuable insights into large datasets. However, its linear nature limited its ability to reconstruct complex FRB structures. In contrast, the IOB-augmented CAE excelled at capturing intricate features, with high reconstruction accuracy and effective denoising at modest signal-to-noise ratios. The IOB layer's ability to prioritize relevant features enabled efficient data compression, preserving key morphological characteristics with minimal latent variables. When applied to real FRBs from CHIME, the IOB-CAE generalized effectively, revealing a latent space that highlighted the continuum of FRB morphologies and the potential for distinguishing intrinsic differences between burst types. This framework demonstrates that while FRBs may not naturally cluster into discrete groups, advanced representation learning techniques can uncover meaningful structures, offering new insights into the diversity and origins of these bursts.

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arXiv2024

The Radius of the High-mass Pulsar PSR J0740+6620 with 3.6 yr of NICER Data

We report an updated analysis of the radius, mass, and heated surface regions of the massive pulsar PSR J0740+6620 using Neutron Star Interior Composition Explorer (NICER) data from 2018 September 21 to 2022 April 21, a substantial increase in data set size compared to previous analyses. Using a tight mass prior from radio timing measurements and jointly modeling the new NICER data with XMM-Newton data, the inferred equatorial radius and gravitational mass are $12.49_{-0.88}^{+1.28}$ km and $2.073_{-0.069}^{+0.069}$ $M_\odot$ respectively, each reported as the posterior credible interval bounded by the $16\,\%$ and $84\,\%$ quantiles, with an estimated systematic error $\lesssim 0.1$ km. This result was obtained using the best computationally feasible sampler settings providing a strong radius lower limit but a slightly more uncertain radius upper limit. The inferred radius interval is also close to the $R=12.76_{-1.02}^{+1.49}$ km obtained by Dittmann et al., when they require the radius to be less than $16$ km as we do. The results continue to disfavor very soft equations of state for dense matter, with $R<11.15$ km for this high-mass pulsar excluded at the $95\,\%$ probability. The results do not depend significantly on the assumed cross-calibration uncertainty between NICER and XMM-Newton. Using simulated data that resemble the actual observations, we also show that our pipeline is capable of recovering parameters for the inferred models reported in this paper.

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