arXiv subjects

Daniela Huppenkothen

Publications and source records attributed to Daniela Huppenkothen.

At least 37 records · Page 2Linked to original sources

arXiv2024

A NICER View of the Nearest and Brightest Millisecond Pulsar: PSR J0437$\unicode{x2013}$4715

We report Bayesian inference of the mass, radius and hot X-ray emitting region properties - using data from the Neutron Star Interior Composition ExploreR (NICER) - for the brightest rotation-powered millisecond X-ray pulsar PSR J0437$\unicode{x2013}$4715. Our modeling is conditional on informative tight priors on mass, distance and binary inclination obtained from radio pulsar timing using the Parkes Pulsar Timing Array (PPTA) (Reardon et al. 2024), and we use NICER background models to constrain the non-source background, cross-checking with data from XMM-Newton. We assume two distinct hot emitting regions, and various parameterized hot region geometries that are defined in terms of overlapping circles; while simplified, these capture many of the possibilities suggested by detailed modeling of return current heating. For the preferred model identified by our analysis we infer a mass of $M = 1.418 \pm 0.037$ M$_\odot$ (largely informed by the PPTA mass prior) and an equatorial radius of $R = 11.36^{+0.95}_{-0.63}$ km, each reported as the posterior credible interval bounded by the 16% and 84% quantiles. This radius favors softer dense matter equations of state and is highly consistent with constraints derived from gravitational wave measurements of neutron star binary mergers. The hot regions are inferred to be non-antipodal, and hence inconsistent with a pure centered dipole magnetic field.

astro-ph.HE↗

arXiv2024

FlopPITy: Enabling self-consistent exoplanet atmospheric retrievals with machine learning

Interpreting the observations of exoplanet atmospheres to constrain physical and chemical properties is typically done using Bayesian retrieval techniques. Because these methods require many model computations, a compromise is made between model complexity and run time. Reaching this compromise leads to the simplification of many physical and chemical processes (e.g. parameterised temperature structure). Here we implement and test sequential neural posterior estimation (SNPE), a machine learning inference algorithm, for exoplanet atmospheric retrievals. The goal is to speed up retrievals so they can be run with more computationally expensive atmospheric models, such as those computing the temperature structure using radiative transfer. We generate 100 synthetic observations using ARCiS (ARtful Modeling Code for exoplanet Science, an atmospheric modelling code with the flexibility to compute models in varying degrees of complexity) and perform retrievals on them to test the faithfulness of the SNPE posteriors. The faithfulness quantifies whether the posteriors contain the ground truth as often as we expect. We also generate a synthetic observation of a cool brown dwarf using the self-consistent capabilities of ARCiS and run a retrieval with self-consistent models to showcase the possibilities that SNPE opens. We find that SNPE provides faithful posteriors and is therefore a reliable tool for exoplanet atmospheric retrievals. We are able to run a self-consistent retrieval of a synthetic brown dwarf spectrum using only 50,000 forward model evaluations. We find that SNPE can speed up retrievals between $\sim2\times$ and $\geq10\times$ depending on the computational load of the forward model, the dimensionality of the observation, and the signal-to-noise ratio of the observation. We make the code publicly available for the community on Github.

astro-ph.EP↗

arXiv2023

The Sonified Hertzsprung-Russell Diagram

Understanding the physical properties of stars, and putting these properties into the context of stellar evolution, is a core challenge in astronomical research. A key visualization in studying stellar evolution is the Hertzsprung-Russell diagram (HRD), organizing data about stellar luminosity and colour into a form that is informative about stellar structure and evolution. However, connecting the HRD with other sources of information, including stellar time series, is an outstanding challenge. Here we present a new method to turn stellar time series into sound. This method encodes physically meaningful features such that auditory comparisons between sonifications of different stars preserve astrophysical differences between them. We present an interactive multimedia version of the HRD that combines both visual and auditory components and that allows exploration of different types of stars both on and off the main sequence through both visual and auditory media.

astro-ph.IM↗

arXiv2023

GRB 180128A: A Second Magnetar Giant Flare Candidate from the Sculptor Galaxy

Magnetars are slowly rotating neutron stars that possess the strongest magnetic fields ($10^{14}-10^{15} \mathrm{G}$) known in the cosmos. They display a range of transient high-energy electromagnetic activity. The brightest and most energetic of these events are the gamma-ray bursts (GRBs) known as magnetar giant flares (MGFs), with isotropic energy $E\approx10^{44}-10^{46} \mathrm{erg}$. There are only seven detections identified as MGFs to date: three unambiguous events occurred in our Galaxy and the Magellanic Clouds, and the other four MGF candidates are associated with nearby star-forming galaxies. As all seven identified MGFs are bright at Earth, additional weaker events remain unidentified in archival data. We conducted a search of the Fermi Gamma-ray Burst Monitor (GBM) database for candidate extragalactic MGFs and, when possible, collected localization data from the Interplanetary Network (IPN) satellites. Our search yielded one convincing event, GRB 180128A. IPN localizes this burst with NGC 253, commonly known as the Sculptor Galaxy. This event is the second MGF in modern astronomy to be associated with this galaxy and the first time two bursts are associated with a single galaxy outside our own. Here, we detail the archival search criteria that uncovered this event and its spectral and temporal properties, which are consistent with expectations for a MGF. We also discuss the theoretical implications and finer burst structures resolved from various binning methods. Our analysis provides observational evidence for an eighth identified MGF.

astro-ph.HE↗

arXiv2023

Discovery of spin-phase dependent QPOs in the super-critical accretion regime from the X-ray pulsar RX J0440.9+4431

RX J0440.9+4431 is an accreting X-ray pulsar (XRP) that remained relatively unexplored until recently, when major X-ray outburst activity enabled more in-depth studies. Here, we report on the discovery of ${\sim}0.2$ Hz quasi-periodic oscillations (QPOs) from this source observed with $Fermi$-GBM. The appearance of QPOs in RX J0440.9+4431 is thricely transient, that is, QPOs appear only above a certain luminosity, only at certain pulse phases (namely corresponding to the peak of its sine-like pulse profile), and only for a few oscillations at time. We argue that this newly discovered phenomenon (appearance of thricely transient QPOs -- or ATTO) occurs if QPOs are fed through an accretion disk whose inner region viscosity is unstable to mass accretion rate and temperature variations. Such variations are triggered when the source switches to the super-critical accretion regime and the emission pattern changes. We also argue that the emission region configuration is likely responsible for the observed QPOs spin-phase dependence.

astro-ph.HE↗

arXiv2023

Dense Forests of Microshots in Bursts from FRB 20220912A

We report on exceptionally bright bursts (>400 Jy ms) detected from the repeating fast radio burst source FRB 20220912A using the Nançay Radio Telescope (NRT), as part of the ECLAT (Extragalactic Coherent Light from Astrophysical Transients) monitoring campaign. These bursts exhibit extremely luminous, broadband, short-duration structures (~ 16 microseconds), which we term 'microshots' and which can be especially well studied in the NRT data given the excellent signal-to-noise and dynamic range (32-bit samples). The estimated peak flux density of the brightest microshot is 450 Jy. We show that the microshots are clustered into dense 'forests', by modelling them as Weibull distributions and obtaining Weibull shape parameters of approximately 0.5. Our polarimetric analysis reveals that the bursts are nearly 100% linearly polarised; have < 10% circular polarisation fractions; a near-zero average rotation measure of 0.10(6) rad/m^2; and varying polarisation position angles over the burst duration. For one of the bursts, we analyse raw voltage data from simultaneous observations with the Westerbork RT-1 single 25-m dish. These data allow us to measure the scintillation bandwidth, 0.30(3) MHz, and to probe the bursts on (sub-)microsecond timescales. Some important nuances related to dedispersion are also discussed. We propose that the emission mechanism for the broadband microshots is potentially different from the emission mechanism of the broader burst components which still show a residual drift of a few hundred MHz/ms after correcting for dispersion using the microshots. We discuss how the observed emission is phenomenologically analogous to different types of radio bursts from the Sun.

astro-ph.HE↗

arXiv2023

An updated mass-radius analysis of the 2017-2018 NICER data set of PSR J0030+0451

In 2019 the NICER collaboration published the first mass and radius inferred for PSR J0030+0451, thanks to NICER observations, and consequent constraints on the equation of state characterising dense matter. Two independent analyses found a mass of $\sim 1.3-1.4\,\mathrm{M_\odot}$ and a radius of $\sim 13\,$km. They also both found that the hot spots were all located on the same hemisphere, opposite to the observer, and that at least one of them had a significantly elongated shape. Here we reanalyse, in greater detail, the same NICER data set, incorporating the effects of an updated NICER response matrix and using an upgraded analysis framework. We expand the adopted models and jointly analyse also XMM-Newton data, which enables us to better constrain the fraction of observed counts coming from PSR J0030+0451. Adopting the same models used in previous publications, we find consistent results, although with more stringent inference requirements. We also find a multi-modal structure in the posterior surface. This becomes crucial when XMM-Newton data is accounted for. Including the corresponding constraints disfavors the main solutions found previously, in favor of the new and more complex models. These have inferred masses and radii of $\sim [1.4 \mathrm{M_\odot}, 11.5$ km] and $\sim [1.7 \mathrm{M_\odot}, 14.5$ km], depending on the assumed model. They display configurations that do not require the two hot spots generating the observed X-rays to be on the same hemisphere, nor to show very elongated features, and point instead to the presence of temperature gradients and the need to account for them.

astro-ph.HE↗

arXiv2023

Fourier Domain

The changes in brightness of an astronomical source as a function of time are key probes into that source's physics. Periodic and quasi-periodic signals are indicators of fundamental time (and length) scales in the system, while stochastic processes help uncover the nature of turbulent accretion processes. A key method of studying time variability is through Fourier methods, the decomposition of the signal into sine waves, which yields a representation of the data in frequency space. With the extension into \textit{spectral timing} the methods built on the Fourier transform can not only help us characterize (quasi-)periodicities and stochastic processes, but also uncover the complex relationships between time, photon energy and flux in order to help build better models of accretion processes and other high-energy dynamical physics. In this Chapter, we provide a broad, but practical overview of the most important relevant methods.

astro-ph.IM↗

arXiv2023

Quasi-Periodic Peak Energy Oscillations in X-ray Bursts from SGR J1935+2154

Magnetars are young neutron stars powered by the strongest magnetic fields in the Universe (10$^{13-15}$ G). Their transient X-ray emission usually manifests as short (a few hundred milliseconds), bright, energetic ($\sim$ 10$^{40-41}$ erg) X-ray bursts. Since its discovery in 2014, magnetar J1935+2154 has become one of the most prolific magnetars, exhibiting very active bursting episodes, and other fascinating events such as pulse timing anti-glitches and Fast Radio Bursts. Here, we present evidence for possible 42 Hz (24 ms) quasi-periodic oscillations in the $νF_ν$ spectrum peak energy (Ep) identified in a unique burst detected with the Fermi Gamma-ray Burst Monitor in January 2022. While quasi-periodic oscillations have been previously reported in the intensity of magnetar burst lightcurves, quasi-periodic oscillations in the Ep have not. We also find an additional event from the same outburst that appears to exhibit similar character in Ep, albeit of lower statistical quality. For these two exceptional transients, such Ep oscillations can be explained by magnetospheric density and pressure perturbations. For burst-emitting plasma consisting purely of $e^+e^-$ pairs, these acoustic modes propagate along a highly magnetized flux tube of length up to around $L\sim 130$ neutron star radii, with $L$ being lower if ions are present in the emission zone. Detailed time-resolved analyses of other magnetar bursts are encouraged to evaluate the rarity of these events and their underlying mechanisms.

astro-ph.HE↗

arXiv2023

Mapping the X-ray variability of GRS1915+105 with machine learning

Black hole X-ray binary systems (BHBs) contain a close companion star accreting onto a stellar-mass black hole. A typical BHB undergoes transient outbursts during which it exhibits a sequence of long-lived spectral states, each of which is relatively stable. GRS 1915+105 is a unique BHB that exhibits an unequaled number and variety of distinct variability patterns in X-rays. Many of these patterns contain unusual behaviour not seen in other sources. These variability patterns have been sorted into different classes based on count rate and color characteristics by Belloni et al (2000). In order to remove human decision-making from the pattern-recognition process, we employ an unsupervised machine learning algorithm called an auto-encoder to learn what classifications are naturally distinct by allowing the algorithm to cluster observations. We focus on observations taken by the Rossi X-ray Timing Explorer's Proportional Counter Array. We find that the auto-encoder closely groups observations together that are classified as similar under the Belloni et al (2000) system, but that there is reasonable grounds for defining each class as made up of components from 3 groups of distinct behaviour.

astro-ph.HE↗

arXiv2023

Practical Guidance for Bayesian Inference in Astronomy

In the last two decades, Bayesian inference has become commonplace in astronomy. At the same time, the choice of algorithms, terminology, notation, and interpretation of Bayesian inference varies from one sub-field of astronomy to the next, which can lead to confusion to both those learning and those familiar with Bayesian statistics. Moreover, the choice varies between the astronomy and statistics literature, too. In this paper, our goal is two-fold: (1) provide a reference that consolidates and clarifies terminology and notation across disciplines, and (2) outline practical guidance for Bayesian inference in astronomy. Highlighting both the astronomy and statistics literature, we cover topics such as notation, specification of the likelihood and prior distributions, inference using the posterior distribution, and posterior predictive checking. It is not our intention to introduce the entire field of Bayesian data analysis -- rather, we present a series of useful practices for astronomers who already have an understanding of the Bayesian "nuts and bolts" and wish to increase their expertise and extend their knowledge. Moreover, as the field of astrostatistics and astroinformatics continues to grow, we hope this paper will serve as both a helpful reference and as a jumping off point for deeper dives into the statistics and astrostatistics literature.

astro-ph.IM↗

arXiv2022

The Future of Hackathon Research and Practice

Hackathons are time-bounded collaborative events which have become a global phenomenon adopted by both researchers and practitioners in a plethora of contexts. Hackathon events are generally used to accelerate the development of, for example, scientific results and collaborations, communities, and innovative prototypes addressing urgent challenges. As hackathons have been adopted into many different contexts, the events have also been adapted in numerous ways corresponding to the unique needs and situations of organizers, participants and other stakeholders. While these interdisciplinary adaptions, in general affords many advantages - such as tailoring the format to specific needs - they also entail certain challenges, specifically: 1) limited exchange of best practices, 2) limited exchange of research findings, and 3) larger overarching questions that require interdisciplinary collaboration are not discovered and remain unaddressed. We call for interdisciplinary collaborations to address these challenges. As a first initiative towards this, we performed an interdisciplinary collaborative analysis in the context of a workshop at the Lorentz Center, Leiden in December 2021. In this paper, we present the results of this analysis in terms of six important areas which we envision to contribute to maturing hackathon research and practice: 1) hackathons for different purposes, 2) socio-technical event design, 3) scaling up, 4) making hackathons equitable, 5) studying hackathons, and 6) hackathon goals and how to reach them. We present these areas in terms of the state of the art and research proposals and conclude the paper by suggesting next steps needed for advancing hackathon research and practice.

cs.HC↗

arXiv2022

Orbital decay in M82 X-2

M82 X-2 is the first pulsating ultraluminous X-ray source (PULX) discovered. The luminosity of these extreme pulsars, if isotropic, implies an extreme mass transfer rate. An alternative is to assume a much lower mass transfer rate, but with an apparent luminosity boosted by geometrical beaming. Only an independent measurement of the mass transfer rate can help discriminate between these two scenarios. In this Paper, we follow the orbit of the neutron star for seven years, measure the decay of the orbit ($\dot{P}_{orb}/{P}_{orb}\approx-8\cdot10^{-6}\mathrm{yr}^{-1}$), and argue that this orbital decay is driven by extreme mass transfer of more than 150 times the mass transfer limit set by the Eddington luminosity. If this is true, the mass available to the accretor is more than enough to justify its luminosity, with no need for beaming. This also strongly favors models where the accretor is a highly-magnetized neutron star.

astro-ph.HE↗

arXiv2022

Phase-resolved spectroscopy of a quasi-periodic oscillation in the black hole X-ray binary GRS 1915+105 with NICER and NuSTAR

Quasi-periodic oscillations (QPOs) are often present in the X-ray flux from accreting stellar-mass black holes (BHs). If they are due to relativistic (Lense-Thirring) precession of an inner accretion flow which is misaligned with the disc, the iron emission line caused by irradiation of the disc by the inner flow will rock systematically between red and blue shifted during each QPO cycle. Here we conduct phase-resolved spectroscopy of a $\sim2.2$ Hz type-C QPO from the BH X-ray binary GRS 1915+105, observed simultaneously with NICER and NuSTAR. We apply a tomographic model in order to constrain the QPO phase-dependent illumination profile of the disc. We detect the predicted QPO phase-dependent shifts of the iron line centroid energy, with our best fit featuring an asymmetric illumination profile ($>2σ$ confidence). The observed line energy shifts can alternatively be explained by the spiral density waves of the accretion-ejection instability model. However we additionally measure a significant ($>3σ$) modulation in reflection fraction, strongly favouring a geometric QPO origin. We infer that the disc is misaligned with previously observed jet ejections, which is consistent with the model of a truncated disc with an inner precessing hot flow. However our inferred disc inner radius is small ($r_\text{in}{\sim} 1.4 GM/c^2$). For this disc inner radius, Lense-Thirring precession cannot reproduce the observed QPO frequency. In fact, this disc inner radius is incompatible with the predictions of all well-studied QPO models in the literature.

astro-ph.HE↗

arXiv2021

Characterizing Sparse Asteroid Light Curves with Gaussian Processes

In the era of wide-field surveys like the Zwicky Transient Facility and the Rubin Observatory's Legacy Survey of Space and Time, sparse photometric measurements constitute an increasing percentage of asteroid observations, particularly for asteroids newly discovered in these large surveys. Follow-up observations to supplement these sparse data may be prohibitively expensive in many cases, so to overcome these sampling limitations, we introduce a flexible model based on Gaussian Processes to enable Bayesian parameter inference of asteroid time series data. This model is designed to be flexible and extensible, and can model multiple asteroid properties such as the rotation period, light curve amplitude, changing pulse profile, and magnitude changes due to the phase angle evolution at the same time. Here, we focus on the inference of rotation periods. Based on both simulated light curves and real observations from the Zwicky Transient Facility, we show that the new model reliably infers rotational periods from sparsely sampled light curves, and generally provides well-constrained posterior probability densities for the model parameters. We propose this framework as an intermediate method between fast, but very limited period detection algorithms and much more comprehensive, but computationally expensive shape modeling based on ray-tracing codes.

astro-ph.EP↗

arXiv2021

Light curve fingerprints: an automated approach to the extraction of X-ray variability patterns with feature aggregation -- an example application to GRS 1915+105

Time series data mining is an important field of research in the era of "Big Data". Next generation astronomical surveys will generate data at unprecedented rates, creating the need for automated methods of data analysis. We propose a method of light curve characterisation that employs a pipeline consisting of a neural network with a Long-Short Term Memory Variational Autoencoder architecture and a Gaussian mixture model. The pipeline performs extraction and aggregation of features from light curve segments into feature vectors of fixed length which we refer to as light curve "fingerprints". This representation can be readily used as input of down-stream machine learning algorithms. We demonstrate the proposed method on a data set of Rossi X-ray Timing Explorer observations of the galactic black hole X-ray binary GRS 1915+105, which was chosen because of its observed complex X-ray variability. We find that the proposed method can generate a representation that characterises the observations and reflects the presence of distinct classes of GRS 1915+105 X-ray flux variability. We find that this representation can be used to perform efficient classification of light curves. We also present how the representation can be used to quantify the similarity of different light curves, highlighting the problem of the popular classification system of GRS 1915+105 observations, which does not account for intermediate class behaviour.

astro-ph.IM↗

arXiv2021

Pitfalls of periodograms: The non-stationarity bias in the analysis of quasi-periodic oscillations

Quasi-periodic oscillations (QPOs) are an important key to understand the dynamic behavior of astrophysical objects during transient events like gamma-ray bursts, solar flares, and magnetar flares. Searches for QPOs often use the periodogram of the time series and perform spectral density estimation using a Whittle likelihood function. However, the Whittle likelihood is only valid if the time series is stationary since the frequency bins are otherwise not statistically independent. We show that if time series are non-stationary, the significance of QPOs can be highly overestimated and estimates of the central frequencies and QPO widths can be overconstrained. The effect occurs if the QPO is only present for a fraction of the time series and the noise level is varying throughout the time series. This can occur for example if background noise from before or after the transient is included in the time series or if the low-frequency noise profile varies strongly over the time series. We confirm the presence of this bias in previously reported results from solar flare data and show that significance can be highly overstated. Finally, we provide some suggestions that help identify if an analysis is affected by this bias.

astro-ph.HE↗

arXiv2021

Photometric Classifications of Evolved Massive Stars: Preparing for the Era of Webb and Roman with Machine Learning

In the coming years, next-generation space-based infrared observatories will significantly increase our samples of rare massive stars, representing a tremendous opportunity to leverage modern statistical tools and methods to test massive stellar evolution in entirely new environments. Such work is only possible if the observed objects can be reliably classified. Spectroscopic observations are infeasible with more distant targets, and so we wish to determine whether machine learning methods can classify massive stars using broadband infrared photometry. We find that a Support Vector Machine classifier is capable of coarsely classifying massive stars with labels corresponding to hot, cool, and emission line stars with high accuracy, while rejecting contaminating low mass giants. Remarkably, 76\% of emission line stars can be recovered without the need for narrowband or spectroscopic observations. We classify a sample of ${\sim}2500$ objects with no existing labels, and identify fourteen candidate emission line objects. Unfortunately, despite the high precision of the photometry in our sample, the heterogeneous origins of the labels for the stars in our sample severely inhibits our classifier from distinguishing classes of stars with more granularity. Ultimately, no large and homogeneously labeled sample of massive stars currently exists. Without significant efforts to robustly classify evolved massive stars -- which is feasible given existing data from large all-sky spectroscopic surveys -- shortcomings in the labeling of existing data sets will hinder efforts to leverage the next-generation of space observatories.

astro-ph.SR↗