arXiv subjects

Cecilia Garraffo

Publications and source records attributed to Cecilia Garraffo.

At least 37 records · Page 2Linked to original sources

arXiv2024

Heating of the Atmospheres of Short-orbit Exoplanets by Their Rapid Orbital Motion Through an Extreme Space Environment

Exoplanets with short orbit period reside very close to their host stars. They transition very rapidly between different sectors of the circumstellar space environment along their orbit, leading to large variations of the magnetic field in the vicinity of the planet on short timescales. This rapid change of the magnetic flux through the conducting and resistive layer of the planetary upper atmosphere may drive currents that dissipate in the form of Joule Heating. Here, we estimate the amount of Joule Heating dissipation in the upper atmosphere of Trappist-1e, and two hypothetical planets orbiting the Sun in close-in orbits. We find that the rapid orbital motion could drive a significant amount of atmospheric heating and could significantly affect the planetary atmosphere escape rate. Thus, the process should be accounted for when studying the long-term evolution of exoplanetary atmospheres.

astro-ph.EP↗

arXiv2023

Hyperspectral shadow removal with Iterative Logistic Regression and latent Parametric Linear Combination of Gaussians

Shadow detection and removal is a challenging problem in the analysis of hyperspectral images. Yet, this step is crucial for analyzing data for remote sensing applications like methane detection. In this work, we develop a shadow detection and removal method only based on the spectrum of each pixel and the overall distribution of spectral values. We first introduce Iterative Logistic Regression (ILR) to learn a spectral basis in which shadows can be linearly classified. We then model the joint distribution of the mean radiance and the projection coefficients of the spectra onto the above basis as a parametric linear combination of Gaussians. We can then extract the maximum likelihood mixing parameter of the Gaussians to estimate the shadow coverage and to correct the shadowed spectra. Our correction scheme reduces correction artefacts at shadow borders. The shadow detection and removal method is applied to hyperspectral images from MethaneAIR, a precursor to the satellite MethaneSAT.

physics.data-an↗

arXiv2023

Generating Images of the M87* Black Hole Using GANs

In this paper, we introduce a novel data augmentation methodology based on Conditional Progressive Generative Adversarial Networks (CPGAN) to generate diverse black hole (BH) images, accounting for variations in spin and electron temperature prescriptions. These generated images are valuable resources for training deep learning algorithms to accurately estimate black hole parameters from observational data. Our model can generate BH images for any spin value within the range of [-1, 1], given an electron temperature distribution. To validate the effectiveness of our approach, we employ a convolutional neural network to predict the BH spin using both the GRMHD images and the images generated by our proposed model. Our results demonstrate a significant performance improvement when training is conducted with the augmented dataset while testing is performed using GRMHD simulated data, as indicated by the high R2 score. Consequently, we propose that GANs can be employed as cost effective models for black hole image generation and reliably augment training datasets for other parameterization algorithms.

astro-ph.GA↗

arXiv2023

Linking circumstellar disk lifetimes to the rotational evolution of low-mass stars

The high-energy radiation emitted by young stars can have a strong influence on their rotational evolution at later stages. This is because internal photoevaporation is one of the major drivers of the dispersal of circumstellar disks, which surround all newly born low-mass stars during the first few million years of their evolution. Employing an internal EUV/X-ray photoevaporation model, we have derived a simple recipe for calculating realistic inner disk lifetimes of protoplanetary disks. This prescription was implemented into a magnetic morphology-driven rotational evolution model and is used to investigate the impact of disk-locking on the spin evolution of low-mass stars. We find that the length of the disk-locking phase has a profound impact on the subsequent rotational evolution of a young star, and the implementation of realistic disk lifetimes leads to an improved agreement of model outcomes with observed rotation period distributions for open clusters of various ages. However, for both young star-forming regions tested in our model, the strong bimodality in rotation periods that is observed in hPer could not be recovered. hPer is only successfully recovered, if the model is started from a double-peaked distribution with an initial disk fraction of $65\,\%$. However, at an age of only $\sim 1\,\mathrm{Myr}$, such a low disk fraction can only be achieved if an additional disk dispersal process, such as external photoevaporation, is invoked. These results therefore highlight the importance of including realistic disk dispersal mechanisms in rotational evolution models of young stars.

astro-ph.SR↗

arXiv2023

Breakthroughs in Cool Star Physics with the Line Emission Mapper X-ray Probe

We outline some of the highlights of the scientific case for the advancement of stellar high energy physics using the Line Emission Mapper X-ray Probe ({\it LEM}). The key to advancements with LEM lie in its large effective area -- up to 100 times that of the {\it Chandra} MEG -- and 1~eV spectral resolution. The large effective area opens up for the first time the ability to study time-dependent phenomena on their natural timescales at high resolution, such as flares and coronal mass ejections, and also opens the sky to much fainter targets than available to {\it Chandra} or {\it XMM-Newton}.

astro-ph.IM↗

arXiv2023

Simulation-based Inference for Exoplanet Atmospheric Retrieval: Insights from winning the Ariel Data Challenge 2023 using Normalizing Flows

Advancements in space telescopes have opened new avenues for gathering vast amounts of data on exoplanet atmosphere spectra. However, accurately extracting chemical and physical properties from these spectra poses significant challenges due to the non-linear nature of the underlying physics. This paper presents novel machine learning models developed by the AstroAI team for the Ariel Data Challenge 2023, where one of the models secured the top position among 293 competitors. Leveraging Normalizing Flows, our models predict the posterior probability distribution of atmospheric parameters under different atmospheric assumptions. Moreover, we introduce an alternative model that exhibits higher performance potential than the winning model, despite scoring lower in the challenge. These findings highlight the need to reevaluate the evaluation metric and prompt further exploration of more efficient and accurate approaches for exoplanet atmosphere spectra analysis. Finally, we present recommendations to enhance the challenge and models, providing valuable insights for future applications on real observational data. These advancements pave the way for more effective and timely analysis of exoplanet atmospheric properties, advancing our understanding of these distant worlds.

astro-ph.EP↗

arXiv2023

Numerical quantification of the wind properties of cool main sequence stars

As a cool star evolves, it loses mass and angular momentum due to magnetized stellar winds which affect its rotational evolution. This change has consequences that range from the alteration of its activity to influences over the atmosphere of any orbiting planet. Despite their importance, observations constraining the properties of stellar winds in cool stars are extremely limited. Therefore, numerical simulations provide a valuable way to understand the structure and properties of these winds. In this work, we simulate the magnetized winds of 21 cool main-sequence stars (F-type to M-dwarfs), using a state-of-the-art 3D MHD code driven by observed large-scale magnetic field distributions. We perform a qualitative and quantitative characterization of our solutions, analyzing the dependencies between the driving conditions (e.g., spectral type, rotation, magnetic field strength) and the resulting stellar wind parameters (e.g., Alfvén surface size, mass loss rate, angular momentum loss rate, stellar wind speeds). We compare our models with the current observational knowledge on stellar winds in cool stars and explore the behaviour of the mass loss rate as a function of the Rossby number. Furthermore, our 3D models encompass the entire classical Habitable Zones (HZ) of all the stars in our sample. This allows us to provide the stellar wind dynamic pressure at both edges of the HZ and analyze the variations of this parameter across spectral type and orbital inclination. The results here presented could serve to inform future studies of stellar wind-magnetosphere interactions and stellar wind erosion of planetary atmospheres via ion escape processe.

astro-ph.SR↗

arXiv2023

QPOML: A Machine Learning Approach to Detect and Characterize Quasi-Periodic Oscillations in X-ray Binaries

Astronomy is presently experiencing profound growth in the deployment of machine learning to explore large datasets. However, transient quasi-periodic oscillations (QPOs) which appear in power density spectra of many X-ray binary system observations are an intriguing phenomena heretofore not explored with machine learning. In light of this, we propose and experiment with novel methodologies for predicting the presence and properties of QPOs to make the first ever detections and characterizations of QPOs with machine learning models. We base our findings on raw energy spectra and processed features derived from energy spectra using an abundance of data from the NICER and RXTE space telescope archives for two black hole low mass X-ray binary sources, GRS 1915+105 and MAXI J1535-571. We advance these non-traditional methods as a foundation for using machine learning to discover global inter-object generalizations between - and provide unique insights about - energy and timing phenomena to assist with the ongoing challenge of unambiguously understanding the nature and origin of QPOs. Additionally, we have developed a publicly available Python machine learning library, QPOML, to enable further Machine Learning aided investigations into QPOs.

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

Three-dimensional, Time-dependent MHD Simulation of Disk-Magnetosphere-Stellar Wind Interaction in a T Tauri, Protoplanetary System

We present a three-dimensional, time-dependent, MHD simulation of the short-term interaction between a protoplanetary disk and the stellar corona in a T Tauri system. The simulation includes the stellar magnetic field, self-consistent coronal heating and stellar wind acceleration, and a disk rotating at sub-Keplerian velocity to induce accretion. We find that initially, as the system relaxes from the assumed initial conditions, the inner part of the disk winds around and moves inward and close to the star as expected. However, the self-consistent coronal heating and stellar wind acceleration build up the original state after some time, significantly pushing the disk out beyond $10R_\star$. After this initial relaxation period, we do not find clear evidence of a strong, steady accretion flow funneled along coronal field lines, but only weak, sporadic accretion. We produce synthetic coronal X-ray line emission light curves which show flare-like increases that are not correlated with accretion events nor with heating events. These variations in the line emission flux are the result of compression and expansion due to disk-corona pressure variations. Vertical disk evaporation evolves above and below the disk. However, the disk - stellar wind boundary stays quite stable, and any disk material that reaches the stellar wind region is advected out by the stellar wind.

astro-ph.SR↗

arXiv2023

Cosmology-informed neural networks to solve the background dynamics of the Universe

The field of machine learning has drawn increasing interest from various other fields due to the success of its methods at solving a plethora of different problems. An application of these has been to train artificial neural networks to solve differential equations without the need of a numerical solver. This particular application offers an alternative to conventional numerical methods, with advantages such as lower memory required to store solutions, parallelization, and, in some cases, a lower overall computational cost than its numerical counterparts. In this work, we train artificial neural networks to represent a bundle of solutions of the differential equations that govern the background dynamics of the Universe for four different models. The models we have chosen are $Λ\mathrm{CDM}$, the Chevallier-Polarski-Linder parametric dark energy model, a quintessence model with an exponential potential, and the Hu-Sawicki $f(R)$ model. We use the solutions that the networks provide to perform statistical analyses to estimate the values of each model's parameters with observational data; namely, estimates of the Hubble parameter from cosmic chronometers, type Ia supernovae data from the Pantheon compilation, and measurements from baryon acoustic oscillations. The results we obtain for all models match similar estimations done in the literature using numerical solvers. In addition, we estimate the error of the solutions that the trained networks provide by comparing them with the analytical solution when there is one, or to a high-precision numerical solution when there is not. Through these estimations we find that the error of the solutions is at most $\sim1\%$ in the region of the parameter space that concerns the $95\%$ confidence regions that we find using the data, for all models and all statistical analyses performed in this work.

astro-ph.CO↗

arXiv2022

Revisiting the space weather environment of Proxima Centauri b

Close-in planets orbiting around low-mass stars are exposed to intense energetic photon and particle radiation and harsh space weather. We have modeled such conditions for Proxima Centauri b (Garraffo et al. 2016b), a rocky planet orbiting in the habitable-zone of our closest neighboring star, finding a stellar wind pressure three orders of magnitude higher than the solar wind pressure on Earth. At that time, no Zeeman-Doppler observations of the surface magnetic field distribution of Proxima Cen were available and a proxy from a star with similar Rossby number to Proxima was used to drive the MHD model. Recently, the first ZDI observation of Proxima Cen became available (Klein et al. 2021). We have modeled Proxima b's space weather using this map and compared it with the results from the proxy magnetogram. We also computed models for a high-resolution synthetic magnetogram for Proxima b generated by a state-of-the-art dynamo model. The resulting space weather conditions for these three scenarios are similar with only small differences found between the models based on the ZDI observed magnetogram and the proxy. We conclude that our proxy magnetogram prescription based on Rossby number is valid, and provides a simple way to estimate stellar magnetic flux distributions when no direct observations are available. Comparison with models based on the synthetic magnetogram show that the exact magnetogram details are not important for predicting global space weather conditions of planets, reinforcing earlier conclusions that the large-scale (low-order) field dominates, and that the small-scale field does not have much influence on the ambient stellar wind.

astro-ph.SR↗

arXiv2022

Simulating the Space Weather in the AU Mic System: Stellar Winds and Extreme Coronal Mass Ejections

Two close-in planets have been recently found around the M-dwarf flare star AU Microscopii (AU Mic). These Neptune-sized planets (AU Mic b and c) seem to be located very close to the so-called "evaporation valley" in the exoplanet population, making this system an important target for studying atmospheric loss on exoplanets. This process, while mainly driven by the high-energy stellar radiation, will be strongly mediated by the space environment surrounding the planets. Here we present an investigation on this last area, performing 3D numerical modeling of the quiescent stellar wind from AU Mic, as well as time-dependent simulations describing the evolution of a highly energetic Coronal Mass Ejection (CME) event in this system. Observational constraints on the stellar magnetic field and properties of the eruption are incorporated in our models. We carry out qualitative and quantitative characterizations of the stellar wind, the emerging CMEs, as well as the expected steady and transient conditions along the orbit of both exoplanets. Our results predict an extreme space weather for AU Mic and its planets. This includes sub-Alfvénic regions for the large majority of the exoplanet orbits, very high dynamic and magnetic pressure values in quiescence (varying within $10^{2} - 10^{5}$ times the dynamic pressure experienced by the Earth), and an even harsher environment during the passage of any escaping CME associated with the frequent flaring observed in AU Mic. These space weather conditions alone pose an immense challenge for the survival of the exoplanetary atmospheres (if any) in this system.

astro-ph.SR↗

arXiv2021

Coronal Mass Ejections and Exoplanets: A Numerical Perspective

Coronal mass ejections (CMEs) are more energetic than any other class of solar phenomena. They arise from the rapid release of up to $10^{33}$ erg of magnetic energy mainly in the form of particle acceleration and bulk plasma motion. Their stellar counterparts, presumably involving much larger energies, are expected to play a fundamental role in shaping the environmental conditions around low-mass stars, in some cases perhaps with catastrophic consequences for planetary systems due to processes such as atmospheric erosion and depletion. Despite their importance, the direct observational evidence for stellar CMEs is almost non-existent. In this way, numerical simulations constitute extremely valuable tools to shed some light on eruptive behavior in the stellar regime. Here we review recent results obtained from realistic modeling of CMEs in active stars, highlighting their key role in the interpretation of currently available observational constraints. We include studies performed on M-dwarf stars, focusing on how emerging signatures in different wavelengths related to these events vary as a function of the magnetic properties of the star. Finally, the implications and relevance of these numerical results are discussed in the context of future characterization of host star-exoplanet systems.

astro-ph.SR↗

arXiv2021

MESA models with magnetic braking

Two magnetic braking models are implemented in MESA for use in the MIST stellar model grids. Stars less than about 1.3 $M_{\odot}$ are observed to spin down over time through interaction with their magnetized stellar winds (i.e., magnetic braking). This is the basis for gyrochronology, and fundamental to the evolution of lower mass stars. The detailed physics behind magnetic braking are uncertain, as are 1D stellar evolution models. Thus, we calibrate our models and compare to data from open clusters. Each braking model tested here is capable of reproducing the data, albeit with some important distinctions. The Matt et al. (2015) prescription matches the slowly rotating stars observed in open clusters, but tends to overestimate the presence of rapidly rotating stars. The Garraffo et al. (2018) prescription often produces too much angular momentum loss to accurately match the observed slow sequence for lower mass stars, but reproduces the bimodal nature of slow and rapidly rotating stars observed in open clusters fairly well. We find additional evidence that some level of mass dependency may be missing in these braking models to match the rotation periods observed in clusters older than 1 Gyr better.

astro-ph.SR↗

arXiv2021

StelNet: Hierarchical Neural Network for Automatic Inference in Stellar Characterization

Characterizing the fundamental parameters of stars from observations is crucial for studying the stars themselves, their planets, and the galaxy as a whole. Stellar evolution theory predicting the properties of stars as a function of stellar age and mass enables translating observables into physical stellar parameters by fitting the observed data to synthetic isochrones. However, the complexity of overlapping evolutionary tracks often makes this task numerically challenging, and with a precision that can be highly variable, depending on the area of the parameter space the observation lies in. This work presents StelNet, a Deep Neural Network trained on stellar evolutionary tracks that quickly and accurately predicts mass and age from absolute luminosity and effective temperature for stars with close to solar metallicity. The underlying model makes no assumption on the evolutionary stage and includes the pre-main sequence phase. We use bootstrapping and train many models to quantify the uncertainty of the model. To break the model's intrinsic degeneracy resulting from overlapping evolutionary paths, we also built a hierarchical model that retrieves realistic posterior probability distributions of the stellar mass and age. We further test and train StelNet using a sample of stars with well-determined masses and ages from the literature.

astro-ph.SR↗

arXiv2020

Stellar Winds Drive Strong Variations in Exoplanet Evaporative Outflows and Transit Absorption Signatures

Stellar wind and photon radiation interactions with a planet can cause atmospheric depletion, which may have a potentially catastrophic impact on a planet's habitability. While the implications of photoevaporation on atmospheric erosion have been researched to some degree, studies of the influence of the stellar wind on atmospheric loss are in their infancy. Here, we use three-dimensional magnetohydrodynamic simulations to model the effect of the stellar wind on the magnetosphere and outflow of a hypothetical planet, modeled to have an H-rich evaporating envelope with a pre-defined mass loss rate, orbiting in the habitable zone close to a low-mass M dwarf. We take the TRAPPIST-1 system as a prototype, with our simulated planet situated at the orbit of TRAPPIST-1e. We show that the atmospheric outflow is dragged and accelerated upon interaction with the wind, resulting in a diverse range of planetary magnetosphere morphologies and plasma distributions as local stellar wind conditions change. We consider the implications of the wind-outflow interaction on potential hydrogen Lyman-alpha (Lya) observations of the planetary atmosphere during transits. The Lya observational signatures depend strongly on the local wind conditions at the time of the observation and can be subject to considerable variation on timescales as short as an hour. Our results indicate that observed variations in exoplanet Lya transit signatures could be explained by wind-outflow interaction.

astro-ph.EP↗

arXiv2020

An Earth-like stellar wind environment for Proxima Centauri c

A new planet has been recently discovered around Proxima Centauri. With an orbital separation of $\sim$$1.44$ au and a minimum mass of about $7$ $M_{\oplus}$, Proxima c is a prime direct imaging target for atmospheric characterization. The latter can only be performed with a good understanding of the space environment of the planet, as multiple processes can have profound effects on the atmospheric structure and evolution. Here, we take one step in this direction by generating physically-realistic numerical simulations of Proxima's stellar wind, coupled to a magnetosphere and ionosphere model around Proxima c. We evaluate their expected variation due to the magnetic cycle of the host star, as well as for plausible inclination angles for the exoplanet orbit. Our results indicate stellar wind dynamic pressures comparable to present-day Earth, with a slight increase (by a factor of 2) during high activity periods of the star. A relatively weak interplanetary magnetic field at the distance of Proxima c leads to negligible stellar wind Joule heating of the upper atmosphere (about $10\%$ of the solar wind contribution on Earth) for an Earth-like planetary magnetic field ($0.3$ G). Finally, we provide an assessment of the likely extreme conditions experienced by the exoplanet candidate Proxima d, tentatively located at $0.029$ au with a minimum mass of $0.29$ $M_{\oplus}$.

astro-ph.SR↗