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SUMMARY:Time-normalized plasma flow mapping during the quadrature of SolO 
 and SDO
DTSTART;VALUE=DATE-TIME:20230512T101500Z
DTEND;VALUE=DATE-TIME:20230512T103000Z
DTSTAMP;VALUE=DATE-TIME:20240704T115610Z
UID:indico-contribution-63-352@meetings.aip.de
DESCRIPTION:Speakers: Gabriel Muro (Aberystwyth University)\nObservations 
 of plasma motions in the low corona are often limited to magnetic field li
 nes originating in active regions\, which are ideal for spatial domain enh
 ancements across individual extreme ultraviolet (EUV) images to see loops\
 , flares\, and other bright activity contrasted against dim background fea
 tures.\n\nThe quiet Sun is essentially all dim background features\, which
  requires advanced image processing and ideal observation parameters to em
 phasize the temporal domain in order to visualize faint\, fine-scale plasm
 a flows. We utilize time-normalized optical flow (TNOF) on large sets of h
 igh cadence EUV data by reducing instrumental noise to a high degree and t
 hen emphasizing the minor brightness variations indicative of plasma motio
 n. Maps of plasma flow paths are produced via optical flow tracking algori
 thms by the computer vision method of Lucas-Kanade and the underlying velo
 city field is estimated with line integral convolution.\n\nTo test the eff
 ectiveness of the TNOF approach\, we have applied this method to an EUV ca
 se study of data from EUI 174 and AIA 171 on 29 March 2022. This date mark
 ed a near-perpendicular line of sight orientation between the two spacecra
 ft\, had similarly short observation intervals\, and provided the opportun
 ity to compare contrast enhanced plasma features off-limb with temporally 
 enhanced on-disk plasma motion. \n\nIn this case study\, we generated movi
 es and flow paths that show TNOF succeeds at qualitatively outlining plasm
 a flow along magnetic field lines from both Solar Orbiter’s and SDO’s 
 point of view which are in general agreement with potential field models. 
 Additionally\, detailed velocities of plasma motion within coronal loops\,
  overall velocity trends\, and a new quasi-magnetic flow trend within the 
 quiet Sun are presented.\n\nhttps://meetings.aip.de/event/24/contributions
 /352/
LOCATION:Haus H\, Telegrafenberg
URL:https://meetings.aip.de/event/24/contributions/352/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Generative deep learning with high-resolution SDO EUV images of th
 e Sun
DTSTART;VALUE=DATE-TIME:20230512T100000Z
DTEND;VALUE=DATE-TIME:20230512T101500Z
DTSTAMP;VALUE=DATE-TIME:20240704T115610Z
UID:indico-contribution-63-351@meetings.aip.de
DESCRIPTION:Speakers: Frederic Effenberger (Ruhr-University Bochum)\nWe pr
 esent results of generative deep learning as applied to a large database o
 f solar images and discuss challenges in training and validation\, in part
 icular with distributed training on GPU supercomputers. Our dataset is bas
 ed on SDO EUV data with high resolution and with excellent coverage since 
 2010. This dataset is thus well suited to study the application of advance
 d machine learning techniques that require large amounts of data for train
 ing\, such as deep learning approaches. We focus on diffusion type models 
 and generative adversarial deep learning (GANs). We address the potential 
 of data augmentation techniques for improved learning and image quality an
 d the opportunities for latent space structure exploration and control. Th
 e challenges in reaching the highest resolutions with good image fidelity 
 are analyzed. Potential application downstream that can make use of such g
 enerated images are briefly discussed and the need for a community-driven\
 , physics-based basis to establish evaluation criteria for generative mode
 ls will be emphasized.\n\nhttps://meetings.aip.de/event/24/contributions/3
 51/
LOCATION:Haus H\, Telegrafenberg
URL:https://meetings.aip.de/event/24/contributions/351/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Physics-informed neural networks for solar magnetic field simulati
 ons
DTSTART;VALUE=DATE-TIME:20230512T094500Z
DTEND;VALUE=DATE-TIME:20230512T100000Z
DTSTAMP;VALUE=DATE-TIME:20240704T115610Z
UID:indico-contribution-63-350@meetings.aip.de
DESCRIPTION:Speakers: Robert Jarolim ()\nPhysics-informed neural networks 
 (PINNs) provide a novel approach for numerical simulations\, tackling chal
 lenges of discretization and enabling seamless integration of noisy data a
 nd physical models (e.g.\, partial differential equations). In this presen
 tation\, we highlight the new opportunities for solar magnetic field model
 ling that are enabled through physics-informed machine learning.\nWe prese
 nt a novel approach for coronal magnetic field extrapolation using physics
  informed neural networks. The neural network is optimized to match observ
 ations of the photospheric magnetic field vector at the bottom-boundary\, 
 while simultaneously satisfying the force-free and divergence-free equatio
 ns in the entire simulation volume. We demonstrate that our method can acc
 ount for noisy data and deviates from the physical model where the force-f
 ree magnetic field assumption cannot be satisfied.\nWe utilize meta-learni
 ng concepts to simulate the evolution of the active region 11158. Our simu
 lation of 5 days of observations at full cadence\, requires about 12 hours
  of total computation time. The derived evolution of the free magnetic ene
 rgy and helicity in the active region\, shows that our model captures flar
 e signatures\, and that the depletion of free magnetic energy spatially al
 igns with the observed EUV emission. With this we present the first method
  that can perform realistic magnetic field extrapolations in quasi real-ti
 me\, which enables advanced space weather monitoring. \nWe conclude with a
 n outlook on our ongoing work where we extend this approach to create a ne
 w class of MHD simulations\, that can flexibly incorporate additional obse
 rvational constraints and perform fast computations.\n\nhttps://meetings.a
 ip.de/event/24/contributions/350/
LOCATION:Haus H\, Telegrafenberg
URL:https://meetings.aip.de/event/24/contributions/350/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Identification of large scale resonant modes in sunspots in the lo
 wer solar atmosphere
DTSTART;VALUE=DATE-TIME:20230512T092000Z
DTEND;VALUE=DATE-TIME:20230512T094500Z
DTSTAMP;VALUE=DATE-TIME:20240704T115610Z
UID:indico-contribution-63-359@meetings.aip.de
DESCRIPTION:Speakers: Marco Stangalini (ASI Italian Space Agency)\nA solar
  magnetic flux tube\, like any physical system\, is capable of supporting 
 a variety of resonances\, which constitute its natural response to externa
 l forcing. Apart from a few single low order eigenmodes in small scale mag
 netic structures\, several simultaneous resonant modes were not found unti
 l recently. These eigenmodes are associated to small but coherent oscillat
 ions and require the application of particular spatial filtering technique
 s to be detected.\nIn this talk I will report and discuss about the detect
 ion and physical implications of large scale coherent oscillations in a su
 nspot\, with a spectrum significantly different from p-modes.\n\nhttps://m
 eetings.aip.de/event/24/contributions/359/
LOCATION:Haus H\, Telegrafenberg
URL:https://meetings.aip.de/event/24/contributions/359/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Atomic lines parameters inference using a global spectropolarimetr
 ic inversion
DTSTART;VALUE=DATE-TIME:20230512T075500Z
DTEND;VALUE=DATE-TIME:20230512T081000Z
DTSTAMP;VALUE=DATE-TIME:20240704T115610Z
UID:indico-contribution-63-348@meetings.aip.de
DESCRIPTION:Speakers: Dušan Vukadinović (Max Planck Institute for Solar 
 System Research)\nMany solar spectral lines have poorly determined atomic 
 parameters\, such as the transition probability or the central wavelength\
 , especially in the near-ultraviolet (NUV) range. We present a novel appro
 ach to exploit high-angular-resolution spectropolarimetric observations of
  the Sun to obtain precise atomic parameters of spectral lines\, required 
 for the proper analysis of solar and stellar observations. The inversion-b
 ased approach requires that unique (global) values of atomic parameters mu
 st represent very well spectral line profiles originating from very differ
 ent solar atmospheres within an observed field of view. To test this so-ca
 lled global method\, we analyzed synthetic spectra computed from magneto-h
 ydrodynamic models of the solar atmosphere and compared the results to the
  previously used methods. The method is general enough to be applied to th
 e spectral lines from the NUV to the infrared wavelengths. The global meth
 od is able to retrieve reliable estimates of atomic parameters even for we
 ak and blended spectral lines\, thus extending the list of spectral lines 
 with reliable atomic parameters used to analyze solar and stellar spectra.
 \n\nhttps://meetings.aip.de/event/24/contributions/348/
LOCATION:Haus H\, Telegrafenberg
URL:https://meetings.aip.de/event/24/contributions/348/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Classification Scheme for High-resolution Spectra Using Machine Le
 arning Algorithms
DTSTART;VALUE=DATE-TIME:20230512T074000Z
DTEND;VALUE=DATE-TIME:20230512T075500Z
DTSTAMP;VALUE=DATE-TIME:20240704T115610Z
UID:indico-contribution-63-349@meetings.aip.de
DESCRIPTION:Speakers: Ekaterina Dineva (Katholieke Universiteit Leuven)\nC
 ontemporary solar physics deals with increasing volumes of high-dimensiona
 l data from observations and realistic theoretical models alike. Machine L
 earning (ML) is rapidly integrated into solar and heliophysics research\, 
 facilitating dimensionality reduction\, visualization and analysis. We dev
 eloped a pipeline\, which employed carefully selected unsupervised ML algo
 rithms for classification and cluster analysis\, to extract information re
 garding the physical properties of the solar atmosphere contained in the l
 arge variety of spectral profiles. The pipeline is tested on the synthetic
  spectra of the Fe I $\\lambda\\\,7090.38$ Å photospheric absorption line
 \, computed with the CO$^5$BOLD radiation hydrodynamics code. This line is
  also part of the observing setup for the Fast Multi-line Universal Spectr
 ograph (FaMuLUS) camera system at the Vacuum Tower Telescope (VTT). CO$^5$
 BOLD snapshot time-series serve as a simulation of high-resolution\, fast-
 cadence solar observations\, thus confronting the pipeline with the scenar
 io of dynamic solar feature evolution. This project aims to deliver a robu
 st classification scheme with minimal user interaction\, which also prepar
 es the spectral dataset for further analysis\, such as spectral inversions
 . Successful classification allows quick identification of structure and d
 ynamics in the region of interest\, as well as diagnostics of the ambient 
 plasma based on spectra line parameters.\n\nhttps://meetings.aip.de/event/
 24/contributions/349/
LOCATION:Haus H\, Telegrafenberg
URL:https://meetings.aip.de/event/24/contributions/349/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Data Analysis Tools
DTSTART;VALUE=DATE-TIME:20230512T071500Z
DTEND;VALUE=DATE-TIME:20230512T074000Z
DTSTAMP;VALUE=DATE-TIME:20240704T115610Z
UID:indico-contribution-63-409@meetings.aip.de
DESCRIPTION:Speakers: Nazaret Bello Gonzalez  (Leibniz-Institut für Sonne
 nphysik (KIS))\nhttps://meetings.aip.de/event/24/contributions/409/
LOCATION:Haus H\, Telegrafenberg
URL:https://meetings.aip.de/event/24/contributions/409/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Machine Learning methods for solar spectroscopy and imaging
DTSTART;VALUE=DATE-TIME:20230512T081000Z
DTEND;VALUE=DATE-TIME:20230512T083500Z
DTSTAMP;VALUE=DATE-TIME:20240704T115610Z
UID:indico-contribution-63-354@meetings.aip.de
DESCRIPTION:Speakers: Andrés Asensio Ramos (Instituto de Astrofísica de 
 Canarias)\nSolar spectropolarimetry is entering the realm of big data. Cur
 rent and future telescopes will produce data at a rate that will make it h
 ard to store in a single machine and even harder to operate on the data. T
 hankfully\, in the last decade\, machine learning has experienced an enorm
 ous advance\, thanks to the open possibility of training very deep and com
 plex neural networks. In this \ncontribution I show options to explore to 
 deal with the big data problem and also how deep learning can be used to e
 fficiently solve difficult problems in Solar Physics. I will focus on how 
 differentiable programming (aka deep learning) is helping us to have acces
 s to velocity fields in the solar atmosphere\, correct for the atmospheric
  degradation of spectropolarimetric data and carry out fast 3D inversions 
 of the Stokes parameters to get physical information of the solar atmosphe
 re.\n\nhttps://meetings.aip.de/event/24/contributions/354/
LOCATION:Haus H\, Telegrafenberg
URL:https://meetings.aip.de/event/24/contributions/354/
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