Monday, 1 December 2025

Substellar Astrophysics meeting in Tordesillas, Spain

 



Substellar science has emerged in the last few decades as new branch of Astrophysics that connects Stars, Exoplanets and the Solar System. The advent of new surveys such as Euclid and Rubin LSST is poised to increase the numbers of known substellar objects by more than an order of magnitude, while the James Webb Telescope is providing new details about their properties. Rapid advances in observational capabilities have spurred the development of a new generation of theoretical models, now reaching unprecedented levels of accuracy and physical completeness.

This meeting will bring together researchers interested in Substellar science with special emphasis on exploiting the wealth of data provided by Euclid and complementing it with other surveys and follow-up observations.

The conference is supported by the European Research Council Advanced grant nicknamed Substellar.

A total eclipse of the Sun will take place during the conference (August 12, 2026).

The proceedings of the conference will be formally published as part of the conference series of Astronomische Nachrichten.

 More information at the conference website. 

Program highlights

Key themes include:
  • Detection methods (Searching methods, astrometry, photometry, spectroscopy)
  • Confirmation of UCD candidates
  • Atmospheric properties of UCD's
  • UCD in connection with Milky Way
  • Multiplicity, planetary systems, disks
  • Substellar luminosity and mass functions
  • Connection with exoplanets
  • Synergies of Euclid and other surveys
  • Big data, machine learning for substellar science
  • Prospects for Exolife in Substellar Worlds
  • Theory of UCD's (atmospheric and evolutionary models, microphysics)

Scientific Organizing Committee

  • Eduardo Martin (chair)
  • Maruša Žerjal (co-chair)
  • Nikola Vitas (co-chair)
  • Patricia Cruz
  • Pin-Gao Gu
  • Nuria Huélamo
  • Nicolas Lodieu
  • Koraljka Mužić
  • Ngoc Phan
  • Annie Robin
  • Johannes Sahlmann
  • Kun Wang

Invited speakers

  • Khalid Barkaoui, Instituto de Astrofísica de Canarias, Tenerife, Spain
  • David Barrado, Centro de Astrobiología, Madrid, Spain
  • Beth Biller, Royal Observatory, Edinburgh, UK
  • Clemence Fontanive, Royal Observatory, Edinburgh, UK
  • Kevin Luhman, Pennsylvania State University, USA
  • Elena Manjavacas, Space Telescope Science Institute, USA
  • Javier Olivares, UNED, Madrid, Spain
  • Antonio Pérez Garrido, Universidad Politécnica de Cartagena, Spain
  • Rafael Rebolo, Instituto de Astrofísica de Canarias, Tenerife, Spain
  • Céline Reylé, Observatoire de Besançon, France
  • Richard Smart, Ossevatorio Astrofisico di Torino, INAF, Italy
  • Enrique Solano, Centro de Astrobiología, Madrid, Spain
  • Xianyu Tan, Shanghai Jiao Tong University, China
  • Ramarao Tata, Ohio University, USA
  • Maria Rosa Zapatero Osorio, Centro de Astrobiología, Madrid, Spain
  • Jun-Yan Zhang, Western University, Ontario, Canada

Wednesday, 2 April 2025

A photometric search for ultracool dwarfs in the Euclid Deep Fields

 

A new paper from Euclid Q1, is just out on arxiv. I’m particularly happy to be part of!

In this work, led by Maruša Žerjal (my collaborator at the IAC and the SUBSTELLAR project), we explored what Euclid can already tell us about the population of ultracool dwarfs. Using a remarkably simple photometric selection based on the very red Euclid ($I_\mathrm{E}-Y_\mathrm{E}$) colour, we identified 5306 new ultracool-dwarf candidates in the three Euclid Deep Fields, ranging from late-M to late-T dwarfs. Around 1200 are L and T dwarfs, and 546 objects are spectroscopically confirmed.

Euclid was designed primarily for cosmology, but its combination of deep optical and near-infrared photometry and spectroscopy makes it an extraordinary machine for finding and characterising the faintest members of the stellar and substellar populations.

And Q1 is only a tiny taste of what is coming. We find roughly 100 UCDs per square degree, which suggests that the final Euclid Wide Survey could contain at least 1.4 million ucd candidates, including around 300 000 L dwarfs and thousands of T dwarfs.

These estimates come from deliberately strict selection criteria that favour purity over completeness, so they are essentially a lower limit. Millions of ultracool dwarfs waiting in the Euclid data, exciting times ahead!


Monday, 15 July 2024

Three-dimensional radiative MHD simulations of near-surface convection in main sequence cool stars

 Andrea Perdomo García has recently completed her PhD thesis, Three-dimensional radiative MHD simulations of near-surface convection in main sequence cool stars, supervised by Manuel Collados and myself.

Andrea’s thesis explored the atmospheres of cool main-sequence stars through realistic three-dimensional radiative MHD simulations with the MANCHA code. A major part of her work was devoted to one of the central challenges of such simulations: treating radiative transfer accurately while keeping it computationally feasible. She developed and tested opacity-binning strategies for stars ranging from F to M spectral types, investigated the increasingly important role of molecular opacity toward cooler stars, and studied how different opacity treatments affect atmospheric structure and radiative energy exchange.

She then applied these methods to 3D simulations of G2V, K0V, and M2V stars, exploring their convection and photospheric structure and extending the calculations to magnetic simulations in which fields generated through the Biermann battery were amplified by small-scale dynamo action.

It has been a true pleasure to work with Andrea over the years and to follow her development as a researcher. I wish her all the best in her future career and in her new position at the Max Planck Institute in Heidelberg. I am sure there are many interesting problems, simulations, and probably quite a few opacity tables still ahead!

Monday, 3 June 2024

Hydrodynamic simulations of cool stellar atmospheres with MANCHA

 

 

Perdomo García, A., Vitas, N., Khomenko, E., Collados, M., A&A, 688, A27 (2024)

arxiv

Three-dimensional simulations do much more than reproduce what we observe on stellar surfaces. They give us a unique laboratory in which we can experiment with stellar atmospheres: change the physical ingredients, follow their interaction, and understand how convection, radiation and atmospheric structure are connected.

In this paper, we used the MANCHA code to simulate the atmospheres of three cool main-sequence stars, G2V, K0V and M2V, and explored one particularly important ingredient: radiative transfer and opacity. We tested how different approximations to the enormously complex stellar opacity affect radiative energy exchange and, ultimately, the atmospheric structure.

The results show how increasingly important a realistic treatment of opacity becomes as we move toward cooler stars, where molecules start to dominate the radiative properties of the atmosphere.

Another important piece of Andrea’s PhD work, and another step toward realistic 3D modelling across the cool-star sequence. 

Wednesday, 14 February 2024

Moving to the dark side!

 


I have some big news to share. From March 1, I’ll be starting a new chapter and moving to the dark side of astrophysics: the substellar world of ultracool dwarfs!

This seems like a natural moment to make the move. With the Euclid ESA mission now opening an enormous new window on the faint and cool populations of the Milky Way, we are entering an era in which the number of known ultracool and substellar objects will increase substantially. Turning this wealth of new observations into physical understanding will require careful work on the modelling side.

In some ways, though, I am not moving very far at all. My main interests will remain what they have been for years: developing numerical agoriths for radiative transfer, equations of state, opacities, and numerical modelling of atmospheres. The difference is that I will now be applying them much further down the temperature scale. At the temperatures of ultracool dwarfs, molecules dominate the opacity, chemistry becomes increasingly tricky, condensates and clouds appear, and the coupling between chemistry, radiation and atmospheric structure gets the central stage in modelling problem. 

I’ll be joining the SUBSTELLAR project led by Eduardo Martín, one of the co-discoverers of Teide 1, the first confirmed brown dwarf, announced in 1995. The work is supported by the European Research Council through the ERC Advanced Grant SUBSTELLAR, devoted to pushing the frontier of substellar science with the Euclid mission.

A new wavelength regime, a new class of objects, and plenty of new physics — but, fortunately, still lots of opacities, radiative transfer and numerical challenges. I hope that this move will also allow me to bring some of my experience in stellar astrophysics across the boundary between these fields. I’m looking forward to seeing where it leads.

May the (gravity and Lorentz) Force be with me!

Wednesday, 7 June 2023

Opacity for realistic 3D MHD simulations of cool stellar atmospheres

The first paper of Andrea Perdomo Garcia is just submitted for publication in Astronomy & Astrophysics, and out on arxiv.org/abs/2306.03744. The paper is all about computing the opacities for realistic modelling of cool stellar atmospheres. It is divided in three unities. First (Section 3) it describes the computation of detailed monochromatic opacity including millions of atomic and molecular spectral lines and millions of wavelength points. For this the code SYNSPEC (Hubeny and Lanz, 2011, 2017a, b) is used. Then (Section 4) the monochromatic opacities are used to construct opacity distribution function which reduces the number of wavelength points from millions to thousands. The results are compared in detail with ones produced by Kurucz. Some striking similarities and some warning differences are found. Finally (Section 5), the opacity distribution function to construct opacity bins. This method, originally proposed by Nordlund (1982) is the key ingredient for realistically simulating stellar atmospheres in 3D as it reduced the problem further, from thousands of wavelength points to only a few. However, the method depends on a choice of some free parameters. In our paper the possible choices are carefully analyzed and some interesting conclusions are offered. 

In Sect.3 there are two figures (Figs.2 and 3) that I find very useful and illustrative. The monochromatic opacity (Fig.2) and the radiative heating rate (Fig.3) are shown as 2D functions of wavelength (X-axis) and height in the atmosphere (Y-axis) for four different cool stars (all with solar metalicity). Optical depths in the continuum and continuum+lines are overplotted.

(Andrea is the final year PhD student at Instituto de Astrofisica de Canarias and Univeridad de La Laguna, supervised by Manolo Collados Vera and myself. Stay tuned, more cool stuff is coming out from her research this year.)

Saturday, 25 March 2023

Charles Hermite (1822 - 1901)

unday morning in Paris offered an opportunity to walk to the Montparnasse Cemetery and pay my respects to some of my personal heroes buried there.

While the graves of Beckett, Cortázar and Poincaré attract plenty of attention and visitors, it is perhaps less well known that the great French mathematician Charles Hermite is buried there as well.

Hermite was not only a mentor to Henri Poincaré, Henri Padé, Thomas Stieltjes and Mihajlo Petrović Alas. His work on interpolation and function approximation lies at the very foundations of many modern numerical methods used in computational fluid dynamics and radiative transfer, even if that connection is not always obvious, or properly acknowledged.

His 1878 paper, Sur la formule d'interpolation de Lagrange, is still worth reading for anyone interested in function approximation, almost 150 years later.

Today, even the name on his gravestone is barely readable.

Monday, 3 October 2022

Wednesday, 7 September 2022

1D Solar Atmosphere Models in IDL: Standard Solar Model (from "The Sun", by Stix)

Stix, in his book ("The Sun: An Introduction", Springer, 2002) in Sect.2.4 introduces a standard solar model from the core to the surface defined as $\tau = 2/3$. His Table 2.4 (on p.56) lists the values of various quantities of this model versus the column mass or the height. Check the book for the details of the model. The table in ascii format is here: stix_tab.2.4.txt The columns are: $m/m_\odot$, $r/r_\odot$, $p \mathrm[Pa]$, $T \mathrm[K]$, $\rho \mathrm[kg/m^3]$, $L/L_\odot$, $X$, $\mu$, $\Gamma_1$.

Wednesday, 13 July 2022

NGC 7319: GTC vs JWST

The first pictures from James Webb Space Telescope are simply mind blowing! Here is a comparison of NGC 7319 (from Stephan's quintet of galaxies) from Gran Telescopio Canarias (still the largest single aperture optical telescope) and from JWST. One should keep in mind that these are different instruments observing and different wavelengths. The alignment is also not perfect.

Wednesday, 1 April 2020

Quadratic splines: Part I

Some basic properties of parabola in the context of the second-order Hermite interpolation equation. 

There is a one-dimensional dataset with $n$ points: $x = (x_0, \dots, x_{n-1})$ and $y = (y_0, \dots, y_{n-1})$, such that $x_{i} > x_{i-1}$ for any $i$. We introduce the following labels: $$h_{i-1} = x_i - x_{i-1}$$ $$\delta_{i-1} = \frac{y_i - y_{i-1}}{x_i - x_{i-1}}$$ $h$ is always positive, while $\delta$ can have any value. On each segment, we approximate the unknown function with a parabola. On the $[i-1, i]$ segment: \begin{equation}f(x) = a_0 + a_1 (x - x_{i-1}) + a_2 (x - x_{i-1})^2\end{equation} The first and the second derivative of the parabola are: \begin{equation}f'(x) = a_1 + 2 a_2 (x - x_{i-1})\end{equation} \begin{equation}f''(x) = 2 a_2 \end{equation} The Hermite interpolation theorem provides us with a tool to determine the $a$ coefficients on the $[i-1, i]$ segment if we know $y_{i-1}$, $y_i$ and one of the first derivative in the end points, either $y'_{i-1}$ or $y'_i$. Let's assume that we know ("know" in the sense "we can compute") $y'_{i-1}$. Then there are three equations that the parabola must obey: \begin{equation}f(x_{i-1}) = y_{i-1} = a_0\end{equation} \begin{equation}f'(x_{i-1}) = y'_{i-1} = a_1\end{equation} \begin{equation}f(x_{i}) = y_i = a_0 + a_1 h_{i-1} + a_2 h_{i-1}^2\end{equation} The solution is then trivial: \begin{equation}a_0 = y_{i-1}\end{equation} \begin{equation}a_1 = y'_{i-1}\end{equation} \begin{equation}a_2 = \frac{y_{i} - y_{i-1} - y'_{i-1} h_{i-1}}{h_{i-1}^2}\end{equation} Once the $a$ coefficients are known, the parabola is fully determined. That also means that the derivative in the point $i$ is fixed: $$f'(x_{i}) = y'_i = a_1 + 2 a_2 (x_i - x_{i-1}) = y'_{i-1} + 2 \frac{y_{i} - y_{i-1} - y'_{i-1} h_{i-1}}{h_{i-1}^2} h_{i-1}$$ $$y'_i = \frac{2(y_{i} - y_{i-1}) - y'_{i-1} h_{i-1}}{h_{i-1}} $$ \begin{equation}y'_i = 2\delta_{i-1} - y'_{i-1}\end{equation} Local extremum

The extremum of this parabola is located at $x_m$: $$f'(x_m) = 0 = a_1 + 2 a_2 (x_m - x_{i-1})$$ \begin{equation}x_m = x_{i-1} - \frac{a_1}{2a_2}\end{equation} Monotonicity

The function $f(x)$ is monotone on the $[i-1, i]$ segment if it does not have an extremum on it, i.e. if $$x_m < x_{i-1}\;\;\;\;\;\;\;\;\;\;\mathrm{or}\;\;\;\;\;\;\;\;\;\; x_m > x_i$$ The first condition readily translates into: $$x_{i-1} - \frac{a_1}{2a_2} < x_{i-1}$$ $$ \frac{a_1}{2a_2} > 0$$ or, when we substitute $a_1$ and $a_2$: $$ \frac{y'_{i-1} h_{i-1}^2}{2(y_{i} - y_{i-1} - y'_{i-1} h_{i-1})} > 0$$ $$ \frac{y'_{i-1} h_{i-1}}{2(\delta_{i-1} - y'_{i-1})} > 0$$ As by definition $h_{i-1} > 0$ $$ \frac{y'_{i-1}}{2(\delta_{i-1} - y'_{i-1})} > 0$$ If $y'_{i-1} > 0$ then $\delta_{i-1} - y'_{i-1} > 0$ and $\delta_{i-1} > y'_{i-1}$. If $y'_{i-1} < 0$, then $\delta_{i-1} < y'_{i-1}$. Therefore, the first condition ($x_m < x_{i-1}$) reduces to: \begin{equation}\boxed{|\delta_{i-1}| > |y'_{i-1}|}\end{equation} The second condition leads to: $$x_{i-1} - \frac{a_1}{2a_2} > x_{i}$$ $$-x_{i} + x_{i-1} - \frac{a_1}{2a_2} > 0$$ $$h_{i-1} + \frac{a_1}{2a_2} < 0$$ $$h_{i-1} + \frac{y'_{i-1} h_{i-1}}{2(\delta_{i-1} - y'_{i-1})} < 0$$ $$\frac{2\delta_{i-1} - y'_{i-1}}{2(\delta_{i-1} - y'_{i-1})} < 0$$ There are two cases. First, if $2\delta_{i-1} - y'_{i-1} > 0$, i.e. $2\delta_{i-1} > y'_{i-1}$. then it must be $\delta_{i-1} - y'_{i-1}< 0$, i.e.$\delta_{i-1} < y'_{i-1}$. Therefore, the first case leads to $2\delta_{i-1} > y'_{i-1} > \delta_{i-1}$. Secondly, if $2\delta_{i-1} - y'_{i-1} < 0$, i.e. $2\delta_{i-1} < y'_{i-1}$, it must be $\delta_{i-1} - y'_{i-1}> 0$, i.e. $\delta_{i-1} > y'_{i-1}$. So the second case leades to $\delta_{i-1} > y'_{i-1}> 2\delta_{i-1}$. The first case is possible only when both $\delta_{i-1}$ and $y'_{i-1}$ are positive and the second case is possible only when both are negative. Therefore, together, the two cases reduce the second condition ($x_m > x_{i}$) to: \begin{equation}\boxed{|2\delta_{i-1}| > |y'_{i-1}| > |\delta_{i-1}|}\end{equation} Together, the two conditions combine into \begin{equation}\boxed{|2\delta_{i-1}| > |y'_{i-1}|}\end{equation} A much faster way to reach the same result is to realize that the the derivatives $y'_{i-1}$ and $y'_{i}$ must be of the same sign if the function is monotone on the $[i-1, i]$ segment, i.e. $$y'_{i-1} \cdot (2\delta_{i-1} - y'_{i-1}) > 0$$ which obviously leads to $|2\delta_{i-1}| > |y'_{i-1}|$.

Therefore, for a Hermite parabola defined by $y_{i-1}$, $y_i$ and $y'_{i-1}$ to be monotone on the interval $(i-1, i)$, the derivative $y'_{i-1}$ must be of the same sign as the linear slope $\delta_{i-1}$ and smaller in magnitude than $2\delta_{i-1}$.

Convexity

For the function $f$ to be convex on $(i-1, i)$ (if parabola is convex on a segment, it's convex everywhere) the condition is $f''(x) > 0$. In the case of our parabola: $$f''(x) = 2a_2 > 0$$. $$\frac{y_{i} - y_{i-1} - y'_{i-1} h_{i-1}}{h_{i-1}^2} > 0$$ or $$y_{i} - y_{i-1} - y'_{i-1} h_{i-1} > 0$$ \begin{equation}\delta_{i-1} > y'_{i-1}\end{equation} This result is obvious $\delta_{i-1} = y'_{i-1}$ is the straight line going through the end-points of the segment. For the parabola to be concave, it must hold $f''(x) < 0$, i.e. \begin{equation}\delta_{i-1} < y'_{i-1}\end{equation} 



 

Saturday, 7 December 2019

XXXI Canary Islands Winter School: Computational Fluid Dynamics in Astrophysics (Photos)

Two weeks of interesting lectures, intensive hands-on exercises, discussions and meeting new people... A couple of photos by Claudio Dalla Vecchia that perfectly capture the spirit of the school. 


Friday, 26 April 2019

XXXI Canary Islands Winter School: Computational Fluid Dynamics in Astrophysics


Registration is now open for the XXXI Canary Islands Winter School of Astrophysics, which will be held this year in La Laguna, Tenerife, Spain from 19-28 November, on the topic of "Computational Fluid Dynamics in Astrophysics". Confirmed invited lecturers are

Fernando Moreno-Insertis (Fluid dynamics, conservation laws and waves)
Ake Nordlund (Stellar and planetary formation)
Maarit Käpylä (Dynamics of solar and stellar convection zones)
Matthias Rempel (Solar Atmosphere)
Sascha Husa (General relativity and compact objects)
Stefanie Walch-Gassner (Interstellar matter)
Tom Theus (Galaxy evolution)

The school will consist of lectures, seminars tutorials, and social events including visits to the observatories in Tenerife and La Palma. It is aimed at advanced graduate students in astrophysics, as well as postdoctoral researchers, with a strong interest in the computational fluid dynamics and it applications astrophysics.

For further information and to register, please visit

http://www.iac.es/winterschool/2019/

Claudio Dalla Vecchia
Nikola Vitas
Elena Khomenko

Wednesday, 17 October 2018

Three-dimensional simulations of solar magneto-convection including effects of partial ionization



Astronomy & Astrophysics Volume 618 (October 2018) is just out with a figure from our paper on the cover!

The paper (Khomenko, Vitas, Collados & de Vicente 2018: A&A, ADS) describes the effects of the partial ionization on the structure, dynamics and energy balance of the low chromosphere.

Tuesday, 11 July 2017

Ideal EOS for partially ionized hydrogen plasma

Plasma in the solar photosphere can be safely approximated by the ideal gas (non-interacting, randomly moving point-like particles) in the local thermodynamical equilibrium (TE). That means that

(1) the equation of state can be approximated by the ideal gas law:
\begin{equation}p = n\,kT,\end{equation}
where $p$ is the gas pressure, $n$ is the total number density, $T$ is the temperature and $k$ is the Boltzmann constant.

For partially ionized gas:

(2) the ionization fraction can be approximated by its equilibrium value that is set by Saha's ionization formula:
\begin{equation}\frac{n_{i+1}n_{\mathrm{e}}}{n_i} = \frac{1}{\Phi(T)},\end{equation}
$$\frac{1}{\Phi(T)} = \frac{2 U_{i+1}}{U_i} \left(\frac{2 \pi m_e k T}{h^2}\right)^{3/2} \mathrm{e}^{-\chi/kT},$$
where $U_i$ and $U_{i+1}$ are the partition functions of the ionization stages $i$ and $i+1$, $m_\mathrm{e}$ is the electron mass, $h$ is Planck's constant and $\chi$ is the ionization energy.

For a mixture of gases, it means that each component can be approximated by ideal gas, so that the partial pressure of the $i$ component is:
\begin{equation}p_j = n_j\,kT, \end{equation}
and the total pressure is equal to the sum of the partial pressures (Dalton's law):
 \begin{equation}p = \sum p_\mathrm{j},\end{equation}
where the summation goes over all components. The components in this case are the free electrons and all the species of the neutral and charged atoms and molecules that are present in the plasma. In the general case the system of the equations is non-linear and contains large number of equations (one for each atomic or molecular specie in every relevant ionization stage). In the theory of stellar atmosphere it is often necessary to solve this system when two parameters are know.

Nevertheless, there are some trivial solutions. The most notorious is the example of the atmosphere  composed of pure hydrogen when the H2 molecules and the negative hydrogen ions are neglected. In that case there are only three types of particles present: protons, neutral H-atoms and free electrons.  The rest of this blog refers to that case only.


Solution for given temperature and pressure

This simple exercise is described in Mihalas (1970, p.73, 1970stat.book.....M). The total number of particles in this case is:
\begin{equation}n = n_\mathrm{e} + n_\mathrm{H} + n_\mathrm{H^+} = n_\mathrm{e} + n_\mathrm{H}^\mathrm{tot},\label{eq:pureh1}\end{equation}
where $n_\mathrm{H}^\mathrm{tot} = n_\mathrm{H} + n_\mathrm{H^+}$ is the total number of neutral and ionized H atoms (the total number of H nuclei). The ratio of  $n_\mathrm{H^+}$ and $n_\mathrm{H}$ is given by Saha's equation:
\begin{equation}\frac{n_\mathrm{H^+} n_{\mathrm{e}}}{n_\mathrm{H}} = \frac{1}{\Phi(T)}. \label{eq:pureh2}\end{equation}
In addition we know that the number of electrons must be equal to the number of protons:  \begin{equation}n_\mathrm{H^+} = n_\mathrm{e}.\label{eq:pureh3}\end{equation}
If we eliminate $n_\mathrm{H}$ and $n_\mathrm{H^+}$ from Eq.$\ref{eq:pureh2}$ and Eq.$\ref{eq:pureh3}$, then Eq.$\ref{eq:pureh1}$ becomes quadratic equation for the electron pressure:
\begin{equation}n = 2 n_\mathrm{e} + n_\mathrm{e}^2 \Phi(T),\label{eq:pureh4}\end{equation}
with the solution:
\begin{equation}n_{\mathrm{e}} = \left(\sqrt{1 + n\,\Phi(T)}-1 \right)\,\frac{1}{\Phi(T)}.\label{eq:pureh5}\end{equation}
To express this solution in terms of the total pressure $p$ and the electron pressure $p_\mathrm{e}$, we assume that the ideal gas law is valid not only for the mixture, but for all of its components as well. Thus: $p_\mathrm{e} = n_\mathrm{e}\,kT$ and the solution for the electron pressure becomes:
\begin{equation}p_{\mathrm{e}} = \left(\sqrt{1 + \frac{p}{kT}\,\Phi(T)}-1 \right)\,\frac{kT}{\Phi(T)}.\label{eq:pureh6}\end{equation}
(In Mihalas' book there is a typo in this equation.) The partial pressure of the hydrogen atoms (neutral and ionized) is obviously:
$$p_\mathrm{H}^\mathrm{tot} = p - p_\mathrm{e}.$$Once we know the electron pressure, it is easy to find other variables.

Ionization fraction

The ionization fraction is defined as:
\begin{equation}x \equiv \frac{n_\mathrm{H^+}}{n_\mathrm{H}^\mathrm{tot}}= \frac{n_\mathrm{e}}{n_\mathrm{H}^\mathrm{tot}}.\label{eq:x}\end{equation}

Density

The mass density of a mixture specified by the number densities of its components is:
\begin{equation}\rho \equiv \sum_i n_i\,m_i = n_\mathrm{H}\,m_\mathrm{H}+n_\mathrm{H^+}\,m_\mathrm{H^+}+n_\mathrm{e}\,m_\mathrm{e},\label{eq:rho}\end{equation}
where  $m_\mathrm{H}$, $m_\mathrm{H^+}$ and $m_\mathrm{e}$ are masses of one neutral hydrogen atom, one proton and one electron. Since $n_\mathrm{H^+} = n_\mathrm{e}$ and $m_\mathrm{H} \approx m_\mathrm{H^+} \gg m_\mathrm{e}$, the density is
$$\rho \approx m_\mathrm{H}\,n_\mathrm{H}^\mathrm{tot}.$$


Alternatives

If the density is given instead of the total pressure,  then the system becomes:
\begin{eqnarray}\rho &=& n_\mathrm{H}\,m_\mathrm{H} + n_\mathrm{e}(m_\mathrm{H} + m_\mathrm{e}),\nonumber\\ \Phi\,n_\mathrm{e}^2 &=& n_\mathrm{H},\nonumber\end{eqnarray}
yielding again to a quadratic equation for the electron density:
$$\Phi\,m_\mathrm{H}\,n_\mathrm{e}^2 +(m_\mathrm{H} + m_\mathrm{e}) n_\mathrm{e} - \rho = 0.$$

If the electron pressure is given instead of the density of the pressure, then the solution for the total pressure is $$p = p_\mathrm{e}(\Phi\,p_\mathrm{e} + 2kT),$$ what is equivalent to Eq.$\ref{eq:pureh4}$.


Mean molecular weight

The mean molecular weight is defined as
$$\mu = \frac{\rho}{n\,m_\mathscr{A}},$$
where $m_\mathscr{A}$ is the atomic mass unit in grams. If all gas in neutral,
$$\mu = \frac{m_\mathrm{H}\,n_\mathrm{H}}{n_\mathrm{H}\,m_\mathscr{A}} = A_\mathrm{H},$$
with  $A_\mathrm{H}$ being the atomic mass of hydrogen ($\approx 1.008$). If all atoms are ionized, the density remains unchanged, but the number of particles is doubled and, therefore, $\mu \approx 0.5$. (The mean molecular weight is always roughly equal to the total number of nuclei per particle.)


Specific internal energy

The internal energy is equal to the sum of the kinetic part and the part due to the ionization (in principle, there should be another term due to the excitation here, but we neglect it in this example):
$$E_\mathrm{int} = \frac{3}{2}N\,kT + N_\mathrm{e}\,\chi.$$
The specific internal energy pre volume is then $$\varepsilon_V = \frac{E_\mathrm{int}}{V} = \frac{3}{2}n\,kT + n_\mathrm{e}\,\chi,$$ and the specific internal energy per mass is simply
$$\varepsilon_m = \frac{\varepsilon_V}{\rho}.$$


Specific heat capacity at constant volume (density)

The heat capacity at constant volume $C_V$ is defined as: $$C_V = \left(\frac{\partial E_\mathrm{int}}{\partial T}\right)_V.$$ The heat capacity per mass at constant volume $c_V$ is defined as: \begin{equation}c_V = \left(\frac{\partial \varepsilon_m}{\partial T}\right)_\rho = \left[\frac{\partial }{\partial T}\left(\frac{3}{2}\frac{p}{\rho} + \frac{n_\mathrm{e}}{\rho}\,\chi\right) \right]_\rho = \frac{3}{2}\frac{1}{\rho}\left(\frac{\partial p }{\partial T}\right)_\rho + \frac{\chi}{\rho} \left(\frac{\partial n_\mathrm{e}}{\partial T}\right)_\rho. \label{eq:cv}\end{equation} From the defition of the ionization fraction (Eq.$\ref{eq:x}$), it directly follows: $$\left(\frac{\partial n_\mathrm{e}}{\partial T}\right)_\rho = \left(\frac{\partial x n_\mathrm{H}^\mathrm{tot}}{\partial T}\right)_\rho = n_\mathrm{H}^\mathrm{tot}\left(\frac{\partial x }{\partial T}\right)_\rho.$$
The gas pressure is now \begin{equation}p = n\,kT = (n_\mathrm{H}^\mathrm{tot} + n_\mathrm{e})\,kT = n_\mathrm{H}^\mathrm{tot}(1 + x)\,kT = \frac{\rho}{m_\mathrm{H}}(1 + x)\,kT,\label{eq:p}\end{equation} and thus \begin{equation}\left(\frac{\partial p }{\partial T}\right)_\rho = n_\mathrm{H}^\mathrm{tot} k \left[(1 + x) + T \left(\frac{\partial x}{\partial T}\right)_\rho \right].\label{eq:dpdt}\end{equation}The heat capacity for the constant volume becomes:\begin{eqnarray}c_V &=& \frac{n_\mathrm{H}^\mathrm{tot}}{\rho}\left[\frac{3}{2} k \left((1 + x) + T \left(\frac{\partial x}{\partial T}\right)_\rho \right) + \chi  \left(\frac{\partial x }{\partial T}\right)_\rho\right].\nonumber\\  &=& \frac{n_\mathrm{H}^\mathrm{tot}}{\rho}\left[\frac{3}{2} k (1 + x) + \left(\frac{3}{2} kT + \chi\right) \left( \frac{\partial x }{\partial T}\right)_\rho\right].\label{eq:cv0}\end{eqnarray}

To find $\partial x/\partial T$, let's rewrite Saha's equation in terms of $x$:
\begin{equation}\frac{n_\mathrm{H^+} n_{\mathrm{e}}}{n_\mathrm{H}} = \frac{x^2}{1-x}\,n_\mathrm{H}^\mathrm{tot}\end{equation} \begin{equation}\frac{ x^2}{1-x} = \frac{m_\mathrm{H}}{\rho} \left( \frac{2\pi m_\mathrm{e} \,kT}{h^2}\right)^{3/2} \mathrm{e}^{-\frac{\chi}{kT}} = q_\rho\,T^{3/2} \mathrm{e}^{-\frac{\chi}{kT}},\label{eq:sahax}\end{equation} where $q_\rho$ contains all the constants. Now the derivative of Eq.$\ref{eq:sahax}$ with respect to $T$ and constant $\rho$ is\begin{eqnarray}\frac{2x(1-x)+x^2}{(1-x)^2}\,\left(\frac{\partial x}{\partial T}\right)_\rho &=& q_\rho \left[\frac{3}{2}T^{1/2} + T^{3/2}\left(\frac{\chi}{kT^2}\right)\right]\,\mathrm{e}^{-\frac{\chi}{kT}},\nonumber\\\frac{x(2-x)}{(1-x)^2}\,\left(\frac{\partial x}{\partial T}\right)_\rho &=& \frac{1}{T} \left(q_\rho T^{3/2} \,\mathrm{e}^{-\frac{\chi}{kT}}\right) \left(\frac{3}{2} + \frac{\chi}{kT}\right),\nonumber\\
\left(\frac{\partial x}{\partial T}\right)_\rho &=&  \frac{(1-x)^2}{x(2-x)}\,\frac{x^2}{1-x}\,\frac{1}{T}  \left(\frac{3}{2} + \frac{\chi}{kT}\right),\nonumber\\ \left(\frac{\partial x}{\partial T}\right)_\rho &=&  \frac{x(1-x)}{(2-x)}\, \frac{1}{T}\, \left(\frac{3}{2} + \frac{\chi}{kT}\right).\label{eq:dxdt}\end{eqnarray} At this point it is worth to introduce to two new variables to simplify the notation:\begin{equation}D = D(x) \equiv \frac{x\,(1-x)}{(1+x)\,(2-x)},\end{equation}and\begin{equation}H = H(T) \equiv \frac{3}{2} + \frac{\chi}{kT}.\end{equation}
Note that $D\rightarrow 0$ when $x\rightarrow 0$ or $x\rightarrow 1$. Note as well that $1 - D$ reduces to:
$$1-D = \frac{2}{(1+x)\,(2-x)}.$$
Equation $\ref{eq:dxdt}$ we now write simply as: \begin{equation}\left(\frac{\partial x}{\partial T}\right)_\rho = (1 + x)\,\frac{1}{T}\,D\,H.\label{eq:dxdt1}\end{equation}
Substituting Eq.$\ref{eq:dxdt1}$ into Eq.$\ref{eq:cv0}$ yields to:
\begin{equation}c_V = \frac{n_\mathrm{H}^\mathrm{tot}}{\rho}\left[\frac{3}{2} k (1 + x) + \left(\frac{3}{2} kT + \chi\right)\, (1 + x)\,\frac{1}{T}\,D\,H\right]\end{equation}
\begin{equation} c_V= \frac{k}{m_\mathrm{H}}\,(1 + x)\, \left[\frac{3}{2}  + D\,H^2\right],\label{eq:cv7}\end{equation}where we used $n_\mathrm{H}^\mathrm{tot}/\rho = 1/m_\mathrm{H}$. Explicitly in terms of $x$ the specific heat at the constant density is:
\begin{equation} c_V= \frac{k}{m_\mathrm{H}}\,(1 + x)\, \left[\frac{3}{2}  + \frac{x\,(1-x)}{(1+x)\,(2-x)}\,\left(\frac{3}{2} + \frac{\chi}{kT}\right)^2\right].\label{eq:cv8}\end{equation}


Specific heat capacity at constant pressure

The next thing to derive is an equivalent expression for the heat capacity per mass at constant pressure. We start from a relation that can easily be derived from the first principle of thermodynamics:
\begin{equation}c_p = c_V + \frac{T}{\rho^2}\,\left(\frac{\partial p}{\partial T}\right)_\rho^2\left(\frac{\partial p}{\partial \rho}\right)_T^{-1}.\end{equation}

First we rewrite it as:
\begin{eqnarray}c_p &=& c_V + \frac{T}{\rho^2}\,\left[\frac{p}{T} \left(\frac{\partial \ln p}{\partial \ln T}\right)_\rho\right]^2\,\left[\frac{p}{\rho}\,\left(\frac{\partial \ln p}{\partial \ln \rho}\right)_T\right]^{-1}.\nonumber\\ &=& c_V + \frac{k}{m_\mathrm{H}}\,(1+x)\,\left(\frac{\partial \ln p}{\partial \ln T}\right)_\rho^2\,\left(\frac{\partial \ln p}{\partial \ln \rho}\right)_T^{-1}.\end{eqnarray}

The two derivatives we find by differentiating the logarithm of Eq.$\ref{eq:p}$:
$$\ln p = \ln (k/m_\mathrm{H}) + \ln\rho + \ln T + \ln (1 + x),$$
$$\mathrm{d} \ln p = \mathrm{d} \ln\rho + \mathrm{d} \ln T + \mathrm{d} \ln (1 + x),$$
yielding to:
\begin{equation}\left(\frac{\partial \ln p}{\partial \ln T}\right)_\rho = 1 +  \frac{1}{1+x}\left(\frac{\partial x}{\partial \ln T}\right)_\rho = 1 + D\,H,\label{eq:dlnp_rho}\end{equation}
\begin{equation}\left(\frac{\partial \ln p}{\partial \ln \rho}\right)_T = 1 +  \frac{1}{1+x}\left(\frac{\partial x}{\partial \ln \rho}\right)_T = 1 - D.\label{eq:dlnp_t}\end{equation}
Therefore, for $c_p$ we can write:
\begin{equation}
c_p = c_V + \frac{k}{m_\mathrm{H}}\,(1+x)\,\frac{(1 + D\,H)^2}{1-D}.
\end{equation}
This expression can be written explicitly in terms of $x$ as:
\begin{eqnarray}
c_p &=& \frac{k}{m_\mathrm{H}}\,(1+x) \left[\frac{3}{2} +  \frac{1 + 2 D\,H + D\,H^2}{1 - D} \right]\nonumber\\
&=& \frac{k}{m_\mathrm{H}}\,(1+x) \left[\frac{3}{2} +  \frac{(2-x)(1+x) + 2(1-x)x \,H + (1-x)x\,H^2}{2} \right]\nonumber\\
&=& \frac{k}{m_\mathrm{H}}\,(1+x) \left[\frac{5}{2} +  \frac{(1-x)x}{2}( 1 + 2 \,H + x\,H^2) \right]\nonumber\\ &=& \frac{k}{m_\mathrm{H}}\,(1+x) \left[\frac{5}{2} +  \frac{(1-x)x}{2}(1 +H)^2 \right]\nonumber\\
&=& \frac{k}{m_\mathrm{H}}\,(1+x) \left[\frac{5}{2} +  \frac{(1-x)x}{2}\left(\frac{5}{2} +\frac{\chi}{kT}\right)^2 \right]
\end{eqnarray}

The ratio of the heat capacities (sometimes labeled with $\gamma$) for the partially ionized pure hydrogen is:
\begin{equation}\gamma \equiv \frac{c_p}{c_V} = 1 + \frac{(1+ D\,H)^2}{(1 - D)\,(3/2 + D\,H^2)}
.\label{eq:cpcv}\end{equation}


Mayer's relation

Mayer's relation for ideal gas states that $c_p = c_v + nk$. In a gas mixture, this should apply to each component of free particles that contribute to the internal energy. Before we had: \begin{equation} c_p = c_V + \frac{k}{m_\mathrm{H}}\,(1+x)\,\frac{(1 + D\,H)^2}{1-D}.
\end{equation} Therefore, for Mayer's equation to be true, we have to show that: $$n = \frac{1}{m_\mathrm{H}}\,(1+x)\,\frac{(1 + D\,H)^2}{1-D} $$

Adiabatic exponents

In addition we derive the explicit expression of the adiabatic exponents $\Gamma$ of the partially ionized pure hydrogen gas. The exponents are defined as:
\begin{equation}\gamma \equiv \Gamma_1 \equiv \left(\frac{\partial \ln p}{\partial \ln \rho}\right)_S,\end{equation}
\begin{equation}\nabla_\mathrm{ad}\equiv\frac{\Gamma_2 -1}{\Gamma_2} \equiv \left(\frac{\partial \ln T}{\partial \ln p}\right)_S,\label{eq:G1}\end{equation}
\begin{equation}\gamma\,\nabla_\mathrm{ad} \equiv \Gamma_3 -1 \equiv \left(\frac{\partial \ln T}{\partial \ln \rho}\right)_S.\end{equation}
Comment on the meaining of $\Gamma$'s. Note that both $\gamma$ in Eq.$\ref{eq:cpcv}$ and in Eq.$\ref{eq:G1}$ is not the same. It is easy to express the three adiabatic coefficients as function of the gradients in Eqs.$\ref{eq:dlnp_rho}$ and $\ref{eq:dlnp_t}$:
\begin{equation}\Gamma_1 =\left( \frac{\partial \ln p}{\partial \ln \rho}\right)_T,\label{eq:G1_1}\end{equation}
\begin{equation}\frac{\Gamma_2 -1}{\Gamma_2} = \frac{k}{m_\mathrm{H}}\,\frac{1}{c_p} \left( \frac{\partial \ln p}{\partial \ln T}\right)_\rho \left( \frac{\partial \ln p}{\partial \ln \rho}\right)_T^{-1},\label{eq:G2_1}\end{equation}
\begin{equation}\Gamma_3 - 1 = \frac{k}{m_\mathrm{H}}\,\frac{1}{c_v}  \left( \frac{\partial \ln p}{\partial \ln T}\right)_\rho.\label{eq:G3_1}\end{equation}
After substituting the results for the gradients Eqs.$\ref{eq:dlnp_rho}$ and $\ref{eq:dlnp_t}$, the final expression of the adiabatic exponents for the partially ionized pure hydrogen plasma are:
\begin{equation}\Gamma_1= \frac{c_p}{c_v}\,(1 - D),\end{equation}
\begin{equation}\frac{\Gamma_2 -1}{\Gamma_2} = \frac{c_p - c_V}{c_p}\,\frac{1}{1 + D\,H},\end{equation}
\begin{equation}\Gamma_3 - 1=  \frac{c_p - c_V}{c_V}\,\frac{1-D}{1 + D\,H}.\end{equation}

Entropy

Let's now compute the specific entropy per mass $s$. It can be shown that (see my notes on computing entropy for $T$ and $\rho$ as independent variables: $$ds = \frac{1}{T} \left[\left(\frac{\partial \varepsilon_m}{\partial \rho}\right)_T - \frac{p}{\rho^2}\right] d\rho + \frac{1}{T}\left(\frac{\partial \varepsilon_m}{\partial T}\right)_\rho dT $$ To solve for these derivatives analytically, we need to find $\varepsilon_m = \varepsilon_m(T, \rho)$: $$\varepsilon_m = \frac{1}{\rho} \left[\frac{3}{2}n\,kT + n_\mathrm{e}\,\chi\right]$$ and its derivatives: $$\left(\frac{\partial \varepsilon_m}{\partial \rho}\right)_T = -\frac{1}{\rho^2} \left[\frac{3}{2}n\,kT + n_\mathrm{e}\,\chi\right] + \frac{3}{2} \frac{kT}{\rho} \left(\frac{\partial n}{\partial \rho}\right)_T + \frac{\chi}{\rho}\left(\frac{\partial n_\mathrm{e}}{\partial \rho}\right)_T$$ and $$\left(\frac{\partial \varepsilon_m}{\partial T}\right)_\rho =$$ Where we still need to express $n$ and $n_e$ as functions of $T$, $\rho$. By definition: $$\rho = n_\mathrm{H}\,m_\mathrm{H} + n_\mathrm{e}(m_\mathrm{H} + m_\mathrm{e})$$ and $$n = n_\mathrm{H} + 2 n_\mathrm{e}$$ while Saha gives us: $$\Phi\,n_\mathrm{e}^2 = n_\mathrm{H}$$
As we saw before, the last two equations combine into a quadratic equation with a solution: $$n_{\mathrm{e}} = \left(\sqrt{1 + n\,\Phi(T)}-1 \right)\,\frac{1}{\Phi(T)}.$$ And for $\rho(n)$ we get explicitly: $$\rho = (n - n_\mathrm{e}) \,m_\mathrm{H} + n_\mathrm{e} m_\mathrm{e}$$ $$\rho = \left(n - \left(\sqrt{1 + n\,\Phi(T)}-1 \right)\,\frac{1}{\Phi(T)}\right) \,m_\mathrm{H} + \left(\sqrt{1 + n\,\Phi(T)}-1 \right)\,\frac{1}{\Phi(T)} m_\mathrm{e}$$ $$\rho = n m_\mathrm{H} - \frac{\sqrt{1 + n\,\Phi(T)}-1 }{\Phi(T)} (m_\mathrm{H} -m_\mathrm{e})$$ Therefore: $$\left(\frac{\partial \rho }{\partial n}\right)_T = m_\mathrm{H} - (m_\mathrm{H} -m_\mathrm{e}) \frac{1}{2 \sqrt{1 + n\,\Phi(T)}}$$ $$\left(\frac{\partial n }{\partial \rho}\right)_T = \frac{2 \sqrt{1 + n\,\Phi(T)}}{2 m_\mathrm{H}\, \sqrt{1 + n\,\Phi(T)} - (m_\mathrm{H} -m_\mathrm{e})}$$
References

The general thermodynamical expressions and their derivation can be found in any textbook on the thermodynamics or on the stellar structure, e.g. in Chandrasekhar, S. (1967, 1967aits.book.....C). There is a brief mentioning of the equation of state for the pure hydrogen in Mihalas, D. (1970, p.73, 1970stat.book.....M). Biermann, L. (1942, 1942ZA.....21..320B) was the first to derive the explicit expressions for the $c_p$ and $c_V$ (see Lobel, A. (2001, 2001ApJ...558..780L)). Knapp, G (lecture notes) gives most of the derivations above neatly and tidily. Hansen, C.J. et al (2004, 2004sipp.book.....H) derive several cases including the pure hydrogen and discuss the solution.




IDL implementation

IDL function that evaluates the equation of state for pure hydrogen for given temperature and gas pressure can be downloaded from here: eos_pureh.pro.

Tuesday, 4 July 2017

How to display magnetic field lines in IDL?

It is a common problem in visualization of magnetic fields. If we assume that the potential $A$ of the magnetic field is knows, the field lines are, by definition, iso-$A$ curves. The quickest way to display them is by using the CONTOUR procedure. If the potential field is given as a 2D variable potential, then:
IDL> CONTOUR, potential, levels = levels, /xs, /ys
gives:

However, the information here is not complete without showing the actual direction of the field along the lines. It is easy to do it in IDL:

IDL> CONTOUR, potential, levels = levels, /xs, /ys
IDL> CONTOUR, potential, levels = levels, /xs, /ys, path_xy = c
IDL> FOR i = 1, N_ELEMENTS(c)/2-1, 50 DO $     ARROW, c[0, i-1], c[1, i-1], c[0, i], c[1, i], /norm, /solid, hsize = 5         

produces:
It is much better now, but the arrow heads from different field lines make it crowded. If we are interested in individual field lines, we can add color to this:


Tuesday, 11 April 2017

Deep-learning about horizontal velocities at the solar surface

The velocity fields are of great importance for understanding dynamics and structure of the solar atmosphere. The line of sight velocities are coded in the wavelength shifts of the spectral lines, thanks to the Doppler effect, and relatively easy to measure. On the other hand, the orthogonal ("horizontal") components of the velocity vector are impossible to measure directly.

The most popular method for estimating the horizontal velocities is so-called local correlation tracking (LCT, November & Simon, 1988). It is based on comparing successive images of the solar surface in the continuum light and transforming their differences into information about the horizontal fields. However, the LCT algorithm suffers from several limitations.

In a paper by Andres Asensio Ramos and Iker S. Requerey (with a small contribution from my side) accepted by A&A and published on Arxiv some weeks ago (2017arXiv170305128A) this problem is tackled by the deep-learning approach. A deep fully convolutional neural network is trained on synthetic observations from 3D MHD simulations of the solar photosphere and then applied to the real observation with the IMaX instrument on board the SUNRISE balloon (Martinez Pillet et al, 2011; Solanki, 2010). The method is validated using simulation snapshots of the quiet sun produced with the MANCHA code that I have been developing in the last couple of years.


Monday, 20 March 2017

K-means clustering

The problem of clustering is a rather general one: If one has $m$ observations or measurements in $n$ dimensional space, how to identify $k$ clusters (classes, groups, types) of measurements and their centroids (representatives)?

The k-means method is extremely simple, rather robust and widely used in it numerous variants. It is essentially very similar (but not identical) to Lloyd's algorithm (aka Voronoi relaxation or interpolation used in computer sciences).

k-means

Let's use the following indices: $i$ counts measurements, $i \in [0, m-1]$; $j$ counts dimensions, $j \in [0, n-1]$; $l$ counts clusters, $l \in [0, k-1]$.

Each measurement in $n$-dimensional space is represented by a vector $x_i = \{x_{i, 0}, \dots x_{i, n-1}\}$, where index $i$ is counting different measurements ($i = 0, \dots, m-1$). The algorithm can be summarized as:

1. Choose randomly $k$ measurements as initial cluster centers: $c_0, ..., c_{k-1}$. Obviously, each of the clusters is also $n$-dimensional vector.

2. Compute Euclidean distance $D_{i, l}$ between every measurement $x_i$ and every cluster center $c_l$:
$$D_{i, l} = \sqrt{\sum_{j=0}^{n-1} (x_{i, j} - c_{l, j})^2}.$$
3. Assign every measurement $x_i$ to the cluster represented by the closest cluster center $c_l$.

4. Now compute new cluster centers by simply averaging all the measurements in each cluster.

5. Go back to 2. and keep iterating until none of the measurements changes its cluster in two successive iterations.

This procedure is initiated randomly and the result will be slightly different in every run. The result of clustering (and the actual number of necessary iteration) significantly depends on the initial choice of cluster centers. The easiest way to improve the algorithm is to improve the initial choice, i.e. to alter only the step 1. and then to iterate as before. There are to simple alternatives for the initialization.

Wednesday, 1 February 2017

First observation of linear polarization in the forbidden [OI] 630.03 nm line

In a new paper (de Wijn, Socas-Navarro & Vitas, 2017, ApJ, 836, 29D) we present the first results of our observations of a sunspot and an active region using the SP/SOT instrument on board the Hinode satellite. The novelty in our observation is a trick that we used to double the standard wavelength range observed by the instrument. Thanks to that, we were able to see the sun not only in the two iron lines at 630.2 nm, but also in four other lines. One of those is particularly interesting: the forbidden ground-based line of neutral oxygen ([OI] 630.03 nm). It is one of only few oxygen lines in the solar spectrum and probably the best diagnostics of the solar oxygen abundance. For the first time ever we observed the linear polarization in this line! As an M2 (magnetic dipole) transition, it is predicted by the theory (Landi degl'Innocenti and Landi, 2004, Section 6.8) that this line produces the linear polarization signal with the opposite sign to the lines produced by E2 transitions. It is also the first time that linear polarization in M2 and nearby E2 lines is measured simultaneously, so that the flip in sign is obvious (see the left-most spectral line in the red circle in the Figure; in linear polarization it has "W" shape, while all other lines in the wavelength range have "M" shapes). This result may bring new light to the ongoing debate on the solar oxygen crisis.

  

More details of this unique observation will appear soon in a follow-up publication.

Wednesday, 30 November 2016

1D Solar Atmosphere Models in IDL: Penumbra by Ding & Fang (1989)

Plane-parallel atmosphere in hydrostatic equilibrium published by Ding & Fang (1989, "A semi-empirical model of sunspots penumbra", 1989A&A...225..204D). Statistical equilibrium for hydrogen model-atom with 12 levels plus continuum. The model is produced by fitting observations of penumbra in  2 lines of H and 5 lines of Ca. The observations were carried out on McMath telescope at Kitt Peak National Observatory. The observed sunspot was small, rounded and close to the disk center. The field strength in the umbra was around 1.25 kG and 560 G in the penumbra.

It is interesting to note their Fig.2 (see it below). In the deep photosphere, the temperature in this model is similar to the temperature in the model of Yun et al. (1981). However, between the optical depths -3 and -4 it becomes close to the VALC model (Vernazza et al, 1981).