Hyperspectral detection of spicules in Ca II K

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Hyperspectral detection of spicules in Ca ii K

Mats Ola Sand

Thesis submitted for the degree of Master of Science in Astronomy

Institute of Theoretical Astrophysics University of Oslo

21.05.2021

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ii

This work, entitled “Hyperspectral detection of spicules in

Caii K” is distributed under the terms of the Public Library of Science Open Access License, a copy of which can be found at http://www.publiclibraryofscience.org.

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Abstract

Solar spicules are chromospheric jets of up-flowing plasma observed all over the Sun, and can be categorized into type iand type ii spicules. Type ii spicules are more dynamic and more ubiquitous, yet their origin and ubiquity are not understood.

We use, for the first time, high-resolution observations of type iispicules in Caii K 393.4 nm to characterize the bright/dark (B/D) characteristics we observe in Ca ii K spicules. We also compare the apparent length of spicules in CaiiK to spicules in Hα. The observations are of the quiet Sun close to the limb (µ= 0.41) and were acquired by the CHROMospheric Imaging Spectrometer (CHROMIS) and the CRisp Imaging SpectroPolarimeter (CRISP) at the Swedish 1-metre Solar Telescope on La Palma.

We find that the B/D characteristics are evident due to suppression of the K2 peak in the upper part corresponding to the K3 Doppler shift, combined with significant enhancement of that same K2 peak along the lower part of the spicule, yielding a direct demonstration of heating along the spicule. We often see the effect of enhanced K2 peaks in upper parts of spicules due to the “opacity window” reported byBose et al. (2019), but we also sometimes observe enhanced upper K2 peaks from the bright surroundings or the spicule itself. Out of 39 spicules, we do not see this “window”-effect for the lower parts of spicules, except one spicule where we see enhancement of lower part K2 due to neighbouring spicule in the background. We also found that spicules appear a little longer in Ca ii K than Hα due to the signal generally reaching higher in Ca ii K but they also appear to reach deeper due to the brightening in the bottom.

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Acknowledgments

I would like to thank my supervisor Tiago M. D. Pereira and co-supervisor Luc Rouppe van der Voort for being two invaluable sources of knowledge for all aspects of this project.

Thank you both for always setting aside time for me and my curiosity, significantly improving the quality of my studies. Thank you also to fellow students that started this run with me five years ago for all academic and not-so-academic discussions. Thank you to my family for always being supportive and making me believe in myself. I also want to thank Kristian Torgalsen, Casper Aleksandersen, and Petter Solrud for all the late- night discussions about the wonders of the Universe; they were indeed the discussions that convinced me to follow my dream of working with astrophysics. Finally, I want to thank my partner Kine and daughter Ingrid; Kine for always keeping me on the right track, and making sure that I have time to work on my project; Ingrid for brightening up my days with your cheerful and charming nature.

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Introduction

The star of our solar system, the Sun, is a white-yellow star, brightening up our days by its radiative nature. Seen from outer space, it does not appear as any more in- teresting than any other star in the galaxy, but due to its distance from Earth, it is an extraordinary astronomical object to us. We can observe this particular star with excellent precision; both spatially and temporally, enabling us to observe solar events and features with considerable detail.

This is naturally done by telescopes observing the electromagnetic (EM) radiation from the Sun. All of this radiation escaping the Sun is created by fusion reactions in the core. The photons travel from the core through the Sun in∼10⁵ years, before reaching the photosphere, which is a few hundred km thick layer that defines the solar surface.

At the photosphere, the plasma/gas becomes transparent for most photons, meaning that most of the photons we observe emerge from this layer. The photospheric layer contains features such as granulation, magnetic bright points, and sunspots. Moving from the surface, the temperature will decrease before hitting a temperature minimum;

from this point, the temperature will increase as we move further away from the surface.

From this temperature minimum, the next layer of the solar atmosphere begins;

the chromosphere. The chromosphere is about 2000km thick, highly dynamic, and an inhomogeneous part of the solar atmosphere. Here we see features such as prominences and filaments, so-called plage regions (might also be seen in the photosphere), and fibrils. The target for this thesis is spicules, more precisely spicules observed on the disk, which can be seen as a subset of the chromospheric fibrils.

1.1 Motivation and background

Spicules are thin, highly dynamic, jet-like features observed all over the solar atmosphere. They were first discovered by the Catholic priest and astronomer Angelo Secchi in 1877. Hence, they have been observed and analyzed for a long time, and their physical and dynamical properties have long been debated (reviews by;Beckers 1968,1972;

Sterling 2000;Tsiropoula et al. 2012).

Spicules are observed in the wings of chromospheric lines such as, e.g., Hα, CaiiH

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2 Introduction

& K, and their ubiquity makes them a potential candidate for feeding the corona with mass and energy. A hundred years after their discovery, they were connected to help drive the solar wind (Pneuman & Kopp 1977, 1978), and a few years later to heating of the chromosphere, transition region, and corona (Athay & Holzer 1982). However, Withbroe(1983) found no significant trace of spicules in Mg x625 and concluded that heating from spicules may not extend to the corona.

The studies reviewed byBeckers(1968) ascribed the apparent motion to actual mass motions, with a much more defined ascending phase than descending phase, as many spicules fade out after their ascent. They described spicules with lifetimes of about 4-5 min and apparent upwards velocities of around 30 km s⁻¹. These physical and dynamical properties established the “classical” characterization of spicules.

1.1.1 Spicules and heating of the outer atmosphere

It was not before the launch ofHinode (Kosugi et al. 2007) that the classical description of spicules was divided into two types (De Pontieu et al. 2007); typeiand typeii, with typeii behaving very different from what was earlier described.

De Pontieu et al. (2007) describe type i as spicules with typical lifetimes of 3-7 minutes, an apparent ascending and descending phase, and apparent velocities of 10- 40 km s⁻¹. These spicules’ behaviour is reported to be identical to dynamic fibrils observed in active regions (De Pontieu et al. 2007), whose properties are in excellent agreement with simulations (Hansteen et al. 2006; Heggland et al. 2007). They also report that typeiseems dominant in active regions.

De Pontieu et al.(2007) describe typeiispicules as much more violent, with apparent velocities of 50-150 km s⁻¹; and shorter-lived, with lifetimes of 10-150 s. They have no descending phase, but rapidly fade out from the Ca ii H passband, suggesting that Ca⁺ ions get ionized by strong heating of the spicule. These findings of the much more violent type of spicules revived the interest for spicules as a significant contributor to coronal heating.

However, not every study agreed with the arrangement of two different types of spicules. EspeciallyZhang et al.(2012) found no convincing examples of typeiispicules, using the sameHinode data, but tracing 207 spicules in only the quiet Sun and coronal hole where type i was not mentioned as dominant. Therefore, they reported that the suggestion of heating of the corona by type ii spicules should be taken cautiously.

Another study of spicules from the same data set, including quiet Sun, coronal holes, and active regions, could not reproduce their findings. Pereira et al. (2012) measured the properties of 519 spicules for the different regions, and supported the findings of De Pontieu et al.(2007), reporting clear evidence of two types of spicules. This statistical study also reported that typeii spicules are the dominant type in most regions of the Sun, except active regions.

With the launch ofInterface Region Imaging Spectrograph (IRIS;De Pontieu et al.

2014), it was confirmed by Pereira et al. (2014) that the fading of type ii spicules appeared to be caused by rapid heating; these spicules did not fade from IRIS but continued to rise after fading from Ca ii H passbands. IRIS images also showed that

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1.2 Problem statement 3

the typeiispicules had longer lifetimes ranging between 500-800 s, and would eventually fall back down like typei. Pereira et al. (2014) also suggested that spicules are heated to at least transition region temperatures, by comparingIRIS data to the Atmospheric Imaging Assembly’s (AIA;Lemen et al. 2012) 30.4 nm channel.

1.2 Problem statement

The origin of typeiispicules is still not known, and simulations have challenges explain- ing all their observed properties as well as reproducing their ubiquity (Martínez-Sykora et al. 2017). Spicules often carry different features in different spectral lines, so charac- terizing spicules’ appearance in the different lines can prove useful.

Most of the studies on spicules and their properties are done by looking at spicules off the limb as there is no contamination from the bright surface there. A limitation for observing off-limb spicules is superposition; it can be very challenging to distinguish single spicules for analysis. Detecting and analyzing single spicules is, therefore, more accessible on the solar disk.

Observing spicules on the disk is mainly done from ground-based telescopes and has typically been done in the wing of Hα. It is common to distinguish between spicules seen off-limb and on-disk. Several studies of on-disk spicules in the quiet Sun have reported densely packed “bushes” of rising spicules from the magnetic network (Langangen et al.

2008;Rouppe van der Voort et al. 2009;Sekse et al. 2012;Pereira et al. 2014).

Using data from the CHROMospheric Imaging Spectrometer (CHROMIS) from the 1-meter Swedish solar telescope (SST; Scharmer et al. 2003) on La Palma, Bose et al.

(2019) characterized for the first time type ii spicules in Ca ii K. They reported that the K2 peaks were either suppressed by the Doppler-shifted K3 or enhanced by intensity from the lower levels, as the K3 shift increase the opacity in one wing and reduce it in the other.

In our work, we are also analyzing type ii spicules in Ca ii K from CHROMIS. As Bose et al.(2019) analyzed spicules close to disk center, we are focusing on spicules close to the limb (µ = 0.41). The main goal of this thesis is to get a better understanding of the bright/dark characteristics that are evident in Ca ii K spicules. We will also compare spicules in Ca ii K to Hα, to see if there is a significant difference in spicule lengths between the two observations.

1.3 Outline

In Chapter2we introduce radiative transfer and how it affects the two different lines we use for analysis. Chapter3 introduces SST and challenges with ground-based observations, as well as details of the observations used for analysis and how they were acquired.

Chapter4describes the methods applied for image analysis, coaligning of observations, how we searched for distinct spicules and extracted the spectra from them, and how we compared spicule lengths in Caii K and Hα. In Chapter5we present the results of our methods. This includes modifications of Ca ii K data, representations of the spectra

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4 Introduction

extracted from bright/dark spicules, and how lengths are compared between the two data sets. In Chapter 6 we discuss the spectra along spicules and how they apply to bright/dark characteristics, why we see differences in apparent lengths in Caii K and Hα, and a concluding section at the end.

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Chapter 2

Theory

2.1 Radiative transfer

When studying the universe outside our planet, our main tool for research and analysis is the electromagnetic spectrum. One can learn many things by analyzing the electromagnetic spectrum we receive from different places in the sky; one can get information of atmospheric composition on exoplanets (specific absorption features corresponds to given elements and molecules); see how the composition of the universe changes over time (see how much absorption there is in a specific part of the spectrum as it gets redshifted through its propagation in space, e.g., Lymanαforest); use a spectral line to look through different layers of the Sun (the opacity will increase as we observe closer to the line core).

2.1.1 Spectral lines

When analyzing spectra from a light source in space, one can often see smaller bumps in the continuum at discrete places. These bumps are calledabsorption oremission lines, where absorption lines are seen as a reduction of the spectra, while emission lines are seen as an increase of the spectra. These lines are related to atomic and quantum physics;

electrons orbiting nuclei are located in orbitals with discrete energy levels, where the ground state (closest to the nucleus) is the most probable state. An electron orbiting in the ground state can be excited to a higher state by collisional or radiative excitation, i.e., by absorbing kinetic energy from surrounding matter, or by absorbing the energy of an incoming photon. An electron can also deexcite by the same interactions, and it can deexcite spontaneously. These processes are called bound-bound transitions. There are also bound-free transitions, which involve electrons receiving enough energy to escape the electromagnetic pull from the nucleus. These processes are not relevant to our work, so we will not talk about them in this thesis.

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6 Theory

2.1.2 Bound-bound transitions

Looking at the bound-bound transitions, these processes can create, destroy or scatter photons. A photon is created when an electron is collisionally excited to a higher energy state, followed by radiative deexcitation. The radiative deexcitation can happen in two ways: the electron can decay spontaneously, radiating a photon with a frequency equivalent to the difference in energy between the upper and lower energy states of the decaying electron; or the electron can be affected by an incoming photon with the same energy as the electron’s energy state, resulting in two coherent photons radiating from the decaying electron. This last form of emission is calledinduced emission.

A photon can also be destroyed in similar processes. An incoming photon can be absorbed by exciting an electron to a specific energy state, followed by a collisional deexcitation, destroying the photon. A photon can also be converted into more than one photon, destroying the incoming photon and creating new outgoing photons; consider a photon with frequencyν₀ exciting an electron to an energy state≥2than its original state, e.g., from n = 1 (ground state) to n = 3. The electron can now first decay to energy level n = 2, radiating a new photon with frequency ν₁, before decaying from n= 2 to the ground state, radiating another photon with frequency ν2. The incoming photon is destroyed in the process, creating two new photons (ν₀ → ν₁ +ν₂, where ν₀> ν₂> ν₁).

Lastly, an incoming photon can scatter. This happens when an electron is radiatively excited, followed by a spontaneous or induced deexcitation involving the same upper and lower levels as the excitation. The photon scattered are done so into a random direction, effectively removing or adding it to the spectrum observed. I.e., an incoming photon can be removed from the spectrum by being scattered into another direction than the beam of light we are observing; or a photon originally coming from a different direction can scatter into the said beam, adding to the spectrum.

This (creation and) destruction of photons is what makes the discontinuities in the continuum mentioned before, and one can learn a lot of an object by analyzing these features in the electromagnetic spectrum. When a beam of light (from now on just the beam) escapes the surface of the Sun, it still has to travel through the solar atmosphere before reaching us. This will result in photons being added and removed from the beam by processes described from the bound-bound transitions. A great deal of data can be extracted by looking at spectral lines created from such events. To get a better understanding of why this is the case, we will dig a little deeper into important parameters in radiative transfer.

2.1.3 Intensity

Theintensity (in reality,monochromaticintensity, but we will disregard the monochromatic part, as this is represented by the subscript ν; this applies for everything with this subscript in this thesis) is defined from the equation

dEν =IνdAdtdνdΩ,

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2.1 Radiative transfer 7

which describes the amount of energy dEν passing perpendicularly through an area dA over time dt, in the bandwidth dν into a solid angle dΩ. I_ν comes in units J m⁻² s⁻¹ Hz⁻¹ sr⁻¹ (SI) or erg cm⁻² s⁻¹ Hz⁻¹ sr⁻¹ (CGS). CGS is included here since they are often used in solar physics; however, we will use SI base units for the remainder of this thesis.

Iν is the monochromatic intensity. This representation of intensity is generally used, but if one rather will use the wavelength-dependent intensity, the conversion from frequency dependency to wavelength dependency is then extracted from

I_tot = Z _∞

0

I_νdν= Z 0

∞

I_λdλ=− Z _∞

0

I_λdλ

⇒ I_νdν =−I_λdλ

⇒ Iν =−Iλdλ dν =Iλ

c ν²

⇒ I_λ =cI_ν λ²,

whereλis the wavelength, andc is the speed of light in vacuum.

I_ν is independent of distance, unless, of course, there is matter between the emitting object and the observer. If this is the case, two things can happen to the intensity; it can be reduced by destruction or scattering of photons, or it can increase by creation or scattering of photons into the beam.

These processes are described by theemissivity and extinction coefficient. 2.1.4 Emissivity

Let’s consider a small volume, dV, in the Sun; the addition of photons to the beam from this volume is given by

dEν =jνdVdtdνdΩ,

wherejνis the emissivity, and dV =dAdzwith dzalong the beam and dAperpendicular to it.

The emissivity, jν, then represents how a given location in an observed medium, e.g., a part of the Sun’s atmosphere, is adding photons to the beam. This addition of photons to the beam is not affected by the incoming intensity, i.e., it comes solely from spontaneous de-excitation. Before this de-excitation, the electron could either be excited collisionally or radiatively. The collisional part is making jν affected by the local conditions as higher density and/or temperature leads to more frequent collisions between particles. The radiative excitation comes from photons originally propagating in another direction than the incoming intensity, making jν affected by the radiation field as well; the stronger the radiation field, the bigger the pool of photons available for scattering into the beam. This radiation field is also called mean intensity Jν, i.e., the intensity averaged over all solid angles.

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8 Theory

The change in intensity due to the emissivity as the beam propagates a short distance, dz, through the medium, e.g., a layer in the solar atmosphere with thickness dz, is defined as

dIν(z) =jν(z)dz.

The emissivity comes in units J m⁻³ s⁻¹Hz⁻¹sr⁻¹, i.e., unit energy per unit volume per unit time per unit frequency per steradian (or intensity per unit length).

2.1.5 Extinction coefficient

Now, considering the same volume, dV, the removal (or addition) of photons from (to) the beam is given by

dE_ν =−α_νI_νdAdzdtdνdΩ,

whereαν is the extinction coefficient, and Iν is incoming intensity.

The extinction coefficient represents how the medium affects an incoming beam of light. Therefore, the change in energy due to the extinction coefficient must include the incoming intensity, as opposed to the change in energy due to the emissivity. The extinction coefficient is also affected by local conditions, such as density; higher density means more particles to interact with and a higher probability of a photon being absorbed, followed by destruction or scattering of the photon. This will effectively remove it from the beam.

Counter-intuitively to its name, the extinction coefficient can also add photons to the beam; this happens from the induced emission mentioned earlier. When this induced emission is dominating the destruction and scattering of photons, the extinction coefficient has a negative value, i.e., the change in energy becomes positive. However, this does, for all practical purposes, not happen to a beam of light from the Sun. For the rest of this thesis, the extinction coefficient will solely represent the removal of photons.

The change in intensity due to the extinction coefficient as a beam propagates a short distance, dz, through the medium is defined as

dIν(z) =−αν(z)Iν(z)dz.

The extinction coefficient comes in units m⁻¹, i.e., per unit length. This can also be written as m² m⁻³, i.e., unit area (cross-section) per unit volume; this is for some a more intuitive description of the extinction coefficient.

2.1.6 Optical depth

When photons escape the solar surface, the frequency-dependent extinction coefficient will remove those with certain frequencies from the beam, i.e., creating the spectral lines or discontinuities in the spectrum mentioned earlier. This happens continuously during the beam’s propagation through the solar atmosphere, until the beam will reach a certain point in the atmosphere where the density is low enough for most of the

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2.1 Radiative transfer 9

photons within the given spectral line to escape. The beam has then reached a point where theoptical depth is small enough for that to happen.

This optical depth is defined from the extinction coefficient as τ_ν(z₀) =−

Z z0

∞

α_νdz,

and it represents the opacity at a certain point, z, related to a photon with a certain frequency, ν. It is measured from the location of the observer, z =∞, to some point, z=z₀, in the Sun or solar atmosphere.

Inside the Sun, the optical depth is so large that the radiation can not escape (this is the reason we never can observe the solar interior directly using the electromagnetic spectrum). But, as we have mentioned, this is not the case from the surface to higher levels in the atmosphere. And, since the optical depth is frequency-dependent, the height where photons with certain frequencies can escape is different. This makes it possible to directly observe different layers of the solar atmosphere.

2.1.7 Transport equation

The total change in intensity as the beam propagates the short distance, dz, through the medium, is defined from both the emission and extinction coefficients as

dIν = (jν−ανIν)dz, or

dIν

dz =j_ν −α_νI_ν. (2.1)

These are both representations of thetransport equation.

Another representation of the transport equation, the more popular standard differ- ential form of the transport equation, is the change in intensity per unit optical depth;

it is defined by dividing equation (2.1) with the extinction coefficient:

dIν

α_νdz = jν

α_ν −ανIν

α_ν ,

⇒ dIν

dτ_ν =Iν −Sν, (2.2)

whereS_ν is the source function. Notice that from its definition, ^j^ν/αν, it gets the same dimensions as the intensity, i.e., the subtraction on the right-hand side is perfectly legal.

2.1.8 Source function From its definition

Sν ≡ j_ν αν

,

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10 Theory

the source function represents the ratio between the emission and extinction coefficients, i.e., it represents in total how many photons are removed and added to the beam.

If the ratio between the emission and extinction coefficient is such that an equal amount of photons are removed and added to the beam, the source function will be the same as incoming intensity (S_ν = I_ν), and the change in intensity per optical depth is unchanged, i.e., we will not see any emission or absorption features from this place. However, if the ratio is such that, e.g., the extinction coefficient dominates the emissivity, the source function will be smaller than the incoming intensity (S_ν < I_ν) and the change in intensity will be negative, resulting in an absorption line in the spectra.

And vice versa if the emissivity is dominating the extinction coefficient.

We have already mentioned how the parameters affecting the source function, j_ν and αν, are affected by quantities like the density and temperature. Both the density and temperature is decreasing with height from the surface, resulting in the extinction coefficient be dominant in the source function, i.e., the spectral lines we observe from the Sun’s surface are in absorption.

However, at the bottom of the chromosphere, the temperature will hit a minimum before it suddenly starts to increase with height. This sudden increase in temperature will, depending on the spectral line, lead to an increase in the source function resulting in an increase in intensity towards the core of the line as the source function will be stronger towards the core than in the wings (the wings are formed deeper at a lower temperature). Then, propagating further up through the atmosphere, the beam will get to a point where the density gets so low that the source function no longer will be as affected by the temperature rise. The change in intensity per optical depth will once again be negative, and the line gets a depression in the core flanked by two emission peaks formed deeper in the atmosphere.

2.2 Chromospheric lines

Chromospheric lines are spectral lines formed in the chromosphere of the solar atmosphere. Observing the Sun in the core of chromospheric lines shows that the photosphere is covered by a canopy of bright and dark fibrils such as spicules.

The chromospheric lines we use for this thesis, Hαand CaiiK, are two of thousands of absorption lines in the optical spectrum of the Sun. They were first categorized by the German physicist Joseph von Fraunhofer in 1814. CaiiK has kept its original letter given by Fraunhofer, while Hα was originally categorized by the letter ‘C’.

2.2.1 Ca ii K

The CaiiK line is one of the strong resonance lines, i.e., a strong spectral line involving the ground state, in the solar spectrum. Ca ii is one-time ionized calcium, i.e., Ca⁺, and the line is located at 393.37 nm in the electromagnetic spectrum. It is one of the strongest absorption lines we receive from the Sun.

CaiiK are also one of those lines where we see two emission peaks flanking the line core. When these peaks first were discovered, they were labeled K2 from Fraunhofer’s

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2.2 Chromospheric lines 11

label of the line, and later K2V and K2Rwhere V stands for violet and R for red as they are peaks in the violet (blue) and red wings of the line; the absorption in the core were named K3, while the minima on the outside of the emission peaks were named K1V and K1R.

The core of Ca ii is very narrow (.0.1 nm between K1 minima) meaning that we have to sample through the line at a very high spectral resolution. This results in fewer photons per sample, reducing the signal-to-noise ratio. The solar blackbody spectrum is also quite a bit lower for 393 nm (Caii) than, e.g., 656 nm (Hα), so we receive much fewer photons from the Sun in Ca ii K than Hα. On top of that, since Ca ii K is in the lower part of the optical spectrum, significantly more photons are removed from the beam by Rayleigh scattering than photons with longer wavelengths, as Rayleigh scattering ∝λ⁻⁴. This leads to an even more reduced signal-to-noise ratio, compared to other lines such as Hα.

The refractive index is also dependent on wavelength (∝λ⁻¹) meaning that Ca ii K is more affected by seeing, which we will talk more about in Chapter3.

2.2.2 Hα

Hα is located at 656.28 nm in the electromagnetic spectrum. It is part of the Balmer series, named after the Swiss mathematician Johan Jakob Balmer. The Balmer series is part of the hydrogen spectral series, which is a named series for bound-bound transitions in the hydrogen atom. Each part of these hydrogen spectral series are distinguished by the lower level in these transitions.

The Balmer series consists of spectral lines created from bound-bound transitions with lower energy level n = 2, i.e., the first energy level above the ground state, and Hα represents the radiative transition between n= 3 and n= 2. The Balmer series is flanked by the Lyman series with lower leveln= 1, and the Paaschen series withn= 3. Hα is the most used chromospheric spectral line for ground-based observations. It is in the red part of the optical spectrum (longer wavelengths than, e.g., Ca ii H&K).

And the hydrogen atom is very small, i.e., it has low mass, resulting in higher thermal velocities broadening the core, which again means that one does not need a very high spectral resolution.

Hence, Hα has a high signal-to-noise ratio and is less prone to seeing than bluer lines.

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12 Theory

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Chapter 3

Data

The data used for this thesis was acquired at the Swedish 1-m Solar Telescope (SST;

Scharmer et al. 2003) on La Palma. This chapter will describe challenges with ground- based observations and how to deal with them (Rouppe van der Voort 2021), as well as the technical details of the observations for this thesis.

3.1 SST

3.1.1 Challenges with ground-based telescopes

When observing astronomical objects with ground-based telescopes, there is particularly one thing to be wary of; seeing, which is movement and/or distortion of astronomical images due to turbulence in the Earth’s atmosphere. These disturbances are easily seen with the naked eye asheat distortion from a hot surface, e.g., just above the ground in a hot desert or on a highway. To optimize for seeing, there are four essential measures done for ground-based observations: the location of the observatory site, the architecture of the telescope, and the physical and computational technique for restoring images:

1. The SST is located at 2400 meters altitude far out in the Atlantic Ocean. Placing the observatory here at this altitude puts the telescope above the clouds most of the time, providing many days and nights with clear skies. The location of La Palma, as the northwestern island of the Canary Islands, makes winds coming in from northwest and west very stable, as they have travelled thousands of kilometres without hindrance. SST is also placed northwest on the island where the slope is particularly steep, adding to the effect of stable winds.

2. Observing the Sun affects the architecture and design of the telescope a great deal.

The heating of the telescope will lead to turbulence inside the telescope, calledin- ternal turbulence. This has been resolved by mounting the lens on top of a 17-metre vacuum tower, effectively removing the possibility for turbulence to occur inside the telescope. This leads to a difference in pressure on both sides of the lens, which is polished in such a way that this pressure difference is crucial for the lens to keep optimal shape for sharp images.

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14 Data

The 17-metre elevation of the telescope from the ground is also a measure to mitigate atmospheric turbulence. From when the Sun rises, it will heat the ground leading to the heat distortion mentioned above. This distortion is more evident as we move closer to the ground, i.e., the higher the telescope the better.

All the optical elements in the telescope are polished as precisely as modern technology can achieve. However, no matter how perfect and precise the surface is;

every lens, mirror, or other physical components will absorb some light, leading to a reduction of observations to some degree. The telescope is therefore designed to have as few optical components as possible to minimize this effect.

3. No matter how much we try to avoid atmospheric turbulence by selecting the site and architecture of our ground-based telescopes, there will always be turbulence to some degree. A physical technique to mitigate this is called adaptive optics. The adaptive optics at the SST is a system of two mirrors that can adjust their orientation and deformation at a very high speed.

The first mirror is a flat tip-tilt mirror that can adjust its orientation by moving in two directions. This mirror corrects for movement of the whole image entering the telescope. Without this mirror, the whole image would fluctuate rapidly in random directions.

The second mirror is a very thin 85-electrode monomorph deformable mirror (Scharmer, G. B. et al. 2019). These electrodes can contract or extend, deforming the mirror in virtually random shapes, effectively countering complex local seeing- effects in the astronomical image.

The system at SST has a frequency of 2 kHz, meaning that the mirrors change 2000 times per second.

4. Even though adaptive optics works wonders for the seeing, it is still difficult to get overall sharp images. To counter this, the data are restored with multi-object multi- frame blind deconvolution methods (MFBD and MOMFBD;Löfdahl 2002;van Noort et al. 2005). As the telescope observes the Sun, many sequences, where each sequence consists of many images (∼ 100) taken over a short period (∼ 10 s), are acquired successively. Next, we assume that the Sun has not changed during this period;

this is a fair assumption for, e.g., the photosphere, as structures over a few hundred kilometres do not change much in this layer. Hence, we can safely say that the differences in the images are solely due to seeing. The images are then run through a computer algorithm that calculates one true solar image from each of the sequences with hundreds of images.

Observing the chromosphere requires a different approach. The assumption that the Sun does not change over 10 seconds does not hold for the chromosphere as it is highly dynamic, changing very fast. Additionally, the chromosphere is best observed by sampling over several wavelengths through a narrow-band filter through a spectral line. The narrow-band filter only allows one wavelength in the spectral line to pass at a time, so sampling of the line is done successively and very fast with a burst of images per wavelength point, typically in the order of ∼10. I.e., observing through the line, is in a way observing through time. It is too short to easily see any temporal

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3.2 Observations 15

evolution of the observations as we move along the line, though.

With all these carefully deliberate precautions to counter seeing, the SST can “get sharp images of the Sun and see details as small as 70 kilometers. That is like seeing somebody waving on Nordkapp from Lindesnes” (Rouppe van der Voort 2021).

3.2 Observations

3.2.1 Details

The observations were acquired on 29 August 2017 by both the CRisp Imaging Spectro- Polarimeter (CRISP;Scharmer et al. 2008) at 08:00:10-09:02:26 UT with a cadence of∼ 19.5s (Hα); and the CHROMospheric Imaging Spectrometer (CHROMIS) at 08:00:09- 09:02:42 UT with a cadence of∼ 14.1 s (Caii K). The target area was the quiet Sun close to the limb, centred at heliocentric-Cartesian coordinates (x, y) = (145⁰⁰,859⁰⁰), corresponding toµ= 0.41.

The images acquired with CHROMIS have an observed field of view (FoV) of66⁰⁰× 38⁰⁰. The instrument sampled the Ca ii K line with steps of ∼6.5 pm spanning from

−0.13 to+0.13nm from the nominal line center, i.e., 393.37 nm. One continuum point in the far wing at400.11 nm was also included. For each wavelength, 20 images were taken per observation frame. The plate scale for the Ca ii K line with CHROMIS is 0.⁰⁰038per pixel.

The images acquired with CRISP have a FoV of 54⁰⁰×54⁰⁰, with most of the view overlapping with the Caii K images. This instrument sampled the Hα line with steps of0.01 nm, spanning from −0.16 to +0.13 nm from the nominal line center, i.e.,656.3 nm. Two outer wing points at ±0.185nm were also included. The plate scale for Hα with CRISP is0.⁰⁰058per pixel.

The data were reduced using the CRISPRED (de la Cruz Rodríguez et al. 2015) and CHROMISRED (Löfdahl et al. 2019) pipelines. This reduction includes the image restoration method, MOMFBD. Afterward, the CRISP data were upscaled to match the CHROMIS pixel scale, before it was coaligned with the CHROMIS data. The CRISP and CHROMIS scans were paired in time using nearest-neighbour interpolation.

3.2.2 FoV outline

Due to the seeing changing considerably through observations, we present one scan at 08:35:43 UT, where the seeing conditions were good. An overview of the observations acquired at 08:35:43 is presented in Figure3.1. The FoV was very quiet, containing a few patches of the magnetic network, as is evident from the facular brightening in the continuum and bushes of spicules, as shown by the upper and lower images in Figure 3.1respectively. The images to the right in Figure3.1displaying Hα are the coaligned observations, and therefore do not capture the full FoV for CRISP.

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16 Data

Figure 3.1: Overview of the observations acquired with CHROMIS (CRISP) on 29 August 2017 at 08:35:43 (08:35:50). From upper left to lower right: continuum intensity at 400.1 nm; wideband intensity at 656.5 nm; the nominal line core intensity for Ca ii K; the nominal line core intensity for Hα; CaiiK intensity in the red wing at∆v= +20 km s⁻¹, i.e., ∆λ= 0.026 nm; Hα intensity in the red wing at ∆v = +32 km s⁻¹, i.e.,

∆λ= 0.07 nm.

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3.2 Observations 17

Ca ii K +20km s⁻¹ and Hα +32 km s⁻¹ in Figure 3.1 show a few regions with dense bushes of spicules, rooted above the photospheric brightenings. The spicules are more distinct in Hα than in CaiiK, where they are more subtle. Therefore, identifying such fine structures as spicules in Ca ii K may prove a complicated task, and this is why we have chosen to compare the Caii K observations with Hα.

Bright features are a frequent appearance in the lower parts of spicules in Ca ii K. These brightenings fade out moving upwards, giving many spicules a bright/dark (B/D) characteristic. They are more densely spaced and the surrounding material is also bright closer to the magnetic concentrations than further away from where they are rooted.

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18 Data

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Chapter 4

Methods

4.1 Image analysis

4.1.1 CRISPEX

We used the CRisp SPectral EXplorer (CRISPEX; Vissers & Rouppe van der Voort 2012) for image and spectral analysis. CRISPEX is a widget based tool for efficient exploration of multi-dimensional datasets, presenting different quantities for the FoV.

For full functionality it must be supplied with two cubes of the same data; onetemporal cube with dimensions (nx, ny, nt·nλ), i.e., number of pixels in x- and y-direction, and number of frames multiplied with number of wavelength points; and one spectral cube with dimensions (n_λ, n_t, n_x·n_y), i.e., it is the temporal cube re-ordered. If the spectral cube is not supplied when opening CRISPEX, the last dimension in the temporal cube will be read as number of wavelength points only, i.e.,nt·n_λ =n_λ.

When CRISPEX is supplied with both the temporal and spectral cubes, it opens three displays as shown in Figure 4.1; a control panel with the display of the intensity image; a display for detailed spectrum showing the spectral information for any given pixel at any given frame; and a display for the spectral time evolution for that same pixel.

4.1.2 Working with CRISPEX cubes

At the beginning of our work we wanted to see if we could use solely Ca ii K for the detection of spicules, and therefore we tried several approaches of data modifications to see if we could enhance the data.

To modify the observations, we needed the ability to load the original cubes for modification, and then create new cubes with the modified data. For loading the cubes we used the Python library Helita(Pereira et al. 2019). The temporal cube’s dimensions are not ideal to work with as it is easier to modify the data frame by frame, i.e., we needed to reshape them into (nx, ny, nt, nλ). Hence, we needed the number of wavelength points, which we get from the temporal cube’s corresponding spectral file.

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20 Methods

Figure 4.1: Overview of CRISPEX. The frame presented is the same as in Figure 3.1.

Upper left image presents the intensity image and control panel; upper right image presents the spectral evolution over time for the given pixel (marked by a small cross in the image); lower image presents the detailed spectrum from that pixel. The vertical red lines represent the spectral position of the frame, while the horizontal line in the spectral-time image represents the time frame.

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4.2 Coaligning CRISP and CHROMIS 21

When the reshape of dimension was done, the data were ready for whatever modifications we wanted to do with them. To prevent using too much memory, the modified data were not written all at once, but frame by frame into new temporal and spectral cubes.

Writing the spectral cubes was done by first loading any given temporal cube as mentioned before. Then the temporal cube was reshaped into dimensions, e.g., (nx, ny, nt, n_λ). Lastly, the dimensions are transposed in a certain order; if the dimensions are in the order as the example order from the last sentence, then the dimensions are transposed into (nλ, nt, nx, ny). The reshaped and transposed temporal cube are then written in the same manner as the original cube by looping over the last dimension (in this example n_y).

Exploring the appearance of spicules in Caii K, we tried three different approaches to highlight them:

• One approach to highlight the spicules was eliminating some of the background with the help of the mean spectra. For this, we created two cubes; one representing division by the mean spectra, and one where we subtracted the mean spectra from the signal. For both cubes, the given operation was done for every pixel in every frame.

• Another approach to highlight the spicules was to enhance their signal by exploit- ing the Doppler shifts we see in spicules. For this we made seven Dopplergrams counting from the nominal line centre; a Dopplergram is created by taking the difference between two intensity frames at symmetric wavelength positions from the nominal line centre.

We also made seven Dopplergrams defined by the ratio between the frames instead of the difference.

• Lastly, we tried to highlight the spicule signal by calculating the running difference, i.e., the intensity difference between subsequent frames in time. We created two cubes with running differences; one cube where the step for the running difference was one time step, and another where the time step was two.

4.2 Coaligning CRISP and CHROMIS

When we coaligned the two observations, several measures had to be made due to several factors: the observations have different pixel scales; the dimensions and FoV of the images have different dimensions; they have different cadences, and; the rotational orientation for every Ca ii K & Hα frame pair is slightly off.

Since analyzing spicules in Ca ii K is the focus of this thesis, we wanted this data to be as authentic as possible; it did not matter much if we lost some information in Hα, which is why CRISP data was adjusted to coalign with CHROMIS.

4.2.1 Adjusting Hα

For adjustments of Hα, we used the Python librariesscipy.ndimageand numpy.

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22 Methods

1. To match the pixel scale for the observations, Hα images were upscaled to match Ca ii K’s plate scale. Just using the ratio between the given values0.⁰⁰058(Hα) and 0.⁰⁰038(Ca ii K), did not give matching plate scales (^0.58/0.38≈1.52632) after visual inspection. The ratio we used for upscaling Hα was 1.53892.

2. As mentioned in Chapter3, CRISP has bigger FoV (54⁰⁰) in they-direction than Caii K (38⁰⁰). Therefore, most of the FoV in Hαthat did not overlap with CaiiK for this axis was cut off. To match the bigger FoV dimensions of Ca iiK in thex-direction, a padding with constant values were added to this axis for the Hα frames.

3. Since the cadence is faster in CaiiK than in Hα, the CaiiK observations have considerably more frames (267 to 193). The nearest neighbour interpolation algorithm mentioned earlier is simply finding the frame in Hα closest in time to Ca ii K for coalignment. Since we are pairing Hα to Ca ii K, this results in several frames in Hαbeing duplicated, but that is better than doing it in the opposite manner, which would make us lose several frames in Ca ii K.

4. Luckily, the rotational deviation was constant through observations, so we needed only one rotational value. This value was found by experimentation.

4.2.2 image_registration

When the adjustment of an Hα image was done, the image was ready for coalignment with Ca ii K. We used the wideband channels from both data sets for coalignment, with the Python libraryimage_registration (Ginsburg 2014). First, the pixel offsets in both the x- and y-directions in the given Hα image was calculated. These offsets were then used with the image we wanted to coalign, e.g., the Hα narrow-band image, effectively coaligning this image to the corresponding Caii K narrow-band image.

This procedure did sometimes result in parts of Hα FoV being pushed over to the opposite side of the image, i.e., pushed out of the image on one side, reappearing on the other. We did not address this data artifact, as it did not concern the Caii K FoV.

4.2.3 Width of Hα

In addition to comparing the Hα narrow-band images with Ca ii K, we compared the width of Hα with Caii K as well. Hα width has been reported to be a reliable way to follow the dynamics of spicules as they are not sensitive to changes of Doppler shifts (Cauzzi et al. 2009;Pereira et al. 2016).

The Hα width was calculated with the use of the Python library Helita (Pereira et al. 2019). First, the wavelength positions (blue and red wing) of Hα width were calculated for every pixel in a given frame. For this, we had to provide the wavelength sampling of Hα. Then, the Hα width, ∆λ, was calculated for every pixel and every frame. Lastly, a new temporal and spectral cube was made from the combination of all the frames. Note that this gives a spectral cube with only one wavelength dimension that is indeed not a wavelength, but values representing∆λof the Hα width.

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4.2 Coaligning CRISP and CHROMIS 23

0

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Figure 4.2: Illustration of three of the spicules analyzed. The upper scans show spicules in Caii K and the lower scans show corresponding spicules in Hα.

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24 Methods

4.3 Finding spicules

The orientation of the original observations displayed the spicules propagating to the left from the surface, as shown in Figure3.1. To make analysis slightly easier and the data more representative, we chose to rotate the coaligned data 90 degrees clockwise, as shown in Figure4.3.

Searching for spicules was typically done in the wings of Hαand around the nominal K2 peaks of Caii K. As the second half of the data appeared to have better seeing, the search for spicules was quickly set to focus on this half of observations. A lot of time in the analyzing part was spent scanning through the spectral line on both sides of the nominal cores, staring at frames with good seeing. In this search for distinct spicules in Caii K, the coalignment came in very handy.

4.3.1 Spectral blink

Finding frames with good seeing and spicules that were distinct in CaiiK was important for getting minimal noise in the observed spectral line. However, since we also wanted to compare the lengths of the spicules between Caii K and Hα, we focused on finding spicules that were distinct in Hαas well. An illustration of spicules in CaiiK compared to Hα is presented by Figure4.2.

When we compared Ca ii K with Hα and Hα width, we used CRISPEX’ spectral blink function. CRISPEX’ spectral blink function is one of two blink functionalities, with the other one being temporal. The temporal blink function lets you blink between frames different in time, but with the same wavelength point; the spectral lets you blink between different wavelength points but within the same time frame.

This spectral blink function makes the search for spicules in Ca ii K remarkably more straightforward. This is a lot of thanks to the blink function’s “on/off”-button, making the blink consistent as we move through the observations in time.

With this spectral blink set to “on”, we did plenty of runs through the second half of the data sets. And for every run, we tried to vary between different wavelength points. A combination of blinking while moving through time and blinking while moving through wavelength points was done each run; when we found a spicule we thought was distinct enough in both data sets, we, e.g., ran the blink along the spectral line to see if it appeared more distinct for another wavelength point.

4.3.2 Ca ii K Pitfalls

Before we included Hα in our search for spicules in CaiiK, there were several potential pitfalls we had to avoid.

One of the things to watch out for in the CaiiK frames are gaps appearing as dark features, i.e., spicule look-alikes, in the intensity frames. These can sometimes appear in the regions where we find many spicules and are gaps in the bright area above the magnetic network. They are fortunately easy to detect as just gaps by stepping between frames; these features do not, as far as we have seen, survive between frames such as

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4.4 Extracting spectra 25

the spicules do. They are also easily distinguishable by comparing with Hα as we do not see these gaps in Hα.

Another thing to watch out for is a superposition of spicules. Quite often we see spicules that are distinct in CaiiK as one spicule but are two spicules propagating from two different places of the photosphere/lower chromosphere. This kind of superposition can be harder to spot, as they can survive over time. This is where coalignment with Hα comes in handy; because of the clear distinction in Hα, this superposition is much easier to recognize.

An example of a superposition of two separate spicules is shown by the lower frames in Figure 4.3; the top part of the spicule in focus, where the dashed red line does not follow, is another spicule propagating from (x, y) ≈ (17.⁰⁰5,7⁰⁰) as shown by the axes.

This superposition is easily spotted by comparing with the red wing in Hα.

4.4 Extracting spectra

After all spicules for analysis were chosen, it was time to extract the spectra as it behaves along the spicule structures. We used matplotlib.pyplot’s ginput and the same approach as inKianfar et al.(2020), Figure 4. The intensity frames picked out for analysis were opened one by one. Then we choose a few points manually in interactive mode and returned thex and y coordinates of the chosen pixels. Choosing the points, we tried to follow the elongation from bottom to top along the centre of any given spicule.

4.4.1 Interpolating points

From the chosen x and y coordinates we interpolated ∼43 (inspired byKianfar et al.

(2020), but we chose slightly fewer pixels as that seemed more appropriate for our data) equidistant pixel points with the manually chosen pixels included. This was done by the combination of the full pixel length of the spicule, and the pixel length between each manually chosen pixel point. If the full pixel length of any chosen spicule were close to 43 pixels, we would choose a new number of pixels as 90% of the full pixel length to avoid extracting spectra from the same pixel twice; this did not happen with any of the spicules we chose. This is probably because the spatial resolution is so high, making even the shorter spicules span over more than enough pixels.

The interpolated pixel points now represent the path from where the spectra are extracted as shown in the right frames of Figure 4.3. To increase the signal-to-noise ratio in the spectra extracted, the spectra were averaged over three neighbouring pixels, with the interpolated/manually chosen pixel point as the middle pixel. Since the spatial resolution is quite high, the spectra do not change much from one pixel to the other, i.e., the spatial direction of the neighbouring pixels did not matter much; so, for simplicity, we chose the neighbouring pixels to always come from thex-axis of the rotated frames.

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26 Methods

0

4 8 12

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0

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arcsec 0

4 8 12

arcsec

+20km s⁻¹

0

⁴ ⁸ ¹² ¹⁶ ²⁰

arcsec

Figure 4.3: Overview of the spicules chosen for extracting spectra. They represent three scans sampled at 08:35:43, 08:42:19, and 08:52:16 UT respectively from top to bottom, i.e., left and right images are the same. The upper and lower images are scans in the red wing from the middle network region in Figure3.1, while the images in the middle are scans in the blue wing from the left-most region, i.e., the upper region when the data are rotated as in this figure. Detailed spectral profiles of the spicules marked in the right column are shown in Figure5.2.

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4.5 Spicule lengths 27

4.4.2 Displaying the spectra

The spectra we extracted from each pixel, were plotted above each other with a constant value as the difference between each line, as shown in Figure 5.2. The y-axis then represents intensity signatures along the length of the spicule, i.e., the differences in spectral signature for the lines relative to each other are in spatial dimensions, and must not be mistaken for the actual difference in intensity value or spectral evolution over time.

4.5 Spicule lengths

When we calculated the length of the spicules, we used the same full pixel length as we found from thex andy coordinates returned from manually chosen pixels from earlier.

Now, the pixel length of a spicule does not tell us anything, so the full pixel length had to be converted to length in km.

Since we know how many arcseconds are represented by one pixel, as this is the plate scale of Caii K with CHROMIS, we need to know how many km are represented by one arcsecond observed on the Sun. Since Hα is coaligned with Caii K, we have to use the same conversion factor for Hα.

Because the plate scale is fixed for the telescope, it is the distance between the telescope and the Sun that affects how many km are represented by one arcsecond; this makes sense as the closer we are to the Sun, the better the spatial resolution. As Earth’s orbit around the Sun is elliptic, the distance from us to the Sun is affected by Earth’s location in its orbit, i.e., the time of the year.

The procedure for converting from plate scale to physical distance on the solar surface was done by the use of Python librariesastropyandsunpy. First, we initialized twoSkyCoordobjects fromastropywith helioprojective frame defined bysunpy, Earth as the observer, and the date of our observations as observation time. The two objects were also initialized by x and y coordinates in units arcseconds, where the distance between the coordinates for the two objects was one arcsecond.

We then transformed the frame to heliocentric, and in that way fetched the solar coordinates represented by the helioprojective frame’s arcsecond coordinates. Lastly, we calculated the difference between the coordinates of the two SkyCoordobjects and then converted this solar coordinate difference to km.

With that done we got the conversion factor from arcseconds to km and could calculate the full length in km of the spicules from their full length in pixels. Note that choosing pixel points inmatplotlib.pyplot’s interactive mode returnsxandy as float numbers, i.e., we are not limited by pixelated paths along the spicules.

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28 Methods

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Chapter 5

Results

We analyzed spectra from 39 different spicules using several scans between 08:30:15- 09:02:11. We found that all of the 39 spicules had a bright/dark (B/D) characteristic when looking at Ca ii K. This does in no way mean that we find more spicules or only spicules with B/D characteristics rather than dark characteristics; as analyzing this B/D characteristic for Caii K spicules is one of the main goals for this thesis, the search for distinct spicules in both data sets is presumably strongly biased by this goal.

Comparing the lengths of the spicules in Caii K with Hαwe found that the length of the spicules appears to be a little different, with the typical difference in length in the order of10² km. Overall, the spicule lengths range from 2500 to 8890 km, with an average of ∼4850km

5.1 Enhancing Ca ii K

An overview of the modifications we applied to the Caii K data is presented in Figure 5.1.

The modification done to eliminate the background is presented by the two upper frames. Looking at the images, it is hard to spot any difference from the original image presented by Figure 3.1, i.e., the procedure was not very useful for this thesis.

The Dopplergrams made for enhancing the signal for the spicules appear to be doing so to some degree, but not overall. Especially the Dopplergrams made with the ratio instead of the difference, appear to highlight some parts of the spicule features more.

They might have proven useful for a different goal, but for us, they did not prove very useful.

The running difference presented by the two lower frames appears to be highlighting the more extreme differences in time evolution. Might prove useful for other features, but for this thesis, it did not prove useful.

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30 Results

Figure 5.1: Overview of the modifications applied to Ca ii K. The four upper images represents the same time frame as presented in Figure3.1, while the lower images is the running difference related to this time frame.

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5.2 Bright dark spicules 31

5.2 Bright dark spicules

In Figure5.2 we plotted the spectra of the spicules presented in Figure 4.3. We chose these three spicules as they represent three typical spectral signatures we see along the spicules in the data set.

1. The first spectral signature we see a lot of through the observations is a change in the Doppler shift from the lower part to the upper part of the spicule as presented by Figure 5.2(a). In this particular spicule, presented by the upper images in Figure 4.3, we see a Doppler signature from a blue-shift in the lower part to a red-shift in the upper part.

2. The next spectral signature we see is the absence of a Doppler shift in the lower part as presented by Figure 5.2(b). The spectra show no clear Doppler shift as none of the K2 peaks are dominating in the lower part of the spicule structure, i.e., the line profile is almost symmetrical. The absence of Doppler shift is only related to the lower part of the spicule.

3. The last spectral signature we see is the same Doppler shift along the spicule as presented by Figure 5.2(c). This Doppler signature remains in the blue or red wing along the elongation. The presented spicule has red-shifted spectra all the way through.

5.2.1 Change of Doppler shift

A change of Doppler shift along the spicules is the spectral signature we most often encounter for spicules in Caii K, at least for the spicules analyzed in this work. Out of the 39 spicules, we found with B/D characteristics, 25 of them had a spectral signature resembling a change of Doppler shift along the elongation.

Looking at the figure representing this change of Doppler, it appears that the spicule is more or less divided in half with relation to the separate Doppler shifts; the lower half has a blue-shifted signature, while the upper half has a red-shifted signature. In the middle of the spicule, where the spectra transition from blue-shift to red-shift, we see a short distance with a spectral signature resembling no Doppler shift.

The lower part is a blue-shift even though it is the K2Rpeak that is enhanced. The reason for this comes from the symmetry of a spectral line; the core of a line is always the part that is formed highest in the atmosphere. This means that the K3 feature of the Ca ii K line can be formed at a height where the line of sight velocity is different from the velocity field in the height where the K2 peaks are formed, giving the K3

feature a different Doppler shift than the K2 peaks. This can, if the difference between the velocity fields is large enough, result in a suppression of the peak’s intensity, e.g., K2V if K3 is blue-shifted. The same goes for the K2Rin the red wing, as illustrated by the upper part of the spicule in Figure5.2(a).

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32 Results

0.1 0.0 0.1

[nm]

I

(a)

0.1 0.0 0.1

[nm]

I

(b)

0.1 0.0 0.1

[nm]

I

(c)

Figure 5.2: An overview of the detailed spectral profiles of the spicules as presented by Figure4.3: left: spectra from dashed line in upper right image, length of spicule is

∼3480km; middle: from dashed line in middle right image, length of spicule is∼5035 km; right: from dashed line in lower right image, length of spicule is ∼ 5525 km. 5 marks the bottom of the spicule,marks the middle part and4marks the top of the spicule. Blue dashed line represents the wavelength position of the intensity frames in Figure4.3

.

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5.3 Ca ii K vs Hα 33

5.2.2 Absence of Doppler shift

Out of all the spicules we detected with B/D characteristics, 5 of them had no Doppler shift in the lower part, i.e., K2V and K2R with equal intensity. For all 5 spicules the spectral signature changes to either blue-shift or red-shift in the upper part of the spicules.

From the figure representing this spectral signature, it appears the spicule quickly changes to a slightly red-shifted signature for most of the lower half as K2V dominates here and K3 is slightly red-shifted. For the upper part of the presented spicule, the K3

appears to be slightly more red-shifted, but we interpret the signature in the upper part as a blue-shift as the suppression of K2V is significantly stronger than for K2R.

5.2.3 One Doppler shift

Out of the 39 B/D spicules analyzed, 9 spicules appeared to have a single Doppler shift along the elongation. Most of the spicules have a distinct enhancement in both K2 peaks, even though the Doppler shift is consistent along the elongation; only two of the spicules analyzed did not have a distinct enhancement in the wing corresponding to the Doppler shift of the spectra.

The spicule presented by the lower images in Figure4.3in Chapter4is one of these spicules; looking at Figure 5.2(c)the spicule has a very small enhancement of the K2R

in the lower part. This particular spicule does not have the distinct B/D characteristic as the other two spicules presented, but instead has a dim brightening in the lower part.

5.3 Ca ii K vs Hα

Comparing spicules in Ca ii K to Hα, shows that the spicules in both data sets agree well, with a few offsets; they appear to overlap spatially, with spicules in Caii K more often reaching a little longer than in Hα, and they appear to have similar lifetimes.

However, we sometimes see spicules in Caii K that are not so evident in Hα and vice versa. The spicules in Hα appear more densely packed close to the line core.

Comparing the lengths between the two spectral lines we found that 25 out of the 27 spicules appeared longer in Caiithan in Hα. The number of spicules here is lower than the total spicules analyzed as the CRISP FoV does not cover the left-most magnetic region in CHROMIS FoV.

The differences in lengths between the two data sets are mostly in the order of 10² km. An overview of the length of spicules in CaiiK and Hα, and the difference between corresponding spicules is presented by Figure5.3.

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34 Results

4000

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CaiiK − Hα

Figure 5.3: Histograms representing spicule lengths in Caii K and Hα (left panel), and the difference in spicule lengths between the two data sets (right panel). Positive values in the right panel represent spicules in CaiiK being longer than corresponding spicules in Hα. Solid lines in the left panel are the length of spicules in CaiiK and dashed lines are the length of spicules in Hα.

Hyperspectral detection of spicules in Ca II K