Astrobiology of University of Arizona - Identifying Habitable Worlds B

                            

This is the 2nd Graded Assignment from the University of Arizona in Astrobiology: Exploring Other Worlds. Taking into account the Visual Storytelling Image in this article as well as having detected 3 exoplanets, here we analyze, we prioritize and we explore habitable worlds in exoplanetary systems that can be much different than our solar system.

Writer: Menelaos Gkikas

For the purposes of this assignment, I discuss key concerns in the search for habitable worlds, ranging from the host star and its type, the orbital distance from the star that can affect the "Goldilocks Zone", meaning the range from the host star in Astronomical Units where water can exist on the surface of the exoplanet as liquid hence referring to a surface temperature as well, plus the atmosphere indicated by whether the exoplanet is massive enough to possess geologic activity that affects the retaining of a thick atmosphere.

Furthermore, to define of whether exoplanetary systems resemble our solar system or not, we take into account that host stars are classified by their spectral type which indicates the mass of the star (computed as a figure of solar masses). The existence of liquid water or not is the major criterion in this assignment.

Science Prompt: 

Three Earth-like exoplanets are detected.  Exoplanet-1 orbits an A star, exoplanet-2 orbits a G star, and exoplanet-3 orbits an M star.  Each exoplanet orbits at the same orbital distance of 1 AU.  

Yousef says that all three Earth-like planets likely have liquid surface water because they all orbit at 1 AU.  Since the Earth orbits at 1 AU from the sun, and we know Earth has liquid surface water, then these exoplanets should as well.

Lora says that both exoplanets 2 & 3 will have liquid water, but not exoplanet-1.  The star for exoplanet-1 is spectral type A, which is too big and hot and would evaporate water on exoplanets. But exoplanet-2 and exoplanet-3 orbit around favorable spectral types G and M, therefore they likely have liquid surface water.

Consider both Yousef and Lora’s statements.  In 250-750 words, write a response to Yousef and Lora about whether you agree or disagree with their assessment of the surface conditions on each exoplanet.  The table below has information that may be helpful for supporting your reasoning.

BEFORE YOU BEGIN!

Plan ahead: Create a rough-outline so that you may better express your thoughts in a clear and ordered fashion. Be sure to use facts and relevant examples to reinforce your response.

BEFORE YOU SUBMIT!

Before you submit your writing, be sure to self-assess based on the rubric.

Instructions:

You are limited to a 750 word response.

There is no set writing format we ask you to follow, but we do recommend that you carefully read the writing prompt and address exactly what is asked. A good way to ensure this is to restate the question(s). For example, should the writing prompt ask:

"What is the Scientific Method?"

You might begin by stating:

"The Scientific Method is..."

And then order your paragraphs/sections accordingly.

You might consider composing your response in a word processor (e.g., Microsoft Word) in order to better facilitate your writing. Once complete, simply copy and paste your text.

Do not attach external documents or images or cite outside sources. There is no need to do so. The information provided by Dr. Impey in his video lectures and slides is sufficient to answer all questions.

Grade: 100%

Answer:

To state my agreement or disagreement with Yousef and Lora, my argumentation begins with the order and the hierarchy of the spectral types of stars, beginning from the least massive, coolest and least luminous towards the most massive, hottest and most luminous from left to right and indicated by the letters MKGFABO.

Exoplanets detected, beginning from exoplanet 3, continuing with exoplanet 2 and then exoplanet 1, orbit around an M, G and A star respectively. To search for habitable exoplanets means to define criteria related with the host star, the orbital distance from the star, the surface temperature of the exoplanet as well as the atmosphere of the exoplanet.

Since all exoplanets orbit their host star at a distance of 1 AU, which is the identical orbital distance of our Earth hosted by the Sun, we use this figure as our measure of a possible orbital distance where water can exist in the surface of the exoplanet as liquid, the “Goldilocks Zone” in other words.

Based on the previous discussion we judge the M, G and A hosts.

In general, the F, G, K, M stars in the order are the best habitable candidates with lifetimes spanning from 3 billion years and above, whereas O, B and A stars possess shorter lifetimes where complex life would be annihilated by a supernova at the end of the star’s lifetime.

Additionally, since the M host is the coolest, least massive and least luminous but a more probable candidate, I make a first distinction between the M, G hosts from one side and the A host from the other side. Planets that are more massive in general can possess significant geologic activity that could affect the existence of a thick atmosphere.  Sufficient atmospheric pressure which is necessary for liquid water is another criterion but these last arguments are merely the comparison evidence.

A smaller planet but with sufficient mass to retain an atmosphere but limited heating from geological activity could potentially maintain liquid water near the star.

Host star M is the least massive but has much lesser range of a Goldilocks zone if compared with the 1 AU, it’s only 0.3-0.4 AUs. The previous mean that the criterion for liquid water is not satisfied.

Host star G, a very much Sun-like host – including mass and lifetime - is almost equalized with the orbital distance of 1 AU to be inside the range of 0.9–1.8 that defines the limits of habitability, so this can be the only exoplanetary system that resembles our solar system.

Host star A is the most massive, hottest and most luminous of the 3 but since the exoplanet orbits at 1 AU and here the Goldilocks Zone addresses 3.5 to 7.1 AUs, then this exoplanet is not habitable in terms of maintaining liquid water on surface, since it orbits around the hottest star with this concrete habitable zone, water will probably be evaporated.

Neither Yousef nor Lora are completely right or completely wrong. Yousef is right about G but wrong about M and A. Lora Is right about G and A but wrong about M. The exoplanet orbiting M probably reaches or even surpasses the cryogenic biosphere.

Astrobiology of University of Arizona - Discovering New Worlds A

                                 

This is the 1st Graded Assignment at the University of Arizona course of Astrobiology: Exploring Other Worlds. 

Writer: Menelaos Gkikas

We're actually searching for exoplanets and potentiality for habitability and it's essential to realize the singularity in scientific judgements. When you travel in space you may not have others "holding your hand"... For the purposes of the course and this assignment, even the knowledge, the inspiring instructor, the cloud environments as well as notes and calculations, plus the criteria of grading that focus mainly on the structure and the different parts and logic of this assignment concept, are not enough to make a "diagnosis", even if we are graded by an AI... When you make a diagnosis you need the exact calculations and the dozens of astronomical and comparative data not present in this course, not even in other courses, but accessible only to real time physicists and astronomers making observations or experimenting. Here, we become experimentalists beyond "right or wrong" bearing in mind that the viewing angle with which we choose to write is paramount.

Here, we mingle methods for the observations of exoplanets with Newton's law of gravity and global attraction, Kepler laws of planetary motions and then potentiality for habitability at a system that should not be confused with our solar system...

Science Prompt:

As a fledgling astrobiologist, you are given the following two sets of data, corresponding to two newly discovered exoplanets.  There are two graphs for Star A, and one for Star B:

Your research advisor wants to do follow-up observations on the exoplanet that is most Earth-like.  It is your job to analyze these data and determine which, if either, exoplanet has the potential for habitability.  In 250-750 words:

Identify the method/s used to gather the data for each exoplanet (radial velocity, transit, gravitational lensing or direct imaging). Briefly explain how each method works.

Discuss what physical characteristics can be learned from the data for each exoplanet and explain your reasoning.

Identify which planet is more Earth-like, and the more likely habitable candidate. Use the information from the observational data to explain your reasoning. 

BEFORE YOU BEGIN!

Plan ahead: Create a rough-outline so that you may better express your thoughts in a clear and ordered fashion. Be sure to use facts and relevant examples to reinforce your response.

BEFORE YOU SUBMIT!

Before you submit your writing, be sure to self-assess based on the rubric. 

Instructions:

You are limited to a 750 word response.

There is no set writing format we ask you to follow, but we do recommend that you carefully read the writing prompt and address exactly what is asked. A good way to ensure this is to restate the question(s). For example, should the writing prompt ask:

"What is the Scientific Method?"

You might begin by stating:

"The Scientific Method is..."

And then order your paragraphs/sections accordingly.

You might consider composing your response in a word processor (e.g., Microsoft Word) in order to better facilitate your writing. Once complete, simply copy and paste your text.

Do not attach external documents or images or cite outside sources. There is no need to do so. The information provided by Dr. Impey in his video lectures and slides is sufficient to answer all questions.

Grade: 90%

Answer:

1.) The method used for gathering data about exoplanet A was the radial velocity method portrayed with its first graph and the transit method portrayed with its second graph.

The method used for gathering data about exoplanet B, was again the radial velocity method portrayed with its only graph for exoplanet B.

The radial velocity method – for both exoplanets - is founded so to measure the reflex motion of a star caused by an exoplanet as measured by an outside observer considering the approaching or receding body. This reflex motion that is caused by Newton’s universal law of attraction, a gravitational law indeed, affects the electromagnetic radiation emitted by the star, hence it also affects its wavelength and color of light if compared with the distance from the observer. It is therefore founded on the Doppler shift basis.

The transit method especially for the star – exoplanet system A, talks about a periodical dimming of the brightness of the star, displayed as an actual eclipse of the star caused by the periodical orbit of the exoplanet. This means that the exoplanet blocks a portion of the starlight of its host. The relative periodical decrease of the star’s brightness is portrayed in the graph.

2.) The physical characteristics that can be learned from the data for each exoplanet take place with the data portrayed by the radial velocity method and the transit method if combined with Kepler’s universal laws of planetary motions.

From the radial velocity diagrams of the host star for exoplanets A and B, we get to calculate the periods of the phenomena. We can then use Kepler’s 3rd law to calculate the orbital distances of exoplanet A and exoplanet B. By these, using the orbital distance and the implications of Kepler’s 1st law, we can calculate the velocities of the exoplanets A and B. Bear in mind that the orbital distance addresses elliptical orbits where if they are approximated as circles, we can simplify calculations. Once the velocities of the exoplanets are known we can calculate exoplanet mass using conservation of momentum.

We can then use the information provided from the transit method, especially for exoplanet A to calculate the physical size of the exoplanet, meaning, an approximate sense of how big it is, addressing its radius. Knowing the radius of the exoplanet A and assuming it a perfect sphere we can calculate the density of the exoplanet and argue of whether this density addresses more like gas giant planets or better rocky compositions suitable for habitability and life.

We can also visualize that the stronger reflex motion of exoplanet B caused to its star addresses a higher mass for exoplanet B, a longer orbital period for exoplanet A and by Kepler’s 3rd law calculations we can also deduct that exoplanet B is closer to the host than A. Knowing also that the transit depth of A is minimal and larger planets block more starlight, we derive a higher physical size for exoplanet B that can also be calculated by density equation.

3.) The period of exoplanet A is closer – even though not similar – to the period of Earth around the Sun. The orbital period of exoplanet A is a little above 150 days while the period of exoplanet B is a little bit higher of only 6 days…! Much faster. This means that exoplanet A has a little less than half the orbital period of Earth.

Knowing that the density of exoplanet B is higher than that of A, makes this exoplanet of more solid material which is more suitable for humans. Making the actual calculations of the Kepler’s 3 laws of planetary motions, we can also compare the physical characteristics and figures computed with those of Earth and other more habitable planets, to compare and contrast advantages and disadvantages of hosting life. 

We can also determine the planet with more liquid or more solid composition. Nevertheless, knowing the mean density of the planet is not a full disclosure of its composition as there are many models that can match the single mean density measured. We just know that exoplanet B might be a more suitable candidate for habitability, a more Earth-like planet.

The Spin Mechanism In QFT - Quantum Field Theory - & Quantum Computing

The concept sits right at the crossroads of foundations and emerging tech. I’ll break it down in layers, moving from spin in QFT, to spin as information, and finally to spin in quantum computing and memory, tying the concepts together as one coherent story.

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1. What “spin” really is in Quantum Field Theory (QFT)

Spin is not rotation

Despite the name, spin is not a particle literally spinning. In QFT, spin is:

an intrinsic quantum number that labels how a quantum field transforms under spacetime symmetries.

More precisely:

• Spin arises from representations of the Lorentz group

• It tells us how a field behaves under rotations and boosts

Examples:

• Spin-0 → scalar fields (Higgs)

• Spin-½ → fermion fields (electrons, quarks)

• Spin-1 → vector fields (photons, gluons)

• Spin-2 → graviton (hypothetical, but important conceptually)

So in QFT, particles are excitations of fields, and spin is a property of the field itself, not a classical motion.

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2. Spin quantum number: the formal backbone

The spin quantum number $s$ determines:

• The dimensionality of the spin space

• The allowed spin projections $m_s$

For a particle with spin $s$:

$$m_s = -s, -s+1, \ldots, s$$

Examples:

• Electron: $s = \frac{1}{2} \Rightarrow m_s = \pm \frac{1}{2}$

• Photon: $s = 1 \Rightarrow m_s = \pm 1$ (no zero state due to gauge symmetry)

In QFT:

• Spin is encoded in spinor, vector, or tensor fields

• The spin-statistics theorem links spin to behavior:

  • Half-integer spin → fermions → Pauli exclusion

  • Integer spin → bosons → collective occupation

This connection is deeply structural, not optional.

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3. Spin as information 

Here’s where things get really interesting.

Spin = a minimal quantum information carrier

Spin systems are:

• Discrete

• Robust

• Naturally quantum

A spin-½ particle is literally a qubit:

$$|\psi\rangle = \alpha |↑\rangle + \beta |↓\rangle$$

This means:

• Spin orientation encodes information

• Superposition encodes parallel information

• Entanglement encodes correlations beyond classical limits

In QFT terms:

• Spin information is stored in the internal degrees of freedom of a field excitation

• Measurements extract classical bits from quantum spin states

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4. Spin memory: how spin stores information

Physical meaning of spin memory

“Spin memory” refers to the ability of a spin system to preserve its quantum state over time.

Key mechanisms:

• Long coherence times

• Weak coupling to environment

• Controlled interactions

Examples:

• Electron spins in quantum dots

• Nuclear spins in NV centers (diamond)

• Phosphorus donor spins in silicon

Spin memory is powerful because:

• Spin couples weakly to charge noise

• Spin states can persist for milliseconds to hours (nuclear spins!)

In QFT language:

• Decoherence = unwanted coupling between the spin field and environmental fields

• Spin memory survives when those couplings are suppressed or engineered

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5. Spin dynamics: how information moves

Spin doesn’t just store information — it processes it.

Mechanisms:

• Spin precession (via magnetic fields)

• Spin–spin interactions (exchange coupling)

• Spin–orbit coupling (bridges motion and internal state)

Mathematically:

$$\frac{d\vec{S}}{dt} = \vec{S} \times \vec{B}$$

In quantum computing:

• Gates = controlled rotations in spin space

• Entanglement = correlated spin states across particles or sites

In QFT:

• These interactions arise from field couplings

• Gauge fields mediate spin-dependent forces

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6. Spin in quantum computing

Why spin is ideal for qubits

Spin qubits are attractive because they are:

• Compact

• Scalable

• Naturally two-level systems

Implementations:

• Electron spin qubits (fast, sensitive)

• Nuclear spin qubits (slow, extremely stable)

• Hybrid systems (electron for control, nuclear for memory)

Logical operations:

• Single-qubit gates → spin rotations

• Two-qubit gates → spin–spin interaction or mediation via photons

From a field-theoretic view:

• A quantum computer is a controlled, low-energy quantum field system

• Computation = engineered evolution in Hilbert space

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7. Big-picture synthesis (QFT → QC)

Here’s the unifying idea:

Spin is the smallest unit where spacetime symmetry, quantum mechanics, and information meet.

• In QFT, spin encodes how matter exists in spacetime

• In quantum information, spin encodes how information exists in Hilbert space

• In quantum computing, spin encodes how computation exists physically

Spin quantum number → defines what is possible
Spin information → defines what is known
Spin memory → defines what can persist

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8. Conceptual takeaway

Spin is not just:

• a quantum number

• a magnetic property

• a computational resource

It is:

a symmetry-labeled container for quantum information

That’s why spin shows up everywhere — from particle physics to quantum chips.

Creative Digital Marketing Vs STEAM Education

Writer: Menelaos Gkikas

Happy New Year creatives, marketers and science people! Today’s scientists as well as creative digital marketers are quested not to invent the wheel and furthermore, learn the lessons of digital transformation in the 21st century, align with hybrid visual development solutions in AI and focus on better productive models. Let me just state my inspiration of how the entire today’s discussion started!

AI cannibalizes its own foundations as well. From the very moment we came up with Gemini and ChatGPT, the era of developers stochastically theorizing for weeks to produce one simple piece of code started to erode. Why should we need over 1 week to stochastically theorize for commands over one piece of code, when we can put the task into ChatGPT, produce the program, fix the code on our own and come up with a solution in less than 1 hour? This disruptive change in traditional programming and industry shifts is boosted by software platforms that function as hybrid visual development asking for tinier code samples together with object-oriented tasks, as well as the need of outsourcing digital marketing to independent martech as long as we conceptualize effectively our bottom need which is data, meaning, data production displayed with a data production line.

No matter the industry, for a computer to produce effective data, means that all its components function harmonically and effectively together that is the same logic with a production line inside a food industry. You can’t override the daily tasks of the factory and if they’re not daily, weekly or bi-weekly…

For digital marketers, we need content production. This means scripts, photos, music, videos. This production line has to be followed and applied regularly inside our mediums and from the very moment of regular flow, digital tools like Google Analytics are immediately activated and authorized to display qualitative and quantitative data.

The above truly mean you have activated machine learning, data analytics and AI qualitative and quantitative data further deployed with extra martech and SEO and digital PR tools.

Data flow on the other hand, a time-dependent dynamical system, reminds us something from differential equations possibly redirecting our thoughts to the Schrodinger equation in quantum physics. Here we have elements of the connections of advanced math to how data scientists think with data and numbers evolved… Different notions in data shift to different notions in equations. If you wake up one day and you’re in the mood of thinking whether you shall make articles about Cirque du Soleil or K-Means Clustering AI, means that your perception on what to talk about shapes outcomes…! If you could model that, perception shaping outcomes means pure quantum physics.

But what about the real logic and the rationale of bridging creative digital marketing with STEAM education initiatives? Pay attention to the following bullets:

1.) Martech

Complex digital marketing happens with tailor made complex technologies. To scale-up digital marketing projects means to carefully outsource expertise to low cost or free outside products in terms of SEO and outreach campaigns, digital PR, analytics as well as manual programming.

2.) Hybrid Visual Development

Coding is no longer king. The era of conceptualizing for weeks and thinking you’re Schrodinger only to write essential code is now gone. This does not mean we don’t need programming. It means programming should be combined with complex visual development products.

3.) Traditional Programming Starts Becoming Obsolete

The evolutions in AI in the last 10 years start threatening traditional programmers. Tools like ChatGPT, Gemini and automated production of code start to emerge, that make the demand for programming less and shifted to more valuable options. Which brings us to the next bullet argument.

4.) AI cannibalizes its own roots

The previous do not mean we don’t need math and programming. If we live in the 4th industrial revolution of AI, then coding and math are part of that AI as well. It just means that not ‘you’, but ‘other’ coding and science examples will survive.

5.) Data Vs Mathematics

It’s important to think like a data scientist. Different notions in data are equivalent with different parts in mathematic equations. For example, we know even before we enter the university that f is defined as the derivative of F and we learn statistics in university that say that this f is the probability density function of the cumulative distribution function F, the major definition of all statistics and since we know differential and integral calculus, F can be expressed as the integral of f. Here, we have founded math for data analysis. Using the same logic we should seek equivalences of data flow with a differential equation and possibly seek the differential equation of Schrodinger in Quantum Physics. Less exercises and problem solving as a university student and more like applying elemental basic principles.

6.) STEAM Education As Content

Science and especially STEM are being defined quantitatively. Nevertheless, equations in books and courses are classified as general statistics that may not comply with the unique example of us, furthermore, they do not comply when we make our own single measurements. Unique measurements may be something else completely. So, it’s important to embrace science as content and see what we can learn as digital marketers by other products that incorporate metrics, not inventing the wheel on our own.

7.) Redefining Science & Coding

In the 21st century of the 4th industrial revolution we’ll be needing less coders and more scientists. It’s important to bridge the gaps and apply inclusion successfully but it’s also important to focus on the science and codes that matter, not on our personal story…

8.) Paradigm Shifts

All the previous mean that as we live in the age of computers, there will be other paradigms that will lead the reigns of evolution and these paradigms will not be the same with the previous generations or the scientists and coders that have been left behind.

9.) Why Digital Marketing

Projects need ROI and Lead Generation. The same counts for the entire company as well. A big company that cannot sell anymore, faces the risk of closing down operations. My personal evaluations on the other hand say that heads of marketing and heads of companies will not allow complete and hostile take overs by AI… Electronic commerce will not beat physical commerce and physical presences completely in all industries. It’s just that there are paradigm shifts, new equilibrium points defined and we have to comply with disruptive change modelling and digital transformations. All the previous bring at the frontiers of the revolution marketing and digital marketing.

10.) Acceptance Vs Reinvention

It’s not what you say it’s how you say it and more specifically the delicate way you will serve it to others. Discovering the how and the mediums to apply STEM are more important than saying your lesson as a university parrot. Meaning, to discover what’s truly important in science and how it’s applied rather than merely applying your classroom contents by-heart.

11.) Nature’s Smarter Than People Think…

We cannot defeat science and science will defeat us more if we override it. Whatever you abandon, it abandons you. Future generations will be heavily science oriented, so it’s important to realize not only the mediums we use but the fact that today’s scientists are a ship that finds no harbor for we are making wrong usage of our tools. We have to thoroughly investigate our previous and current tools before we venture with new ones and more importantly, science is not for the faint at heart. Science is for the brave ones, so make brave choices and let the future evolve at its own…!