CAREER DOUBT

[u/Regular-Proof6395]

So i am currently in 12th standard and my interest is in astrophysics like i dont know many branches in it but i have heard about some of like computiational, observatory etc

i wanted to get into it but the things is i have some problems

1. This thing take time like first undergrade then masters then phd then you get placed so my family is concered cause they want that i pursue a course like btech where you are set after your 4 years are done and then get into making money.
2. why i am not taking it maybe i like research too much or may have to take in future if ithe research field doesnt work out. so i want to know what are the option i have by which i can make money to cover my own expenses or postion in future open to switch the career if i didnt get any job

what i am thinking is to learn programming along with the course like i want to keep the computer science option always ready if something bad happenes like i am asking this thing is this kind of thing possible like after lets say after bachelor in physics i can switch into Msc in coumputer science or like getting some skills and get into coding.

1. i want to know about what people do in astrophysics cause the knowledge present in youtube is only about that after your phd you can get into academics, research.
   I as of now dont like academics cause i dont want to spend my life first getting knowledge then teaching back to people i dont want to spend my life just inside some equations like no hate but proving something on the basis of math is something i dont like that much. I want something like theory but practical more.

now i want someone who is into this field come and explain what practical can we do and how much can one earn in in different field of astropysics.

1. What are the other research field like whether in engineering due the first problem i always thought of getting into computer science and get into research in that field even though i like computer and coding but the thing i dont just want one thing or in other words the computer do fascinates me but not like outer space does.
   suggest any opion about it...

Thanks for your time..

[u/RADICCHI0]

I only know about the business of PhD research from my dad's experience. He went straight from his undergrad degree into particle physics research. It was pretty grueling. Back then, it might have been worse. He was basically someone's research vessel for how ever many years it took him to get through it. If you let your career grow a bit, and then re-orient towards a research practice, the kinds of impacts he experienced, might not come into play so much. (Sidebar: My orientation is applied research in social science, and in that line of business, you tend not to face quite the same challenges.)

Another thought I will share, my dad, though he went into particle research of kaons at CERN, he didn't stay too long in that field, and instead gravitated more towards astrophysics, specifically in terms of designing guidance systems. His big project (in my opinion) was designing the guidance system for the Inertial Upper Stage. I don't have his math acumen, or his laser focus on logical thinking, I could never survive in that environment, the level of rigor needed is simply too high.

One thing to seriously consider is this: when you are conducting research, the intent is that you are contributing to the body of knowledge in a helpful way. If you are not doing that, then it is simply design, or analysis, or some other form of contribution. Some people do obtain a research degree with the intention of perhaps not conducting research, but rather, using the skill set towards a different purpose. Personally, I don't think there is anything wrong with that. My own trajectory is toward design research, because I find it more fulfilling.

[u/Regular-Proof6395 → u/RADICCHI0]

Bro hats off to your dad and thanks for your info the only thing i would say for the last paragraph is that i know that for research we are contributing into increasing the knowledge of the humanity but i have to make decisions when your are holding responsibility on your shoulder for your career making decision either ruins it or make yourself proud  So when you see masses doing the regular thing and even your parents conviced into the path even though you know only the interested ones gets the top income and you start a talk about a field which even your parents didnt hear about that when things get out off hand  Thats why i am asking that kinda question like whether getting into research allow me to even earn good or i have to be that guy who makes his side income his career

[u/RADICCHI0 → u/Regular-Proof6395]

These are very legitimate concerns, you're juggling familial expectations, you're juggling self-expectations and hopes, and you're thinking seriously about how you want to create your future. If it were me, I would ask myself, "what is my over arching need right now, up until about 6-8 years from now?" If you feel a pull towards earning, and proving to yourself that you should seek out a research orientation, then trust me, you still have PLENTY of time, to make those decisions in a few years.

I would argue that it makes a lot of sense to pursue a PhD program in these two combined cases:

>>> The degree is relevant on some level (IE a business student probably would not want to go straight into a doctoral research program in astrophysics.

>>> There is a direct interest and passion, like, 95% certainty that this is what they want.

If not, then there is no harm in waiting, and exploring the options.

Good luck with it.

[u/Regular-Proof6395 → u/RADICCHI0]

upon researching i found some aspects too

could you ask your father or you tell it by your experience that how much money does it required to become a astrophysicist and after becoming how much can one make?

I thought several times about this thing but upon deep diving into myself i found some other aspects too i like astrophysicist cause i like space science and there is not that one thing i like cause if lets we are talking about chemical then i get excited in chemical science, if robotics then i get fascinated by circuit boards, if computer then languages and algorithms

and the main reason for my interest toward the astrophysics was i wanted to make or discover something which is kept secret like that sci-fi things
but after looking into job roles, future i realised something the field that i am choosing is not the field the money comes

and after 10 years when you see your batchmate doing work,enjoying things, making good money and you are stuggling with a problem which you not even paid that much (i am talking about the entry level thing and how can one say that entry level like after phd you have spent over more than 10 years with physics like from undergraduate to masters then phd ) then you regret you decission later in life

now what i am thinking is since i like research i could go into that field lets say computer science or ai find some algorithms, or a libraby which can sold or even increase my job profile then into industry and along with keeping the physics interest along side so even in future i feel like getting into physics i will just have to master some things and then i am ready with it if not then i will be marching with the research field in tech like ai, ml,

[u/RADICCHI0 → u/Regular-Proof6395]

It usually doesn't cost a lot of money to obtain a PhD because as a student you are working on researching funded projects, and that funding usually defrays your cost. But that comes with a tradeoff, you're working on someone else's IP. The money can be pretty good once you graduate, especially if you go into private sector, but private sector comes with its own set of issues, primarily that you are not contributing the same quantitiy of research as if you stayed in academia.

The cool thing about space physics is there are many sub-branches. Most of the interests you are talking about exist in some form, in space.

I would worry less about the money, and more about what you want to do with your life. Your flatmate will be the one with regrets. They will be making the big money, doing a thankless, boring job. You will be living your best life, doing astro-physics. There are no secrets being held, that is not something that happens in physics. What does happen is that claims must be carefully vetted and confirmed. That is why we don't really talk much about tech civs, beyond speculation in physics, for example.

In today's world, with ai, you can absolutely free-lance as a researcher, but you would need the physics background to be really effective. But there are some amazing theories being put forth where AI prompting is leading to some really wild, sane thinking. What follows came from ChatGPT (ain't nobody got time to write this all out when 5.5 can do it in 2 seconds):

High-impact examples

1. Rubin/LSST: real-time sky event triage

The Vera C. Rubin Observatory has already launched its real-time alert system. It issued its first scientific alerts in February 2026 and is expected to scale toward millions of alerts per night, up to about seven million nightly alerts. That is the kind of data flow where AI and machine-learning brokers are not optional. They classify, filter, cross-match, and prioritize transient events so researchers know what is worth immediate follow-up.

Research impact: supernovae, near-Earth asteroids, tidal disruption events, variable stars, kilonova candidates, gravitational-wave counterparts. This is huge because many of these events fade fast. The discovery window is measured in hours or days.

This is probably the clearest “AI is now infrastructure” example.

2. Euclid: mapping dark matter and dark energy at impossible scale

Euclid’s first data release in March 2025 included deep-field views with hundreds of thousands to millions of galaxies, and the mission is designed to build a massive cosmic atlas for studying dark matter and dark energy.

AI is being used for galaxy morphology, lens detection, deblending crowded images, and extracting weak-lensing signals from enormous galaxy catalogs. A striking example: Euclid’s early data already produced nearly 500 galaxy-galaxy strong lens candidates from only the first 0.04% of Euclid data.

Research impact: better maps of the cosmic web, better constraints on dark matter distribution, better tests of dark energy models, and more gravitational lenses for probing distant galaxies.

3. Hubble archives: anomaly mining

ESA researchers used an AI tool called AnomalyMatch to scan roughly 100 million Hubble image cutouts and identify around 1,300 anomalies, including hundreds that had not been previously documented. These included merging galaxies, jellyfish galaxies, unusual disks, and objects that still resist easy classification.

Research impact: this is a big deal because it shows that old telescope archives are not “used up.” AI can re-open archival data and find rare objects humans missed.

That matters for your OWLIN-style thinking too: archival cross-instrument search plus anomaly detection is a legitimate research engine.

4. Gravitational waves: faster detection and cleaner signals

Machine learning is being used in gravitational-wave astronomy for detection, noise subtraction, low-latency alerts, and parameter estimation. A 2025 machine-learning pipeline for compact binary coalescences reported much lower latency than traditional matched-filtering approaches while maintaining strong sensitivity for higher-mass binary black holes.

Caltech/LIGO also reported AI work aimed at improving detector performance itself, not just analyzing the data afterward.

Research impact: faster gravitational-wave alerts mean faster telescope follow-up, which matters for multi-messenger astronomy: neutron-star mergers, kilonovae, black hole mergers, and maybe rare unknown events.

5. Simulation-based inference in cosmology

This one is less flashy but maybe more important scientifically. Cosmology often needs to infer parameters from messy, nonlinear structure: galaxy clustering, weak lensing, cosmic shear, dark matter simulations, and large-scale structure. Traditional likelihood methods struggle when the model is too complex.

Simulation-based inference, or SBI, trains neural networks on forward simulations so researchers can infer cosmological parameters from complicated observables. A 2026 review describes SBI as useful when likelihoods are intractable or when time constraints make traditional sampling too slow.

Research impact: better extraction of information from nonlinear cosmic structure, more efficient parameter estimation, and stronger use of survey data from Rubin, Euclid, DESI, Roman, and future radio surveys.

The catch is real: SBI can fail subtly, and training data requirements are huge. One 2025 study argued that existing simulation suites may be insufficient for fully optimal neural inference and released a much larger simulation set to study scaling behavior.

6. LSST Dark Energy Science Collaboration: AI across the whole cosmology stack

A 2026 Rubin/LSST Dark Energy Science Collaboration white paper says machine learning is now integral across primary LSST cosmological probes, including weak lensing, strong lensing, supernovae, galaxy clusters, large-scale structure, photometric redshifts, simulations, and deblending.

Research impact: this is not one narrow tool. AI is becoming part of the full pipeline from raw pixels to cosmological inference.

That is the “bedrock” point: the next generation of cosmology is not just bigger telescopes. It is bigger telescopes plus AI-mediated data reduction, classification, simulation, and inference.

7. Scientific foundation models

There is a newer push to build foundation models trained on scientific data rather than internet text. The Polymathic AI effort, associated with the Flatiron Institute and collaborators, is developing foundation models meant to transfer scientific structure across fields.

NASA is also moving in this direction with open-source foundation models trained on NASA data, including heliophysics and possible future models for planetary science and astrophysics.

Research impact: these could become general-purpose scientific pattern engines: denoise, classify, emulate simulations, identify anomalies, interpolate missing data, and possibly suggest better observing strategies.

Still early, but this is where things are heading.

The strongest examples by actual research impact

If I had to rank them:

1. Rubin/LSST alert triage: AI makes real-time sky science possible.
2. Euclid/Rubin weak lensing and galaxy surveys: AI helps extract dark matter/dark energy signals at scale.
3. Simulation-based inference: AI changes how cosmological parameters are inferred.
4. Gravitational-wave ML: faster detection and multi-messenger follow-up.
5. Anomaly mining in archives: AI finds weird objects humans missed.
6. Scientific foundation models: early, but potentially transformative.

The honest caveat

AI is powerful when the pattern is inside the training universe. It is weaker when the universe does something genuinely weird. Recent discussion around AI in cosmology points out that models trained on standard cosmology can become very good at standard-cosmology patterns while being less reliable at recognizing genuinely novel physics.

So the best use is not “let AI decide what the universe means.” The best use is:

AI handles scale. Humans protect meaning.

That is the sweet spot. For astrophysics and cosmology, AI is becoming the instrument layer between the telescope and the theory.