LIGO Follow up not looking good

We need to somehow fix the follow up imaging problems and I feel like this place may not be the best place to break into at the moment

Look into what are the main issues that GRANDMA and alike are struggling with

can we not stack a shit ton of data to compensate for lack of brightness?

So the situation is that finding them isn't the biggest problem, if they are not blackholes we usually find them; hell finding them is hard cause of how dim they are. However we can continue to monitor them for lightcurves however we run into 2 problems

Networks struggle post magnitude 18
The Fucking sun

The situation with the LIGO folowups is that we are bascially finding everything but the blackholes?

Equipment Gap	Current Status	Future Fix
Localization	Poor (3 detectors)	LIGO-India (adds 4th baseline)
Scanning	Slow & Shallow	Rubin Observatory (Deep & Fast)
Sensitivity	Nearby Universe only	Einstein Telescope (Whole Universe)
Frequency	High Freq only	LISA (Low Freq / Space-based)

2. The Heavy Hitters (The Robots)¶

We currently rely on a few specific automated facilities that are designed to scan huge swathes of the sky rapidly.

ZTF (Zwicky Transient Facility - California): This is currently the "MVP" of the Northern Hemisphere. It has an incredibly wide field of view (47 square degrees per image). It can scan the entire visible Northern sky in a single night. If a merger happens in the North, ZTF is usually the first to catch it.
ATLAS (Hawaii, Chile, South Africa): A global network designed for asteroid detection, but excellent for this. Because it has units in both hemispheres, it provides "all-sky" coverage, though it is not as deep (sensitive) as ZTF.
BlackGEM (Chile): This is a newer facility specifically built for gravitational waves. It consists of multiple telescopes linked together to instantly cover the specific shape of LIGO's error boxes in the Southern Hemisphere.

https://rubinobservatory.org

I think this kill this

we cannot beat them in brightness however we can infact beat them in terms of coverage and follow up -- what value would a network like this bring for follow ups and

A Light Curve is fundamentally a graph of brightness over time.1

Think of it as a "fever chart" for an explosion. By plotting how bright a dot of light is today, tomorrow, and next week, astronomers can determine exactly what caused the explosion without ever seeing the object in detail.

Here is why this simple graph is the Holy Grail of transient astronomy and why amateur data is often the missing puzzle piece.

1. The Anatomy of a Light Curve¶

A light curve typically has Time on the X-axis (days) and Brightness (Magnitude) on the Y-axis.

The Rise: How fast does it get bright? (Kilonovae shoot up in less than a day; Supernovae take weeks).
The Peak: The maximum brightness.
The Decay (The Fade): This is the most critical part. Does it crash rapidly or fade slowly?
The Color Shift: A light curve isn't just one line. Astronomers plot the curve in Blue filters, Red filters, and Infrared filters simultaneously.

2. The Science is in the "Slope"¶

You might wonder: If we saw the explosion on Day 1, why do we care how bright it is on Day 4?

The rate at which the light fades (the slope of the curve) tells us what heavy metals were created.

Opacity (The Fog): Heavy elements like Lanthanides (Gold, Platinum, Neodymium) are very "opaque"—they trap light efficiently.2
The Result: If a neutron star merger creates a lot of Gold/Platinum, the debris cloud acts like a thick fog. The light struggles to escape, causing the object to fade slowly and turn deep red (infrared).
The Blue vs. Red Kilonova:
- Blue Component: Fast fade (days). Caused by lighter elements.
- Red Component: Slow fade (weeks). Caused by heavy "r-process" elements (the gold stuff).3

If we miss the data points on Day 4, 5, or 6, we might miss the transition from Blue to Red, and we lose the ability to calculate how much gold was created.

3. Why Amateurs Win on Day 4¶

This is where the "Amateur Distributed Network" becomes scientifically lethal.

The Problem with Pros: "Telescope A.D.D."¶

Professional telescopes (like Keck or VLT) are shared by thousands of astronomers.

Day 0-2: The LIGO alert goes off. Every pro telescope slews to the target. We get great data.
Day 3: A new, exciting Gamma Ray Burst happens elsewhere. The big telescopes slew away to the new shiny object.
Day 4: The original Kilonova is now "old news" and slightly dimmer. The big scopes are busy. The data gap begins.

The Amateur Solution: "Staring Contests"¶

Amateurs don't have allocation committees telling them what to do.

Gap Filling: An amateur network can decide: "We will image this target every night for 3 weeks, no matter what."
The Result: You provide the data points that connect Day 2 to Day 7. Without your points, the line on the graph is just a guess.
Global Relay: If the target is fading fast, you need data points every few hours. A single pro telescope in Chile can't see it when the sun rises. But a network (Chile -> Australia -> India -> Canary Islands -> USA) can keep the target in darkness continuously, building a "high cadence" light curve that catches flickering behavior pros would miss.

4. Real-World Example: The "Bump"¶

Sometimes, the light curve doesn't just fade smoothly. It might hit a "bump" or a "plateau" (staying bright for a few days) due to a secondary energy source, like a magnetar spinning inside the debris.

If this bump happens on Day 4, and the pros are looking at something else, only the amateur network will see it. Finding (or not finding) that bump changes our entire understanding of the physics of the collision.

Summary: The Value Proposition¶

Timeline	Who Dominates?	Scientific Goal
T = 0 to 24 Hours	Pros (ZTF, Swift)	Find the location; catch the UV flash.
T = 2 to 10 Days	Amateur / Small Scope Networks	Measure the decay rate. Determine heavy metal composition (Gold/Platinum).
T = 10+ Days	Space Telescopes (Hubble/JWST)	Detect the "remnant" after it becomes too faint for ground scopes.

Would you like me to...¶

Explain Photometry (the technique of converting your CCD image into a specific magnitude number)?
Suggest specific software that amateurs use to upload their measurements to the scientific community (like the AAVSO)?

Is this a valid point -- is this some place where we can work with

Yes, there are groups doing exactly this, but it remains a "patchwork" effort rather than a solved problem. The 2-to-10-day window is currently the battleground for Pro-Am collaborations—networks that mix professional astronomers with serious amateurs to ensure nothing slips through the cracks.

Here is who is currently operating in that specific niche:

1. GRANDMA (The Heavyweight)¶

GRANDMA (Global Rapid Advanced Network Devoted to the Multi-messenger Addicts) is the most prominent network specifically designed for this.

What they do: It is a French-led initiative that coordinates about 30 professional telescopes and over 80 amateur astronomers worldwide.
The Strategy: They treat the network like a single giant instrument. When the "big boys" (like the VLT in Chile) move on to other targets after Day 2, GRANDMA activates its legion of smaller scopes (including amateur 14-inch telescopes) to keep taking pictures every night.
The "Kilonova-Catcher": This is their citizen science portal. If you own a telescope, you can sign up. They send you coordinates, you upload your images, and their algorithms extract the photometry (brightness data) to build that Day 2–10 light curve.

2. AAVSO (American Association of Variable Star Observers)1¶

Historically focused on variable stars, the AAVSO has pivoted hard into "High Energy Network" alerts.

The Shift: They now issue "Alert Notices" for gravitational wave counterparts.
The Role: They have thousands of members with standardized equipment. While they don't have a dedicated "LIGO pipeline" like GRANDMA, they provide the infrastructure for amateurs to submit scientific-grade data. If a bright kilonova (Mag < 18) appears, AAVSO members are often the ones filling in the light curve gaps.

3. GOTO (Gravitational-wave Optical Transient Observer)¶

This is a dedicated professional project designed specifically to stop "telescope A.D.D."

The Gear: Instead of using one big telescope, GOTO uses arrays of smaller, cheaper telescopes (roughly 16 inches—close to high-end amateur size) linked together on a single mount.
Locations: They have nodes in La Palma (Canary Islands) and Australia.2
The Mission: Because their telescopes are "cheap" and dedicated only to this science, they don't have to move on. They can sit on a target for weeks, perfectly filling that 2-to-10-day window that expensive telescopes can't justify.

4. The Unistellar Network (Citizen Science Swarm)¶

This is the newest player, using the "eVscope" (a consumer digital telescope).

The Concept: These are small (4.5 inch) telescopes, but there are thousands of them.
The Method: For a bright target, Unistellar can send a push notification to users' phones. If 50 users in Europe observe the same target for 30 minutes, their data can be "stacked" (combined) to reduce noise, effectively creating a virtual large telescope that can monitor the object for days.

Is it enough?¶

No.

Even with these groups, we still lose data.

Weather: If GRANDMA's few viewers in the Southern Hemisphere are clouded out, the data chain breaks.
Magnitude limits: Most amateur networks struggle past Magnitude 18. If the kilonova is faint (which most are), GRANDMA's amateur wing can't see it, and we are forced to beg for time on scarce professional 2-meter telescopes.

Would you like me to...¶

Show you how to join the GRANDMA/Kilonova-Catcher program?
Explain the difference between "stacking" images (what Unistellar does) vs. single long exposures?

This "Magnitude Wall" is the single biggest physical barrier in Multi-Messenger Astronomy. It is the reason why a global army of amateur astronomers often has to sit on the sidelines while a few overworked professional telescopes scramble to collect data.

Here is the deep dive into why that Magnitude 18 limit exists, why breaking it is so expensive, and why the "2-meter" telescopes are the scarcest commodity in astronomy.

1. The Deception of the Magnitude Scale¶

To understand the struggle, you have to understand the scale. Astronomers use a "reverse logarithmic" scale, which is counter-intuitive.

Small number = Bright. (The Sun is -26).1
Big number = Dim.2 (The limit of the human eye is +6).3
The Trap: Because it is logarithmic, a difference of 5 magnitudes is a factor of 100x in brightness.4

Magnitude	Object Example	Who can see it?
6	Faintest star visible to eye	Humans (Dark Sky)
12	Quasar	Small Amateur Scope (4-inch)
17–18	"The Amateur Wall"	High-End Amateur (14-inch + Camera)
21–22	Typical Kilonova	2-Meter Professional Scope
28	Distant Galaxy	Hubble / James Webb

The Reality: A typical Kilonova might be Magnitude 17 at peak (doable for amateurs) but drops to Magnitude 21 within 3 days. The moment it crosses Mag 18, the amateur network goes blind.

2. Why is Magnitude 18 the "Wall"?¶

Why can't an amateur just leave the camera shutter open longer to see fainter things?

The answer is Signal-to-Noise Ratio (SNR).

The Bucket Problem (Aperture): A telescope is a light bucket.5 A 2-meter professional mirror has roughly 25 times the surface area of a high-end 16-inch (0.4m) amateur mirror. It collects photons 25x faster.
The Sky Noise Problem: The sky is never perfectly black. It glows (light pollution, airglow, zodiacal light).
- If you are an amateur exposing for 10 minutes to catch a faint signal, you are also collecting 10 minutes of "sky glow."
- Eventually, the noise from the sky overwhelms the faint signal from the kilonova. You can't just expose longer; the image just turns gray.
The "2-Meter" Advantage: Because a 2-meter telescope collects "good" light (signal) so much faster than the "bad" light (sky noise) accumulates, it can see faint objects that an amateur scope physically cannot distinguish from the background hiss.

3. The "Begging" for 2-Meter Telescopes¶

You mentioned we have to "beg" for time. This is literal.

In the astronomy world, telescopes are tiered:

Tier 1 (8-meter to 10-meter): The Giants (Keck, VLT, Gemini). There are only about 15 of these on Earth. Getting time on them is extremely difficult. You don't use them for "monitoring" a fading dot; you use them for spectra.
Tier 3 (0.4-meter to 0.8-meter): The Robotic/Amateur class. There are thousands. They are great but blind past Mag 18.
Tier 2 (2-meter to 4-meter): The "Goldilocks" Class.

The Crisis of the Middle Class:

The 2-meter telescopes (like the Liverpool Telescope in La Palma or the various 2.2m scopes in Chile) are the perfect size to monitor a Mag 20 kilonova.

The Problem: There are very few of them operational. Many were built in the 1970s/80s and have been shut down due to budget cuts.
The Queue: When a kilonova happens, the LIGO team has to submit an emergency proposal ("Target of Opportunity") to interrupt the telescope's schedule. They are competing with people studying asteroids, exoplanets, and variable stars who booked that night 6 months ago.

4. Can Amateurs Break the Wall?¶

Technically, yes, but it pushes "amateur" to the extreme.

To reach Magnitude 20 or 21 as an amateur, you need:

Dark Skies: You must be far from cities (Bortle 1 or 2 skies) to lower the sky noise floor.
CMOS Tech: New, cooled Monochrome CMOS cameras (like those used in industry) are very low noise.6
The "Stacking" Strategy: Instead of one long exposure, you take 100 exposures of 2 minutes each and combine them digitally.
- Constraint: This takes hours. To get one data point at Mag 20, an amateur might need to dedicate their entire night to that one dot. Most amateurs prefer to image pretty nebulas rather than stare at a faint gray pixel for 6 hours.

Summary¶

The gap between Mag 18 (Amateur limit) and Mag 22 (Professional limit) is the "Valley of Death" for kilonova science.

It is where the physics of the explosion is most interesting (the creation of heavy metals), but it is exactly where our global telescope network is weakest.

Would you like me to...¶

Explain the "Target of Opportunity" (ToO) process—how a LIGO scientist actually hacks into a telescope schedule in real-time?
Show a comparison of an image stack (1 hour vs 10 hours) to see how faint objects "pop out" of the noise?

wait rather than trying to use the network for ligo; what if we use the network to stop the usage of 2m telescopes for things the network can work for

we can still try to build this as a why the fuck not but eh whatever the fuck

We can focus on [[Exoplanet Follow up]] which will make a big difference in how much 2m telescopes are being used

What is the consumer telescope situation even like at the moment

A. Spectroscopy (The Main Job)¶

This is the #1 reason they are overbooked.

Imaging vs. Spectroscopy: Taking a picture (imaging) is easy. Taking a spectrum (splitting light into a rainbow to see chemical elements) requires spreading the light out, which makes it much dimmer.
The Need: To get a decent spectrum of a standard supernova or variable star, you need a lot of light buckets. An amateur scope is too small to get a clean spectrum of anything faint. The 8-meter scopes are overkill.
The 2m Niche: They are the perfect size to classify thousands of supernovae, asteroids, and active galactic nuclei.

Spectroscopy may be the best part we can hit

This is a fascinating engineering challenge. You are essentially trying to build a distributed "compound eye" telescope.

Here is the breakdown of how 500 lines/mm plays with different sensors, and the reality of your "20-camera galaxy stack" idea.

1. Will 500 lines/mm work for any sensor? (The Heterogeneous Network)¶

No. It will not work for every sensor setup blindly, but it will work for most if you match the Lens to the Sensor Size.

The grating (500 l/mm) bends light at a fixed angle. The physical length of the rainbow that lands on your chip depends entirely on the Focal Length of the lens you put behind it.

The "Crop Factor" Trap:

If you have a heterogeneous network (some cameras are 1/3" sensors, some are tiny 1/4" phone sensors, some are large DSLR sensors), you face a geometry problem.

Scenario A (The Problem): You use a Telephoto Lens (zoomed in) on a Small Sensor.
- Result: The rainbow is stretched out so long that it physically falls off the edge of the chip. You might capture the blue part, but the red part lands on the plastic casing inside the camera.
Scenario B (The Fix): You use a Wide Angle Lens (zoomed out) on a Small Sensor.
- Result: The rainbow is short and compact. It fits on the sensor, but resolution decreases.

The Universal Rule for your Network:

For a 500 lines/mm grating, ensure your Ratio of Focal Length to Sensor Width is roughly 1:1 or less.

Example: If your sensor is 4mm wide (common 1/3" sensor), use a 4mm to 6mm focal length lens. If you use a 50mm lens, you will lose the spectrum.

Tip for your network users: Tell them to use variable zoom lenses (varifocal). This allows them to "zoom out" until the full rainbow fits on their specific sensor.

2. Stacking 20+ Cameras for Galaxy Spectroscopy¶

You asked if pointing 20+ cameras at a faint galaxy and stacking the data would reveal a real spectrum.

The Short Answer:

For Galaxies, this will likely fail.

For Quasars or Supernovae, this is a brilliant idea.

Here is why "Stacking" works differently for Spectroscopy than it does for regular Photography:

A. The "Slitless" Problem (Why Galaxies Fail)¶

Your setup is "Slitless Spectroscopy." You are not using a narrow slit to isolate the object; you are using the whole sky.

Point Sources (Stars/Quasars): A star is a pinprick of light. When diffracted, it creates clean, thin spectral lines.
Extended Sources (Galaxies): A galaxy is a "blob" or a shape. When diffracted, the grating creates a "Green image of the galaxy," slightly offset from a "Red image of the galaxy."
The Result: These overlapping colored blobs smear together. You don't get sharp spectral lines; you get a muddy smear. Stacking 20 images just gives you a brighter muddy smear.

B. The Signal-to-Noise Math (Why Point Sources Work)¶

If you target a point source (like a Quasar or a Supernova in a distant galaxy), the "smearing" problem disappears, and your 20-camera array becomes powerful.

The SNR Boost: Signal-to-Noise Ratio improves by the square root of the number of cameras ($\sqrt{N}$).
- 1 Camera = 1x Signal
- 20 Cameras = $\sqrt{20} \approx 4.5x$ Signal boost.
The Reality: A 4.5x boost is significant! It effectively turns your cheap security sensors into a much higher-end entry-level astronomy camera.

C. The "Alignment from Hell"¶

Ideally, you stack images by aligning the stars. In spectroscopy, you must align the dispersion axis too.

If Camera A has the spectrum running horizontal (0°), and Camera B is slightly tilted (5°), you cannot simply stack them. The spectral lines will crisscross and blur out.

Requirement: Every camera in your network must be rotated so the spectrum runs exactly horizontally across the frame, OR you need complex software to de-rotate the spectral data before stacking.

Summary: Is it "Worth the Science"?¶

Target	20-Camera Stack Feasibility	Scientific Value	Why?
Galaxies	Low	Low	"Slitless" setup smears the data because galaxies are shapes, not points.
Supernovae	High	Very High	They are point sources. You could be the first to classify a supernova using a "swarm" of cheap cameras.
Quasars	Medium	High	Faint point sources. You could measure the redshift (recession velocity) of ancient objects like 3C 273.

Next Step for your Network¶

Since you are building a heterogeneous network, you need a way to standardize the data.

Would you like me to generate a simple "Python Script Logic" that your network users can run? It would take an image of a star, find the zero-order and first-order spectrum, and automatically tell them: "Your calibration is X nm/pixel"? This solves the problem of everyone having different sensors.

Yes, exactly. Supernovae are the ultimate transient events, and this characteristic is precisely what makes your proposed "20-camera swarm" scientifically viable.

Because they appear suddenly, brighten rapidly, and then fade over weeks or months, they solve the two biggest problems you were facing with your network:

1. The "Boring Sky" Problem¶

Professional telescopes (like Hubble or James Webb) are booked years in advance. They cannot just sit around staring at a random patch of sky waiting for something to explode.

Your Advantage: Your network of cheap, always-on security cameras acts as a wide-field dragnet. You are far more likely to catch the first moments of a supernova in a nearby galaxy than a professional observatory is.

2. The "Stacking" Problem¶

Since a supernova is a single exploding star, it appears as a Point Source of light, not a fuzzy blob.

Why this matters: As we discussed, point sources do not smear their spectra. This means your plan to point 20+ cameras at a new supernova and stack the data will actually work. You will get clean, sharp spectral lines.

The "Killer App" for your Network: Supernova Classification¶

If your network detects a new bright point in a galaxy, the scientific community doesn't just need to know "it's there." They need to know what type it is. Your 500 lines/mm setup is perfectly capable of answering this.

You are looking for a specific chemical fingerprint to make the classification:

Type Ia Supernova: You will see a deep absorption line for Silicon (around 615nm) and No Hydrogen.1
- Science Value: These are "standard candles" used to measure the size of the universe.
Type II Supernova: You will see huge, bright emission lines for Hydrogen (H-alpha at 656nm).
- Science Value: This marks the death of a massive star.

How your Network would operate (The Workflow)¶

To make this "worth the science," your network needs to move from passive monitoring to active "swarming."

Detection (The Alarm): One camera in your heterogeneous network spots a "new star" in a known galaxy (M101, M51, etc.).
The Swarm: You alert the other 19 users. Everyone points their security cameras (or amateur telescopes) at that specific coordinate.
The Stack: You combine the noisy, grainy video feeds from all 20 distinct sensors.
The Result: The noise cancels out, the spectral lines for Silicon or Hydrogen pop out, and you publish a "Spectral Classification" telegram to the astronomical community.

Summary¶

You have correctly identified the sweet spot.

Galaxies: Too faint, too fuzzy. (Bad for your setup).
Supernovae: Bright, point-source, and urgent. (Perfect for your setup).

Next Step:

To prepare for this, you need a reference "Library" so your software knows what it's looking for. Would you like a list of the 3 most common bright stars to use as "Practice Supernovae"? (These stars have spectra that mimic Type I and Type II supernovae, allowing your users to practice stacking and processing before a real event happens).

[[hetrogenous spectra stacking]]