This Is What My Revision Wall Looked Like This Time Last Year. So Gutted I Got Rid Of These Note Cards.

here is a list of questions i have already answered about graduate school!! 

please check it before you send me a question about graduate school :) :) i hope it’s useful! xo // updated 08.2019

basic info

what is the PhD and what can you do with it? (+) 

what does a literature PhD entail?

should i do a PhD if i have to pay tuition?

does it look bad to take time off between degrees?

what was your timeline like? 

what’s the difference between a terminal MA and a PhD? (+)

does getting an MA first help you get into a PhD program?

application process

how can i prepare for applying early in my undergrad career? (+) (+)

where should i start looking for programs?

should i choose a program based on rank or fit? (+)

how many programs should i apply to?

parts of the application

advice on the writing sample

advice on the GRE (+)

how should i ask for letters of recommendation?

how should i write a statement of purpose? (+)

how do i demonstrate my “ability to excel”?

how should i address mental health/family/personal issues that impacted my grades?

grad school application spreadsheet

how should i email potential advisors? (+)

how can i survive the waiting period? (+)

how should i prepare for an interview or phone call? (+)

what should i ask at open house?

what should i do if i don’t think i can afford my grad school tuition?

what should i do the summer before i start my program?

seminars/coursework

how should i plan for grad seminar presentations? (+)

what should i bring with me to seminars?

what are grad seminars like?

how can i get better at speaking during seminars?

reading

what should i have read before i start my lit grad program?

how much reading should i expect?

how can i read a lot without getting overwhelmed? (+)

how can i read efficiently? (+) (+)

quals-specific reading advice

how should i take notes on critical articles?

writing

how do i write a lit review?

how do i write an indicative bibliography?

how do i choose a dissertation topic? (+)

how do i plan for a long research paper?

how do i balance all the different kinds of writing i have to do?

money

how do finances work in grad school?

what is adjuncting and why does it suck?

how can i budget while on a stipend?

should i work while in grad school? (+)

fellowship, postdocs, & job stuff

how do i think up a second project when i’m not even done with my dissertation?

CV writing tips

which websites post US fellowship/postdoc/job ads? 

how do dissertation fellowships work? 

tips for grant, award, & fellowship applications

misc

will grad school make my mental health issues worse? (+) 

how do i survive conferences?

how should i deal with burnout? (+)

i think i want to quit

my advisor is ghosting me

how do i work with no structured schedule?

how do i get enough sleep?

how do i balance my work & my teaching?

how can i beat imposter syndrome? (+)

how can i excel in grad school?

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We actually have around 20 senses (depending on how you count) but this infographic outlines the 5 main ones; hearing, seeing, tasting, smelling, and touch. More like this on To Be A Genius.

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When algorithms surprise us

Machine learning algorithms are not like other computer programs. In the usual sort of programming, a human programmer tells the computer exactly what to do. In machine learning, the human programmer merely gives the algorithm the problem to be solved, and through trial-and-error the algorithm has to figure out how to solve it.

This often works really well - machine learning algorithms are widely used for facial recognition, language translation, financial modeling, image recognition, and ad delivery. If you’ve been online today, you’ve probably interacted with a machine learning algorithm.

But it doesn’t always work well. Sometimes the programmer will think the algorithm is doing really well, only to look closer and discover it’s solved an entirely different problem from the one the programmer intended. For example, I looked earlier at an image recognition algorithm that was supposed to recognize sheep but learned to recognize grass instead, and kept labeling empty green fields as containing sheep.

When machine learning algorithms solve problems in unexpected ways, programmers find them, okay yes, annoying sometimes, but often purely delightful.

So delightful, in fact, that in 2018 a group of researchers wrote a fascinating paper that collected dozens of anecdotes that “elicited surprise and wonder from the researchers studying them”. The paper is well worth reading, as are the original references, but here are several of my favorite examples.

Bending the rules to win

First, there’s a long tradition of using simulated creatures to study how different forms of locomotion might have evolved, or to come up with new ways for robots to walk.

Why walk when you can flop? In one example, a simulated robot was supposed to evolve to travel as quickly as possible. But rather than evolve legs, it simply assembled itself into a tall tower, then fell over. Some of these robots even learned to turn their falling motion into a somersault, adding extra distance.

[Image: Robot is simply a tower that falls over.]

Why jump when you can can-can? Another set of simulated robots were supposed to evolve into a form that could jump. But the programmer had originally defined jumping height as the height of the tallest block so - once again - the robots evolved to be very tall. The programmer tried to solve this by defining jumping height as the height of the block that was originally the *lowest*. In response, the robot developed a long skinny leg that it could kick high into the air in a sort of robot can-can. 

[Image: Tall robot flinging a leg into the air instead of jumping]

Hacking the Matrix for superpowers

Potential energy is not the only energy source these simulated robots learned to exploit. It turns out that, like in real life, if an energy source is available, something will evolve to use it.

Floating-point rounding errors as an energy source: In one simulation, robots learned that small rounding errors in the math that calculated forces meant that they got a tiny bit of extra energy with motion. They learned to twitch rapidly, generating lots of free energy that they could harness. The programmer noticed the problem when the robots started swimming extraordinarily fast.

Harvesting energy from crashing into the floor: Another simulation had some problems with its collision detection math that robots learned to use. If they managed to glitch themselves into the floor (they first learned to manipulate time to make this possible), the collision detection would realize they weren’t supposed to be in the floor and would shoot them upward. The robots learned to vibrate rapidly against the floor, colliding repeatedly with it to generate extra energy.

[Image: robot moving by vibrating into the floor]

Clap to fly: In another simulation, jumping bots learned to harness a different collision-detection bug that would propel them high into the air every time they crashed two of their own body parts together. Commercial flight would look a lot different if this worked in real life.

Discovering secret moves: Computer game-playing algorithms are really good at discovering the kind of Matrix glitches that humans usually learn to exploit for speed-running. An algorithm playing the old Atari game Q*bert discovered a previously-unknown bug where it could perform a very specific series of moves at the end of one level and instead of moving to the next level, all the platforms would begin blinking rapidly and the player would start accumulating huge numbers of points. 

A Doom-playing algorithm also figured out a special combination of movements that would stop enemies from firing fireballs - but it only works in the algorithm’s hallucinated dream-version of Doom. Delightfully, you can play the dream-version here

[Image: Q*bert player is accumulating a suspicious number of points, considering that it’s not doing much of anything]

Shooting the moon: In one of the more chilling examples, there was an algorithm that was supposed to figure out how to apply a minimum force to a plane landing on an aircraft carrier. Instead, it discovered that if it applied a *huge* force, it would overflow the program’s memory and would register instead as a very *small* force. The pilot would die but, hey, perfect score.

Destructive problem-solving

Something as apparently benign as a list-sorting algorithm could also solve problems in rather innocently sinister ways.

Well, it’s not unsorted: For example, there was an algorithm that was supposed to sort a list of numbers. Instead, it learned to delete the list, so that it was no longer technically unsorted.

Solving the Kobayashi Maru test: Another algorithm was supposed to minimize the difference between its own answers and the correct answers. It found where the answers were stored and deleted them, so it would get a perfect score.

How to win at tic-tac-toe: In another beautiful example, in 1997 some programmers built algorithms that could play tic-tac-toe remotely against each other on an infinitely large board. One programmer, rather than designing their algorithm’s strategy, let it evolve its own approach. Surprisingly, the algorithm suddenly began winning all its games. It turned out that the algorithm’s strategy was to place its move very, very far away, so that when its opponent’s computer tried to simulate the new greatly-expanded board, the huge gameboard would cause it to run out of memory and crash, forfeiting the game.

In conclusion

When machine learning solves problems, it can come up with solutions that range from clever to downright uncanny. 

Biological evolution works this way, too - as any biologist will tell you, living organisms find the strangest solutions to problems, and the strangest energy sources to exploit. Sometimes I think the surest sign that we’re not living in a computer simulation is that if we were, some microbe would have learned to exploit its flaws.

So as programmers we have to be very very careful that our algorithms are solving the problems that we meant for them to solve, not exploiting shortcuts. If there’s another, easier route toward solving a given problem, machine learning will likely find it. 

Fortunately for us, “kill all humans” is really really hard. If “bake an unbelievably delicious cake” also solves the problem and is easier than “kill all humans”, then machine learning will go with cake.

Mailing list plug

If you enter your email, there will be cake!

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2017 US Medical School Application Timeline

Hope you all find this helpful! Good luck chickadees! My inbox is always open if you have questions!

Download my printable 2017 application cycle checklist here

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I’m going to uni this autumn, and I feel like most of studyblr’s incoming freshmen are just as clueless as I am. Here’s a bunch of tips from the more experienced among us, and I hope it’s just as useful to you as it is to me!

this took forever to make so i’m really hoping it’s good

Money Matters

Textbooks

Sites where you can get free textbooks by @thearialligraphyproject

Get textbooks online

Tips for textbooks by @theorganizedcoyote

Websites to get cheap textbooks by @theorganisedstudent

Ultimate guide to buying college textbooks

Safe ways to get free textbooks

Saving Money

A girl called jack: eating under the line

Qriket

Scholarship masterpost by @wallcalendar

Save money while shopping online

College scholarships 2016-17 by @wonderstudying

Tips for finding & getting scholarships

How to budget

Where to find student discounts by @collegerefs

Searching for scholarships by @collegesmarts

Creative ways to save money in college

Places that offer discounts with student id

Ways to save/earn money

Paying for college by @collegerefs

Student Life

Dorm & Living

PSA for college freshmen

Dorm tips from @humanitaes

Ultimate school locker (uni bag) kit by @girl-studying

Resolving issues with roommates

Tips for living like an adult

How to eat healthy in dining halls

DIY dorm decor by @notquitenightingale

Everything I actually used in my freshman dorm room

Guide to living alone by @piratestudy

Living with a roommate by @collegerefs

Packing Lists

Thing I forgot to bring to college by @myberkeleyadventure

Sam’s ultimate failproof guide to packing by @staticsandstationery

Ultimate college packing list for freshmen

College packing list by @kimberlystudies

What to bring to college

Checklist for dorms & campus living

Packing up: preparing for college in @theacademiczine

College dorm packing list by @produitivity

Recipes

One-pot chicken fajita pasta

100 dirt cheap recipes for students

57 go-to recipes for college students

Cheap & healthy recipes 

Over 400 fast & healthy recipes

Classes

Studying & Taking Notes

Organizing notes with Google Docs by @academla

How to write a lecture summary efficiently by @collegerefs

Symbols & abbreviations for note-taking 

How to take lecture notes by @hstrystdyblr

How to take notes in college by @determinationandcaffeine

Getting the most out of lecture by @strive-for-da-best

How to get your best grades in college by @saralearnswell

If you have a bad college professor

Essays

Transition words for essays by @soniastudyblr

How to analyze historical sources by @rewritign

How to write a university level essay by @healthyeyes

Analyzing a written text

Essay writing: university vs. high school

How to write a history paper by @thehistorygrad

How I plan and write literature papers by @notaperfectstudent

Exams

A quick guide to finals by @emmastudies​

10 revision tips for final & first year exams

High school exams vs. college exams

Crucial study tips for finals week in college 

3 day study plan by @getstudyblr

Low stress college study strategy by @plannerdy 

+ More

Masterposts

A college student’s masterpost by @eruditekid

Random college tips by @determinationandcaffeine

College advice by @studenting

Giant college masterpost by @heyiwantyoutostay

Advice

Advice for college by @collegerefs

10 tips for starting uni by @studycubs

Advice from a college senior 

Great tips from @fuckstudy

10 more excellent tips

Things nobody tells you about university by @polcry 

Miscellaneous

8 things successful students do by @frankfurter-studies

Email tips by @haileymostudies

@collegerefs‘ entire blog– so much good stuff that’s incredibly useful

my grace.uni tag– all the posts I’ve saved for university

Staying in contact with high school friends

How to make friends in college

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Python as a first language: a roadmap to getting started.

I personally prefer Java as a first language. Put non-technically, it is a lot less convenient, so you get a more realistic idea of how computers work. Nonetheless, Python is an amazing language (with convenience as one of it’s values) so it makes sense as a gentle introduction. It’s also a popular language for data science and machine learning, so it’s great to have experience with.

The 3 ingredients you need to get started:

The Python Language Interpreter: when you write some code in a text file and save it as a .py file, the Python interpreter is what turns that code into commands that your computer can then actually preform. This is necessary.

An Integrated Development Environment (IDE): An IDE is like a helpful text editor for programming. Some basic features include auto-complete, typo and mistake catching, and automatic text coloring to make some parts of your code easier to find. This is optional but highly recommended.

Some learning resources: We’re going to need something about programming basics, problem solving in computer science, using an API, learning how to use google and stackexchange, data types, control structures, and then maybe an object-oriented programming intro, and eventually all the neat advanced features of the python language. Then we need to learn how to use Numpy (for scientific computing), Pandas (for easy data storage), and Tensorflow (machine learning!). Add some handy cheat-sheets for python, numpy, pandas, and Tensorflow, and we’re good to go.

Other posts will adress download, installation, and resources.

A curriculum:

Like I said up above, we need to know how to do the following. Save this and make it a checklist.

Learn to use google to answer questions about installing or using python, any packages, or computer science.

This also includes getting to know how to search Stackexchange, the website for coding questions n’ stuff.

How to install python 3 and get set up

How to install an IDE like Eclipse (with PyDev), IDLE, or Notebook++.

Programming basics: how does python work? What does the language look like? How does tabbing work?

Understand basic logic, including AND, OR, XOR, NOR, NAND, XNOR, Implies, and If…Else statements.

Variables: what are they, how do I set one and change it?

Basic math in Python.

Data types: what kinds of variables can I have? How does my computer store data? How do I use those types of data? What are the key commands and operations I know how to do?

Control structures: if, else, elif, for loops, while loops, break, continue

Methods! What are they, how do I make one, what can I do with it?

The open() command, the all() command, other neat built-in methods

<function name>= lambda <your variables>: <single line method>

Problem solving in computer science: now do fizbuzz.

What’s a package?

Importing packages, installing packages you don’t have with PIP

Using an API: how do I find one and how do I read it?

object-oriented programming in Python: what’s a class, how do I make one, how do I reference and instantiate one, methods, class vars, etc

Error handling: how to do exceptions

All the neat advanced features of the python language: iterators, generators, list comprehensions, enumerate, range, assert, with…as, etc.

Read through the Numpy API (for scientific computing), data types, matrices, stats, methods, etc. A short detour through scikit would be helpful.

Read through Matplotlib.pyplot API, plotting, plotting options, histograms, scatterplots, etc.

Pandas (for easy data storage), data frames, series, built-in operations on columns and rows, loading from a CSV, saving as a csv, apply, etc

Tensorflow (machine learning!) For basic stuff, shoot for knowing how to use the estimator package, which is discussed elsewhere on this blog. Also get to know the nitty gritty, including tensors, layers, tensorboard, etc.

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Based on this post 

“Failure is the key to success" 

We’ve all heard motivational quotes similar to the above. While they serve as an important reminder on our rough days, sometimes we need something more than hugs and well wishes to get us through. 

 You’ve failed. What next? 

Let it out: Step back. Take a few days off. Shower, catch up on that sleep debt. Remind yourself of who you are outside your academic study. Catch up with a friend for coffee (or just get some coffee). Watch a movie, marathon a tv series, built a pillow fort, tumblr, youtube spiral - anything. Because your academic life is only one piece (of varying size) of a larger puzzle. Remind yourself of who you are outside of university/ school. 

Formal/ Informal Avenues of Review : Check your school/university’s academic policies. Are you eligible to resit the exam? Were there any extenuating circumstances that affected your performance (e.g. medical, emergency etc)? Are you eligible to seek an adjustment to your marks? If you’re eligible, what documentation will you need to provide? Which staff member can you contact to follow up your application? If you’re not eligible, are there any other members of staff or advisor (e.g. course advisors, councillors, student leaders) you can discuss your circumstances with? 

Reset, Recuperate, Retake:  It is likely that you will need to repeat or retake the class/ assessment/ unit. That’s ok. Seek feedback from your lecturer/ tutor or teacher. Consolidate your strong areas. Focus on the areas of the course you find challenging. Use the feedback given to target your study time! 

Same Shit, Different Day: While the content may be the same, using a different method to study may be more effective ( see below) 

Same content? Change it up!

flashcards by @illolita​, 

colour coding 

audio recordings by @riseandstudy​, 

mindmaps  by @reviseordie​

sticky notes  @etudiance​ 

Change your study habits by @whilwheaton​ 

Practice past papers.  (see below). 

Something to help you get back up on your feet 

Videos and speeches to help you push through 

On being wrong 

On regret 

On making difficult choices 

On what the fuck to do with your life 

On "falling behind”   

On rushing things 

On success  

On motivation - for recent graduates 

On perspective by @fishingboatproceeds​

 Just do it 

Find songs to cry and scream to 

 8tracks and this spotify playlist 

Classical music for any mood  by @violaboss​ 

Find something on the Nostalgia Machine  

Infinite Jukebox 

Make your own music

IDGAF by Watsky “Let me tell you about my GPA, four-O, straight A’s and my- idgaf!” 

 Something to help you fucking laugh or smile 

 Honest Trailers 

Dance Mashup 

Thomas Sanders @thatsthat24​ 

This kid and this kid 

Goat Remixes 

This vine 

Need more?

Cute flash games (+amazing background music) 

Motivation wall by @study-ings 

Mug Cake by @sortedfood 

Calming Manatee 

Ultimate motivation song 

SCREAM INTO THE VOID (personal favourite) 

 Things to remember 

 Oi, have you ever failed anything? 

 Yes. See here 

Graduation is a journey. 

Yes, studyblr makes studying look glamourous, neat, aesthetic, "oh, look how these _____ notes helped me get an A+.” But writing notes, having fancy pens, using apps (whilst useful) only form one part of a larger narrative. 

The reality is much less appealing. The road to graduation is not a fairytale. The road to graduation is fucking tough. It may involve tears, frustration, all nighters, crippling anxiety and unhealthy levels of caffeine consumption. One set back does not knock you off the path to graduation. Setbacks are arguably a part of the journey. 

 Sometimes, hard work and effort will not translate into results. 

 There’s a special brand of frustration that comes with dedicating the past month/week/ year to a particular project and not achieving your desired result/ grade. It stings like a bitch and is often accompanied by a sense of doubt. 

Check that you’ve followed your syllabus. Accept that there may have been factors outside of your control. Revisit the process - what did you enjoy? What did you learn from the mechanics of the process? 

The value of hard work does not necessarily lie in the result attained; nor is the result attained a reflection of the worth of your efforts. 

 Experience is an advantage 

Retaking the test/assessment/ unit can be an advantage. You know what questions to expect. More importantly, you know how you react under exam conditions. Prepare. Plan your exam time. Will you start the exam paper from the beginning or the end and work backwards? 

Use this experience in those pesky interview questions! (e.g. the good ol’ “tell me about a time you failed”) 

Perfection is not a prerequisite to success 

 Ah, my pesky perfectionist tendencies. At times, I must remind myself that I don’t need to be perfect in order to reach my goal. Not having the latest app, 10 different coloured pens, that fancy notebook, the newest edition of the textbook does not mean I’ve automatically failed. I just have to be more resourceful. 

Just start. Work with what you have, to the best of your ability. Yes it may be difficult; you might need to access extra resources online, or find material from other sources. Your notes may all be in the same colour, or spread over three different notebooks. But success will not be an option unless you start. 

 Other useful tips 

On bad semesters by @post–grad 

 This masterpost by @areistotle 

Reducing stress by @mindpalacestudy 

 How to fail by @psychstudyblr 

 A final note… 

You’ve probably been through hell to get to where you are. Celebrate your achievements - don’t diminish them. Build a support network (heck, at the very least make a youtube playlist of funny videos). You’ve passed an exam before. You’ve endured X number of years of formal academic education. Sure, each exam is slightly different and each comes with its customised brand of torture. No two exams are exactly the same. But have confidence in yourself - you’ve picked up some transferrable skills along the way - determination, resilience, the endurance to pull all nighters.

Whether you’ve been a straight A student up to this point, a masters student or top of the academic pyramid, failure is going to be one of those annoying things you’ll eventually face. 

What you do with it? 

Well, that’s where shit gets interesting. 

All the best, 

-fuckstudy 

Masterposts are posted every other Monday (asia pacific)/ Sunday (everywhere else). See previous masterposts here. Feel free to request topics here. 

previous topic: law 101 - how to read case law

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This makes me sound stupid but what does a feynman diagram mean?

You don’t sound stupid! They can be pretty confusing at first, and I’m sure you’re not they only one that doesn’t fully understand them (myself included) so let’s learn how to draw Feynman diagrams!

You do not need to know any fancy-schmancy math or physics to do this!

I know a lot of people are intimidated by physics: don’t be! Today there will be no equations, just non-threatening squiggly lines. Even school children can learn how to draw Feynman diagrams. Particle physics: fun for the whole family.

For now, think of this as a game. You’ll need a piece of paper and a pen/pencil. The rules are as follows (read these carefully):

1. You can draw two kinds of lines, a straight line with an arrow or a wiggly line:

You can draw these pointing in any direction.

2. You may only connect these lines if you have two lines with arrows meeting a single wiggly line.

Note that the orientation of the arrows is important! You must have exactly one arrow going into the vertex and exactly one arrow coming out.

3. Your diagram should only contain connected pieces. That is every line must connect to at least one vertex. There shouldn’t be any disconnected part of the diagram.

In the image above, the diagram on the left is allowed while the one on the right is not since the top and bottom parts don’t connect.

4. What’s really important are the endpoints of each line, so we can get rid of excess curves. You should treat each line as a shoelace and pull each line taut to make them nice and neat. They should be as straight as possible. (But the wiggly line stays wiggly!)

That’s it! Those are the rules of the game. Any diagram you can draw that passes these rules is a valid Feynman diagram. We will call this game QED. Take some time now to draw a few diagrams. Beware of a few common pitfalls of diagrams that do not work (can you see why?):

After a while, you might notice a few patterns emerging. For example, you could count the number of external lines (one free end) versus the number of internal lines (both ends attached to a vertex).

How are the number of external lines related to the number of internal lines and vertices?

If I tell you the number of external lines with arrows point inward, can you tell me the number of external lines with arrows pointing outward? Does a similar relation hole for the number of external wiggly lines?

If you keep following the arrowed lines, is it possible to end on some internal vertex?

Did you consider diagrams that contain closed loops? If not, do your answers to the above two questions change?

I won’t answer these questions for you, at least not in this post. Take some time to really play with these diagrams. There’s a lot of intuition you can develop with this “QED” game. After a while, you’ll have a pleasantly silly-looking piece of paper and you’ll be ready to move on to the next discussion:

What does it all mean?

Now we get to some physics. Each line in rule (1) is called a particle. (Aha!) The vertex in rule (2) is called an interaction. The rules above are an outline for a theory of particles and their interactions. We called it QED, which is short for quantum electrodynamics. The lines with arrows are matter particles (“fermions”). The wiggly line is a force particle (“boson”) which, in this case, mediates electromagnetic interactions: it is the photon.

The diagrams tell a story about how a set of particles interact. We read the diagrams from left to right, so if you have up-and-down lines you should shift them a little so they slant in either direction. This left-to-right reading is important since it determines our interpretation of the diagrams. Matter particles with arrows pointing from left to right are electrons. Matter particles with arrows pointing in the other direction are positrons (antimatter!). In fact, you can think about the arrow as pointing in the direction of the flow of electric charge. As a summary, we our particle content is:

(e+ is a positron, e- is an electron, and the gamma is a photon… think of a gamma ray.)

From this we can make a few important remarks:

The interaction with a photon shown above secretly includes information about the conservation of electric charge: for every arrow coming in, there must be an arrow coming out.

But wait: we can also rotate the interaction so that it tells a different story. Here are a few examples of the different ways one can interpret the single interaction (reading from left to right):

These are to be interpreted as: (1) an electron emits a photon and keeps going, (2) a positron absorbs a photon and keeps going, (3) an electron and positron annihilate into a photon, (4) a photon spontaneously “pair produces” an electron and positron.

On the left side of a diagram we have “incoming particles,” these are the particles that are about to crash into each other to do something interesting. For example, at the LHC these ‘incoming particles’ are the quarks and gluons that live inside the accelerated protons. On the right side of a diagram we have “outgoing particles,” these are the things which are detected after an interesting interaction.

For the theory above, we can imagine an electron/positron collider like the the old LEP and SLAC facilities. In these experiments an electron and positron collide and the resulting outgoing particles are detected. In our simple QED theory, what kinds of “experimental signatures” (outgoing particle configurations) could they measure? (e.g. is it possible to have a signature of a single electron with two positrons? Are there constraints on how many photons come out?)

So we see that the external lines correspond to incoming or outgoing particles. What about the internal lines? These represent virtual particles that are never directly observed. They are created quantum mechanically and disappear quantum mechanically, serving only the purpose of allowing a given set of interactions to occur to allow the incoming particles to turn into the outgoing particles. We’ll have a lot to say about these guys in future posts. Here’s an example where we have a virtual photon mediating the interaction between an electron and a positron.

In the first diagram the electron and positron annihilate into a photon which then produces another electron-positron pair. In the second diagram an electron tosses a photon to a nearby positron (without ever touching the positron). This all meshes with the idea that force particles are just weird quantum objects which mediate forces. However, our theory treats force and matter particles on equal footing. We could draw diagrams where there are photons in the external state and electrons are virtual:

This is a process where light (the photon) and an electron bounce off each other and is called Compton scattering. Note, by the way, that I didn’t bother to slant the vertical virtual particle in the second diagram. This is because it doesn’t matter whether we interpret it as a virtual electron or a virtual positron: we can either say (1) that the electron emits a photon and then scatters off of the incoming photon, or (2) we can say that the incoming photon pair produced with the resulting positron annihilating with the electron to form an outgoing photon:

Anyway, this is the basic idea of Feynman diagrams. They allow us to write down what interactions are possible. However, you will eventually discover that there is a much more mathematical interpretation of these diagrams that produces the mathematical expressions that predict the probability of these interactions to occur, and so there is actually some rather complicated mathematics “under the hood.” But just like a work of art, it’s perfectly acceptable to appreciate these diagrams at face value as diagrams of particle interactions.  Let me close with a quick “frequently asked questions”:

What is the significance of the x and y axes?These are really spacetime diagrams that outline the “trajectory” of particles. By reading these diagrams from left to right, we interpret the x axis as time. You can think of each vertical slice as a moment in time. The y axis is roughly the space direction.

So are you telling me that the particles travel in straight lines?No, but it’s easy to mistakenly believe this if you take the diagrams too seriously. The path that particles take through actual space is determined not only by the interactions (which are captured by Feynman diagrams), but the kinematics (which is not). For example, one would still have to impose things like momentum and energy conservation. The point of the Feynman diagram is to understand the interactions along a particle’s path, not the actual trajectory of the particle in space.

Does this mean that positrons are just electrons moving backwards in time?In the early days of quantum electrodynamics this seemed to be an idea that people liked to say once in a while because it sounds neat. Diagrammatically (and in some sense mathematically) one can take this interpretation, but it doesn’t really buy you anything. Among other more technical reasons, this viewpoint is rather counterproductive because the mathematical framework of quantum field theory is built upon the idea of causality.

What does it mean that a set of incoming particles and outgoing particles can have multiple diagrams?In the examples above of two-to-two scattering I showed two different diagrams that take the in-state and produce the required out-state. In fact, there are an infinite set of such diagrams. (Can you draw a few more?) Quantum mechanically, one has to sum over all the different ways to get from the in state to the out state. This should sound familiar: it’s just the usual sum over paths in the double slit experiment that we discussed before. We’ll have plenty more to say about this, but the idea is that one has to add the mathematical expressions associated with each diagram just like we had to sum numbers associated with each path in the double slit experiment.

What is the significance of rules 3 and 4?Rule 3 says that we’re only going to care about one particular chain of interactions. We don’t care about additional particles which don’t interact or additional independent chains of interactions. Rule 4 just makes the diagrams easier to read. Occasionally we’ll have to draw curvy lines or even lines that “slide under” other lines.

Where do the rules come from?The rules that we gave above (called Feynman rules) are essentially the definition of a theory of particle physics. More completely, the rules should also include a few numbers associated with the parameters of the theory (e.g. the masses of the particles, how strongly they couple), but we won’t worry about these. Graduate students in particle physics spent much of their first year learning how to carefully extract the diagrammatic rules from mathematical expressions (and then how to use the diagrams to do more math), but the physical content of the theory is most intuitively understood by looking at the diagrams directly and ignoring the math. If you’re really curious, the expression from which one obtains the rules looks something like this (from TD Gutierrez), though that’s a deliberately “scary-looking” formulation.

You’ll develop more intuition about these diagrams and eventually get to some LHC physics, but hopefully this will get the ball rolling for you.

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11 . 06 . 2016 // more photosynthesis notes!

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Since I get asked a lot about where to learn more about the human brain and behaviour, I’ve made a masterpost of books, websites, videos and online courses to introduce yourself to that piece of matter that sits between your ears.

Books

The Brain Book  by Rita Carter

The Pyschology Book (a good starter book)  by DK

Thinking, Fast and Slow  by Daniel Kahneman

Quiet: The Power of Introverts in a World That Can’t Stop Talking  by Susan Cain

The Man Who Mistook His Wife for a Hat  by Oliver Sacks

The Brain: The Story of You  by David Eagleman

The Brain That Changes Itself: Stories of Personal Triumph from the Frontiers of Brain Science  by Norman Doidge

This Is Your Brain on Music  by Daniel Levitin

The Autistic Brain by Richard Panek and Temple Grandin (highly reccomended)

Sapiens: A Brief History of Humankind  by Yuval Noah Harari (not really brain-related, but it is single handedly the best book I have ever read)

Websites

@tobeagenius (shameless self-promotion)

How Stuff Works

Psych2Go

BrainFacts

Neuroscience for Kids (aimed at kids, but it has some good info)

New Scientist

National Geographic

Live Science

Videos & Youtube Channels

Mind Matters series by TedEd

Crash Course Psychology

SciShow Brain

Psych2Go TV

asapSCIENCE

Brain Craft

Its Okay To Be Smart

Online Courses

The Addicted Brain

Visual Perception and The Brain

Understanding the Brain: The Neurobiology of Everyday Life

Pyschology Of Popularity

Harvard Fundamentals Of Neuroscience

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