Difficulty in Understanding Relativity

Special relativity says: 1. the speed of light in vaccum is an invariable for all inertial frames of reference; 2. the laws of physics are the same in all inertial frames of reference. Then we will find the rest mass of a photon is zero and it takes infinitely large amount of energy to accelerate an object with non-zero rest mass to the speed of light. The derivation is perfectly satisfying.

Since the speed of light is constant, we know that the light itself is in an inertial frame of reference. So, if we attach our standpoint to this frame of reference, everything else is moving at the speed of light! And of course, we knew it for sure that these things all have a non-zero rest mass!

What happened? The transformation from one inertial frame to another (somehow non-trivial?) inertial frame is irreversible. Where does this assymetry come from? Or, did I make some stupid mistake?

时间： 2024-12-09 23:57:00

Difficulty in Understanding Relativity的相关文章

linux/Documentation/kobject.txt

Everything you never wanted to know about kobjects, ksets, and ktypes Greg Kroah-Hartman <[email protected]> Based on an original article by Jon Corbet for lwn.net written October 1,2003 and located at http://lwn.net/Articles/51437/ Last updated Dec

(转)　Written Memories: Understanding, Deriving and Extending the LSTM

R2RT Written Memories: Understanding, Deriving and Extending the LSTM Tue 26 July 2016 When I was first introduced to Long Short-Term Memory networks (LSTMs), it was hard to look past their complexity. I didn’t understand why they were designed they

Auto Layout Guide----(一)-----Understanding Auto Layout

Understanding Auto Layout 理解自动布局 Auto Layout dynamically calculates the size and position of all the views in your view hierarchy, based on constraints placed on those views. For example, you can constrain a button so that it is horizontally centered

understanding backpropagation

几个有助于加深对反向传播算法直观理解的网页,包括普通前向神经网络,卷积神经网络以及利用BP对一般性函数求导(UFLDL) A Visual Explanation of the Back Propagation Algorithm for Neural Networks By Sebastian Raschka, Michigan State University. Let's assume we are really into mountain climbing, and to add a l

Understanding and Selecting a SIEM/LM: Correlation and Alerting

Continuing our discussion of core SIEM and Log Management technology, we now move into event correlation. This capability was the holy grail that drove most investment in early SIEM products, and probably the security technology creating the most con

Understanding the Top command on Linux

Article by AlexioBash published on his website about ArchLinux in italian. Know what is happening in "real time" on your systems is in my opinion the basis to use and optimize your OS. On ArchLinux or better on GNU/Linux in general thetopcommand

Understanding the JVM

The aim to write this blog is that I want to summerize and NOTE what I have learned with the booked called as Understanding the JVM Advandced features and Best prac Now it's the first chapter reviews : ? This chapter didn't talk much ,just the histor

Understanding Convolutions

w从2次丢下一个球,要求距离和固定来解释褶积 http://colah.github.io/posts/2014-07-Understanding-Convolutions/ Understanding Convolutions Posted on July 13, 2014 neural networks, convolutional neural networks, convolution, math, probability In a previous post, we built up

（转）The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3)

Adit Deshpande CS Undergrad at UCLA ('19) Blog About The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3) Introduction Link to Part 1Link to Part 2 In this post, we’ll go into summarizing a lot of the new and important develo