Prime Number Theorem And ...

4 Elementary Corollaries

Lemma 71

For any arithmetic function \(f\) and real number \(x\), one has

\[ \sum _{n \leq x} f(n) = \sum _{n \leq ⌊x⌋_+} f(n) \]

and

\[ \sum _{n {\lt} x} f(n) = \sum _{n {\lt} ⌈x⌉_+} f(n). \]
Proof

Straightforward.

Theorem 17
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One has

\[ \sum _{p \leq x} \log p = x + o(x). \]
Proof

From the prime number theorem we already have

\[ \sum _{n \leq x} \Lambda (n) = x + o(x) \]

so it suffices to show that

\[ \sum _{j \geq 2} \sum _{p^j \leq x} \log p = o(x). \]

Only the terms with \(j \leq \log x / \log 2\) contribute, and each \(j\) contributes at most \(\sqrt{x} \log x\) to the sum, so the left-hand side is \(O( \sqrt{x} \log ^2 x ) = o(x)\) as required.

Corollary 8 Bounds on primorial
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We have

\[ \prod _{p \leq x} p = \exp ( x + o(x) ) \]
Proof

Exponentiate Theorem 17.

Proof

We have the identity

\[ \pi (x) = \frac{1}{\log x} \sum _{p \leq x} \log p + \int _2^x (\sum _{p \leq t} \log p) \frac{dt}{t \log ^2 t} \]

as can be proven by interchanging the sum and integral and using the fundamental theorem of calculus. For any \(\epsilon \), we know from Theorem 17 that there is \(x_\epsilon \) such that \(\sum _{p \leq t} \log p = t + O(\epsilon t)\) for \(t \geq x_\epsilon \), hence for \(x \geq x_\epsilon \)

\[ \pi (x) = \frac{1}{\log x} (x + O(\epsilon x)) + \int _{x_\epsilon }^x (t + O(\epsilon t)) \frac{dt}{t \log ^2 t} + O_\epsilon (1) \]

where the \(O_\epsilon (1)\) term can depend on \(x_\epsilon \) but is independent of \(x\). One can evaluate this after an integration by parts as

\[ \pi (x) = (1+O(\epsilon )) \int _{x_\epsilon }^x \frac{dt}{\log t} + O_\epsilon (1) \]
\[ = (1+O(\epsilon )) \int _{2}^x \frac{dt}{\log t} \]

for \(x\) large enough, giving the claim.

Corollary 9
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One has

\[ \pi (x) = (1+o(1)) \frac{x}{\log x} \]

as \(x \to \infty \).

Proof

An integration by parts gives

\[ \int _2^x \frac{dt}{\log t} = \frac{x}{\log x} - \frac{2}{\log 2} + \int _2^x \frac{dt}{\log ^2 t}. \]

We have the crude bounds

\[ \int _2^{\sqrt{x}} \frac{dt}{\log ^2 t} = O( \sqrt{x} ) \]

and

\[ \int _{\sqrt{x}}^x \frac{dt}{\log ^2 t} = O( \frac{x}{\log ^2 x} ) \]

and combining all this we obtain

\[ \int _2^x \frac{dt}{\log t} = \frac{x}{\log x} + O( \frac{x}{\log ^2 x} ) \]
\[ = (1+o(1)) \frac{x}{\log x} \]

and the claim then follows from Theorem ??.

Let \(p_n\) denote the \(n^{th}\) prime.

Proposition 3
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One has

\[ p_n = (1+o(1)) n \log n \]

as \(n \to \infty \).

Proof

Use Corollary 9 to show that for any \(\epsilon {\gt}0\), and for \(x\) sufficiently large, the number of primes up to \((1-\epsilon ) n \log n\) is less than \(n\), and the number of primes up to \((1+\epsilon ) n \log n\) is greater than \(n\).

Corollary 10
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We have \(p_{n+1} - p_n = o(p_n)\) as \(n \to \infty \).

Proof

Easy consequence of preceding proposition.

Corollary 11
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For every \(\epsilon {\gt}0\), there is a prime between \(x\) and \((1+\epsilon )x\) for all sufficiently large \(x\).

Proof

Use Corollary 9 to show that \(\pi ((1+\epsilon )x) - \pi (x)\) goes to infinity as \(x \to \infty \).

Proposition 4
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We have \(|\sum _{n \leq x} \frac{\mu (n)}{n}| \leq 1\).

Proof

From Möbius inversion \(1_{n=1} = \sum _{d|n} \mu (d)\) and summing we have

\[ 1 = \sum _{d \leq x} \mu (d) \lfloor \frac{x}{d} \rfloor \]

for any \(x \geq 1\). Since \(\lfloor \frac{x}{d} \rfloor = \frac{x}{d} - \epsilon _d\) with \(0 \leq \epsilon _d {\lt} 1\) and \(\epsilon _x = 0\), we conclude that

\[ 1 ≥ x \sum _{d \leq x} \frac{\mu (d)}{d} - (x - 1) \]

and the claim follows.

Proposition 5 Möbius form of prime number theorem
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We have \(\sum _{n \leq x} \mu (n) = o(x)\).

Proof

From the Dirichlet convolution identity

\[ \mu (n) \log n = - \sum _{d|n} \mu (d) \Lambda (n/d) \]

and summing we obtain

\[ \sum _{n \leq x} \mu (n) \log n = - \sum _{d \leq x} \mu (d) \sum _{m \leq x/d} \Lambda (m). \]

For any \(\epsilon {\gt}0\), we have from the prime number theorem that

\[ \sum _{m \leq x/d} \Lambda (m) = x/d + O(\epsilon x/d) + O_\epsilon (1) \]

(divide into cases depending on whether \(x/d\) is large or small compared to \(\epsilon \)). We conclude that

\[ \sum _{n \leq x} \mu (n) \log n = - x \sum _{d \leq x} \frac{\mu (d)}{d} + O(\epsilon x \log x) + O_\epsilon (x). \]

Applying 4 we conclude that

\[ \sum _{n \leq x} \mu (n) \log n = O(\epsilon x \log x) + O_\epsilon (x). \]

and hence

\[ \sum _{n \leq x} \mu (n) \log x = O(\epsilon x \log x) + O_\epsilon (x) + O( \sum _{n \leq x} (\log x - \log n) ). \]

From Stirling’s formula one has

\[ \sum _{n \leq x} (\log x - \log n) = O(x) \]

thus

\[ \sum _{n \leq x} \mu (n) \log x = O(\epsilon x \log x) + O_\epsilon (x) \]

and thus

\[ \sum _{n \leq x} \mu (n) = O(\epsilon x) + O_\epsilon (\frac{x}{\log x}). \]

Sending \(\epsilon \to 0\) we obtain the claim.

Proposition 6
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We have \(\sum _{n \leq x} \lambda (n) = o(x)\).

Proof

From the identity

\[ \lambda (n) = \sum _{d^2|n} \mu (n/d^2) \]

and summing, we have

\[ \sum _{n \leq x} \lambda (n) = \sum _{d \leq \sqrt{x}} \sum _{n \leq x/d^2} \mu (n). \]

For any \(\epsilon {\gt}0\), we have from Proposition 5 that

\[ \sum _{n \leq x/d^2} \mu (n) = O(\epsilon x/d^2) + O_\epsilon (1) \]

and hence on summing in \(d\)

\[ \sum _{n \leq x} \lambda (n) = O(\epsilon x) + O_\epsilon (x^{1/2}). \]

Sending \(\epsilon \to 0\) we obtain the claim.

Proposition 7 Alternate Möbius form of prime number theorem
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We have \(\sum _{n \leq x} \mu (n)/n = o(1)\).

Proof

As in the proof of Theorem 4, we have

\[ 1 = \sum _{d \leq x} \mu (d) \lfloor \frac{x}{d} \rfloor \]
\[ = x \sum _{d \leq x} \frac{\mu (d)}{d} - \sum _{d \leq x} \mu (d) \{ \frac{x}{d} \} \]

so it will suffice to show that

\[ \sum _{d \leq x} \mu (d) \{ \frac{x}{d} \} = o(x). \]

Let \(N\) be a natural number. It suffices to show that

\[ \sum _{d \leq x} \mu (d) \{ \frac{x}{d} \} = O(x/N). \]

if \(x\) is large enough depending on \(N\). We can split the left-hand side as the sum of

\[ \sum _{d \leq x/N} \mu (d) \{ \frac{x}{d} \} \]

and

\[ \sum _{j=1}^{N-1} \sum _{x/(j+1) {\lt} d \leq x/j} \mu (d) (x/d - j). \]

The first term is clearly \(O(x/N)\). For the second term, we can use Theorem 5 and summation by parts (using the fact that \(x/d-j\) is monotone and bounded) to find that

\[ \sum _{x/(j+1) {\lt} d \leq x/j} \mu (d) (x/d - j) = o(x) \]

for any given \(j\), so in particular

\[ \sum _{x/(j+1) {\lt} d \leq x/j} \mu (d) (x/d - j) = O(x/N^2) \]

for all \(j=1,\dots ,N-1\) if \(x\) is large enough depending on \(N\). Summing all the bounds, we obtain the claim.

4.1 Consequences of the PNT in arithmetic progressions

Theorem 18 Chebyshev asymptotic in arithmetic progressions
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If \(a\ (q)\) is a primitive residue class, then one has

\[ \sum _{p \leq x: p = a\ (q)} \log p = \frac{x}{\phi (q)} + o(x). \]
Proof

This is a routine modification of the proof of Theorem 17.

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Corollary 12
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Any primitive residue class contains an infinite number of primes.