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Sums of random multiplicative functions over function fields with few irreducible factors

We establish a normal approximation for the limiting distribution of partial sums of random Rademacher multiplicative functions over function fields, provided the number of irreducible factors of the polynomials is small enough. This parallels work of Harper for random Rademacher multiplicative functions over the integers.


Introduction
Let M be the set of monic polynomials belonging to the polynomial ring F q [t] with coefficients in the finite field F q with q elements, where q ≥ 2 is a prime power. A random Rademacher multiplicative function f : M → {−1, 0, 1} over F q [t] is obtained by picking independent random variables f (P ) uniformly distributed on {±1} (that is, taking the value ±1 with probability 1/2 each) for monic irreducible polynomials P , extending f multiplicatively to all squarefree monic polynomials, and setting f to be zero for all non-squarefree monic polynomials. For example, if F = P 1 · · · P k for distinct irreducible monic polynomials P 1 , . . . , P k , then f (F ) = f (P 1 ) · · · f (P k ). For any positive integers k and n, set where ω(F ) is the number of distinct irreducible factors of the polynomial F , and deg(F ) is the degree of F . The purpose of this article is to establish the following theorem.
Theorem 1. Let f be a random Rademacher multiplicative function over F q [t], where q ≥ 2 is a fixed prime power. If k ≥ 1 satisfies k = o(log n) as n → ∞, then converges in distribution to the standard normal distribution N (0, 1) as n → ∞.
This result is motivated by the study of random multiplicative functions over the integers, which were introduced by Wintner [12] to heuristically model the Möbius function. A random Rademacher multiplicative function f : N → {−1, 0, 1} over the integers is similarly obtained by picking independent random variables f (p) for each prime p, extending it multiplicatively to all squarefree integers, and setting it to be zero for all non-squarefree integers. If π k (x) is the number of squarefree integers ≤ x with k distinct prime factors, then the sum parallels the quantity in (1.1). Indeed, integers of size x are known to correspond to polynomials in F q [t] of degree n ≈ log x. Improving upon a result of Hough [5], Harper [4] established the following theorem which motivates our Theorem 1.
1 Theorem 2 (Harper). Let f be a random Rademacher multiplicative function over the integers. If k ≥ 1 satisfies k = o(log log x) as x → ∞, then (1.2) converges in distribution to the standard normal N (0, 1) as x → ∞.
Notice the range k = o(log log x) in Theorem 2 over the integers corresponds precisely to the range k = o(log n) in Theorem 1 over the polynomial ring F q [t]. Theorem 1 can therefore be viewed as an extension of Theorem 2 to the function field setting. For an introduction to multiplicative functions over function fields, we refer the reader to work of Granville, Harper, and Soundararajan [3], whose conventions we follow here.
The proof strategy for Theorem 1 adapts Harper's key ideas with the verification of three conditions in a martingale central limit theorem (Theorem 3). In Section 2, we prepare this strategy and define our martingale difference sequence. The analysis of this martingale allows us to efficiently reduce the theorem to a natural counting problem (Lemma 4), just as Harper did in Section 4.2 of [4]. However, this counting problem for function fields introduces cases which did not appear for the integers. The source of these new cases is simple: two distinct irreducible polynomials can have the same degree, but two distinct rational primes cannot have the same size. Since our martingale is filtered based on the degree of the largest irreducible factor (similar to the size of the prime for integers), this distinction creates new terms in our sums that we must carefully treat; see the remark following Lemma 4 for details.
In Section 3, we proceed to analyze these sums and complete the proof of Theorem 1 with some technical estimates. Although these combinatorial sums are somewhat more intricate, the estimation of these sums is simpler due to the familiar analytic benefits of function fields over integers. The key technical lemma for this analysis (Lemma 5) is proved in Section 4. We use recent results on the size of P k (n) by Gómez-Colunga et al. [2] and Afshar and Porritt [1], which respectively parallel classical estimates for π k (x) by Hardy and Ramanujan, and Sathe and Selberg.
We conclude the introduction with a few remarks on the sharpness of Theorem 1 and possible extensions. Harper showed that the range k = o(log log x) in Theorem 2 is optimal [4, Corollary 1]. He further established a normal approximation for sums like (1.2) with the looser restriction ω(m) ≤ k and also for a larger class of random multiplicative functions [4,Theorem 3]. It would be of interest to determine whether the range k = o(log n) in Theorem 1 is optimal and whether similar extensions hold in our setting. It seems plausible that such results carry over by similar arguments, but we did not pursue those investigations. If the range k = o(log n) is optimal as Harper's work would suggest, then this indicates that the proof of Theorem 1 is quite delicate and sensitive to even minor losses.
Acknowledgments. This research was conducted as part of the 2020 Fields Undergraduate Summer Research Program. The authors are grateful to the Fields Institute for their financial support and facilitating our online collaboration.

Notation
Let q ≥ 2 be a prime power. Let F q [t] be the polynomial ring with coefficients in the finite field F q with q elements. Let M be the set of monic polynomials belonging to F q [t]. We shall use capital letters to denote a polynomial F in M, writing deg(F ) for the degree of the polynomial F , ω(F ) for the number of distinct irreducible factors of F , and P + (F ) for the maximum degree of an irreducible dividing F . The letters P and Q will be reserved for monic irreducible polynomials. For integers k, n ≥ 1, let P k (n) be the set of squarefree polynomials F in M with ω(F ) = k and deg(F ) = n. The letter f denotes a random Rademacher multiplicative function over F q [t]. The relation u ≪ v means that there exists an absolute positive constant C such that |u| ≤ Cv. If the constant C depends on a parameter, say ε, then we shall write u ≪ ε v.

Plan for the proof of Theorem 1
For integers k, n ≥ 1 and a random Rademacher multiplicative function f over F q [t], define Notice E[f (F )] = 0 for any non-trivial squarefree F because f is multiplicative and (f (P )) P is a sequence of independent random variables with mean zero. Hence, S (k) (n) has mean zero. Also, Thus the mean of (1.1) is zero and its variance is indeed one. Our goal is to prove that S (k) (n), normalized by its standard deviation |P k (n)|, converges in distribution to the standard normal as n → ∞, provided k = o(log n). The strategy follows that of Harper [4] with appropriate modifications and simplifications as mentioned earlier in the introduction. First, notice P 1 (n) is the set of irreducible monic polynomials of degree n, so S (1) (n) is a sum of |P 1 (n)| independent random variables uniform on {±1}. Thus, the classical central limit theorem implies that S (1) (n) converges in distribution to N (0, 1) as n → ∞. We may therefore assume throughout that k ≥ 2.
2.1. Central limit theorem for martingale difference sequences. To prove convergence in distribution to standard normal, we want to use a central limit theorem that gives information on the convergence of the partial sums of a martingale difference sequence. The result we use was obtained by McLeish [6], but we state it as it appeared in [4].

Theorem 3 (McLeish).
For n ∈ N, suppose that k n ∈ N, and that X i,n , 1 ≤ i ≤ k n , is a martingale difference sequence on (Ω, F , (F i,n ) i , P). Write S n := i≤kn X i,n and suppose that the following conditions hold: Then, S n converges in distribution to N (0, 1) as n → ∞.
Let us describe the martingale difference sequence in our problem. Let n ≥ k ≥ 2. Write P + (F ) for the maximum degree of the irreducible factors of F . For d ≥ 1, define Notice the set P k,n (n) is empty as k ≥ 2, so P k (n) is the union of P k,d (n) over 1 ≤ d ≤ n − 1 and therefore S (k) (n) = n−1 d=1 S Hence, if F d denotes the sigma algebra generated by {f (P ) : deg P < d}, then (S (k) d (n)) d≤n−1 is a martingale difference sequence with respect to (F d ) d≤n−1 .
We will therefore apply Theorem 3 to the random variables S (k) d (n)/ |P k (n)|, which still form a martingale difference sequence and whose sum over d ≤ n−1 equals S (k) (n)/ |P k (n)|, the quantity considered in Theorem 1. For convenience, we also use the notation P k,≤d (n) := j≤d P k,j (n).

2.2.
Reduction to some counting problems. By a computation similar to (2.1), it follows that E[S (k) d (n) 2 ] = |P k,d (n)|, so that condition (i) of Theorem 3 holds for all n, not just in the limit. Proving Theorem 1 then boils down to verifying conditions (ii) and (iii) of Theorem 3. The second condition stated in terms of our normalised random variables asks that for all ε > 0, as n → ∞. This quantity is at most Thus, it suffices to prove that Equivalently, we will show as n → ∞. For any 1 ≤ d, e ≤ n − 1, it will therefore be convenient to express E S Proof. Expanding out the sums and applying linearity of expectation, we get that Notice the expectation on the righthand side is nonzero only when W XY Z is a square, in which case it equals 1. This is the counting problem, which we proceed to reformulate. We may write W X as the product of a square part As A ∈ P t,≤d (ℓ), this implies that U ∈ P k−t,≤d (n − ℓ) and M ∈ P 2t,≤d (2ℓ). A similar analysis holds when comparing Y = V B and Z = V (M/B) but, since the polynomial M is common to both arguments, it follows that A and B necessarily have the same degree and same number of prime factors and so do U and V . Hence, B ∈ P t,≤e (ℓ), V ∈ P k−t,≤e (n − ℓ), and M ∈ P 2t,≤min{d,e} (2ℓ). The terms in (2.6) with M = 1, t ∈ {1, . . . , k − 1}, and ℓ ∈ {1, . . . , n − 1} therefore contribute at most I k,d,e (n). Continuing with this notation, the last case to consider is when M = 1 and t = k (or equivalently ℓ = n) in which case U = V = 1. Notice U = V = 1 implies that W XY Z = U 2 V 2 M 2 = M 2 has a prime factor of degree max{d, e} yet P + (M ) ≤ min{d, e}. If d = e, this leads to a contradiction, so this last case occurs if and only if d = e. Thus, M has at least two distinct degree d factors in this case and P + (A) = P + (B) = d where A and B divide M . Thus, there exists a pair of distinct irreducibles P, Q ∈ P 1 (d) such that M = P QM ′ , where M ′ belongs to P 2k−2 (2n − 2d) and at least one of the following holds: Q | A and P | B, P | A and P | B, Q | A and Q | B.
If, say, the first situation holds then A = P A ′ and B = QB ′ for A ′ , B ′ ∈ P k−1 (n − d) dividing M ′ . A similar statement holds for the other cases. Combining all of these observations, we see that the terms in (2.6) with M = 1 and t = k contribute at most J k,d,e (n), as required.
Remark. This lemma and its proof possess the key differences between the function field setting and the integers. Crucially, the product W XY Z can form a square in a new way and contribute to (2.6). Namely, if d ≤ e then W and X do not need to share the same irreducible factor of degree d; these factors of degree d can instead pair with factors from Y and Z. This manifests in (2.4) by allowing M to have these large irreducible factors of degree d = min{d, e} and also by creating the additional terms (2.5) which do not appear in Section 4.2 of [4].
If the irreducible factors of degree d from W and X (resp. of degree e from Y and Z) are paired in a one-to-one manner, then only U (resp. V ) in (2.4) would have these large factors of degree d (resp. degree e) and moreover (2.5) would not exist. This is precisely what happens for Harper in the integer setting. Namely, if integers w and x have largest prime factor p, then p 2 always divides wx since the size of the prime corresponds uniquely to the prime itself. Now, using Lemma 4 with d = e, we see that (2.2) becomes the requirement that Similarly, (2.3) holds provided that both (2.2) and (2.3) will therefore be satisfied provided as n → ∞. This establishes Theorem 1 assuming (2.7) holds.
In particular, if r ≥ 2 is fixed and k = o(log n) as n → ∞, then the above is o(|P k (n)| 2 ).
Assuming Lemma 5, it suffices to show that each of the three sums in (2.7) are o(|P k (n)| 2 ) provided k = o(log n) as n → ∞. We deal with each estimate in separate subsections.
3.1. Estimate for n−1 d=1 |P k,d (n)| 2 . For F ∈ P k,d (n), one has F = P F ′ for some P ∈ P 1 (d) and F ′ ∈ P k−1 (n − d). This implies that |P k,d (n)| ≤ |P 1 (d)||P k−1 (n − d)| and so This is a subsum of Lemma 5 with r = 2 so it is o(|P k (n)| 2 ) as n → ∞, as required. 6 3.2. Estimate for n−1 d=1 n−1 e=1 I k,d,e (n). Consider the definition of I k,d,e (n) in (2.4). The condition P + (U A) = d implies that at least one of the following holds: P + (U ) = d or P + (A) = d. Summing over d and, in some cases, dropping the requirement that the maximum degree of the irreducible factors of our polynomials is ≤ d or ≤ e, this implies that n−1 d=1 I k,d,e (n) is at most Applying the same argument to the condition P + (V B) = e and summing over e, it follows that n−1 d=1 n−1 e=1 I k,d,e (n) is at most 1 .
Fix t ∈ {1, . . . , k − 1} and ℓ ∈ {1, . . . , n − 1}. Notice that the double sum with U and V is equal to |P k−t (n − ℓ)| 2 . Next, consider the triple sum with M, A, and B. Writing G = gcd(A, B), we have that A = GA ′ , B = GB ′ , and M = GA ′ B ′ M ′ for some M ′ coprime to A ′ , B ′ , and G. Since A and B have the same degree and same number of prime factors (and hence so do A ′ and B ′ ), it follows that G and M ′ must have the same degree and same number of prime factors. Namely, if G ∈ P j (g) for some integer 0 ≤ j ≤ t and some integer 0 ≤ g ≤ ℓ, then M ′ ∈ P j (g) and A ′ , B ′ ∈ P t−j (ℓ − g). Note the case j = 0 (and hence g = 0) occurs when G = M ′ = 1 so M = AB, and the case j = t (and hence g = ℓ) occurs when A ′ = B ′ = 1 so M = GM ′ . Combining these observations implies that |P k−t (n − ℓ)| 2 |P j (g)| 2 |P t−j (ℓ − g)| 2 .
Since k = o(log n) as n → ∞, all of these sums are o(|P k (n)| 2 ) by Lemma 5. This completes the proof of (2.7) and the proof of Theorem 1.

Proof of Lemma 5
All that remains is to prove Lemma 5. To do so, we shall first require an estimate for the size of P k (n) that is uniform for all integers k and n. Gómez-Colunga et al. [2] have recently established such a result.