# Modular Forms of Level 1¶

In this chapter we study in detail the structure of level modular forms, i.e., modular forms on We assume some complex analysis (e.g., the residue theorem), linear algebra, and that the reader has read Chapter Modular Forms.

## Examples of Modular Forms of Level 1¶

In this section we will finally see some examples of modular forms of level ! We first introduce the Eisenstein series and then define , which is a cusp form of weight . In Section Structure Theorem for Level 1 Modular Forms we prove the structure theorem, which says that all modular forms of level are polynomials in Eisenstein series.

For an even integer , the nonnormalized weight Eisenstein series is the function on the extended upper half plane given by

(1)

The star on top of the sum symbol means that for each the sum is over all such that .

Proposition 2.1

The function is a modular form of weight , i.e., .

Proof

See [Ser73, Section VII.2.3] for a proof that defines a holomorphic function on . To see that is modular, observe that

where for the last equality we use that the map on is invertible. Also,

where we use that is invertible.

Proposition 2.2

, where is the Riemann zeta function.

Proof

As (along the imaginary axis) in (1), the terms that involve with go to . Thus

This sum is twice , as claimed.

### The Cusp Form ¶

Suppose is an elliptic curve over , viewed as a quotient of by a lattice , with (see [DS05, Section 1.4]). The Weierstrass -function of the lattice is

where the sum is over even integers . It satisfies the differential equation

If we set and , the above is an (affine) equation of the form for an elliptic curve that is complex analytically isomorphic to (see [Ahl78, pg. 277] for why the cubic has distinct roots).

The discriminant of the cubic

is , where

Since is the difference of two modular forms of weight it has weight . Morever,

so is a cusp form of weight .

Let

Lemma 2.3

If , then .

Proof

Let and . Since is an elliptic curve, it has nonzero discriminant .

Proposition 2.4

We have .

Proof

See [Ser73, Thm. 6, pg. 95].

Remark 2.5

Sage computes the -expansion of efficiently to high precision using the command delta_qexp:

sage: delta_qexp(6)
q - 24*q^2 + 252*q^3 - 1472*q^4 + 4830*q^5 + O(q^6)


### Fourier Expansions of Eisenstein Series¶

Recall from (?) that elements of can be expressed as formal power series in terms of and that this expansion is called the Fourier expansion of . The following proposition gives the Fourier expansion of the Eisenstein series .

Definition 2.6

For any integer and any positive integer , the sigma function

is the sum of the powers of the positive divisors of . Also, let , which is the number of divisors of , and let . For example, if is prime, then .

Proposition 2.7

For every even integer , we have

Proof

See [Ser73, Section VII.4], which uses clever manipulations of series, starting with the identity

From a computational point of view, the -expansion of Proposition 2.7 is unsatisfactory because it involves transcendental numbers. To understand these numbers, we introduce the Bernoulli numbers for defined by the following equality of formal power series:

(2)

Expanding the power series, we have

As this expansion suggests, the Bernoulli numbers with odd are (see Exercise 2.2). Expanding the series further, we obtain the following table:

See Section Fast Computation of Bernoulli Numbers for a discussion of fast (analytic) methods for computing Bernoulli numbers.

We compute some Bernoulli numbers in Sage:

sage: bernoulli(12)
-691/2730
sage: bernoulli(50)
495057205241079648212477525/66
sage: len(str(bernoulli(10000)))
27706


A key fact is that Bernoulli numbers are rational numbers and they are connected to values of at positive even integers.

Proposition 2.8

If is an even integer, then

Proof

This is proved by manipulating a series expansion of (see [Ser73, Section VII.4]).

Definition 2.9

The normalized Eisenstein series of even weight is

Combining Proposition 2.7 and Proposition 2.8, we see that

(3)

Warning

Our series is normalized so that the coefficient of is , but often in the literature is normalized so that the constant coefficient is . We use the normalization with the coefficient of equal to , because then the eigenvalue of the Hecke operator (see Section Hecke Operators) is the coefficient of . Our normalization is also convenient when considering congruences between cusp forms and Eisenstein series.

## Structure Theorem for Level 1 Modular Forms¶

In this section we describe a structure theorem for modular forms of level . If is a nonzero meromorphic function on and , let be the largest integer such that is holomorphic at . If with , we set . We will use the following theorem to give a presentation for the vector space of modular forms of weight ; this presentation yields an algorithm to compute this space.

Let denote the complex vector space of modular forms of weight for . The standard fundamental domain for is the set of with and . Let .

Theorem 2.11

Let be any integer and suppose is nonzero. Then

where is the sum over elements of other than and !.

Proof

The proof in [Ser73, Section VII.3] uses the residue theorem.

Let index{} denote the subspace of weight cusp forms for . We have an exact sequence

that sends to . When is even, the space contains the Eisenstein series , and , so the map is surjective. This proves the following lemma.

Lemma 2.12

If is even, then and the following sequence is exact:

Proposition 2.13

For and , we have .

Proof

Suppose is nonzero yet or . By Theorem 2.11,

This is not possible because each quantity on the left is nonnegative so whatever the sum is, it is too big (or , in which case ).

Theorem 2.14

Multiplication by defines an isomorphism .

Proof

By Lemma 2.3, is not identically , so because is holomorphic, multiplication by defines an injective map . To see that this map is surjective, we show that if , then . Since has weight and , Theorem 2.11 implies that has a simple zero at and does not vanish on . Thus if and if we let , then is holomorphic and satisfies the appropriate transformation formula, so .

Corollary 2.15

For , the space has dimension , with basis , , , , , and , respectively, and .

Proof

Combining Proposition 2.13 with Theorem 2.14, we see that the spaces for cannot have dimension greater than , since otherwise for some . Also has dimension at most , since has dimension . Each of the indicated spaces of weight contains the indicated Eisenstein series and so has dimension , as claimed.

Corollary 2.16

Here is the biggest integer .

Proof

As we have already seen above, the formula is true when . By Theorem 2.14, the dimension increases by when is replaced by .

Theorem 2.17

The space has as basis the modular forms , where run over all pairs of nonnegative integers such that .

Proof

Fix an even integer . We first prove by induction that the modular forms generate ; the cases and follow from the above arguments (e.g., when , we have and basis ). Choose some pair of nonnegative integers such that . The form is not a cusp form, since it is nonzero at . Now suppose is arbitrary. Since , there exists such that . Then by Theorem 2.14, there is such that . By induction, is a polynomial in and of the required type, and so is , so is as well. Thus

spans .

Suppose there is a nontrivial linear relation between the for a given . By multiplying the linear relation by a suitable power of and , we may assume that we have such a nontrivial relation with . Now divide the linear relation by the weight form to see that satisfies a polynomial with coefficients in (see Exercise 2.4). Hence is a root of a polynomial, hence a constant, which is a contradiction since the -expansion of is not constant.

Algorithm 2.18

Given integers and , this algorithm computes a basis of -expansions for the complex vector space mod . The -expansions output by this algorithm have coefficients in .

1. [Simple Case] If , output the basis with just in it and terminate; otherwise if or is odd, output the empty basis and terminate.
2. [Power Series] Compute and mod using the formula from (3) and Section Fast Computation of Bernoulli Numbers.
3. [Initialize] Set .
4. [Enumerate Basis] For each integer between and , compute . If is an integer, compute and output the basis element . When computing , find for each , and save these intermediate powers, so they can be reused later, and likewise for powers of .

Proof

This is simply a translation of Theorem 2.17 into an algorithm, since is a nonzero scalar multiple of . That the -expansions have coefficients in follows from (3).

Example 2.19

We compute a basis for , which is the space with smallest weight whose dimension is greater than . It has as basis , , and , whose explicit expansions are

We compute this basis in Sage as follows:

sage: E4 = eisenstein_series_qexp(4, 3)
sage: E6 = eisenstein_series_qexp(6, 3)
sage: E4^6
1/191102976000000 + 1/132710400000*q
+ 203/44236800000*q^2 + O(q^3)
sage: E4^3*E6^2
1/3511517184000 - 1/12192768000*q
- 377/4064256000*q^2 + O(q^3)
sage: E6^4
1/64524128256 - 1/32006016*q + 241/10668672*q^2 + O(q^3)


In Section The Miller Basis, we will discuss the reduced echelon form basis for .

## The Miller Basis¶

Lemma 2.20

The space has a basis such that if is the coefficient of , then for . Moreover the all lie in . We call this basis the Miller basis for .

This is a straightforward construction involving , and . The following proof very closely follows [Lan95, Ch. X,Thm. 4.4], which in turn follows the first lemma of V. Miller’s thesis.

Proof

Let . Since and , we note that

and

have -expansions in with leading coefficient . Choose integers such that

with when , and let

Then it is elementary to check that has weight

Hence the are linearly independent over , so form a basis for . Since , and are all in , so are the . The may then be constructed from the by Gauss elimination. The coefficients of the resulting power series lie in because each time we clear a column we use the power series whose leading coefficient is (so no denominators are introduced).

Remark 2.21

The basis coming from Miller’s lemma is “canonical”“, since it is just the reduced row echelon form of any basis. Also the set of all integral linear combinations of the elements of the Miller basis are precisely the modular forms of level with integral -expansion.

We extend the Miller basis to all by taking a multiple of with constant term and subtracting off the from the Miller basis so that the coefficients of of the resulting expansion are . We call the extra basis element .

Example 2.22

If , then . Choose , since . Then

and

We let and

Example 2.23

When , the Miller basis including is

Example 2.24

The Sage command victor_miller_basis computes the Miller basis to any desired precision given .

sage: victor_miller_basis(28,5)
[
1 + 15590400*q^3 + 36957286800*q^4 + O(q^5),
q + 151740*q^3 + 61032448*q^4 + O(q^5),
q^2 + 192*q^3 - 8280*q^4 + O(q^5)
]


Remark 2.25

To write as a polynomial in and , it is wasteful to compute the Miller basis. Instead, use the upper triangular (but not echelon!) basis , and match coefficients from to .

## Hecke Operators¶

In this section we define Hecke operators on level modular forms and derive their basic properties. We will not give proofs of the analogous properties for Hecke operators on higher level modular forms, since the proofs are clearest in the level case, and the general case is similar (see, e.g., [Lan95]).

For any positive integer , let

Note that the set is in bijection with the set of subgroups of of index , where corresponds to , as one can see using Hermite normal form, which is the analogue over of echelon form (see Exercise 7.5).

Recall from (?) that if , then

Definition 2.26

The Hecke operator of weight is the operator on the set of functions on defined by

Remark 2.27

It would make more sense to write on the right, e.g., , since is defined using a right group action. However, if are integers, then the action of and on weakly modular functions commutes (by Proposition 2.29 below), so it makes no difference whether we view the Hecke operators of given weight as acting on the right or left.

Proposition 2.28

If is a weakly modular function of weight , then so is ; if is a modular function, then so is .

Proof

Suppose . Since induces an automorphism of , X_n \cdot \gamma = \{ \delta \gamma : \delta\in X_n\} is also in bijection with the subgroups of of index . For each element , there is such that (the element transforms to Hermite normal form), and the set of elements is thus equal to . Thus

A finite sum of meromorphic function is meromorphic, so is weakly modular. If is holomorphic on , then each is holomorphic on for . A finite sum of holomorphic functions is holomorphic, so is holomorphic.

We will frequently drop from the notation in , since the weight is implicit in the modular function to which we apply the Hecke operator. Henceforth we make the convention that if we write and if is modular, then we mean , where is the weight of .

Proposition 2.29

On weight modular functions we have

(4)

and

(5)

Proof

Let be a subgroup of index . The quotient is an abelian group of order , and , so decomposes uniquely as a direct sum of a subgroup of order with a subgroup of order . Thus there exists a unique subgroup such that , and has index in . The subgroup corresponds to an element of , and the index subgroup corresponds to multiplying that element on the right by some uniquely determined element of . We thus have

i.e., the set products of elements in with elements of equal the elements of , up to -equivalence. Thus for any , we have . Applying this formula with and swapped yields the equality .

We will show that . Suppose is a weight weakly modular function. Using that , we have

Also

Thus it suffices to show that disjoint union copies of is equal to , where we consider elements with multiplicities and up to left -equivalence (i.e., the left action of ).

Suppose is a subgroup of of index , so corresponds to an element of . First suppose is not contained in . Then the image of in is of order , so if , then and , and is the only subgroup with this property. Second, suppose that if of index and that corresponds to . Then every one of the subgroups of index contains . Thus there are chains with .

The chains with and are in bijection with the elements of . On the other hand the union of with copies of corresponds to the subgroups of index , but with those that contain counted times. The structure of the set of chains that we derived in the previous paragraph gives the result.

Corollary 2.30

The Hecke operator , for prime , is a polynomial in with integer coefficients, i.e., . If are any integers, then

Proof

The first statement follows from (5) of Proposition 2.29. It then follows that when and are both powers of a single prime . Combining this with (4) gives the second statement in general.

Proposition 2.31

Let be a modular function of weight . Then

In particular, if is prime, then

where if .

Proof

This is proved in [Ser73, Section VII.5.3] by writing out explicitly and using that is if and otherwise.

Corollary 2.32

The Hecke operators preserve and .

Remark 2.33

Alternatively, for the above corollary is Proposition 2.28, and for we see from the definitions that if , then also vanishes at .

Example 2.34

Recall from (3) that

Using the formula of Proposition 2.31, we see that

Since has dimension and since we have proved that preserves , we know that acts as a scalar. Thus we know just from the constant coefficient of that

More generally, for prime we see by inspection of the constant coefficient of that

In fact

for any integer and even weight .

Example 2.35

By Corollary Corollary 2.32, the Hecke operators also preserve the subspace of . Since has dimension (spanned by ), we see that is an eigenvector for every . Since the coefficient of in the -expansion of is , the eigenvalue of on is the coefficient of . Since for , we have proved the nonobvious fact that the Ramanujan function that gives the coefficient of is a multiplicative function, i.e., if , then .

Remark 2.36

The Hecke operators respect the decomposition , i.e., for all the series are eigenvectors for all .

## Computing Hecke Operators¶

This section is about how to compute matrices of Hecke operators on .

Algorithm 2.37

This algorithm computes the matrix of the Hecke operator on the Miller basis for .

1. [Dimension] Compute using Corollary 2.16.

2. [Basis] Using Lemma 2.20, compute the echelon basis for .

3. [Hecke operator] Using Proposition 2.31, compute for each the image .

4. [Write in terms of basis] The elements determine linear combinations of

These linear combinations are easy to find once we compute , since our basis of is in echelon form. The linear combinations are just the coefficients of the power series up to and including .

5. [Write down matrix] The matrix of acting from the right relative to the basis is the matrix whose rows are the linear combinations found in the previous step, i.e., whose rows are the coefficients of .

Proof

By Proposition 2.31, the coefficient of involves only and smaller-indexed coefficients of . We need only compute a modular form modulo in order to compute modulo . Uniqueness in step (4) follows from Lemma 2.20 above.

Example 2.38

We compute the Hecke operator on using the above algorithm.

1. [Compute dimension] We have .

2. [Compute basis] Compute up to (but not including) the coefficient of . As given in the proof of Lemma 2.20, we have

Thus $M_{12}$ has basis

Sage does the arithmetic in the above calculation as follows:

sage: R.<q> = QQ[['q']]
sage: F4 =  240 * eisenstein_series_qexp(4,3)
sage: F6 = -504 * eisenstein_series_qexp(6,3)
sage: F4^3
1 + 720*q + 179280*q^2 + O(q^3)
sage: Delta = (F4^3 - F6^2)/1728; Delta
q - 24*q^2 + O(q^3)
sage: F4^3 - 720*Delta
1 + 196560*q^2 + O(q^3)

3. [Compute Hecke operator] In each case letting denote the coefficient of or , respectively, we have

and

(Note that .)

4. [Write in terms of basis] We read off at once that

5. [Write down matrix] Thus the matrix of , acting from the right on the basis , , is

As a check note that the characteristic polynomial of is and that is the sum of the powers of the divisors of .

Example 2.39

The Hecke operator on with respect to the echelon basis is

It has characteristic polynomial

where the cubic factor is irreducible.

The echelon_form command creates the space of modular forms but with basis in echelon form (which is not the default).

sage: M = ModularForms(1,36, prec=6).echelon_form()
sage: M.basis()
[
1 + 6218175600*q^4 + 15281788354560*q^5 + O(q^6),
q + 57093088*q^4 + 37927345230*q^5 + O(q^6),
q^2 + 194184*q^4 + 7442432*q^5 + O(q^6),
q^3 - 72*q^4 + 2484*q^5 + O(q^6)
]


Next we compute the matrix of the Hecke operator .

sage: T2 = M.hecke_matrix(2); T2
[34359738369   0       6218175600 9026867482214400]
[          0   0      34416831456    5681332472832]
[          0   1           194184       -197264484]
[          0   0              -72           -54528]


Finally we compute and factor its characteristic polynomial.

sage: T2.charpoly().factor()
(x - 34359738369) * (x^3 - 139656*x^2 - 59208339456*x - 1467625047588864)


The following is a famous open problem about Hecke operators on modular forms of level . It generalizes our above observation that the characteristic polynomial of on , for , factors as a product of a linear factor and an irreducible factor.

Conjuecture 2.40

The characteristic polynomial of on is irreducible for any .

Kevin Buzzard observed that in several specific cases the Galois group of the characteristic polynomial of is the full symmetric group (see [Buz96]). See also [FJ02] for more evidence for the following conjecture:

Conjuecture 2.41

For all primes and all even the characteristic polynomial of acting on is irreducible.

## Fast Computation of Fourier Coefficients¶

How difficult is it to compute prime-indexed coefficients of

Theorem 2.42

Let be a prime. There is a probabilistic algorithm to compute , for prime , that has expected running time polynomial in

Proof

See [ECdJ+06].

More generally, if is an eigenform in some space , where , then one expects that there is an algorithm to compute in time polynomial in . Bas Edixhoven, Jean-Marc Couveignes and Robin de Jong have proved that can be computed in polynomial time; their approach involves sophisticated techniques from arithmetic geometry (e.g., ‘etale cohomology, motives, Arakelov theory). The ideas they use are inspired by the ones introduced by Schoof, Elkies and Atkin for quickly counting points on elliptic curves over finite fields (see [Sch95]).

Edixhoven describes (in an email to the author) the strategy as follows:

1. We compute the mod Galois representation associated to . In particular, we produce a polynomial such that is the fixed field of . This is then used to obtain and to do a Schoof-like algorithm for computing .
2. We compute the field of definition of suitable points of order on the modular Jacobian to do part (1) (see [DS05, Ch. 6] for the definition of ).
3. The method is to approximate the polynomial in some sense (e.g., over the complex numbers or modulo many small primes ) and to use an estimate from Arakelov theory to determine a precision that will suffice.

## Fast Computation of Bernoulli Numbers¶

This section, which was written jointly with Kevin McGown, is about computing the Bernoulli numbers , for , defined in Section Fourier Expansions of Eisenstein Series by

(6)

One way to compute is to multiply both sides of (?) by and equate coefficients of to obtain the recurrence

This recurrence provides a straightforward and easy-to-implement method for calculating if one is interested in computing for all up to some bound. For example,

and

However, computing via the recurrence is slow; it requires summing over many large terms, it requires storing the numbers , and it takes only limited advantage of asymptotically fast arithmetic algorithms. There is also an inductive procedure to compute Bernoulli numbers that resembles Pascal’s triangle called the Akiyama-Tanigawa algorithm (see [Kan00]).

Another approach to computing is to use Newton iteration and asymptotically fast polynomial arithmetic to approximate . This method yields a very fast algorithm to compute modulo . See [BCS92] for an application of this method modulo a prime to the verification of Fermat’s last theorem for irregular primes up to one million.

Example 2.43

David Harvey implemented the algorithm of [BCS92] in Sage as the command bernoulli_mod_p:

sage: bernoulli_mod_p(23)
[1, 4, 13, 17, 13, 6, 10, 5, 10, 9, 15]


A third way to compute uses an algorithm based on Proposition 2.8, which we explain below (Algorithm 2.45). This algorithm appears to have been independently invented by several people: by Bernd C. Kellner (see [Kel06]); by Bill Dayl; and by H. Cohen and K. Belabas.

We compute as an exact rational number by approximating to very high precision using Proposition 2.8, the Euler product

and the following theorem:

Theorem 2.44

For even ,

Proof

See [Lan95, Ch. X, Thm. 2.1].

## The Number of Digits of ¶

The following is a new quick way to compute the number of digits of the numerator of . For example, using it we can compute the number of digits of in less than a second.

By Theorem 2.44 we have . The number of digits of the numerator is thus

But

and so . Finally, Stirling’s formula (see [Ahl78, pg. 198–206]) gives a fast way to compute :

(7)

We put quotes around the equality sign because does not converge to its Laurent series. Indeed, note that for any fixed value of the summands on the right side go to as ! Nonetheless, we can use this formula to very efficiently compute , since if we truncate the sum, then the error is smaller than the next term in the infinite sum.

### Computing Exactly¶

We return to the problem of computing . Let

so . Write

with , , and . It is elementary to show that for even . Suppose that using the Euler product we approximate from below by a number such that

Then hence It follows that and hence

It remains to compute . Consider the following problem: given and , find so that

satisfies . We always have . Also,

so

Thus if , then

so , as required. For our purposes, we have and , so it suffices to take .

Algorithm 2.45

Given an integer , this algorithm computes the Bernoulli number as an exact rational number.

1. [Special cases] If , return ; if , return ; if is odd, return .
2. [Factorial factor] Compute to sufficiently many digits of precision so the ceiling in step (6) is uniquely determined (this precision can be determined using Section The Number of Digits of ).
3. [Denominator] Compute .
4. [Bound] Compute
5. [Approximate ] Compute .
6. [Numerator] Compute .
7. [Output ] Return .

In step (5) use a sieve to compute all primes efficiently (which is fast, since is so small). In step (4) we may replace by any integer greater than the one specified by the formula, so we do not have to compute to very high precision.

In Section Computing Generalized Bernoulli Numbers Analytically below we will generalize the above algorithm.

Example 2.46

We illustrate Algorithm 2.45 by computing . Using 135 binary digits of precision, we compute

The divisors of are , so

We find and compute

Finally we compute

so

## Exercises¶

Exercise 2.1

Using Proposition 2.8 and the table found here, compute explicitly.

Exercise 2.2

Prove that if is odd, then the Bernoulli number is .

Exercise 2.3

Use (3) to write down the coefficients of , , , and of the Eisenstein series .

Exercise 2.4

Suppose is a positive integer with . Suppose are integers with .

1. Prove .
2. Show that

Exercise 2.5

Compute the Miller basis for with precision . Your answer will look like Example 2.23.

Exercise 2.6

Consider the cusp form in . Write as a polynomial in and (see Remark 2.25).

Exercise 2.7

Let be the weight Eisenstein series from equation (1). Let be the complex number so that the constant coefficient of the -expansion of is . Is it always the case that the -expansion of lies in ?

Exercise 2.8

Compute the matrix of the Hecke operator on the Miller basis for . Then compute its characteristic polynomial and verify it factors as a product of two irreducible polynomials.

What Next? Much of the rest of this book is about methods for computing subspaces of for general and . These general methods are more complicated than the methods presented in this chapter, since there are many more modular forms of small weight and it can be difficult to obtain them. Forms of level have subtle connections with elliptic curves, abelian varieties, and motives. Read on for more!

Modular Forms

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Modular Forms of Weight 2