CoCalc Public Filescode / 11a.ipynb

# Computing $\Lambda_{E,d}(t)$

** Some details below of the definition of $\alpha$ are wrong.**

## The Definition

Let $E$ be a non-CM elliptic curve over $\QQ$.

Let $d>2$ be an odd positive integer.

Let $\chi$ vary over Dirichlet characters of order $d$ whose conductor $m$ is coprime to the conductor $N$ of $E$.

We have $L(E,\overline{\chi},1) = \frac{1}{\tau(\chi)} \cdot \sum_{a\in (\ZZ/m\ZZ)^*} \chi(a)\cdot \left \langle\frac{a}{m} \right \rangle^{\pm}_E$ where $\pm$ is the sign of $\chi$, $\tau(\chi)$ is the Gauss sum, and $\left \langle\frac{a}{m} \right \rangle^{\pm}_E = \pi i \left( \int_{i\infty}^{r} f dz \pm \int_{i\infty}^{-r} f dz \right)$ is the $\pm$ period mapping.

To make things very concrete we assume that $m$ is prime. This is not necessary.

Let $G$ denote the quotient of $(\ZZ/m\ZZ)^*$ of order $d$, so we have an exact sequence $1 \to H \to (\ZZ/m\ZZ)^* \to G \to 1$ where $H$ is the unique subgroup of $(\ZZ/m\ZZ)^*$ of order $n = \varphi(m)/d$.

Choose a generator $h\in (\ZZ/m\ZZ)^*$, i.e., any element of $\varphi(m)$ of order $\varphi(m)/d$, so $H=\langle h \rangle$.

Choose $b \in (\ZZ/m\ZZ)^*$ of order $\varphi(m)$, and write $(\ZZ/m\ZZ)^* = Hb^0 \cup Hb^1 \cdots \cup Hb^{d-1} \qquad \text{(disjoint union)}$

Since $\chi(H)=\{1\}$, we have $L(E,\overline{\chi},1) = \frac{1}{\tau(\chi)} \cdot \sum_{i=0}^{d-1} \left( \chi(b^i) \sum_{j=0}^{n-1} \left\langle \frac{b^i h^j}{m} \right\rangle^{\pm}_E \right)$

Fix $\omega_{E}^{\pm}$ so that $\left \langle\frac{a}{m} \right \rangle^{\pm}_E$ is an algebraic multiple of $\omega_{E}^{\pm}$ (so, e.g., $\omega_{E}^{\pm}$ might just be the least real or imaginary period of $E$). The rational period mapping is $\left [ \frac{a}{m} \right ] ^{\pm}_E = \frac{\left \langle\frac{a}{m} \right \rangle^{\pm}_E} {\omega_E^{\pm}} \in \QQ.$

$L(E,\overline{\chi},1) = \frac{\omega_E^\pm}{\tau(\chi)} \cdot \sum_{i=0}^{d-1} \left( \chi(b^i) \sum_{j=0}^{n-1} \left [ \frac{b^i h^j}{m} \right ]^{\pm}_E \right)$

For $i=0,1,\ldots, \frac{d-1}{2}$, let $\alpha_m^{\pm}(i) = \frac{1}{\sqrt{\varphi(m)\log(m)}} \cdot \sum_{j=0}^{n-1} \left [ \frac{N^{(d-1)/2} b^i h^j}{m} \right ]^{\pm}_E \in \RR.$

The distribution $\Lambda_{E,d}$ is the distribution of real numbers $\alpha_m(i)$, where we vary over all $m$ and $i>0$. More concretely, for each integer $m$ coprime to $N_E$ such that $d \mid \varphi(m)$, compute the $d$ real numbers $\alpha_m(i)$ and add them to our set of values. The distribution $\Lambda_{E,d}$ is then the result of doing this as $m$ goes to $\infty$.

NOTE: The term $\alpha_m(0)$ is the sensitive theta coefficient.

Regarding complexity the work in doing this computation is the work of computing the rational numbers $[a/m]_E^\pm$ for all $a$. It's the same bottlekneck that goes into approximating $p$-adic $L$-series using the classical Riemann sums algorithm. The code in Sage for this is fairly slow, but I have some fast code in psage, which I used for some papers on $p$-adic $L$-series.

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## Example 11a

In terms of Sage, the rational_period_mapping method on a modular symbols space computes a choice of $[a/m]_E^{\pm}$:

In [12]:
M = ModularSymbols(11,sign=1).cuspidal_submodule()
N = M.level()
f = M.rational_period_mapping()
f([oo, 1/11])  # a/m = 1/11

(0)
In [24]:
d = 3
ms = [m for m in prime_range(2000) if gcd(m, 11) == 1 and euler_phi(m) % d == 0 and m%2==1]
print(ms)

[7, 13, 19, 31, 37, 43, 61, 67, 73, 79, 97, 103, 109, 127, 139, 151, 157, 163, 181, 193, 199, 211, 223, 229, 241, 271, 277, 283, 307, 313, 331, 337, 349, 367, 373, 379, 397, 409, 421, 433, 439, 457, 463, 487, 499, 523, 541, 547, 571, 577, 601, 607, 613, 619, 631, 643, 661, 673, 691, 709, 727, 733, 739, 751, 757, 769, 787, 811, 823, 829, 853, 859, 877, 883, 907, 919, 937, 967, 991, 997, 1009, 1021, 1033, 1039, 1051, 1063, 1069, 1087, 1093, 1117, 1123, 1129, 1153, 1171, 1201, 1213, 1231, 1237, 1249, 1279, 1291, 1297, 1303, 1321, 1327, 1381, 1399, 1423, 1429, 1447, 1453, 1459, 1471, 1483, 1489, 1531, 1543, 1549, 1567, 1579, 1597, 1609, 1621, 1627, 1657, 1663, 1669, 1693, 1699, 1723, 1741, 1747, 1753, 1759, 1777, 1783, 1789, 1801, 1831, 1861, 1867, 1873, 1879, 1933, 1951, 1987, 1993, 1999]
In [41]:
def alphas(m, d, normalize=True):
assert d%2 == 1
R = Integers(m)
Npow = R(N)^((d-1)//2)
gen = R(primitive_root(m))
n = euler_phi(m)//d
b = gen
h = gen^d
if normalize:
denom = float(sqrt(euler_phi(m)*log(m)))
else:
denom = 1
alphas = []
for i in range(1, (d-1)//2 + 1):
s = 0
for j in range(n):
symb = [oo, (Npow * b^i * h^j).lift() / ZZ(m)]
period = f(symb)[0]
s += period
alphas.append(s / denom)
return alphas

In [42]:
print ms[0]
alphas(ms[0], d, normalize=False)

7
[7]
In [43]:
alphas(37, d, normalize=False)

[-33]
In [44]:
for i in range(10):
print ms[i], alphas(ms[i], d, false)

7 [7] 13 [-16] 19 [16] 31 [10] 37 [-33] 43 [-11] 61 [20] 67 [22] 73 [9] 79 [21]
In [8]:
data = []
for m in ms:
data += alphas(m, d)

In [9]:
len(data)

360
In [10]:
stats.TimeSeries(data).plot_histogram(bins=30)

In [11]:
# More data...
ms2 = [m for m in prime_range(2000,3000) if gcd(m, 11) == 1 and euler_phi(m) % d == 0 and m%2==1]
for m in ms2:
data += alphas(m, d)

In [12]:
t = stats.TimeSeries(data)
t.plot_histogram(bins=30)

In [13]:
t.plot_histogram(bins=100)

In [14]:
print t.mean(), t.standard_deviation()

-0.0806714227685 0.110769030552
In [15]:
stats.TimeSeries(data).plot()


### Now try $d=29$

In [16]:
d = 29
ms = [m for m in prime_range(3,5000) if gcd(m, 11) == 1 and euler_phi(m) % d == 0]
print(ms)

[59, 233, 349, 523, 929, 1103, 1277, 1451, 1567, 1741, 1973, 2089, 2437, 2843, 3191, 3307, 3539, 4003, 4177, 4409, 4583, 4931]
In [17]:
print alphas(ms[0], d)

[0.04232831912577409, 0.04232831912577409, 0.04232831912577409, 0.04232831912577409, 0.04232831912577409, 0.04232831912577409, -0.16931327650309635, 0.04232831912577409, 0.04232831912577409, -0.16931327650309635, 0.04232831912577409, 0.04232831912577409, -0.16931327650309635, 0.04232831912577409, -0.16931327650309635, 0.04232831912577409, 0.04232831912577409, -0.16931327650309635, 0.04232831912577409, -0.16931327650309635, 0.04232831912577409, 0.04232831912577409, -0.16931327650309635, 0.04232831912577409, 0.04232831912577409, -0.16931327650309635, 0.04232831912577409, 0.04232831912577409, 0.04232831912577409]
In [18]:
data = []
for m in ms:
data += alphas(m, d)

In [19]:
print len(data)
t = stats.TimeSeries(data)
print t.mean()
t.plot_histogram(bins=100)

638 -0.0127456234641
In [20]:
t.plot()

In [21]:
stats.TimeSeries(t[:i].mean() for i in range(5,len(t))).plot(gridlines='minor')

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