用中文解释一下：To account for the search background noise varying across the target signal space, candidate and background events are divided into three search classes based on template length. The right panel of Fig. 4 shows the background for the search class of GW150914. The GW150914 detectionstatistic value of ρˆ c ¼ 23.6 is larger than any background event, so only an upper bound can be placed on its false alarm rate. Across the three search classes this bound is 1 in 203 000 years. This translates to a false alarm probability < 2 × 10−7, corresponding to 5.1σ

为了考虑搜索背景噪声在目标信号空间中变化的影响，候选事件和背景事件被分为三个搜索类别，基于模板长度进行划分。图4的右侧面板显示了GW150914搜索类的背景噪声。GW150914检测统计值ρ̂c=23.6大于任何背景事件，因此只能对其误报率进行上限估计。在三个搜索类别中，上限为203,000年内1次。这相当于误报概率<2×10^-7，相当于5.1σ。

相关问题

我加了STR_TO_DATE函数，但是报错function str_to_date(character varying, unknown) does not exist 建议：No function matches the given name and argument types. You might need to add explicit type casts.

根据错误提示，数据库中似乎不存在名为 `str_to_date` 的函数。这是因为 `str_to_date` 函数是 MySQL 中的函数，在 PostgreSQL 中并不存在。在 PostgreSQL 中可以使用 `to_timestamp` 函数进行字符转日期时间类型的转换。

可以使用类似如下的方式将字符类型的参数转换为时间类型：

to_timestamp(createDate, 'YYYY-MM-DD HH24:MI:SS')

其中，'YYYY-MM-DD HH24:MI:SS' 是表示日期时间格式的字符串，需要根据实际情况进行修改。

需要注意的是，`to_timestamp` 函数返回的是 `timestamp with time zone` 类型的值，如果需要与 `create_date` 字段进行比较，需要使用 `at time zone` 将其转换为相同的时区，例如：

to_timestamp(createDate, 'YYYY-MM-DD HH24:MI:SS') at time zone 'UTC'

这样就可以将返回的时间戳转换为 UTC 时区的时间，然后与 `create_date` 字段进行比较。

improve the code and make it can be used to output different length of data

Sure! Here's an updated code that can handle varying lengths of data. It uses the `max.col()` function to determine the maximum width of each column based on the length of the data, and then formats the output accordingly.

# create a data frame
df <- data.frame(
  name = c("Alice", "Bob", "Charlie", "David", "Elizabeth"),
  age = c(25, 30, 35, 40, 45),
  salary = c(50000, 60000, 70000, 80000, 90000)
)

# open a connection to a file
con <- file("output.txt", "w")

# determine the maximum width of each column
max_widths <- apply(df, 2, function(x) max(nchar(as.character(x))))

# create the format string based on the maximum widths
fmt <- paste0(paste0("%-", max_widths), "s")
fmt <- paste0(fmt, collapse = " ")

# write the header row
writeLines(sprintf(fmt, names(df)), con)

# write the data rows
for (i in seq_len(nrow(df))) {
  row <- df[i, ]
  writeLines(sprintf(fmt, as.character(row)), con)
}

# close the connection
close(con)

In this updated code, we first use the `apply()` function to determine the maximum width of each column based on the length of the data. We then create the format string based on the maximum widths using `paste0()` and `collapse = " "`. Finally, we use `sprintf()` to format each row of data using the format string, and write the output to a file using `writeLines()`.