Recent Flare List (2021-)

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List of EOVSA Flares with Spectrogram Data

April 2021

Date Time (UT) GOES Class Spectrogram STIX Coverage
2021-04-17 16:46 B9.0
EOVSA 20210417 B9flare.png
plot data
Yes
2021-04-19 23:36 M1.0
EOVSA 20210419 M1flare.png
plot data
No

May 2021

Date Time (UT) GOES Class Spectrogram STIX Coverage
2021-05-05 22:30 B5.0
EOVSA 20210505 B1flare.png
plot data
Yes
2021-05-07 19:00 M4.0
EOVSA 20210507 M4flare.png
plot data
Yes
2021-05-08 18:30 C9.0
EOVSA 20210508 C9flare.png
plot data
Yes
2021-05-09 13:55 C4.0
EOVSA 20210509 C4flare.png
plot data
Yes
2021-05-17 19:05 B5.0
EOVSA 20210517 B5flare.png
plot data
Yes
2021-05-21 19:25 C5.0
EOVSA 20210521 C5flare.png
plot data
Yes
2021-05-22 16:10 C1.0
EOVSA 20210522 C1flare.png
plot data
Yes
2021-05-22 17:10 M1.0
EOVSA20210522 M1flare.png
plot data
Yes
2021-05-22 21:30 M1.4
EOVSA 20210522 M1.4flare.png
plot data
Yes
2021-05-22 23:11 C7.0
EOVSA 20210522 C7flare.png
plot data
Yes
2021-05-23 17:00 C2.0
EOVSA 20210523 C2flare.png
plot data
Yes
2021-05-27 22:00 C1.0
EOVSA 20210527 C1flare.png
plot data
No
2021-05-27 23:10 C7.0
EOVSA 20210527 C7flare.png
plot data
No
2021-05-28 22:30 C9.0
EOVSA 20210528 C9flare.png
plot data
No

July 2021

Date Time (UT) GOES Class Spectrogram STIX Coverage
2021-07-03 14:27 X1.5
EOVSA 20210703 X1flare.png
plot data
No
2021-07-06 19:15 C1.1
EOVSA 20210706 flare1.png
plot data
No
2021-07-06 20:58 C1.2
EOVSA 20210706 flare2.png
plot data
No
2021-07-06 21:49 C1.1
EOVSA 20210706 flare3.png
plot data
No
2021-07-09 17:20 C4.7
EOVSA 20210709 C4flare.png
plot data
No

August 2021

Date Time (UT) GOES Class Spectrogram STIX Coverage
2021-08-20 15:55 C3.0
EOVSA 20210820 C3flare.png
plot data
Yes
2021-08-20 21:45 C1.4
EOVSA 20210820 C1.4flare.png
plot data
Yes
2021-08-21 18:41 B3.9
EOVSA 20210821 B3.9flare.png
plot data
Yes
2021-08-21 21:57 B3.9
EOVSA 20210821 B3.9flare2.png
plot data
Yes
2021-08-21 22:29 B4.7
EOVSA 20210821 B4.7flare.png
plot data
Yes
2021-08-26 18:12 C3.0
EOVSA 20210826 C3flare.png
plot data
Yes
2021-08-26 23:20 C3.9
EOVSA 20210826 C3.9flare.png
plot data
Yes
2021-08-27 21:00 C7.3
EOVSA 20210827 C7flare.png
plot data
Yes
2021-08-29 17:21 C2.9
EOVSA 20210829 C3flare.png
plot data
Yes (occulted?)
2021-08-30 16:12 C2.0
EOVSA 20210830 C2flare.png
plot data
Yes (occulted)
2021-08-30 18:23 B7.0
EOVSA 20210830 B7flare.png
plot data
Yes (occulted)
2021-08-30 20:47 B8.0
EOVSA 20210830 B8flare.png
plot data
Yes (occulted)
2021-08-31 14:48 B2.5
EOVSA 20210831 Bflares.png
plot data
No
2021-08-31 19:08 B7.1
EOVSA 20210831 B7flare.png
plot data
No


September 2021

Date Time (UT) GOES Class Spectrogram STIX Coverage
2021-09-08 17:24 C8.3
EOVSA 20210908 C8flare.png
plot data
Yes
2021-09-20 all day low B
EOVSA 20210920 flares0.png
plot data
EOVSA 20210920 flares1.png
plot data
EOVSA 20210920 flares2.png
plot data
Yes
2021-09-21 20:24 B5.1
EOVSA 20210921 Bflare.png
plot data
Yes
2021-09-22 20:12 C1.1
EOVSA 20210922 Cflare.png
plot data
Yes
2021-09-23 15:24 M1.9
EOVSA 20210923 M2flare.png
plot data
Yes
2021-09-23 20:40 C1.0
EOVSA 20210923 Cflare.png
plot data
Yes
2021-09-24 16:45 none?
EOVSA 20210924 Bflare.png
plot data
Yes
2021-09-24 21:16 B2.0
EOVSA 20210924 Bflares.png
plot data
Yes

October 2021

Date Time (UT) GOES Class Spectrogram STIX Coverage
2021-10-11 19:21 B2
EOVSA 20211011 Bflare.png
plot data
Yes
2021-10-12 15:57 B6
EOVSA 20211012 Bflare.png
plot data
Yes
2021-10-28 15:26 X1
EOVSA 20211028 Xflare.png
plot data
Yes

November 2021

Date Time (UT) GOES Class Spectrogram STIX Coverage
2021-11-01 20:43 C2.1
EOVSA 20211101 C2flare.png
plot data
Yes
2021-11-01 21:11 C4.0
EOVSA 20211101 C4flare.png
plot data
Yes
2021-11-01 23:39 C4.5
EOVSA 20211101 C4.5flare.png
plot data
Yes
2021-11-03 21:14 C5.2
EOVSA 20211103 C5flare.png
plot data
Yes

December 2021

Date Time (UT) GOES Class Spectrogram STIX Coverage Comment
2021-12-12 21:13 C1.2
EOVSA 20211212 Cflare.png
plot data
Yes
2021-12-14 20:33 C1.6
EOVSA 20211214 Cflare.png
plot data
Yes
2021-12-31 19:42 C8.1
EOVSA 20211232 C8flare.png
plot data
Yes Surprisingly weak in microwaves considering it is a C8 in soft X-rays.

January 2022

Date Time (UT) GOES Class Spectrogram STIX Coverage
2022-01-18 17:38 M1.2
EOVSA 20220118 Mflare.png
plot data
Yes
2022-01-28 19:31 C2.8
EOVSA 20220128 C2flare.png
plot data
Yes
2022-01-28 20:10-20:50 B2-B6
EOVSA 20220128 Bflares.png
plot data
Yes
2022-01-29 23:00 M1
EOVSA 20220129 M1flare.png
plot data
Yes

February 2022

Date Time (UT) GOES Class Spectrogram STIX Coverage
2022-02-03 20:42 C1.3
EOVSA 20220203 C1flare.png
plot data
Yes
2022-02-05 17:00 C2.1
EOVSA 20220205 C2flare.png
plot data
Yes
2022-02-08 21:40 C5.1
EOVSA 20220208 C5flare.png
plot data
Yes

March 2022

Date Time (UT) GOES Class Spectrogram STIX Coverage Comment
2022-03-30 17:30 X1.4
EOVSA 20220330 Xflare.png
plot data
Yes
EOVSA 20220330 Xflare osc.png
Interesting oscillations
2022-03-31 18:28 M9.7
EOVSA 20220331 M9flare.png
plot data
Yes
2022-03-31 19:32 C5
EOVSA 20220331 Cflare.png
plot data
Yes

May 2022

Date Time (UT) GOES Class Spectrogram STIX Coverage Comment
2022-05-11 18:35 M2.6
EOVSA 20220511 M2flare.png
plot data
Yes

Python code to read plot data file

from __future__ import print_function
def rd_datfile(file):
    ''' Read EOVSA binary spectrogram file and return a dictionary with times 
        in Julian Date, frequencies in GHz, and cross-power data in sfu.
        
        Return Keys:
          'time'     Numpy array of nt times in JD format
          'fghz'     Numpy array of nf frequencies in GHz
          'data'     Numpy array of size [nf, nt] containing cross-power data
          
        Returns empty dictionary ({}) if file size is not compatible with inferred dimensions
    '''
    import struct
    import numpy as np
    def dims(file):
        # Determine time and frequency dimensions (assumes the file has fewer than 10000 times)
        f = open(file,'rb')
        tmp = f.read(83608)  # max 10000 times and 451 frequencies
        f.close()
        nbytes = len(tmp)
        tdat = np.array(struct.unpack(str(int(nbytes/8))+'d',tmp[:nbytes]))
        nt = np.where(tdat < 2400000.)[0]
        nf = np.where(np.logical_or(tdat[nt[0]:] > 18, tdat[nt[0]:] < 1))[0]
        return nt[0], nf[0]
    nt, nf = dims(file)
    f = open(file,'rb')
    tmp = f.read(nt*8)
    times = struct.unpack(str(nt)+'d',tmp)
    tmp = f.read(nf*8)
    fghz = struct.unpack(str(nf)+'d',tmp)
    tmp = f.read()
    f.close()
    if len(tmp) != nf*nt*4:
        print('File size is incorrect for nt=',nt,'and nf=',nf)
        return {}
    data = np.array(struct.unpack(str(nt*nf)+'f',tmp)).reshape(nf,nt)
    return {'time':times, 'fghz':fghz, 'data':data}

IDL code to read plot data file

function rd_datfile,file
    ; Read EOVSA binary spectrogram file and return a structure with times 
    ; in Julian Date, frequencies in GHz, and cross-power data in sfu.
    ;    
    ; Return tags:
    ;      'time'     Array of nt times in JD format
    ;      'fghz'     Array of nf frequencies in GHz
    ;      'data'     Array of size [nf, nt] containing cross-power data
    ;
    ;    Returns empty dictionary ({}) if file size is not compatible with inferred dimensions
    openr,/get_lun,lun,file
    tmp = dblarr(10451)
    readu,lun,tmp
    free_lun,lun
    nt = (where(tmp lt 2400000.))[0]
    nf = (where(tmp[nt[0]:*] gt 18 or tmp[nt[0]:*] lt 1))[0]
    times = dblarr(nt)
    fghz = dblarr(nf)
    data = fltarr(nt, nf)
    openr,/get_lun,lun,file
    readu,lun,times
    readu,lun,fghz
    readu,lun,data
    free_lun,lun
    data = create_struct('time',times,'fghz',fghz,'data',transpose(data))
    return, data
    end